distributions | R Documentation |
Define a distribution for PSA parameters.
normal(mean, sd) lognormal(mean, sd, meanlog, sdlog) gamma(mean, sd) binomial(prob, size) multinomial(...) logitnormal(mu, sigma) beta(shape1, shape2) triangle(lower, upper, peak = (lower + upper)/2) poisson(mean) define_distribution(x) beta(shape1, shape2) triangle(lower, upper, peak = (lower + upper)/2) use_distribution(distribution, smooth = TRUE)
mean |
Distribution mean. |
sd |
Distribution standard deviation. |
meanlog |
Mean on the log scale. |
sdlog |
SD on the log scale. |
prob |
Proportion. |
size |
Size of sample used to estimate proportion. |
... |
Dirichlet distribution parameters. |
mu |
Mean on the logit scale. |
sigma |
SD on the logit scale. |
shape1 |
for beta distribution |
shape2 |
for beta distribution |
lower |
lower bound of triangular distribution. |
upper |
upper bound of triangular distribution. |
peak |
peak of triangular distribution. |
x |
A distribution function, see details. |
distribution |
A numeric vector of observations defining a distribution, usually the output from an MCMC fit. |
smooth |
Use gaussian kernel smoothing? |
These functions are not exported, but only used
in define_psa()
. To specify a user-made
function use define_distribution()
.
use_distribution()
uses gaussian kernel
smoothing with a bandwith parameter calculated
by stats::density()
. Values for unobserved
quantiles are calculated by linear
interpolation.
define_distribution()
takes as argument a
function with a single argument, x
,
corresponding to a vector of quantiles. It
returns the distribution values for the given
quantiles. See examples.
define_distribution( function(x) stats::qexp(p = x, rate = 0.5) ) # a mixture of 2 gaussians x <- c(rnorm(100), rnorm(100, 6)) plot(density(x)) use_distribution(x)
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