As List
Description
Coerces an object to an list. All attributes are removed except any names.
Usage
as_list(x, ...) ## Default S3 method: as_list(x, ...)as_list(x, ...) ## Default S3 method: as_list(x, ...)
Arguments
x |
An object. |
... |
Other arguments passed to methods. |
Value
A list.
Examples
as_list(1:3) as_list(c(x = 1, y = 2))as_list(1:3) as_list(c(x = 1, y = 2))
As List
Description
Coerces an object to an list. All attributes are removed except any names.
Usage
as_list_unnamed(x, ...) ## Default S3 method: as_list_unnamed(x, ...)as_list_unnamed(x, ...) ## Default S3 method: as_list_unnamed(x, ...)
Arguments
x |
An object. |
... |
Other arguments passed to methods. |
Value
A list.
Examples
as_list_unnamed(1:3) as_list_unnamed(c(x = 1, y = 2))as_list_unnamed(1:3) as_list_unnamed(c(x = 1, y = 2))
Check Index
Description
Checks if an object is a vector of one or more positive integer values.
Usage
chk_index(x, x_name = NULL) vld_index(x)chk_index(x, x_name = NULL) vld_index(x)
Arguments
x |
An object. |
x_name |
A string of the name of object x or NULL. |
Value
The chk_ function throws an informative error if the test fails.
The vld_ function returns a flag indicating whether the test was met.
Functions
-
vld_index(): Validate Index
Examples
x <- c(2L, 1L) chk_index(x) y <- c(2L, -1L) try(chk_index(y)) vld_index(c(-1)) vld_index(c(3L, 1L))x <- c(2L, 1L) chk_index(x) y <- c(2L, -1L) try(chk_index(y)) vld_index(c(-1)) vld_index(c(3L, 1L))
Check Indices
Description
Checks if an object is a list of indices ie vectors of one or more positive integer values.
Usage
chk_indices(x, x_name = NULL) vld_indices(x)chk_indices(x, x_name = NULL) vld_indices(x)
Arguments
x |
An object. |
x_name |
A string of the name of object x or NULL. |
Value
The chk_ function throws an informative error if the test fails.
The vld_ function returns a flag indicating whether the test was met.
Functions
-
vld_indices(): Validate Indices
Examples
x <- list(c(2L, 1L)) chk_indices(x) y <- c(2L, 1L) try(chk_indices(y)) vld_indices(c(3L, 1L)) vld_indices(list(c(3L, 1L)))x <- list(c(2L, 1L)) chk_indices(x) y <- c(2L, 1L) try(chk_indices(y)) vld_indices(c(3L, 1L)) vld_indices(list(c(3L, 1L)))
Check Parameter Names
Description
Checks if valid parameter names.
Usage
chk_pars(x, x_name = NULL) vld_pars(x)chk_pars(x, x_name = NULL) vld_pars(x)
Arguments
x |
An object. |
x_name |
A string of the name of object x or NULL. |
Details
The character vector must consist of values that start with an alpha and only include alphanumeric characters and '_' or '.'.
Missing values and duplicates are permitted.
Value
The chk_ function throws an informative error if the test fails.
The vld_ function returns a flag indicating whether the test was met.
Functions
-
vld_pars(): Validate Parameter Names
Examples
x <- c("x", "a1._", "X") chk_pars(x) y <- c("x[1]", "a1", "a1", "._0") try(chk_pars(y)) vld_pars(c("x", "a1._", "X")) vld_pars(c("x[1]", "a1", "a1", "._0"))x <- c("x", "a1._", "X") chk_pars(x) y <- c("x[1]", "a1", "a1", "._0") try(chk_pars(y)) vld_pars(c("x", "a1._", "X")) vld_pars(c("x[1]", "a1", "a1", "._0"))
Bernoulli Distribution
Description
Bernoulli Distribution
Usage
dbern(x, prob, log = FALSE) pbern(q, prob, lower.tail = TRUE, log = FALSE) qbern(p, prob, lower.tail = TRUE, log = FALSE) rbern(n, prob)dbern(x, prob, log = FALSE) pbern(q, prob, lower.tail = TRUE, log = FALSE) qbern(p, prob, lower.tail = TRUE, log = FALSE) rbern(n, prob)
Arguments
x |
A vector of 0s and 1s. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
log |
A flag specifying whether to return the log-transformed value. |
q |
A vector of quantiles. |
lower.tail |
A flag specifying whether to return the lower or upper tail of the distribution. |
p |
A numeric vector of probabilities. |
n |
A non-negative whole number of the number of random samples to generate. |
Value
An numeric vector of the random samples.
Examples
dbern(1, 0.5) pbern(0.75, 0.5) qbern(0.1, 0.5) rbern(1, 0.5)dbern(1, 0.5) pbern(0.75, 0.5) qbern(0.1, 0.5) rbern(1, 0.5)
Bernoulli Deviances
Description
Bernoulli Deviances
Usage
dev_bern(x, prob = 0.5, res = FALSE)dev_bern(x, prob = 0.5, res = FALSE)
Arguments
x |
A vector of 0s and 1s. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_beta_binom(),
dev_binom(),
dev_gamma(),
dev_gamma_pois(),
dev_lnorm(),
dev_neg_binom(),
dev_norm(),
dev_pois(),
dev_pois_zi(),
dev_skewnorm(),
dev_student()
Examples
dev_bern(c(TRUE, FALSE), 0.7)dev_bern(c(TRUE, FALSE), 0.7)
Beta-Binomial Deviances
Description
This parameterization of the beta-binomial distribution uses an expected
probability parameter, prob, and a dispersion parameter, theta. The
parameters of the underlying beta mixture are alpha = (2 * prob) / theta
and beta = (2 * (1 - prob)) / theta. This parameterization of theta is
unconventional, but has useful properties when modelling. When theta = 0,
the beta-binomial reverts to the binomial distribution. When theta = 1 and
prob = 0.5, the parameters of the beta distribution become alpha = 1 and
beta = 1, which correspond to a uniform distribution for the beta-binomial
probability parameter.
Usage
dev_beta_binom(x, size = 1, prob = 0.5, theta = 0, res = FALSE)dev_beta_binom(x, size = 1, prob = 0.5, theta = 0, res = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_binom(),
dev_gamma(),
dev_gamma_pois(),
dev_lnorm(),
dev_neg_binom(),
dev_norm(),
dev_pois(),
dev_pois_zi(),
dev_skewnorm(),
dev_student()
Examples
dev_beta_binom(c(0, 1, 2), 10, 0.5, 0.1)dev_beta_binom(c(0, 1, 2), 10, 0.5, 0.1)
Binomial Deviances
Description
Binomial Deviances
Usage
dev_binom(x, size = 1, prob = 0.5, res = FALSE)dev_binom(x, size = 1, prob = 0.5, res = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_beta_binom(),
dev_gamma(),
dev_gamma_pois(),
dev_lnorm(),
dev_neg_binom(),
dev_norm(),
dev_pois(),
dev_pois_zi(),
dev_skewnorm(),
dev_student()
Examples
dev_binom(c(0, 1, 2), 2, 0.3)dev_binom(c(0, 1, 2), 2, 0.3)
Gamma Deviances
Description
Gamma Deviances
Usage
dev_gamma(x, shape = 1, rate = 1, res = FALSE)dev_gamma(x, shape = 1, rate = 1, res = FALSE)
Arguments
x |
A numeric vector of values. |
shape |
A non-negative numeric vector of shape. |
rate |
A non-negative numeric vector of rate. |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_beta_binom(),
dev_binom(),
dev_gamma_pois(),
dev_lnorm(),
dev_neg_binom(),
dev_norm(),
dev_pois(),
dev_pois_zi(),
dev_skewnorm(),
dev_student()
Examples
dev_gamma(c(0, 1, 2), 1, 2)dev_gamma(c(0, 1, 2), 1, 2)
Gamma-Poisson Deviances
Description
Gamma-Poisson Deviances
Usage
dev_gamma_pois(x, lambda = 1, theta = 0, res = FALSE)dev_gamma_pois(x, lambda = 1, theta = 0, res = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_beta_binom(),
dev_binom(),
dev_gamma(),
dev_lnorm(),
dev_neg_binom(),
dev_norm(),
dev_pois(),
dev_pois_zi(),
dev_skewnorm(),
dev_student()
Examples
dev_gamma_pois(c(1, 3, 4), 3, 2)dev_gamma_pois(c(1, 3, 4), 3, 2)
Zero-Inflated Gamma-Poisson Deviances
Description
Zero-Inflated Gamma-Poisson Deviances
Usage
dev_gamma_pois_zi(x, lambda = 1, theta = 0, prob = 0, res = FALSE)dev_gamma_pois_zi(x, lambda = 1, theta = 0, prob = 0, res = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
Examples
dev_gamma_pois_zi(c(1, 3, 4), 3, 2)dev_gamma_pois_zi(c(1, 3, 4), 3, 2)
Log-Normal Deviances
Description
Log-Normal Deviances
Usage
dev_lnorm(x, meanlog = 0, sdlog = 1, res = FALSE)dev_lnorm(x, meanlog = 0, sdlog = 1, res = FALSE)
Arguments
x |
A numeric vector of values. |
meanlog |
A numeric vector of the means on the log scale. |
sdlog |
A non-negative numeric vector of the standard deviations on the log scale. |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_beta_binom(),
dev_binom(),
dev_gamma(),
dev_gamma_pois(),
dev_neg_binom(),
dev_norm(),
dev_pois(),
dev_pois_zi(),
dev_skewnorm(),
dev_student()
Examples
dev_lnorm(exp(-2:2))dev_lnorm(exp(-2:2))
Negative Binomial Deviances
Description
Negative Binomial Deviances
Usage
dev_neg_binom(x, lambda = 1, theta = 0, res = FALSE)dev_neg_binom(x, lambda = 1, theta = 0, res = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_beta_binom(),
dev_binom(),
dev_gamma(),
dev_gamma_pois(),
dev_lnorm(),
dev_norm(),
dev_pois(),
dev_pois_zi(),
dev_skewnorm(),
dev_student()
Examples
dev_neg_binom(c(1, 2, 5), 2, 3)dev_neg_binom(c(1, 2, 5), 2, 3)
Normal Deviances
Description
Normal Deviances
Usage
dev_norm(x, mean = 0, sd = 1, res = FALSE)dev_norm(x, mean = 0, sd = 1, res = FALSE)
Arguments
x |
A numeric vector of values. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_beta_binom(),
dev_binom(),
dev_gamma(),
dev_gamma_pois(),
dev_lnorm(),
dev_neg_binom(),
dev_pois(),
dev_pois_zi(),
dev_skewnorm(),
dev_student()
Examples
dev_norm(c(-2:2))dev_norm(c(-2:2))
Poisson Deviances
Description
Poisson Deviances
Usage
dev_pois(x, lambda, res = FALSE)dev_pois(x, lambda, res = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_beta_binom(),
dev_binom(),
dev_gamma(),
dev_gamma_pois(),
dev_lnorm(),
dev_neg_binom(),
dev_norm(),
dev_pois_zi(),
dev_skewnorm(),
dev_student()
Examples
dev_pois(c(1, 3, 4), 3)dev_pois(c(1, 3, 4), 3)
Zero-Inflated Poisson Deviances
Description
Zero-Inflated Poisson Deviances
Usage
dev_pois_zi(x, lambda, prob = 0, res = FALSE)dev_pois_zi(x, lambda, prob = 0, res = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_beta_binom(),
dev_binom(),
dev_gamma(),
dev_gamma_pois(),
dev_lnorm(),
dev_neg_binom(),
dev_norm(),
dev_pois(),
dev_skewnorm(),
dev_student()
Examples
dev_pois_zi(c(1, 3, 4), 3)dev_pois_zi(c(1, 3, 4), 3)
Skew Normal Deviances
Description
Skew Normal Deviances
Usage
dev_skewnorm(x, mean = 0, sd = 1, shape = 0, res = FALSE)dev_skewnorm(x, mean = 0, sd = 1, shape = 0, res = FALSE)
Arguments
x |
A numeric vector of values. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
shape |
A numeric vector of shape. |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_beta_binom(),
dev_binom(),
dev_gamma(),
dev_gamma_pois(),
dev_lnorm(),
dev_neg_binom(),
dev_norm(),
dev_pois(),
dev_pois_zi(),
dev_student()
Examples
dev_skewnorm(c(-2:2)) dev_skewnorm(-2:2, 0, 1, 5) dev_skewnorm(-2:2, 0, 1, 5, res = TRUE)dev_skewnorm(c(-2:2)) dev_skewnorm(-2:2, 0, 1, 5) dev_skewnorm(-2:2, 0, 1, 5, res = TRUE)
Student's t Deviances
Description
Student's t Deviances
Usage
dev_student(x, mean = 0, sd = 1, theta = 0, res = FALSE)dev_student(x, mean = 0, sd = 1, theta = 0, res = FALSE)
Arguments
x |
A numeric vector of values. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
res |
A flag specifying whether to return the deviance residual as opposed to the deviance. |
Value
An numeric vector of the corresponding deviances or deviance residuals.
