MimModels {mimR}R Documentation

Create undirected and block recursive MIM models

Description

...........

Usage

mim(mimFormula, data, letter=FALSE, marginal=data$name)
chainmim(mimFormula.list, data)

Arguments

mimFormula A text string following the MIM syntax. Long variable names are allowed however. See 'details'.
mimFormula.list A list of mim formulae
data A gmData object
letter If TRUE, the variables used in mim.formula are single letters.
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" or (if letter is TRUE) the shorter form "ab/abx,aby/abxy" or "ab/abx+aby/abxy". A mimFormula can also be "." (the main effects (the independence) model), "*" (the saturated model) or "*h" (the homogeneous saturated model). See 'examples'.

Value

A mimModel or mimBRModel 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, as.mimData

Examples

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

m1   <- mim("Sex:Drug/Sex:Drug:W1+Sex:Drug:W2/W1:W2", data=gmd.rats.nodata)
m2   <- mim("ab/abc+abd/cd", data=gmd.rats.nodata, letter=TRUE)

summary(m1)
summary(m2)

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

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)
m2   <- mim("ab/abc+abd/cd", data=gmd.rats, letter=TRUE)

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)
m2f  <- fit(m2)

summary(m1f)
summary(m2f)

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)


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