doBy (version 4.5-15)

LSmeans: Compute linear estimates, including LS-means (aka population means or marginal means)

Description

Compute linear estimates for a range of models. One example of linear estimates is LS-means (least squares means, also known as population means and as marginal means).

Usage

LSmeans(object, effect = NULL, at = NULL, level=0.95,   ...)

Arguments

object

Model object

effect

A vector of variables. For each configuration of these the estimate will be calculated.

at

A list of values of covariates (including levels of some factors) to be used in the calculations

level

The level of the (asymptotic) confidence interval.

...

Additional arguments; currently not used.

Value

A dataframe with results from computing the contrasts.

Warning

Notice that LSmeans and LSmatrix fails if the model formula contains an offset (as one would have in connection with e.g. Poisson regression. It is on the todo-list to fix this

Details

There are restrictions on the formulas allowed in the model object. For example having y ~ log(x) will cause an error. Instead one must define the variable logx = log(x) and do y~logx.

See Also

LSmatrix linest

Examples

## Two way anova:

data(warpbreaks)

m0 <- lm(breaks ~ wool + tension, data=warpbreaks)
m1 <- lm(breaks ~ wool * tension, data=warpbreaks)
LSmeans(m0)
LSmeans(m1)

## same as:
K <- LSmatrix(m0);K
linest(m0, K)
K <- LSmatrix(m1);K
linest(m1, K)

LSmatrix(m0, effect="wool")
LSmeans(m0, effect="wool")

LSmatrix(m1, effect="wool")
LSmeans(m1, effect="wool")

LSmatrix(m0, effect=c("wool","tension"))
LSmeans(m0, effect=c("wool","tension"))

LSmatrix(m1, effect=c("wool","tension"))
LSmeans(m1, effect=c("wool","tension"))


## Regression; two parallel regression lines:

data(Puromycin)

m0 <- lm(rate ~ state + log(conc), data=Puromycin)
## Can not use LSmeans / LSmatrix here because of
## the log-transformation. Instead we must do:
Puromycin$lconc <- log( Puromycin$conc )
m1 <- lm(rate ~ state + lconc, data=Puromycin)

LSmatrix(m1)
LSmeans(m1)

LSmatrix(m1, effect="state")
LSmeans(m1, effect="state")

LSmatrix(m1, effect="state", at=list(lconc=3))
LSmeans(m1, effect="state", at=list(lconc=3))

## Non estimable contrasts

## ## 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)

LSmeans(mod.bal, effect=c("BB", "CC"))
LSmeans(mod.nst, effect=c("BB", "CC"))
LSmeans(mod.nst, at=list(BB=1, CC=1))

LSmeans(mod.nst, at=list(BB=1, CC=2))
## Above: NA's are correct; not an estimable function

if( require( lme4 )){
 warp.mm <- lmer(breaks ~ -1 + tension + (1|wool), data=warpbreaks)
 LSmeans(warp.mm, effect="tension")
 class(warp.mm)
 fixef(warp.mm)
 coef(summary(warp.mm))
 vcov(warp.mm)
 if (require(pbkrtest))
   vcovAdj(warp.mm)
}