R: Compute linear estimates

linest {doBy} | R Documentation

## Compute linear estimates

### Description

Compute linear estimates for a range of models. One example of linear estimates is population means (also known as LSMEANS).

### Usage

    linest(object, K=NULL, level=0.95, ...)

### Arguments

`object` | Model object
`K` | Either `NULL` or a matrix with p columns where p is the number of parameters in the systematic effects in the model. If `NULL` then `K` is taken to be the p times p identity matrix
`level` | The level of the (asymptotic) confidence interval.
`...` | Additional arguments; currently not used.

### Value

A dataframe with results from computing the contrasts.

### Author(s)

Søren Højsgaard, sorenh@math.aau.dk

### See Also

`LSmeans` `LSmatrix`

### Examples

    ## Make balanced dataset
    dat.bal <- expand.grid(list(AA=factor(1:2), BB=factor(1:3), CC=factor(1:3)))
    dat.bal$y <- rnorm(nrow(dat.bal))

    ## Make unbalanced dataset
    #   'BB' is nested within 'CC' so BB=1 is only found when CC=1
    #   and BB=2,3 are found in each CC=2,3,4
    dat.nst <- dat.bal
    dat.nst$CC <-factor(c(1,1,2,2,2,2,1,1,3,3,3,3,1,1,4,4,4,4))

    mod.bal  <- lm(y ~ AA + BB*CC,    data=dat.bal)
    mod.nst  <- lm(y ~ AA + BB : CC, data=dat.nst)

    K <- LSmatrix(mod.nst, effect=c("BB","CC"))
    linest( mod.nst, K )

[Package doBy version 4.5-15 Index]