MimModels {mimR} | R Documentation |
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mim(mimFormula, data, fit=TRUE, marginal=data$name)
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 |
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'.
A mim model object
Before using mimR, make sure that the MIM program is runnning.
Søren Højsgaard, sorenh@agrsci.dk
David Edwards, An Introduction to Graphical Modelling, Springer Verlag, 2002
# 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"))) m12 <- mim("Sex:Drug/Sex:Drug:W1+Sex:Drug:W2/W1:W2", data=gmd.rats.nodata) summary(m12) 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) 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))