See Also
Other dev_dist:
dev_bern(),
dev_beta_binom(),
dev_binom(),
dev_gamma(),
dev_gamma_pois(),
dev_lnorm(),
dev_neg_binom(),
dev_norm(),
dev_pois(),
dev_pois_zi(),
dev_skewnorm()
Examples
dev_student(c(1, 3.5, 4), 3)dev_student(c(1, 3.5, 4), 3)
Direction of a distribution
Description
The direction of a distribution is the side (left/right) that the distribution's center falls on, relative to a threshold. The center can be calculated using a user-specified function, including the median (default), mean, geometric mean, mode, or any other custom function. By convention, values below the threshold fall to the left, while values above the threshold fall to the right. Center estimates equal to the threshold are assumed to fall to the right.
Usage
direction(x, estimate = xtr_median, threshold = 0, na_rm = FALSE)direction(x, estimate = xtr_median, threshold = 0, na_rm = FALSE)
Arguments
x |
A numeric vector of MCMC values or any other numeric vector of samples. |
estimate |
A function for estimating the center of the distribution.
Defaults to |
threshold |
A number of the threshold value. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A string indicating if at least half of the observations are above
the threshold ("right") or not ("left"), or NA_character_ in the case
of missing values when na_rm = FALSE.
See Also
Other summary:
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
direction(c(1, 2, 3)) direction(c(-1)) direction(c(0, 0, 0)) direction(c(-100, 1, 1)) direction(c(-100, 1, 1), mean) direction(c(100, 0.01, 0.01), function(.x) exp(mean(log(.x))))direction(c(1, 2, 3)) direction(c(-1)) direction(c(0, 0, 0)) direction(c(-100, 1, 1)) direction(c(-100, 1, 1), mean) direction(c(100, 0.01, 0.01), function(.x) exp(mean(log(.x))))
Directional information
Description
The occurrence of an event (or lack thereof) transmits more or less information depending on the event's probability.
Usage
directional_information( x, side = "median", threshold = 0, threshold_split = "proportional", na_rm = FALSE ) p2info(p, n = Inf)directional_information( x, side = "median", threshold = 0, threshold_split = "proportional", na_rm = FALSE ) p2info(p, n = Inf)
Arguments
x |
A numeric vector of MCMC values. |
side |
A string indicating whether to calculate
the directional information relative to the left side ( |
threshold |
A number of the threshold value. |
threshold_split |
A string indicating how to deal with threshold values:
|
na_rm |
A flag specifying whether to remove missing values. |
p |
A numeric vector of probabilities of direction. |
n |
A numeric vector of the number of posterior samples used to estimate
each value of |
Details
Quantifies the information about direction in a posterior distribution based on the directional probability.
This function calculates such information using the difference in the probability of direction (see probability_direction()), after converting each probability to bits (also see svalue().
Value
A number indicating the directional information in bits.
If x has NA values but na_rm is FALSE, returns NA_real.
Functions
-
directional_information(): Calculate the directional information from a posterior distribution. -
p2info(): Calculate the information from a vector of probabilities.
References
Kery, M., and Schaub, M. 2011. Bayesian population analysis using WinBUGS: a hierarchical perspective. Academic Press, Boston. Available from https://www.vogelwarte.ch/en/research/population-biology/book-bpa/.
See Also
Other summary:
direction(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
directional_information(0) directional_information(1) # one coin flip of information directional_information(c(1, 1)) # two coin flips directional_information(c(1, 1, -1)) # x[2] and x[3] cancel out directional_information(c(1, 1, -1, -1)) # both sides cancel out directional_information(rnorm(1e3, mean = 0)) directional_information(rnorm(1e3, mean = 1)) directional_information(rnorm(1e3, mean = 10)) # all coin flips are positive directional_information(rnorm(1e3, mean = -10)) # all coin flips are negative directional_information(rnorm(1e3, mean = 1e3)) # only quantiles matter directional_information(rnorm(1e6, mean = 1e3)) # more `x` implies more info p2info(seq(0, 1, by = 0.1)) p2info(seq(0, 1, by = 0.1), n = 10) # limit information to be in [-10, 10]directional_information(0) directional_information(1) # one coin flip of information directional_information(c(1, 1)) # two coin flips directional_information(c(1, 1, -1)) # x[2] and x[3] cancel out directional_information(c(1, 1, -1, -1)) # both sides cancel out directional_information(rnorm(1e3, mean = 0)) directional_information(rnorm(1e3, mean = 1)) directional_information(rnorm(1e3, mean = 10)) # all coin flips are positive directional_information(rnorm(1e3, mean = -10)) # all coin flips are negative directional_information(rnorm(1e3, mean = 1e3)) # only quantiles matter directional_information(rnorm(1e6, mean = 1e3)) # more `x` implies more info p2info(seq(0, 1, by = 0.1)) p2info(seq(0, 1, by = 0.1), n = 10) # limit information to be in [-10, 10]
Skew-Normal Distribution
Description
Skew-Normal Distribution
Usage
dskewnorm(x, mean = 0, sd = 1, shape = 0, log = FALSE) pskewnorm(q, mean = 0, sd = 1, shape = 0) qskewnorm(p, mean = 0, sd = 1, shape = 0) rskewnorm(n = 1, mean = 0, sd = 1, shape = 0)dskewnorm(x, mean = 0, sd = 1, shape = 0, log = FALSE) pskewnorm(q, mean = 0, sd = 1, shape = 0) qskewnorm(p, mean = 0, sd = 1, shape = 0) rskewnorm(n = 1, mean = 0, sd = 1, shape = 0)
Arguments
x |
A numeric vector of values. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
shape |
A numeric vector of values. |
log |
A flag specifying whether to return the log-transformed value. |
q |
A vector of quantiles. |
p |
A numeric vector of probabilities. |
n |
A non-negative whole number of the number of random samples to generate. |
Value
dskewnorm gives the density, pskewnorm gives the distribution function, qskewnorm gives the quantile function, and rskewnorm generates random deviates.
pskewnorm and qskewnorm use the lower tail probability.
Examples
dskewnorm(x = -2:2, mean = 0, sd = 1, shape = 0.1) dskewnorm(x = -2:2, mean = 0, sd = 1, shape = -1) qskewnorm(p = c(0.1, 0.4), mean = 0, sd = 1, shape = 0.1) qskewnorm(p = c(0.1, 0.4), mean = 0, sd = 1, shape = -1) pskewnorm(q = -2:2, mean = 0, sd = 1, shape = 0.1) pskewnorm(q = -2:2, mean = 0, sd = 1, shape = -1) rskewnorm(n = 3, mean = 0, sd = 1, shape = 0.1) rskewnorm(n = 3, mean = 0, sd = 1, shape = -1)dskewnorm(x = -2:2, mean = 0, sd = 1, shape = 0.1) dskewnorm(x = -2:2, mean = 0, sd = 1, shape = -1) qskewnorm(p = c(0.1, 0.4), mean = 0, sd = 1, shape = 0.1) qskewnorm(p = c(0.1, 0.4), mean = 0, sd = 1, shape = -1) pskewnorm(q = -2:2, mean = 0, sd = 1, shape = 0.1) pskewnorm(q = -2:2, mean = 0, sd = 1, shape = -1) rskewnorm(n = 3, mean = 0, sd = 1, shape = 0.1) rskewnorm(n = 3, mean = 0, sd = 1, shape = -1)
Exponential Transformation of Base 10
Description
Returns the transformation of 10^x.
Usage
exp10(x)exp10(x)
Arguments
x |
An numeric atomic object. |
Value
A numeric atomic object with the value of 10^x.
See Also
Other translations:
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
x <- c(5, 10.5) exp10(x)x <- c(5, 10.5) exp10(x)
Exponential Transformation of Base 2
Description
Returns the transformation of 2^x.
Usage
exp2(x)exp2(x)
Arguments
x |
An numeric atomic object. |
Value
A numeric atomic object with the value of 2^x.
See Also
Other translations:
exp10(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
x <- c(5, 10.5) exp2(x)x <- c(5, 10.5) exp2(x)
Absolute
Description
Computes the absolute value of x.
Used in TMB as replacement for abs() which is seemingly ambiguous.
Usage
fabs(x)fabs(x)
Arguments
x |
An existing R object. |
Details
A wrapper on abs().
Value
A numeric vector of the corresponding absolute values.
See Also
Other translations:
exp10(),
exp2(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
fabs(c(0, -1, 2))fabs(c(0, -1, 2))
Fill All Values
Description
Fills all of an object's (missing and non-missing) values while preserving the object's dimensionality and class.
Usage
fill_all(x, value, ...) ## S3 method for class 'logical' fill_all(x, value = FALSE, nas = TRUE, ...) ## S3 method for class 'integer' fill_all(x, value = 0L, nas = TRUE, ...) ## S3 method for class 'numeric' fill_all(x, value = 0, nas = TRUE, ...) ## S3 method for class 'character' fill_all(x, value = "0", nas = TRUE, ...)fill_all(x, value, ...) ## S3 method for class 'logical' fill_all(x, value = FALSE, nas = TRUE, ...) ## S3 method for class 'integer' fill_all(x, value = 0L, nas = TRUE, ...) ## S3 method for class 'numeric' fill_all(x, value = 0, nas = TRUE, ...) ## S3 method for class 'character' fill_all(x, value = "0", nas = TRUE, ...)
Arguments
x |
An object. |
value |
A scalar of the value to replace values with. |
... |
Other arguments passed to methods. |
nas |
A flag specifying whether to also fill missing values. |
Details
It should only be defined for objects with values of consistent class ie not standard data.frames.
Value
The modified object.
Methods (by class)
-
fill_all(logical): Fill All for logical Objects -
fill_all(integer): Fill All for integer Objects -
fill_all(numeric): Fill All for numeric Objects -
fill_all(character): Fill All for character Objects
See Also
Other fill:
fill_na()
Examples
# logical fill_all(c(TRUE, NA, FALSE)) fill_all(c(TRUE, NA, FALSE, nas = FALSE)) fill_all(c(TRUE, NA, FALSE, value = NA)) # integer fill_all(matrix(1:4, nrow = 2), value = -1) # numeric fill_all(c(1, 4, NA), value = TRUE) fill_all(c(1, 4, NA), value = TRUE, nas = FALSE) # character fill_all(c("some", "words"), value = TRUE)# logical fill_all(c(TRUE, NA, FALSE)) fill_all(c(TRUE, NA, FALSE, nas = FALSE)) fill_all(c(TRUE, NA, FALSE, value = NA)) # integer fill_all(matrix(1:4, nrow = 2), value = -1) # numeric fill_all(c(1, 4, NA), value = TRUE) fill_all(c(1, 4, NA), value = TRUE, nas = FALSE) # character fill_all(c("some", "words"), value = TRUE)
Fill Missing Values
Description
Fills all of an object's missing values while preserving the object's dimensionality and class.
Usage
fill_na(x, value, ...) ## S3 method for class 'logical' fill_na(x, value = FALSE, ...) ## S3 method for class 'integer' fill_na(x, value = 0L, ...) ## S3 method for class 'numeric' fill_na(x, value = 0, ...) ## S3 method for class 'character' fill_na(x, value = "0", ...)fill_na(x, value, ...) ## S3 method for class 'logical' fill_na(x, value = FALSE, ...) ## S3 method for class 'integer' fill_na(x, value = 0L, ...) ## S3 method for class 'numeric' fill_na(x, value = 0, ...) ## S3 method for class 'character' fill_na(x, value = "0", ...)
Arguments
x |
An object. |
value |
A scalar of the value to replace values with. |
... |
Other arguments passed to methods. |
Details
It should only be defined for objects with values of consistent class ie not standard data.frames.
Value
The modified object.
