| gm.modelsim {gmvalid} | R Documentation |
Generates a random data frame of discrete variables given a dependence model and marginals.
gm.modelsim(N, model, categories = 0)
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),...)".
|
A data frame with number of rows approximately equal to N and number of columns equal
to the number of variables used in model.
Observed marginal probabilities reflect the given marginal probabilites only approximatively. Works best with large sample sizes, like e.g. N=10,000.
Ronja Foraita, Fabian Sobotka
Bremen Institute for Prevention Research and Social Medicine
(BIPS) http://www.bips.uni-bremen.de
gm.generate, gm.sim.ixj, r2dtable
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)