dbcnorm | R Documentation |
Density, cumulative distribution, quantile functions and random number generation for the distribution that becomes normal after the Box-Cox transformation. Note that this is based on the original Box-Cox paper.
dbcnorm(q, mu = 0, sigma = 1, lambda = 0, log = FALSE)
pbcnorm(q, mu = 0, sigma = 1, lambda = 0)
qbcnorm(p, mu = 0, sigma = 1, lambda = 0)
rbcnorm(n = 1, mu = 0, sigma = 1, lambda = 0)
q |
vector of quantiles. |
mu |
vector of location parameters (means). |
sigma |
vector of scale parameters. |
lambda |
the value of the Box-Cox transform parameter. |
log |
if |
p |
vector of probabilities. |
n |
number of observations. Should be a single number. |
The distribution has the following density function:
f(y) = y^(lambda-1) 1/sqrt(2 pi) exp(-((y^lambda-1)/lambda -mu)^2 / (2 sigma^2))
Both pbcnorm
and qbcnorm
are returned for the lower
tail of the distribution.
In case of lambda=0, the values of the log normal distribution are returned. In case of lambda=1, the values of the normal distribution are returned with mu=mu+1.
All the functions are defined for non-negative values only.
Depending on the function, various things are returned (usually either vector or scalar):
dbcnorm
returns the density function value for the
provided parameters.
pbcnorm
returns the value of the cumulative function
for the provided parameters.
qbcnorm
returns quantiles of the distribution. Depending
on what was provided in p
, mu
and sigma
, this
can be either a vector or a matrix, or an array.
rbcnorm
returns a vector of random variables
generated from the bcnorm distribution. Depending on what was
provided in mu
and sigma
, this can be either a vector
or a matrix or an array.
Ivan Svetunkov, ivan@svetunkov.com
Box, G. E., & Cox, D. R. (1964). An Analysis of Transformations. Journal of the Royal Statistical Society. Series B (Methodological), 26(2), 211–252. Retrieved from https://www.jstor.org/stable/2984418
Distributions
x <- dbcnorm(c(-1000:1000)/200, 0, 1, 1)
plot(c(-1000:1000)/200, x, type="l")
x <- pbcnorm(c(-1000:1000)/200, 0, 1, 1)
plot(c(-1000:1000)/200, x, type="l")
qbcnorm(c(0.025,0.975), 0, c(1,2), 1)
x <- rbcnorm(1000, 0, 1, 1)
hist(x)
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