Methods (by class)
-
fill_na(logical): Fill Missing Values for logical Objects -
fill_na(integer): Fill Missing Values for integer Objects -
fill_na(numeric): Fill Missing Values for numeric Objects -
fill_na(character): Fill Missing Values for character Objects
See Also
Other fill:
fill_all()
Examples
# logical fill_na(c(TRUE, NA)) # integer fill_na(c(1L, NA), 0) # numeric fill_na(c(1, NA), Inf) # character fill_na(c("text", NA)) fill_na(matrix(c("text", NA)), value = Inf)# logical fill_na(c(TRUE, NA)) # integer fill_na(c(1L, NA), 0) # numeric fill_na(c(1, NA), Inf) # character fill_na(c("text", NA)) fill_na(matrix(c("text", NA)), value = Inf)
Inverse Log Transformation
Description
Inverse log transforms a numeric atomic object.
Usage
ilog(x)ilog(x)
Arguments
x |
An object. |
Details
A wrapper on exp(value).
Value
A numeric atomic object.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
x <- 1 ilog(x)x <- 1 ilog(x)
Inverse Log Base 10 Transformation
Description
Inverse log transforms a numeric atomic object with base 10.
Usage
ilog10(x)ilog10(x)
Arguments
x |
An object. |
Details
A wrapper on exp10(value).
Value
A numeric atomic object.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
x <- c(2, 4.5) ilog10(x)x <- c(2, 4.5) ilog10(x)
Inverse Log Base 2 Transformation
Description
Inverse log transforms a numeric atomic object with base 2.
Usage
ilog2(x)ilog2(x)
Arguments
x |
An object. |
Details
A wrapper on exp2(value).
Value
A numeric atomic object.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
x <- c(2, 4.5) ilog2(x)x <- c(2, 4.5) ilog2(x)
Inverse Logistic Transformation
Description
Inverse logistically transforms a numeric atomic object.
Usage
ilogit(x)ilogit(x)
Arguments
x |
A numeric atomic object. |
Details
A wrapper on stats::plogis().
Value
A numeric atomic object.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
ilogit(c(-1, 0, 5))ilogit(c(-1, 0, 5))
Inverse Logistic Transformation
Description
Inverse logistically transforms a numeric atomic object.
Usage
inv_logit(x)inv_logit(x)
Arguments
x |
A numeric atomic object. |
Details
A wrapper on stats::plogis().
Value
A numeric atomic object.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
inv_logit(c(-1, 0, 5))inv_logit(c(-1, 0, 5))
Inverse Odds
Description
Calculates the probabilities for odds.
Usage
inv_odds(x)inv_odds(x)
Arguments
x |
A numeric object (vector, matrix or array) of odds. |
Value
A numeric object of the the probabilities for each odd.
See Also
Other odds:
log_odds(),
log_odds<-(),
log_odds_ratio(),
odds(),
odds<-(),
odds_ratio()
Examples
inv_odds(c(0, 1, 9, 9999))inv_odds(c(0, 1, 9, 9999))
Inverse Logistic Transformation
Description
Inverse logistically transforms a numeric atomic object.
Usage
invlogit(x)invlogit(x)
Arguments
x |
A numeric atomic object. |
Details
A wrapper on stats::plogis().
Value
A numeric atomic object.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
invlogit(c(-1, 0, 5))invlogit(c(-1, 0, 5))
Kurtosis
Description
Kurtosis
Usage
kurtosis(x, na_rm = FALSE)kurtosis(x, na_rm = FALSE)
Arguments
x |
A numeric object of MCMC values. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A number.
See Also
Other summary:
direction(),
directional_information(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
kurtosis(1:10)kurtosis(1:10)
Bernoulli Log-Likelihood
Description
Bernoulli Log-Likelihood
Usage
log_lik_bern(x, prob = 0.5)log_lik_bern(x, prob = 0.5)
Arguments
x |
A vector of 0s and 1s. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_bern(c(TRUE, FALSE), 0.7)log_lik_bern(c(TRUE, FALSE), 0.7)
Beta Log-Likelihood
Description
Beta Log-Likelihood
Usage
log_lik_beta(x, alpha = 1, beta = 1, tlower = 0, tupper = 1)log_lik_beta(x, alpha = 1, beta = 1, tlower = 0, tupper = 1)
Arguments
x |
A vector of values between 0s and 1s. |
alpha |
The first shape parameter of the beta distribution. |
beta |
The second shape parameter of the beta distribution. |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_beta(c(0, 0.5, 0.7, 1), 0.7)log_lik_beta(c(0, 0.5, 0.7, 1), 0.7)
Beta-Binomial Log-Likelihood
Description
This parameterization of the beta-binomial distribution uses an expected
probability parameter, prob, and a dispersion parameter, theta. The
parameters of the underlying beta mixture are alpha = (2 * prob) / theta
and beta = (2 * (1 - prob)) / theta. This parameterization of theta is
unconventional, but has useful properties when modelling. When theta = 0,
the beta-binomial reverts to the binomial distribution. When theta = 1 and
prob = 0.5, the parameters of the beta distribution become alpha = 1 and
beta = 1, which correspond to a uniform distribution for the beta-binomial
probability parameter.
Usage
log_lik_beta_binom( x, size = 1, prob = 0.5, theta = 0, tlower = 0, tupper = Inf, memoize = FALSE )log_lik_beta_binom( x, size = 1, prob = 0.5, theta = 0, tlower = 0, tupper = Inf, memoize = FALSE )
Arguments
x |
A non-negative whole numeric vector of values. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
memoize |
Whether or not to memoize the function. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_beta_binom(c(0, 1, 2), 3, 0.5, 0)log_lik_beta_binom(c(0, 1, 2), 3, 0.5, 0)
Binomial Log-Likelihood
Description
Binomial Log-Likelihood
Usage
log_lik_binom(x, size = 1, prob = 0.5, tlower = 0, tupper = Inf)log_lik_binom(x, size = 1, prob = 0.5, tlower = 0, tupper = Inf)
Arguments
x |
A non-negative whole numeric vector of values. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_binom(c(0, 1, 2), 2, 0.3)log_lik_binom(c(0, 1, 2), 2, 0.3)
Exponential Log-Likelihood
Description
The exponential distribution is parameterized in terms of the rate.
Usage
log_lik_exp(x, rate = 1, tlower = 0, tupper = Inf)log_lik_exp(x, rate = 1, tlower = 0, tupper = Inf)
Arguments
x |
A non-negative numeric vector of values. |
rate |
A non-negative numeric vector of rate. |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
A numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_exp(c(0, 1, 2), 2)log_lik_exp(c(0, 1, 2), 2)
Gamma Log-Likelihood
Description
Gamma Log-Likelihood
Usage
log_lik_gamma(x, shape = 1, rate = 1, tlower = 0, tupper = Inf)log_lik_gamma(x, shape = 1, rate = 1, tlower = 0, tupper = Inf)
Arguments
x |
A numeric vector of values. |
shape |
A non-negative numeric vector of shape. |
rate |
A non-negative numeric vector of rate. |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_gamma(c(0, 1, 2), 1, 2)log_lik_gamma(c(0, 1, 2), 1, 2)
Gamma-Poisson Log-Likelihood
Description
Gamma-Poisson Log-Likelihood
Usage
log_lik_gamma_pois(x, lambda = 1, theta = 0, tlower = 0, tupper = Inf)log_lik_gamma_pois(x, lambda = 1, theta = 0, tlower = 0, tupper = Inf)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_gamma_pois(c(0, 1, 2), 1, 1)log_lik_gamma_pois(c(0, 1, 2), 1, 1)
Zero-Inflated Gamma-Poisson Log-Likelihood
Description
Zero-Inflated Gamma-Poisson Log-Likelihood
Usage
log_lik_gamma_pois_zi( x, lambda = 1, theta = 0, prob = 0, tlower = 0, tupper = Inf )log_lik_gamma_pois_zi( x, lambda = 1, theta = 0, prob = 0, tlower = 0, tupper = Inf )
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_gamma_pois_zi(c(1, 3, 4), 3, 1, prob = 0.5)log_lik_gamma_pois_zi(c(1, 3, 4), 3, 1, prob = 0.5)
Log-Normal Log-Likelihood
Description
Log-Normal Log-Likelihood
Usage
log_lik_lnorm(x, meanlog = 0, sdlog = 1, tlower = 0, tupper = Inf)log_lik_lnorm(x, meanlog = 0, sdlog = 1, tlower = 0, tupper = Inf)
Arguments
x |
A numeric vector of values. |
meanlog |
A numeric vector of the means on the log scale. |
sdlog |
A non-negative numeric vector of the standard deviations on the log scale. |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_lnorm(10, 0, 2)log_lik_lnorm(10, 0, 2)
Negative Binomial Log-Likelihood
Description
Negative Binomial Log-Likelihood
Usage
log_lik_neg_binom(x, lambda = 1, theta = 0, tlower = 0, tupper = Inf)log_lik_neg_binom(x, lambda = 1, theta = 0, tlower = 0, tupper = Inf)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_neg_binom(c(0, 1, 2), 2, 1)log_lik_neg_binom(c(0, 1, 2), 2, 1)
Normal Log-Likelihood
Description
Normal Log-Likelihood
Usage
log_lik_norm(x, mean = 0, sd = 1, tlower = -Inf, tupper = Inf)log_lik_norm(x, mean = 0, sd = 1, tlower = -Inf, tupper = Inf)
Arguments
x |
A numeric vector of values. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_norm(c(-2:2))log_lik_norm(c(-2:2))
Poisson Log-Likelihood
Description
Poisson Log-Likelihood
Usage
log_lik_pois(x, lambda = 1, tlower = 0, tupper = Inf)log_lik_pois(x, lambda = 1, tlower = 0, tupper = Inf)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_pois(c(1, 3, 4), 3)log_lik_pois(c(1, 3, 4), 3)
Zero-Inflated Poisson Log-Likelihood
Description
Zero-Inflated Poisson Log-Likelihood
Usage
log_lik_pois_zi(x, lambda = 1, prob = 0, tlower = 0, tupper = Inf)log_lik_pois_zi(x, lambda = 1, prob = 0, tlower = 0, tupper = Inf)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_skewnorm(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_pois_zi(c(1, 3, 4), 3, prob = 0.5)log_lik_pois_zi(c(1, 3, 4), 3, prob = 0.5)
Skew Normal Log-Likelihood
Description
Skew Normal Log-Likelihood
Usage
log_lik_skewnorm(x, mean = 0, sd = 1, shape = 0, tlower = -Inf, tupper = Inf)log_lik_skewnorm(x, mean = 0, sd = 1, shape = 0, tlower = -Inf, tupper = Inf)
Arguments
x |
A numeric vector of values. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
shape |
A numeric vector of shape. |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_student(),
log_lik_unif()
Examples
log_lik_skewnorm(c(-2:2)) log_lik_skewnorm(c(-2:2), shape = -2) log_lik_skewnorm(c(-2:2), shape = 2)log_lik_skewnorm(c(-2:2)) log_lik_skewnorm(c(-2:2), shape = -2) log_lik_skewnorm(c(-2:2), shape = 2)
Student's t Log-Likelihood
Description
Student's t Log-Likelihood
Usage
log_lik_student(x, mean = 0, sd = 1, theta = 0, tlower = -Inf, tupper = Inf)log_lik_student(x, mean = 0, sd = 1, theta = 0, tlower = -Inf, tupper = Inf)
Arguments
x |
A numeric vector of values. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
tlower |
A numeric vector of the lower truncation point. |
tupper |
A numeric vector of the upper truncation point. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_unif()
Examples
log_lik_student(c(1, 3.5, 4), mean = 1, sd = 2, theta = 1 / 3)log_lik_student(c(1, 3.5, 4), mean = 1, sd = 2, theta = 1 / 3)
Uniform Log-Likelihood
Description
Uniform Log-Likelihood
Usage
log_lik_unif(x, min = 0, max = 1)log_lik_unif(x, min = 0, max = 1)
Arguments
x |
A numeric vector of values. |
min |
A numeric vector of the minimums. |
max |
A numeric vector of the maximums. |
Value
An numeric vector of the corresponding log-likelihoods.
See Also
Other log_lik_dist:
log_lik_bern(),
log_lik_beta(),
log_lik_beta_binom(),
log_lik_binom(),
log_lik_exp(),
log_lik_gamma(),
log_lik_gamma_pois(),
log_lik_gamma_pois_zi(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois(),
log_lik_pois_zi(),
log_lik_skewnorm(),
log_lik_student()
Examples
log_lik_unif(c(0, 0.7, 1))log_lik_unif(c(0, 0.7, 1))
Log Odds
Description
Calculates the log odds for probabilities.
