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, L = NULL, ...)

    ## S3 method for class 'linest_class'
    tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

    ## S3 method for class 'linest_class'
    confint(object, parm, level = 0.95, ...)

    ## S3 method for class 'linest_class'
    coef(object, ...)

    ## S3 method for class 'linest_class'
    summary(object, ...)

### Arguments

`object` | Model object
`L` | 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 `L` is taken to be the p times p identity matrix
`...` | Additional arguments; currently not used.
`x` | A 'linest_class' object (produced by `linest` methods).
`conf.int` | Should confidence intervals be added.
`conf.level` | Desired confidence level.
`parm` | Specification of the parameters estimates for which confidence inctervals are to be calculated.
`level` | The level of the (asymptotic) confidence interval.
`confint` | Should confidence interval appear in output.

### Value

A dataframe with results from computing the contrasts.

### Author(s)

Søren Højsgaard, sorenh@math.aau.dk

### See Also

`LSmeans`, `LE_matrix`

### 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)

    L <- LE_matrix(mod.nst, effect=c("BB", "CC"))
    linest( mod.nst, L )

[Package doBy version 4.6.5 Index]