LINPREDICT {mimR}R Documentation

mimR version of MIM command 'display'

Description

This function returns the conditional mean and covariance of y given x if y is coninuous and the linear predictor of y given x if x is discrete.

Usage

linpredict(mim,y, x=NULL,letter=FALSE, submitData=TRUE, submitModel=TRUE)
displayMIM(y, x=NULL)

Arguments

mim A mim model object
submitData If TRUE data are submitted to MIM, otherwise not. If data is already loaded in MIM, setting submitData=FALSE saves some time
submitModel If TRUE the model is submitted to MIM, otherwise not. If the model is already the model in, setting submitModel=FALSE saves some time
y Vector of response variables
x Vector of explanatory variables
letter If TRUE then x and y contains the variables as letters (instead of as proper names)

Details

The implementation of linpredict is slightly fragile and certainly not elegant. Please use it with caution. The displayMIM function is not intended for the user

Value

Returns a linpredictMIM object which is a list of lists containing the quantities returned.

WARNING

If a and b are discrete and x and y are coninuous, then the MIM command display by,ax will return the linear predictor of b given a,x and the conditional mean and variance of y given a,x.

In mimR, on the other hand, the response types can not be mixed, so you should do it in two steps.

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, Second Edition, Springer Verlag, 2000

See Also

fitted

Examples

data(rats)
gmd.rats <- as.gmData(rats)
m1   <- mim("Sex:Drug/Sex:Drug:W1+Sex:Drug:W2/W1:W2", data=gmd.rats)
mf1  <- fit(m1)
d1  <- linpredict(mf1, y="W2",    x="W1:Sex") 
d2  <- linpredict(mf1, y="Sex",   x="W1:W2")
d3  <- linpredict(mf1, y="W1:W2", x="Sex")
d4  <- linpredict(mf1, y="W1:W2")
d5  <- linpredict(mf1, y="Sex")
d1; d2; d3; d4; d5

[Package Contents]