Usage
log_odds(x)log_odds(x)
Arguments
x |
A numeric object (vector, matrix or array) of probabilities. |
Value
A numeric object of the the log odds for each probability.
See Also
Other odds:
inv_odds(),
log_odds<-(),
log_odds_ratio(),
odds(),
odds<-(),
odds_ratio()
Examples
log_odds(c(0, 0.5, 0.9, 1))log_odds(c(0, 0.5, 0.9, 1))
Log-Odds Ratio
Description
Calculates the log odds ratio for two probabilities.
Usage
log_odds_ratio(x, x2)log_odds_ratio(x, x2)
Arguments
x |
A numeric object (vector, matrix or array) of probabilities. |
x2 |
A second numeric object of probabilities. |
Value
A numeric object of the log odds ratios.
See Also
Other odds:
inv_odds(),
log_odds(),
log_odds<-(),
odds(),
odds<-(),
odds_ratio()
Examples
log_odds_ratio(0.5, 0.75)log_odds_ratio(0.5, 0.75)
Log Odds Ratio2
Description
Calculates the log odds ratio for a vector of two probabilities.
Usage
log_odds_ratio2(x)log_odds_ratio2(x)
Arguments
x |
A numeric vector of length 2. |
Value
A number.
See Also
Other odds fun2:
odds_ratio2()
Examples
log_odds_ratio2(c(0.5, 0.9)) log_odds_ratio2(c(0.9, 0.5))log_odds_ratio2(c(0.5, 0.9)) log_odds_ratio2(c(0.9, 0.5))
Inverse Log Odds Transformation
Description
Replaces an object with the inverse log odds of value.
Usage
log_odds(x) <- valuelog_odds(x) <- value
Arguments
x |
An existing R object. |
value |
A numeric atomic object. |
Value
Called for the side effect of updating x.
See Also
Other odds:
inv_odds(),
log_odds(),
log_odds_ratio(),
odds(),
odds<-(),
odds_ratio()
Examples
x <- NULL log_odds(x) <- 0.5 xx <- NULL log_odds(x) <- 0.5 x
Log Transformation
Description
Replaces a object with the exponent of value.
Usage
log(x) <- valuelog(x) <- value
Arguments
x |
An object. |
value |
A numeric atomic object. |
Details
A wrapper on exp(value).
Value
Called for the side effect of updating x.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
x <- NULL log(x) <- 0.5 xx <- NULL log(x) <- 0.5 x
Log Base 10 Transformation
Description
Replaces a object with the base 10 exponent of value.
Usage
log10(x) <- valuelog10(x) <- value
Arguments
x |
An object. |
value |
A numeric atomic object. |
Details
A wrapper on exp10(value).
Value
Called for the side effect of updating x.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
x <- NULL log10(x) <- c(0.5, 5) xx <- NULL log10(x) <- c(0.5, 5) x
Log Base 2 Transformation
Description
Replaces a object with the base 2 exponent of value.
Usage
log2(x) <- valuelog2(x) <- value
Arguments
x |
An object. |
value |
A numeric atomic object. |
Details
A wrapper on exp2(value).
Value
Called for the side effect of updating x.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow(),
step()
Examples
x <- NULL log2(x) <- c(0.5, 5) xx <- NULL log2(x) <- c(0.5, 5) x
Logistic Transformation
Description
Logistic transforms a numeric atomic object.
Usage
logit(x)logit(x)
Arguments
x |
A numeric atomic object. |
Details
A wrapper on stats::qlogis().
Value
The logistically transformed numeric atomic object.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit<-(),
phi(),
pow(),
step()
Examples
logit(c(0.25, 0.5, 0.75))logit(c(0.25, 0.5, 0.75))
Logistic Transformation
Description
Logistic Transformation
Usage
logit(x) <- valuelogit(x) <- value
Arguments
x |
An existing object. |
value |
A numeric atomic object of the value to inverse logistically transform. |
Details
A wrapper on stats::plogis(value).
Value
Called for the side effect of updating x.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
phi(),
pow(),
step()
Examples
x <- 1 logit(x) <- 0.5 xx <- 1 logit(x) <- 0.5 x
Lower Credible Limit
Description
Calculates the quantile-based lower credible limit.
Usage
lower(x, conf_level = 0.95, na_rm = FALSE)lower(x, conf_level = 0.95, na_rm = FALSE)
Arguments
x |
A numeric vector of MCMC values. |
conf_level |
A numeric scalar between 0 and 1 specifying the confidence level. |
na_rm |
A flag specifying whether to remove missing values. |
Details
By default it returns the 95% credible limit which corresponds to the 2.5% quantile.
Value
A number.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
lower(as.numeric(0:100))lower(as.numeric(0:100))
Numericise (or Numericize)
Description
Coerce an R object to a numeric atomic object.
Usage
numericise(x, ...) numericize(x, ...) ## S3 method for class 'logical' numericise(x, ...) ## S3 method for class 'integer' numericise(x, ...) ## S3 method for class 'double' numericise(x, ...) ## S3 method for class 'factor' numericise(x, ...) ## S3 method for class 'Date' numericise(x, ...) ## S3 method for class 'POSIXct' numericise(x, ...) ## S3 method for class 'hms' numericise(x, ...) ## S3 method for class 'matrix' numericise(x, ...) ## S3 method for class 'array' numericise(x, ...) ## S3 method for class 'data.frame' numericise(x, ...)numericise(x, ...) numericize(x, ...) ## S3 method for class 'logical' numericise(x, ...) ## S3 method for class 'integer' numericise(x, ...) ## S3 method for class 'double' numericise(x, ...) ## S3 method for class 'factor' numericise(x, ...) ## S3 method for class 'Date' numericise(x, ...) ## S3 method for class 'POSIXct' numericise(x, ...) ## S3 method for class 'hms' numericise(x, ...) ## S3 method for class 'matrix' numericise(x, ...) ## S3 method for class 'array' numericise(x, ...) ## S3 method for class 'data.frame' numericise(x, ...)
Arguments
x |
An object. |
... |
Other arguments passed to methods. |
Details
numericize() is an alias for numericise.
If you want to implement a method for a class "foo", implement
numericise.foo().
Value
A numeric atomic object.
Methods (by class)
-
numericise(logical): Numericise a logical Object -
numericise(integer): Numericise an integer Object -
numericise(double): Numericise an double Object -
numericise(factor): Numericise a factor -
numericise(Date): Numericise a Date vector -
numericise(POSIXct): Numericise a POSIXct vector -
numericise(hms): Numericise a hms vector -
numericise(matrix): Numericise a matrix -
numericise(array): Numericise an array -
numericise(data.frame): Numericise a data.frame
Examples
# logical numericise(TRUE) numericise(matrix(c(TRUE, FALSE), nrow = 2)) # integer numericise(2L) # double numericise(c(1, 3)) # factor numericise(factor(c("c", "a"))) # Date numericise(as.Date("1972-01-01")) # POSIXct numericise(as.POSIXct("1972-01-01", tz = "UTC")) # hms numericise(hms::as_hms("00:01:03")) # matrix numericise(matrix(TRUE)) # array numericise(array(TRUE)) # data.frame numericise(data.frame( logical = c(TRUE, FALSE, NA), integer = 1:3, numeric = c(4, 10, NA), factor = as.factor(c("c", "A", "green")) ))# logical numericise(TRUE) numericise(matrix(c(TRUE, FALSE), nrow = 2)) # integer numericise(2L) # double numericise(c(1, 3)) # factor numericise(factor(c("c", "a"))) # Date numericise(as.Date("1972-01-01")) # POSIXct numericise(as.POSIXct("1972-01-01", tz = "UTC")) # hms numericise(hms::as_hms("00:01:03")) # matrix numericise(matrix(TRUE)) # array numericise(array(TRUE)) # data.frame numericise(data.frame( logical = c(TRUE, FALSE, NA), integer = 1:3, numeric = c(4, 10, NA), factor = as.factor(c("c", "A", "green")) ))
Odds
Description
Calculates the odds for probabilities.
Usage
odds(x)odds(x)
Arguments
x |
A numeric object (vector, matrix or array) of probabilities. |
Value
A numeric object of the the odds for each probability.
See Also
Other odds:
inv_odds(),
log_odds(),
log_odds<-(),
log_odds_ratio(),
odds<-(),
odds_ratio()
Examples
odds(c(0, 0.5, 0.9, 1))odds(c(0, 0.5, 0.9, 1))
Odds Ratio
Description
Calculates the odds ratio for two probabilities.
Usage
odds_ratio(x, x2)odds_ratio(x, x2)
Arguments
x |
A numeric object (vector, matrix or array) of probabilities. |
x2 |
A second numeric object of probabilities. |
Value
A numeric object of the odds ratios.
See Also
Other odds:
inv_odds(),
log_odds(),
log_odds<-(),
log_odds_ratio(),
odds(),
odds<-()
Examples
odds_ratio(0.5, 0.75)odds_ratio(0.5, 0.75)
Odds Ratio2
Description
Calculates the odds ratio for a vector of two probabilities.
Usage
odds_ratio2(x)odds_ratio2(x)
Arguments
x |
A numeric vector of length 2. |
Value
A number.
See Also
Other odds fun2:
log_odds_ratio2()
Examples
odds_ratio2(c(0.5, 0.9)) odds_ratio2(c(0.9, 0.5))odds_ratio2(c(0.5, 0.9)) odds_ratio2(c(0.9, 0.5))
Inverse Odds Transformation
Description
Replaces an object with the inverse odds of value.
Usage
odds(x) <- valueodds(x) <- value
Arguments
x |
An existing R object. |
value |
A numeric atomic object. |
Value
Called for the side effect of updating x.
See Also
Other odds:
inv_odds(),
log_odds(),
log_odds<-(),
log_odds_ratio(),
odds(),
odds_ratio()
Examples
x <- NULL odds(x) <- 0.5 xx <- NULL odds(x) <- 0.5 x
Parameter Pattern
Description
Parameter Pattern
Usage
par_pattern()par_pattern()
Value
A string of the regular expression for a parameter name.
Examples
par_pattern()par_pattern()
Extreme Probability
Description
Calculates the probability that a cumulative distribution function
probability is at least that extreme.
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Usage
pextreme(x)pextreme(x)
Arguments
x |
A numeric vector of values between 0 and 1. |
Value
A numeric vector of values between 0 and 1.
See Also
Other residuals:
sextreme()
Examples
pextreme(seq(0, 1, by = 0.1))pextreme(seq(0, 1, by = 0.1))
Phi
Description
The standard normal cumulative density function.
Usage
phi(x)phi(x)
Arguments
x |
A numeric atomic object. |
Details
A wrapper on stats::pnorm().
Value
A numeric atomic object.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
pow(),
step()
Examples
phi(0:2)phi(0:2)
Power
Description
R equivalent to the power function.
Usage
pow(x, n)pow(x, n)
Arguments
x |
A numeric atomic object of the base. |
n |
A numeric atomic object of the exponent. |
Details
Wrapper on x^n.
Value
A numeric atomic object of x raised to n.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
step()
Examples
pow(10, 2)pow(10, 2)
Bernoulli Cumulative Distribution Function
Description
Bernoulli Cumulative Distribution Function
Usage
prob_bern(x, prob = 0.5)prob_bern(x, prob = 0.5)
Arguments
x |
A numeric vector of quantiles. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_bern(c(TRUE, FALSE), 0.7)prob_bern(c(TRUE, FALSE), 0.7)
Beta Cumulative Distribution Function
Description
Beta Cumulative Distribution Function
Usage
prob_beta(x, alpha = 1, beta = 1)prob_beta(x, alpha = 1, beta = 1)
Arguments
x |
A numeric vector of quantiles. |
alpha |
The first shape parameter of the beta distribution. |
beta |
The second shape parameter of the beta distribution. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_beta(c(0, 0.5, 1), 2, 3)prob_beta(c(0, 0.5, 1), 2, 3)
Beta-Binomial Cumulative Distribution Function
Description
This parameterization of the beta-binomial distribution uses an expected
probability parameter, prob, and a dispersion parameter, theta. The
parameters of the underlying beta mixture are alpha = (2 * prob) / theta
and beta = (2 * (1 - prob)) / theta. This parameterization of theta is
unconventional, but has useful properties when modelling. When theta = 0,
the beta-binomial reverts to the binomial distribution. When theta = 1 and
prob = 0.5, the parameters of the beta distribution become alpha = 1 and
beta = 1, which correspond to a uniform distribution for the beta-binomial
probability parameter.
