Compute linear estimates

Compute linear estimates, i.e. L %*% beta for a range of models. One example of linear estimates is population means (also known as LSMEANS).

linest(object, L = NULL, ...)

# 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.

parm

Specification of the parameters estimates for which confidence intervals 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

Author

Søren Højsgaard, sorenh@math.aau.dk

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 )
#>         estimate  std.error  statistic         df p.value
#>  [1,]  0.0070647  0.2439499  0.0289598 10.0000000  0.9775
#>  [2,]         NA         NA         NA         NA      NA
#>  [3,]         NA         NA         NA         NA      NA
#>  [4,]         NA         NA         NA         NA      NA
#>  [5,]  0.0211182  0.4225336  0.0499800 10.0000000  0.9611
#>  [6,]  1.0559073  0.4225336  2.4989899 10.0000000  0.0315
#>  [7,]         NA         NA         NA         NA      NA
#>  [8,]  0.6592607  0.4225336  1.5602562 10.0000000  0.1498
#>  [9,]  1.1315871  0.4225336  2.6780995 10.0000000  0.0232
#> [10,]         NA         NA         NA         NA      NA
#> [11,] -0.9210848  0.4225336 -2.1799087 10.0000000  0.0543
#> [12,]         NA         NA         NA         NA      NA