simstudy (version 0.7.0)

distributions: Distributions for Data Definitions

Description

This help file describes the distributions used for data creation in simstudy.

Arguments

formula

Desired mean as a Number or an R expression for mean as a String. Variables defined via defData() and variables within the parent environment (prefixed with ..) can be used within the formula. Functions from the parent environment can be used without a prefix.

variance

Number. Default is 0.

link

String identifying the link function to be used. Default is identity.

Mixture

The mixture distribution makes it possible to mix to previously defined distributions/variables. Each variable that should be part of the new distribution x_1,...,X_n is assigned a probability p_1,...,p_n. For more information see rdatagen.net.

Details

For details about the statistical distributions please see stats::distributions, any non-statistical distributions will be explained below. Required variables and expected pattern for each distribution can be found in this table:

name	formula	format	variance	link
beta	mean	String or Number	dispersion value	identity or logit
binary	probability for 1	String or Number	NA	identity or logit
binomial	probability of success	String or Number	number of trials	identity or logit
categorical	probabilities	p_1;p_2;..;p_n	category labels: a;b;c , 50;130;20	identity or logit
exponential	mean (lambda)	String or Number	NA	identity or log
gamma	mean	String or Number	dispersion value	identity or log
mixture	formula	x_1 |p_1 + x_2|p_2 ... x_n| p_n	NA	NA
negBinomial	mean	String or Number	dispersion value	identity or log
nonrandom	formula	String or Number	NA	NA
normal	mean	String or Number	variance	NA
noZeroPoisson	mean	String or Number	NA	identity or log
poisson	mean	String or Number	NA	identity or log
trtAssign	ratio	r_1;r_2;..;r_n	stratification	identity or nonbalanced
uniform	range	from;to	NA	NA
uniformInt	range	from;to	NA	NA

Examples

ext_var <- 2.9
def <- defData(varname = "external", formula = "3 + log(..ext_var)", variance = .5)
def
genData(5, def)