Usage
prob_beta_binom(x, size = 1, prob = 0.5, theta = 0)prob_beta_binom(x, size = 1, prob = 0.5, theta = 0)
Arguments
x |
A numeric vector of quantiles. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_beta_binom(c(0, 1, 2), 3, 0.5, 0)prob_beta_binom(c(0, 1, 2), 3, 0.5, 0)
Binomial Cumulative Distribution Function
Description
Binomial Cumulative Distribution Function
Usage
prob_binom(x, size = 1, prob = 0.5)prob_binom(x, size = 1, prob = 0.5)
Arguments
x |
A numeric vector of quantiles. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_binom(c(0, 1, 2), 2, 0.3)prob_binom(c(0, 1, 2), 2, 0.3)
Exponential Cumulative Distribution Function
Description
Exponential Cumulative Distribution Function
Usage
prob_exp(x, rate = 1)prob_exp(x, rate = 1)
Arguments
x |
A numeric vector of quantiles. |
rate |
A non-negative numeric vector of rate. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_exp(c(0, 1, 2), 2)prob_exp(c(0, 1, 2), 2)
Gamma Cumulative Distribution Function
Description
Gamma Cumulative Distribution Function
Usage
prob_gamma(x, shape = 1, rate = 1)prob_gamma(x, shape = 1, rate = 1)
Arguments
x |
A numeric vector of quantiles. |
shape |
A non-negative numeric vector of shape. |
rate |
A non-negative numeric vector of rate. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_gamma(c(0, 1, 2), 1, 2)prob_gamma(c(0, 1, 2), 1, 2)
Gamma-Poisson Cumulative Distribution Function
Description
Gamma-Poisson Cumulative Distribution Function
Usage
prob_gamma_pois(x, lambda = 1, theta = 0)prob_gamma_pois(x, lambda = 1, theta = 0)
Arguments
x |
A numeric vector of quantiles. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_gamma_pois(c(0, 1, 2), 1, 1)prob_gamma_pois(c(0, 1, 2), 1, 1)
Zero-Inflated Gamma-Poisson Cumulative Distribution Function
Description
Zero-Inflated Gamma-Poisson Cumulative Distribution Function
Usage
prob_gamma_pois_zi(x, lambda = 1, theta = 0, prob = 0)prob_gamma_pois_zi(x, lambda = 1, theta = 0, prob = 0)
Arguments
x |
A numeric vector of quantiles. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_gamma_pois_zi(c(1, 3, 4), 3, 1, prob = 0.5)prob_gamma_pois_zi(c(1, 3, 4), 3, 1, prob = 0.5)
Log-Normal Cumulative Distribution Function
Description
Log-Normal Cumulative Distribution Function
Usage
prob_lnorm(x, meanlog = 0, sdlog = 1)prob_lnorm(x, meanlog = 0, sdlog = 1)
Arguments
x |
A numeric vector of quantiles. |
meanlog |
A numeric vector of the means on the log scale. |
sdlog |
A non-negative numeric vector of the standard deviations on the log scale. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_lnorm(10, 0, 2)prob_lnorm(10, 0, 2)
Negative Binomial Cumulative Distribution Function
Description
Negative Binomial Cumulative Distribution Function
Usage
prob_neg_binom(x, lambda = 1, theta = 0)prob_neg_binom(x, lambda = 1, theta = 0)
Arguments
x |
A numeric vector of quantiles. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_neg_binom(c(0, 1, 2), 2, 1)prob_neg_binom(c(0, 1, 2), 2, 1)
Normal Cumulative Distribution Function
Description
Normal Cumulative Distribution Function
Usage
prob_norm(x, mean = 0, sd = 1)prob_norm(x, mean = 0, sd = 1)
Arguments
x |
A numeric vector of quantiles. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_norm(c(-2:2))prob_norm(c(-2:2))
Poisson Cumulative Distribution Function
Description
Poisson Cumulative Distribution Function
Usage
prob_pois(x, lambda = 1)prob_pois(x, lambda = 1)
Arguments
x |
A numeric vector of quantiles. |
lambda |
A non-negative numeric vector of means. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois_zi(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_pois(c(1, 3, 4), 3)prob_pois(c(1, 3, 4), 3)
Zero-Inflated Poisson Cumulative Distribution Function
Description
Zero-Inflated Poisson Cumulative Distribution Function
Usage
prob_pois_zi(x, lambda = 1, prob = 0)prob_pois_zi(x, lambda = 1, prob = 0)
Arguments
x |
A numeric vector of quantiles. |
lambda |
A non-negative numeric vector of means. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_skewnorm(),
prob_student(),
prob_unif()
Examples
prob_pois_zi(c(1, 3, 4), 3, prob = 0.5)prob_pois_zi(c(1, 3, 4), 3, prob = 0.5)
Skew Normal Cumulative Distribution Function
Description
Skew Normal Cumulative Distribution Function
Usage
prob_skewnorm(x, mean = 0, sd = 1, shape = 0)prob_skewnorm(x, mean = 0, sd = 1, shape = 0)
Arguments
x |
A numeric vector of quantiles. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
shape |
A numeric vector of shape. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_student(),
prob_unif()
Examples
prob_skewnorm(c(-2:2)) prob_skewnorm(c(-2:2), shape = -2) prob_skewnorm(c(-2:2), shape = 2)prob_skewnorm(c(-2:2)) prob_skewnorm(c(-2:2), shape = -2) prob_skewnorm(c(-2:2), shape = 2)
Student's t Cumulative Distribution Function
Description
Student's t Cumulative Distribution Function
Usage
prob_student(x, mean = 0, sd = 1, theta = 0)prob_student(x, mean = 0, sd = 1, theta = 0)
Arguments
x |
A numeric vector of quantiles. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_unif()
Examples
prob_student(c(1, 3.5, 4), mean = 1, sd = 2, theta = 1 / 3)prob_student(c(1, 3.5, 4), mean = 1, sd = 2, theta = 1 / 3)
Uniform Cumulative Distribution Function
Description
Uniform Cumulative Distribution Function
Usage
prob_unif(x, min = 0, max = 1)prob_unif(x, min = 0, max = 1)
Arguments
x |
A numeric vector of quantiles. |
min |
A numeric vector of the minimums. |
max |
A numeric vector of the maximums. |
Value
An numeric vector of the corresponding probabilities.
See Also
Other prob_dist:
prob_bern(),
prob_beta(),
prob_beta_binom(),
prob_binom(),
prob_exp(),
prob_gamma(),
prob_gamma_pois(),
prob_gamma_pois_zi(),
prob_lnorm(),
prob_neg_binom(),
prob_norm(),
prob_pois(),
prob_pois_zi(),
prob_skewnorm(),
prob_student()
Examples
prob_unif(c(0, 0.5, 1))prob_unif(c(0, 0.5, 1))
Probability of Direction
Description
The probability of direction (PD) is the proportion of the (posterior) distribution above (right) or below (left) a threshold.
Usage
probability_direction(x, side = "median", threshold = 0, na_rm = FALSE)probability_direction(x, side = "median", threshold = 0, na_rm = FALSE)
Arguments
x |
A numeric vector of MCMC values. |
side |
A character vector of length 1 indicating whether to calculate
the directional probability for the left tail ( |
threshold |
A number of the threshold value, which is excluded from the interval for the probability. |
na_rm |
A flag specifying whether to remove missing values. |
Details
By default, the direction is based on the side of the median value, but it
can be specified to measure support for specific hypotheses.
A right-side PD of 0.9 indicates that the interval spanning from the
threshold to infinity has a coverage of 90%.
Can be used as a measure of certainty in the direction of the estimate
(e.g., positive or negative when using a threshold of 0).
NOTE: probability estimates of 0 or 1 are corrected towards 0.5 by adding
or subtracting 1 / (length(x) + 1), where x is a vector of MCMC samples.
Ideally, x should be large enough as to make the correction negligible.
Value
A number between 0 and 1.
If x has NA values but na_rm is FALSE, returns NA_real.
References
Makowski, D., Ben-Shachar, M.S., Chen, S.H.A., and Lüdecke, D. 2019. Indices of Effect Existence and Significance in the Bayesian Framework. Front. Psychol. 10: 2767. doi:10.3389/fpsyg.2019.02767.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
x <- rnorm(1e6, qnorm(0.05, lower.tail = TRUE)) probability_direction(x, side = "left") probability_direction(x, side = "right") # = 1 - probability_direction(x, side = "left") probability_direction(c(0, 0, 1), side = "right") # returns P(X >0) = 1/3 instead of P(X >= 0) = 1 probability_direction(c(1, 1), side = "right") # p = 1 - 1/(n+1)x <- rnorm(1e6, qnorm(0.05, lower.tail = TRUE)) probability_direction(x, side = "left") probability_direction(x, side = "right") # = 1 - probability_direction(x, side = "left") probability_direction(c(0, 0, 1), side = "right") # returns P(X >0) = 1/3 instead of P(X >= 0) = 1 probability_direction(c(1, 1), side = "right") # p = 1 - 1/(n+1)
Proportional Change
Description
Calculates the proportional change for two sets of numbers.
Usage
proportional_change(x, x2)proportional_change(x, x2)
Arguments
x |
A numeric object (vector, matrix or array) of non-negative numbers. |
x2 |
A second numeric object of non-negative numbers. |
Value
A numeric object of the proportional change.
See Also
Other proportional:
proportional_difference()
Examples
proportional_change(1, 2) proportional_change(2, 1)proportional_change(1, 2) proportional_change(2, 1)
Proportional Change2
Description
Calculates the proportional change for a vector of two non-negative numbers.
Usage
proportional_change2(x)proportional_change2(x)
Arguments
x |
A numeric vector of length 2. |
Value
A number.
See Also
Other proportional fun2:
proportional_difference2()
Examples
proportional_change2(c(1, 2)) proportional_change2(c(2, 1))proportional_change2(c(1, 2)) proportional_change2(c(2, 1))
Proportional Difference
Description
Calculates the proportional difference for two sets of numbers.
Usage
proportional_difference(x, x2)proportional_difference(x, x2)
Arguments
x |
A numeric object (vector, matrix or array) of non-negative numbers. |
x2 |
A second numeric object of non-negative numbers. |
Value
A numeric object of the proportional change.
See Also
Other proportional:
proportional_change()
Examples
proportional_difference(1, 2) proportional_difference(2, 1)proportional_difference(1, 2) proportional_difference(2, 1)
Proportional Difference2
Description
Calculates the proportional difference for a vector of two non-negative numbers.
Usage
proportional_difference2(x)proportional_difference2(x)
Arguments
x |
A numeric vector of length 2. |
Value
A number.
See Also
Other proportional fun2:
proportional_change2()
Examples
proportional_difference2(c(1, 2)) proportional_difference2(c(2, 1))proportional_difference2(c(1, 2)) proportional_difference2(c(2, 1))
Bayesian P-Value
Description
A Bayesian p-value (p) is here defined in terms of the quantile-based (1-p) * 100% credible interval (CRI) that just includes a threshold (Kery and Schaub 2011). By default a p-value of 0.05 indicates that the 95% CRI just includes the threshold value.
Note that the function contains the sample-size correction
to avoid p-values of 0. The function can still
return p-values of 1.
To use as a measure of certainty in the direction of the estimate (i.e.,
positive or negative), see probability_direction().
For p-values converted to bits, see svalue().
To convert MCMC objects to information, see directional_information().
Usage
pvalue(x, side = "both", threshold = 0, na_rm = FALSE)pvalue(x, side = "both", threshold = 0, na_rm = FALSE)
Arguments
x |
A numeric vector of MCMC values. |
side |
A character vector of length 1 indicating whether to calculate
p-values for the left tail ( |
threshold |
A number of the threshold value. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A number between 0 and 1.
If x has NA values but na_rm is FALSE, returns NA_real.
References
Kery, M., and Schaub, M. 2011. Bayesian population analysis using WinBUGS: a hierarchical perspective. Academic Press, Boston. Available from https://www.vogelwarte.ch/en/research/population-biology/book-bpa/.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
x <- rnorm(1e6, qnorm(0.05, lower.tail = TRUE)) pvalue(x) # should be 0.05 * 2 pvalue(x, side = "left") # should be 0.95 pvalue(x, side = "right") # should be 0.05x <- rnorm(1e6, qnorm(0.05, lower.tail = TRUE)) pvalue(x) # should be 0.05 * 2 pvalue(x, side = "left") # should be 0.95 pvalue(x, side = "right") # should be 0.05
Proportion of Zeros
Description
The proportion of zeros in an numeric object.
