MIMmodels {mimR}R Documentation

Create undirected MIM models

Description

Create undirected MIM models

Usage

mim(mimFormula, data, fit=TRUE, marginal=data$name)

Arguments

mimFormula A model formula following the MIM syntax. Long variable names are allowed however. See 'details'. The formula can be given either with a tilde or as a string
fit Should the model be fitted if possible
data A gmData object
marginal Can be used for specifying only a subset of the variables in connection with a main effects, a saturated and a homogeneous saturated model

Details

A mim.formula can be "Sex+Drug/Sex:W1+Drug:W1+Sex:W2+Drug:W2/Sex:W1:W2+Drug:W1:W2". A mimFormula can also be "." (the main effects (the independence) model), ".." (the saturated model) or "..h" (the homogeneous saturated model). See 'examples'.

Value

A mim model object

Note

Before using mimR, make sure that the MIM program is runnning.

Author(s)

Søren Højsgaard, sorenh@agrsci.dk

References

David Edwards, An Introduction to Graphical Modelling, Springer Verlag, 2002

See Also

as.gmData

Examples

# Create som models (no data needed!)
gmd  <-  gmData(c("Sex","Drug","W1","W2"),
    factor=c(2,3,FALSE,FALSE),
    vallabels=list("Sex"=c("M","F"), "Drug"=c("D1","D2","D3")))

m12   <- mim("Sex:Drug/Sex:Drug:W1+Sex:Drug:W2/W1:W2", data=gmd)

summary(m12)

m.main <- mim(".",  data=gmd)
m.sat  <- mim("..",  data=gmd)
m.hsat <- mim("..h", data=gmd)

summary(m.main); 
summary(m.sat); 
summary(m.hsat)

# Next we need some data to work with
data(rats)
gmd.rats <- as.gmData(rats)
vallabels(gmd.rats)
observations(gmd.rats)

m1   <- mim("Sex:Drug/Sex:Drug:W1+Sex:Drug:W2/W1:W2", data=gmd.rats)

m.main <- mim(".",   data=gmd.rats, marginal=c("Sex", "Drug", "W1"))
m.sat  <- mim("..",  data=gmd.rats, marginal=c("Sex", "Drug", "W1"))
m.hsat <- mim("..h", data=gmd.rats, marginal=c("Sex", "Drug", "W1"))

m1f  <- fit(m1)

summary(m1f)

m.main <- fit(mim(".",  data=gmd.rats))
m.sat  <- fit(mim("..",  data=gmd.rats))
m.hsat <- fit(mim("..h", data=gmd.rats))

summary(m.main); 
summary(m.sat); 
summary(m.hsat)

# To generate an nth order hierarchical log-linear model for discrete
# data you can do

data(HairEyeColor)
mim(nthOrderModel(names(dimnames(HairEyeColor)),order=2),data=as.gmData(HairEyeColor))


[Package mimR version 2.0.5 Index]