doBy (version 4.6-3)

linest: 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 linest_class
tidy(x, conf.int = FALSE, conf.level = 0.95,
  ...)
# S3 method for linest_class
confint(object, parm, level = 0.95, ...)
# S3 method for linest_class
coef(object, ...)
# S3 method for 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.

See Also

LSmeans, LE_matrix

Examples

# NOT RUN {

## 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 )

# }