Usage
pzeros(x, na_rm = FALSE)pzeros(x, na_rm = FALSE)
Arguments
x |
A numeric object of MCMC values. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A number between 0 and 1.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
pzeros(c(0:2))pzeros(c(0:2))
Bernoulli Quantile Function
Description
Bernoulli Quantile Function
Usage
quant_bern(x, prob = 0.5)quant_bern(x, prob = 0.5)
Arguments
x |
A numeric vector of probabilities. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_bern(c(0.3, 0.8), 0.7)quant_bern(c(0.3, 0.8), 0.7)
Beta Quantile Function
Description
Beta Quantile Function
Usage
quant_beta(x, alpha = 1, beta = 1)quant_beta(x, alpha = 1, beta = 1)
Arguments
x |
A numeric vector of probabilities. |
alpha |
The first shape parameter of the beta distribution. |
beta |
The second shape parameter of the beta distribution. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_beta(c(0.1, 0.4, 0.6), 2, 3)quant_beta(c(0.1, 0.4, 0.6), 2, 3)
Binomial Quantile Function
Description
Binomial Quantile Function
Usage
quant_binom(x, size = 1, prob = 0.5)quant_binom(x, size = 1, prob = 0.5)
Arguments
x |
A numeric vector of probabilities. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_binom(c(0.1, 0.4, 0.6), 2, 0.3)quant_binom(c(0.1, 0.4, 0.6), 2, 0.3)
Exponential Quantile Function
Description
Exponential Quantile Function
Usage
quant_exp(x, rate = 1)quant_exp(x, rate = 1)
Arguments
x |
A numeric vector of probabilities. |
rate |
A non-negative numeric vector of rate. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_exp(c(0.1, 0.4, 0.6), 2)quant_exp(c(0.1, 0.4, 0.6), 2)
Gamma Quantile Function
Description
Gamma Quantile Function
Usage
quant_gamma(x, shape = 1, rate = 1)quant_gamma(x, shape = 1, rate = 1)
Arguments
x |
A numeric vector of probabilities. |
shape |
A non-negative numeric vector of shape. |
rate |
A non-negative numeric vector of rate. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_gamma(c(0.1, 0.4, 0.6), 1, 2)quant_gamma(c(0.1, 0.4, 0.6), 1, 2)
Gamma-Poisson Quantile Function
Description
Gamma-Poisson Quantile Function
Usage
quant_gamma_pois(x, lambda = 1, theta = 0)quant_gamma_pois(x, lambda = 1, theta = 0)
Arguments
x |
A numeric vector of probabilities. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_gamma_pois(c(0.1, 0.4, 0.6), 1, 1)quant_gamma_pois(c(0.1, 0.4, 0.6), 1, 1)
Zero-Inflated Gamma-Poisson Quantile Function
Description
Zero-Inflated Gamma-Poisson Quantile Function
Usage
quant_gamma_pois_zi(x, lambda = 1, theta = 0, prob = 0)quant_gamma_pois_zi(x, lambda = 1, theta = 0, prob = 0)
Arguments
x |
A numeric vector of probabilities. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_gamma_pois_zi(c(0.1, 0.4, 0.6), 3, 1, prob = 0.5)quant_gamma_pois_zi(c(0.1, 0.4, 0.6), 3, 1, prob = 0.5)
Log-Normal Quantile Function
Description
Log-Normal Quantile Function
Usage
quant_lnorm(x, meanlog = 0, sdlog = 1)quant_lnorm(x, meanlog = 0, sdlog = 1)
Arguments
x |
A numeric vector of probabilities. |
meanlog |
A numeric vector of the means on the log scale. |
sdlog |
A non-negative numeric vector of the standard deviations on the log scale. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_lnorm(c(0.1, 0.4, 0.6), 0, 2)quant_lnorm(c(0.1, 0.4, 0.6), 0, 2)
Negative Binomial Quantile Function
Description
Negative Binomial Quantile Function
Usage
quant_neg_binom(x, lambda = 1, theta = 0)quant_neg_binom(x, lambda = 1, theta = 0)
Arguments
x |
A numeric vector of probabilities. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_neg_binom(c(0.1, 0.4, 0.6), 2, 1)quant_neg_binom(c(0.1, 0.4, 0.6), 2, 1)
Normal Quantile Function
Description
Normal Quantile Function
Usage
quant_norm(x, mean = 0, sd = 1)quant_norm(x, mean = 0, sd = 1)
Arguments
x |
A numeric vector of probabilities. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_norm(c(0.1, 0.4, 0.6))quant_norm(c(0.1, 0.4, 0.6))
Poisson Quantile Function
Description
Poisson Quantile Function
Usage
quant_pois(x, lambda = 1)quant_pois(x, lambda = 1)
Arguments
x |
A numeric vector of probabilities. |
lambda |
A non-negative numeric vector of means. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois_zi(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_pois(c(0.1, 0.4, 0.6), 3)quant_pois(c(0.1, 0.4, 0.6), 3)
Zero-Inflated Poisson Quantile Function
Description
Zero-Inflated Poisson Quantile Function
Usage
quant_pois_zi(x, lambda = 1, prob = 0)quant_pois_zi(x, lambda = 1, prob = 0)
Arguments
x |
A numeric vector of probabilities. |
lambda |
A non-negative numeric vector of means. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_skewnorm(),
quant_student(),
quant_unif()
Examples
quant_pois_zi(c(0.1, 0.4, 0.6), 3, prob = 0.5)quant_pois_zi(c(0.1, 0.4, 0.6), 3, prob = 0.5)
Skew Normal Quantile Function
Description
Skew Normal Quantile Function
Usage
quant_skewnorm(x, mean = 0, sd = 1, shape = 0)quant_skewnorm(x, mean = 0, sd = 1, shape = 0)
Arguments
x |
A numeric vector of probabilities. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
shape |
A numeric vector of shape. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_student(),
quant_unif()
Examples
quant_skewnorm(c(0.1, 0.4, 0.6)) quant_skewnorm(c(0.1, 0.4, 0.6), shape = -2) quant_skewnorm(c(0.1, 0.4, 0.6), shape = 2)quant_skewnorm(c(0.1, 0.4, 0.6)) quant_skewnorm(c(0.1, 0.4, 0.6), shape = -2) quant_skewnorm(c(0.1, 0.4, 0.6), shape = 2)
Student's t Quantile Function
Description
Student's t Quantile Function
Usage
quant_student(x, mean = 0, sd = 1, theta = 0)quant_student(x, mean = 0, sd = 1, theta = 0)
Arguments
x |
A numeric vector of probabilities. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_unif()
Examples
quant_student(c(0.1, 0.4, 0.6), mean = 1, sd = 2, theta = 1 / 3)quant_student(c(0.1, 0.4, 0.6), mean = 1, sd = 2, theta = 1 / 3)
Uniform Quantile Function
Description
Uniform Quantile Function
Usage
quant_unif(x, min = 0, max = 1)quant_unif(x, min = 0, max = 1)
Arguments
x |
A numeric vector of probabilities. |
min |
A numeric vector of the minimums. |
max |
A numeric vector of the maximums. |
Value
An numeric vector of the corresponding quantiles.
See Also
Other quant_dist:
quant_bern(),
quant_beta(),
quant_binom(),
quant_exp(),
quant_gamma(),
quant_gamma_pois(),
quant_gamma_pois_zi(),
quant_lnorm(),
quant_neg_binom(),
quant_norm(),
quant_pois(),
quant_pois_zi(),
quant_skewnorm(),
quant_student()
Examples
quant_unif(c(0.1, 0.4, 0.6))quant_unif(c(0.1, 0.4, 0.6))
Bernoulli Random Samples
Description
Bernoulli Random Samples
Usage
ran_bern(n = 1, prob = 0.5)ran_bern(n = 1, prob = 0.5)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_beta_binom(),
ran_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois(),
ran_pois_zi(),
ran_skewnorm(),
ran_student()
Examples
ran_bern(10)ran_bern(10)
Beta-Binomial Random Samples
Description
This parameterization of the beta-binomial distribution uses an expected
probability parameter, prob, and a dispersion parameter, theta. The
parameters of the underlying beta mixture are alpha = (2 * prob) / theta
and beta = (2 * (1 - prob)) / theta. This parameterization of theta is
unconventional, but has useful properties when modelling. When theta = 0,
the beta-binomial reverts to the binomial distribution. When theta = 1 and
prob = 0.5, the parameters of the beta distribution become alpha = 1 and
beta = 1, which correspond to a uniform distribution for the beta-binomial
probability parameter.
Usage
ran_beta_binom(n = 1, size = 1, prob = 0.5, theta = 0)ran_beta_binom(n = 1, size = 1, prob = 0.5, theta = 0)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois(),
ran_pois_zi(),
ran_skewnorm(),
ran_student()
Examples
ran_beta_binom(10, 1, 0.5, 0)ran_beta_binom(10, 1, 0.5, 0)
Binomial Random Samples
Description
Binomial Random Samples
Usage
ran_binom(n = 1, size = 1, prob = 0.5)ran_binom(n = 1, size = 1, prob = 0.5)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois(),
ran_pois_zi(),
ran_skewnorm(),
ran_student()
Examples
ran_binom(10)ran_binom(10)
Gamma Random Samples
Description
Gamma Random Samples
Usage
ran_gamma(n = 1, shape = 1, rate = 1)ran_gamma(n = 1, shape = 1, rate = 1)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
shape |
A non-negative numeric vector of shape. |
rate |
A non-negative numeric vector of rate. |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_binom(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois(),
ran_pois_zi(),
ran_skewnorm(),
ran_student()
Examples
ran_gamma(10)ran_gamma(10)
Gamma-Poisson Random Samples
Description
Gamma-Poisson Random Samples
Usage
ran_gamma_pois(n = 1, lambda = 1, theta = 0)ran_gamma_pois(n = 1, lambda = 1, theta = 0)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_binom(),
ran_gamma(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois(),
ran_pois_zi(),
ran_skewnorm(),
ran_student()
Examples
ran_gamma_pois(10, theta = 1)ran_gamma_pois(10, theta = 1)
Zero-Inflated Gamma-Poisson Random Samples
Description
Zero-Inflated Gamma-Poisson Random Samples
Usage
ran_gamma_pois_zi(n = 1, lambda = 1, theta = 0, prob = 0)ran_gamma_pois_zi(n = 1, lambda = 1, theta = 0, prob = 0)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois(),
ran_pois_zi(),
ran_skewnorm(),
ran_student()
Examples
ran_gamma_pois_zi(10, lambda = 3, theta = 1, prob = 0.5)ran_gamma_pois_zi(10, lambda = 3, theta = 1, prob = 0.5)
Log-Normal Random Samples
Description
Log-Normal Random Samples
Usage
ran_lnorm(n = 1, meanlog = 0, sdlog = 1)ran_lnorm(n = 1, meanlog = 0, sdlog = 1)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
meanlog |
A numeric vector of the means on the log scale. |
sdlog |
A non-negative numeric vector of the standard deviations on the log scale. |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_neg_binom(),
ran_norm(),
ran_pois(),
ran_pois_zi(),
ran_skewnorm(),
ran_student()
Examples
ran_lnorm(10)ran_lnorm(10)
Negative Binomial Random Samples
Description
Identical to Gamma-Poisson Random Samples.
Usage
ran_neg_binom(n = 1, lambda = 1, theta = 0)ran_neg_binom(n = 1, lambda = 1, theta = 0)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_norm(),
ran_pois(),
ran_pois_zi(),
ran_skewnorm(),
ran_student()
Examples
ran_neg_binom(10, theta = 1)ran_neg_binom(10, theta = 1)
Normal Random Samples
Description
Normal Random Samples
Usage
ran_norm(n = 1, mean = 0, sd = 1)ran_norm(n = 1, mean = 0, sd = 1)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_neg_binom(),
ran_pois(),
ran_pois_zi(),
ran_skewnorm(),
ran_student()
Examples
ran_norm(10)ran_norm(10)
Poisson Random Samples
Description
Poisson Random Samples
Usage
ran_pois(n = 1, lambda = 1)ran_pois(n = 1, lambda = 1)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
lambda |
A non-negative numeric vector of means. |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois_zi(),
ran_skewnorm(),
ran_student()
Examples
ran_pois(10)ran_pois(10)
Zero-Inflated Poisson Random Samples
Description
Zero-Inflated Poisson Random Samples
Usage
ran_pois_zi(n = 1, lambda = 1, prob = 0)ran_pois_zi(n = 1, lambda = 1, prob = 0)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
lambda |
A non-negative numeric vector of means. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois(),
ran_skewnorm(),
ran_student()
Examples
ran_pois_zi(10, prob = 0.5)ran_pois_zi(10, prob = 0.5)
Skew Normal Random Samples
Description
Skew Normal Random Samples
Usage
ran_skewnorm(n = 1, mean = 0, sd = 1, shape = 0)ran_skewnorm(n = 1, mean = 0, sd = 1, shape = 0)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
shape |
A numeric vector of shape. |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois(),
ran_pois_zi(),
ran_student()
Examples
ran_skewnorm(10, shape = -1) ran_skewnorm(10, shape = 0) ran_skewnorm(10, shape = 1)ran_skewnorm(10, shape = -1) ran_skewnorm(10, shape = 0) ran_skewnorm(10, shape = 1)
Student's t Random Samples
Description
Student's t Random Samples
Usage
ran_student(n = 1, mean = 0, sd = 1, theta = 0)ran_student(n = 1, mean = 0, sd = 1, theta = 0)
Arguments
n |
A non-negative whole number of the number of random samples to generate. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
Value
A numeric vector of the random samples.
