gm.modelsim {gmvalid}R Documentation

Random data frames with given dependence model and marginals

Description

Generates a random data frame of discrete variables with a given dependence model and marginals.

Usage

    gm.modelsim(N, model, categories = 0)

Arguments

N Number of observations, sample size.
model A character string assigning a dependence model expressed as clique structure. Each variable has to be expressed as a letter, e.g. "ABC,CDE".
categories a list of weight vectors that assigns the weight of each catogory. Number of list elements must equal the number of variables in model. Default is "list(c(.5,.5),c(.5,.5),...)".

Value

A data frame with number of rows approximately equal to N and number of columns equal to the number of variables used in model.

Note

Observed marginal probabilities reflect the given marginal probabilites only approximatively. Works best with population sizes over N=10,000.

Author(s)

Ronja Foraita, Fabian Sobotka
Bremen Institute for Prevention Research and Social Medicine
(BIPS) http://www.bips.uni-bremen.de

See Also

gm.generate, gm.sim.ixj, r2dtable

Examples

    gm.modelsim(100,"AB,AC")
    table( gm.modelsim(100,"a,b,c") )
    
    tmp.df <- gm.modelsim(10000,"abf,cd,cf,bdeg,bfg")
    
    # with given number of categories
    tmp.df <- gm.modelsim(1000,"AB,C",list(c(1,1,1),c(1,1),c(1,1,1)))

    # with given number of categories and marginals
    tmp.df <- gm.modelsim(1000,"ABC",list(c(0.3,0.3,0.4),c(0.6,.4),c(0.25,0.25,0.5)))
    table(tmp.df)

    ## Not run: 
tmp.df <- gm.modelsim(100,"ABC",list(3,2,3))# (number of categories will be 2 x 2 x 2 )
            gm.modelsim(100,"123")
            
## End(Not run)

[Package gmvalid version 1.0 Index]