See Also
Other ran_dist:
ran_bern(),
ran_beta_binom(),
ran_binom(),
ran_gamma(),
ran_gamma_pois(),
ran_gamma_pois_zi(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois(),
ran_pois_zi(),
ran_skewnorm()
Examples
ran_student(10, theta = 1 / 2)ran_student(10, theta = 1 / 2)
Bernoulli Residuals
Description
Bernoulli Residuals
Usage
res_bern(x, prob = 0.5, type = "dev", simulate = FALSE)res_bern(x, prob = 0.5, type = "dev", simulate = FALSE)
Arguments
x |
A vector of 0s and 1s. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_beta_binom(),
res_binom(),
res_gamma(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois(),
res_pois_zi(),
res_skewnorm(),
res_student()
Examples
res_bern(c(TRUE, FALSE), 0.7)res_bern(c(TRUE, FALSE), 0.7)
Beta-Binomial Residuals
Description
This parameterization of the beta-binomial distribution uses an expected
probability parameter, prob, and a dispersion parameter, theta. The
parameters of the underlying beta mixture are alpha = (2 * prob) / theta
and beta = (2 * (1 - prob)) / theta. This parameterization of theta is
unconventional, but has useful properties when modelling. When theta = 0,
the beta-binomial reverts to the binomial distribution. When theta = 1 and
prob = 0.5, the parameters of the beta distribution become alpha = 1 and
beta = 1, which correspond to a uniform distribution for the beta-binomial
probability parameter.
Usage
res_beta_binom( x, size = 1, prob = 0.5, theta = 0, type = "dev", simulate = FALSE )res_beta_binom( x, size = 1, prob = 0.5, theta = 0, type = "dev", simulate = FALSE )
Arguments
x |
A non-negative whole numeric vector of values. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_binom(),
res_gamma(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois(),
res_pois_zi(),
res_skewnorm(),
res_student()
Examples
res_beta_binom(c(0, 1, 2), 4, 0.5, 0.1)res_beta_binom(c(0, 1, 2), 4, 0.5, 0.1)
Binomial Residuals
Description
Binomial Residuals
Usage
res_binom(x, size = 1, prob = 0.5, type = "dev", simulate = FALSE)res_binom(x, size = 1, prob = 0.5, type = "dev", simulate = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
size |
A non-negative whole numeric vector of the number of trials. |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_gamma(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois(),
res_pois_zi(),
res_skewnorm(),
res_student()
Examples
res_binom(c(0, 1, 2), 2, 0.3)res_binom(c(0, 1, 2), 2, 0.3)
Gamma Residuals
Description
Gamma Residuals
Usage
res_gamma(x, shape = 1, rate = 1, type = "dev", simulate = FALSE)res_gamma(x, shape = 1, rate = 1, type = "dev", simulate = FALSE)
Arguments
x |
A numeric vector of values. |
shape |
A non-negative numeric vector of shape. |
rate |
A non-negative numeric vector of rate. |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_binom(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois(),
res_pois_zi(),
res_skewnorm(),
res_student()
Examples
res_gamma(c(0, 1, 2), 1, 2)res_gamma(c(0, 1, 2), 1, 2)
Gamma-Poisson Residuals
Description
Gamma-Poisson Residuals
Usage
res_gamma_pois(x, lambda = 1, theta = 0, type = "dev", simulate = FALSE)res_gamma_pois(x, lambda = 1, theta = 0, type = "dev", simulate = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_binom(),
res_gamma(),
res_gamma_pois_zi(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois(),
res_pois_zi(),
res_skewnorm(),
res_student()
Examples
res_gamma_pois(c(0, 1, 2), 1, 1)res_gamma_pois(c(0, 1, 2), 1, 1)
Zero-Inflated Gamma-Poisson Residuals
Description
Zero-Inflated Gamma-Poisson Residuals
Usage
res_gamma_pois_zi( x, lambda = 1, theta = 0, prob = 0, type = "dev", simulate = FALSE )res_gamma_pois_zi( x, lambda = 1, theta = 0, prob = 0, type = "dev", simulate = FALSE )
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
prob |
A numeric vector of values between 0 and 1 of the probability of zero-inflation. |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_binom(),
res_gamma(),
res_gamma_pois(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois(),
res_pois_zi(),
res_skewnorm(),
res_student()
Examples
res_gamma_pois_zi(c(0, 1, 2), 1, 1, 0.5)res_gamma_pois_zi(c(0, 1, 2), 1, 1, 0.5)
Log-Normal Residuals
Description
Log-Normal Residuals
Usage
res_lnorm(x, meanlog = 0, sdlog = 1, type = "dev", simulate = FALSE)res_lnorm(x, meanlog = 0, sdlog = 1, type = "dev", simulate = FALSE)
Arguments
x |
A numeric vector of values. |
meanlog |
A numeric vector of the means on the log scale. |
sdlog |
A non-negative numeric vector of the standard deviations on the log scale. |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_binom(),
res_gamma(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_neg_binom(),
res_norm(),
res_pois(),
res_pois_zi(),
res_skewnorm(),
res_student()
Examples
res_lnorm(10)res_lnorm(10)
Negative Binomial Residuals
Description
Negative Binomial Residuals
Usage
res_neg_binom(x, lambda = 1, theta = 0, type = "dev", simulate = FALSE)res_neg_binom(x, lambda = 1, theta = 0, type = "dev", simulate = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_binom(),
res_gamma(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_lnorm(),
res_norm(),
res_pois(),
res_pois_zi(),
res_skewnorm(),
res_student()
Examples
res_neg_binom(c(0, 1, 5), 2, 3)res_neg_binom(c(0, 1, 5), 2, 3)
Normal Residuals
Description
Normal Residuals
Usage
res_norm(x, mean = 0, sd = 1, type = "dev", simulate = FALSE)res_norm(x, mean = 0, sd = 1, type = "dev", simulate = FALSE)
Arguments
x |
A numeric vector of values. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_binom(),
res_gamma(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_lnorm(),
res_neg_binom(),
res_pois(),
res_pois_zi(),
res_skewnorm(),
res_student()
Examples
res_norm(c(-2:2))res_norm(c(-2:2))
Poisson Residuals
Description
Poisson Residuals
Usage
res_pois(x, lambda = 1, type = "dev", simulate = FALSE)res_pois(x, lambda = 1, type = "dev", simulate = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_binom(),
res_gamma(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois_zi(),
res_skewnorm(),
res_student()
Examples
res_pois(c(1, 3, 4), 3)res_pois(c(1, 3, 4), 3)
Zero-Inflated Poisson Residuals
Description
Zero-Inflated Poisson Residuals
Usage
res_pois_zi(x, lambda = 1, prob = 0, type = "dev", simulate = FALSE)res_pois_zi(x, lambda = 1, prob = 0, type = "dev", simulate = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
lambda |
A non-negative numeric vector of means. |
prob |
A numeric vector of values between 0 and 1 of the probability of zero-inflation. |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_binom(),
res_gamma(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois(),
res_skewnorm(),
res_student()
Examples
res_pois_zi(c(1, 3, 4), 6, 0.5, type = "raw")res_pois_zi(c(1, 3, 4), 6, 0.5, type = "raw")
Skew Normal Residuals
Description
Skew Normal Residuals
Usage
res_skewnorm(x, mean = 0, sd = 1, shape = 0, type = "dev", simulate = FALSE)res_skewnorm(x, mean = 0, sd = 1, shape = 0, type = "dev", simulate = FALSE)
Arguments
x |
A numeric vector of values. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
shape |
A numeric vector of shape. |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_binom(),
res_gamma(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois(),
res_pois_zi(),
res_student()
Examples
res_skewnorm(c(-2:2))res_skewnorm(c(-2:2))
Student's t Residuals
Description
Student's t Residuals
Usage
res_student(x, mean = 0, sd = 1, theta = 0, type = "dev", simulate = FALSE)res_student(x, mean = 0, sd = 1, theta = 0, type = "dev", simulate = FALSE)
Arguments
x |
A non-negative whole numeric vector of values. |
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
type |
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data. |
simulate |
A flag specifying whether to simulate residuals. |
Value
An numeric vector of the corresponding residuals.
See Also
Other res_dist:
res_bern(),
res_beta_binom(),
res_binom(),
res_gamma(),
res_gamma_pois(),
res_gamma_pois_zi(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois(),
res_pois_zi(),
res_skewnorm()
Examples
res_student(c(1, 3.5, 4), mean = 6, sd = 0.5, theta = 1 / 3, type = "raw")res_student(c(1, 3.5, 4), mean = 6, sd = 0.5, theta = 1 / 3, type = "raw")
Adjust Beta Distribution Parameters for Sensitivity Analyses
Description
Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation
of the Beta distribution. The Beta distribution has a maximum variance of
mean(x) * (1 - mean(x), where mean(x) = alpha / (alpha + beta). If the
inputs produce a desired variance that is greater than the maximum possible
variance, or provides alpha and/or beta parameters that are < 1 and thus
push more probability weight towards extreme probability values, this
function returns alpha = 1 and beta = 1 (the uniform distribution).
Usage
sens_beta(alpha, beta, sd_mult = 2)sens_beta(alpha, beta, sd_mult = 2)
Arguments
alpha |
The first shape parameter of the beta distribution. |
beta |
The second shape parameter of the beta distribution. |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_exp(),
sens_gamma(),
sens_gamma_pois(),
sens_gamma_pois_zi(),
sens_lnorm(),
sens_neg_binom(),
sens_norm(),
sens_pois(),
sens_skewnorm(),
sens_student()
Examples
sens_beta(10, 10, 2) sens_beta(10, 10, 0.8)sens_beta(10, 10, 2) sens_beta(10, 10, 0.8)
Adjust Exponential Distribution Parameters for Sensitivity Analyses
Description
Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation
of the exponential distribution. Due to the parameterization of this
distribution, adjusting the standard deviation necessarily changes the mean
value.
Usage
sens_exp(rate, sd_mult = 2)sens_exp(rate, sd_mult = 2)
Arguments
rate |
A non-negative numeric vector of rate. |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_beta(),
sens_gamma(),
sens_gamma_pois(),
sens_gamma_pois_zi(),
sens_lnorm(),
sens_neg_binom(),
sens_norm(),
sens_pois(),
sens_skewnorm(),
sens_student()
Examples
sens_exp(10, 2) sens_exp(10, 0.8)sens_exp(10, 2) sens_exp(10, 0.8)
Adjust Gamma Distribution Parameters for Sensitivity Analyses
Description
Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation
of the Gamma distribution.
Usage
sens_gamma(shape, rate, sd_mult = 2)sens_gamma(shape, rate, sd_mult = 2)
Arguments
shape |
A non-negative numeric vector of shape. |
rate |
A non-negative numeric vector of rate. |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_beta(),
sens_exp(),
sens_gamma_pois(),
sens_gamma_pois_zi(),
sens_lnorm(),
sens_neg_binom(),
sens_norm(),
sens_pois(),
sens_skewnorm(),
sens_student()
Examples
sens_gamma(10, 2, 2) sens_gamma(10, 2, 0.2)sens_gamma(10, 2, 2) sens_gamma(10, 2, 0.2)
Adjust Gamma-Poisson Distribution Parameters for Sensitivity Analyses
Description
Expands (sd_mult > 1) the standard deviation of the Negative Binomial
distribution. This function does not currently have the option to reduce the
standard deviation.
Usage
sens_gamma_pois(lambda, theta, sd_mult = 2)sens_gamma_pois(lambda, theta, sd_mult = 2)
Arguments
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_beta(),
sens_exp(),
sens_gamma(),
sens_gamma_pois_zi(),
sens_lnorm(),
sens_neg_binom(),
sens_norm(),
sens_pois(),
sens_skewnorm(),
sens_student()
Examples
sens_gamma_pois(10, 0.1, 2)sens_gamma_pois(10, 0.1, 2)
Adjust Zero-Inflated Gamma-Poisson Distribution Parameters for Sensitivity Analyses
Description
Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation
of the Zero-Inflated Gamma-Poisson distribution.
Usage
sens_gamma_pois_zi(lambda, theta, prob, sd_mult = 2)sens_gamma_pois_zi(lambda, theta, prob, sd_mult = 2)
Arguments
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
prob |
A numeric vector of values between 0 and 1 of the probability of success. |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_beta(),
sens_exp(),
sens_gamma(),
sens_gamma_pois(),
sens_lnorm(),
sens_neg_binom(),
sens_norm(),
sens_pois(),
sens_skewnorm(),
sens_student()
Examples
sens_gamma_pois_zi(10, 0.1, 0.3, 2)sens_gamma_pois_zi(10, 0.1, 0.3, 2)
Adjust Log-Normal Distribution Parameters for Sensitivity Analysis
Description
Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation
of the Log-Normal distribution. With high values of sdlog (i.e., > 9),
and sd_mult > 1, the mean of the adjusted distribution can be expected to
have a mean value that is very different from the original mean, however,
the proportional difference in these values should not be very different.
Usage
sens_lnorm(meanlog, sdlog, sd_mult = 2)sens_lnorm(meanlog, sdlog, sd_mult = 2)
Arguments
meanlog |
A numeric vector of the means on the log scale. |
sdlog |
A non-negative numeric vector of the standard deviations on the log scale. |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_beta(),
sens_exp(),
sens_gamma(),
sens_gamma_pois(),
sens_gamma_pois_zi(),
sens_neg_binom(),
sens_norm(),
sens_pois(),
sens_skewnorm(),
sens_student()
Examples
sens_lnorm(0, 1, 2) sens_lnorm(0, 1, 0.8)sens_lnorm(0, 1, 2) sens_lnorm(0, 1, 0.8)
Adjust Negative Binomial Distribution Parameters for Sensitivity Analyses
Description
Expands (sd_mult > 1) the standard deviation of the Negative Binomial
distribution. This function does not currently have the option to reduce the
standard deviation.
Usage
sens_neg_binom(lambda, theta, sd_mult = 2)sens_neg_binom(lambda, theta, sd_mult = 2)
Arguments
lambda |
A non-negative numeric vector of means. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_beta(),
sens_exp(),
sens_gamma(),
sens_gamma_pois(),
sens_gamma_pois_zi(),
sens_lnorm(),
sens_norm(),
sens_pois(),
sens_skewnorm(),
sens_student()
Examples
sens_neg_binom(10, 0.1, 2)sens_neg_binom(10, 0.1, 2)
Adjust Normal Distribution Parameters for Sensitivity Analyses
Description
Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation
of the Normal distribution without changing the mean.
Usage
sens_norm(mean, sd, sd_mult = 2)sens_norm(mean, sd, sd_mult = 2)
Arguments
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_beta(),
sens_exp(),
sens_gamma(),
sens_gamma_pois(),
sens_gamma_pois_zi(),
sens_lnorm(),
sens_neg_binom(),
sens_pois(),
sens_skewnorm(),
sens_student()
Examples
sens_norm(10, 3, 2) sens_norm(10, 3, 0.8)sens_norm(10, 3, 2) sens_norm(10, 3, 0.8)
Adjust Poisson Distribution Parameters for Sensitivity Analyses
Description
Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation
of the Poisson distribution. Due to the parameterization of this
distribution, adjusting the standard deviation necessarily changes the mean
value.
Usage
sens_pois(lambda, sd_mult = 2)sens_pois(lambda, sd_mult = 2)
Arguments
lambda |
A non-negative numeric vector of means. |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_beta(),
sens_exp(),
sens_gamma(),
sens_gamma_pois(),
sens_gamma_pois_zi(),
sens_lnorm(),
sens_neg_binom(),
sens_norm(),
sens_skewnorm(),
sens_student()
Examples
sens_pois(10, 2) sens_pois(10, 0.8)sens_pois(10, 2) sens_pois(10, 0.8)
Adjust Skew Normal Distribution Parameters for Sensitivity Analyses
Description
Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation
of the Skew Normal distribution without changing the mean.
Usage
sens_skewnorm(mean, sd, shape, sd_mult = 2)sens_skewnorm(mean, sd, shape, sd_mult = 2)
Arguments
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
shape |
A non-negative numeric vector of shape. |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_beta(),
sens_exp(),
sens_gamma(),
sens_gamma_pois(),
sens_gamma_pois_zi(),
sens_lnorm(),
sens_neg_binom(),
sens_norm(),
sens_pois(),
sens_student()
Examples
sens_skewnorm(10, 3, -1, 2) sens_skewnorm(10, 3, 3, 0.8)sens_skewnorm(10, 3, -1, 2) sens_skewnorm(10, 3, 3, 0.8)
Adjust Student's t Distribution Parameters for Sensitivity Analyses
Description
Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation
of the Student's t distribution. Because the variance of this distribution
is not defined for every degree of freedom, the adjustment to the standard
deviation is approximate, and the mean of the adjusted distribution can
be expected to have shifted.
Usage
sens_student(mean, sd, theta, sd_mult = 2)sens_student(mean, sd, theta, sd_mult = 2)
Arguments
mean |
A numeric vector of the means. |
sd |
A non-negative numeric vector of the standard deviations. |
theta |
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial). |
sd_mult |
A non-negative multiplier on the standard deviation of the distribution. |
Value
A named list of the adjusted distribution's parameters.
See Also
Other sens_dist:
sens_beta(),
sens_exp(),
sens_gamma(),
sens_gamma_pois(),
sens_gamma_pois_zi(),
sens_lnorm(),
sens_neg_binom(),
sens_norm(),
sens_pois(),
sens_skewnorm()
Examples
sens_student(10, 3, 0.1, 2) sens_student(10, 3, 0.1, 0.8)sens_student(10, 3, 0.1, 2) sens_student(10, 3, 0.1, 0.8)
Extreme Surprisal
Description
Calculates the surprisal (in bits) that a cumulative distribution function
probability is at least that extreme.
Usage
sextreme(x, directional = FALSE)sextreme(x, directional = FALSE)
Arguments
x |
A numeric vector of values between 0 and 1. |
directional |
A flag specifying whether probabilities less than 0.5 should be returned as negative values. |
Value
A numeric vector of surprisal values.
See Also
Other residuals:
pextreme()
Examples
sextreme(seq(0.1, 0.9, by = 0.1)) sextreme(seq(0.1, 0.9, by = 0.1), directional = TRUE)sextreme(seq(0.1, 0.9, by = 0.1)) sextreme(seq(0.1, 0.9, by = 0.1), directional = TRUE)
Skewness
Description
Skewness
Usage
skewness(x, na_rm = FALSE)skewness(x, na_rm = FALSE)
Arguments
x |
A numeric object of MCMC values. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A number.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
skewness(1:10)skewness(1:10)
Step
Description
Step
Usage
step(x)step(x)
Arguments
x |
A numeric atomic object. |
Value
A logical value.
See Also
Other translations:
exp10(),
exp2(),
fabs(),
ilog(),
ilog10(),
ilog2(),
ilogit(),
inv_logit(),
invlogit(),
log10<-(),
log2<-(),
log<-(),
logit(),
logit<-(),
phi(),
pow()
Examples
step(1)step(1)
Surprisal Value
Description
The surprisal value
(Greenland 2019) is a probability expressed in terms of how many consecutive
heads would have to be thrown on a fair coin in a single attempt to achieve
the same probability: , where is the p-value of
interest. See the details section for some examples.
Usage
svalue(x, side = "both", threshold = 0, na_rm = FALSE) p2svalue(p)svalue(x, side = "both", threshold = 0, na_rm = FALSE) p2svalue(p)
Arguments
x |
A numeric object of MCMC values. |
side |
A character indicating whether to calculate s-values using
p-values for the left tail ( |
threshold |
A number of the threshold value. |
na_rm |
A flag specifying whether to remove missing values. |
p |
A numeric vector of probabilities. |
Details
A near-certain event has an s-value near 0 because it is similar to getting 0 successful coin flips out of 0 tosses, which is certain and unsurprising.
An event with a probability of 0.5 is as surprising as getting a successful coin toss.
A near-impossible event has a very large s-value because its occurrence would be extremely surprising, like observing many consecutive successes on a fair coin.
Value
A non-negative number.
If x has NA values but na_rm is FALSE, returns NA_real.
Functions
-
svalue(): Calculate an s-value from a posterior distribution. -
p2svalue(): Calculate an s-value from a vector of probabilities.
References
Greenland, S. 2019. Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values. The American Statistician 73(sup1): 106–114. doi:10.1080/00031305.2018.1529625.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
svalue(as.numeric(0:100)) svalue(as.numeric(0:100), side = "left") svalue(as.numeric(0:100), side = "right") svalue(rnorm(1e4, mean = 1), side = "left") svalue(rnorm(1e4, mean = 1), side = "right") p2svalue(seq(0, 1, by = 0.1))svalue(as.numeric(0:100)) svalue(as.numeric(0:100), side = "left") svalue(as.numeric(0:100), side = "right") svalue(rnorm(1e4, mean = 1), side = "left") svalue(rnorm(1e4, mean = 1), side = "right") p2svalue(seq(0, 1, by = 0.1))
Upper Credible Limit
Description
Calculates the quantile-based upper credible limit.
Usage
upper(x, conf_level = 0.95, na_rm = FALSE)upper(x, conf_level = 0.95, na_rm = FALSE)
Arguments
x |
A numeric vector of MCMC values. |
conf_level |
A numeric scalar between 0 and 1 specifying the confidence level. |
na_rm |
A flag specifying whether to remove missing values. |
Details
By default it returns the 95% credible limit which corresponds to the 97.5% quantile.
Value
A number.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
upper(as.numeric(0:100))upper(as.numeric(0:100))
Variance
Description
Variance
Usage
variance(x, na_rm = FALSE)variance(x, na_rm = FALSE)
Arguments
x |
A numeric object of MCMC values. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A number.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
variance(1:10)variance(1:10)
Mean
Description
Mean
Usage
xtr_mean(x, na_rm = FALSE)xtr_mean(x, na_rm = FALSE)
Arguments
x |
A numeric object of MCMC values. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A number.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_median(),
xtr_sd(),
zeros(),
zscore()
Examples
xtr_mean(1:10)xtr_mean(1:10)
Median
Description
Median
Usage
xtr_median(x, na_rm = FALSE)xtr_median(x, na_rm = FALSE)
Arguments
x |
A numeric object of MCMC values. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A number.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_sd(),
zeros(),
zscore()
Examples
xtr_mean(1:10)xtr_mean(1:10)
Standard Deviation
Description
Standard Deviation
Usage
xtr_sd(x, na_rm = FALSE)xtr_sd(x, na_rm = FALSE)
Arguments
x |
A numeric object of MCMC values. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A number.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
zeros(),
zscore()
Examples
xtr_sd(1:10)xtr_sd(1:10)
Zeros
Description
The number of zeros in an numeric object.
Usage
zeros(x, na_rm = FALSE)zeros(x, na_rm = FALSE)
Arguments
x |
A numeric object of MCMC values. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A non-negative integer.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zscore()
Examples
zeros(c(0:2))zeros(c(0:2))
Z-Score
Description
The Bayesian z-score is here defined as the number of standard deviations from the mean estimate to zero.
Usage
zscore(x, na_rm = FALSE)zscore(x, na_rm = FALSE)
Arguments
x |
A numeric object of MCMC values. |
na_rm |
A flag specifying whether to remove missing values. |
Value
A number.
See Also
Other summary:
direction(),
directional_information(),
kurtosis(),
lower(),
probability_direction(),
pvalue(),
pzeros(),
skewness(),
svalue(),
upper(),
variance(),
xtr_mean(),
xtr_median(),
xtr_sd(),
zeros()
Examples
zscore(as.numeric(0:100))zscore(as.numeric(0:100))