# linest: Compute linear estimates In doBy: Groupwise Statistics, LSmeans, Linear Contrasts, Utilities

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## S3 method for class 'esticon_class' summary(object, ...) linest(object, L = NULL, level = 0.95, ...) ## S3 method for class 'linest_class' coef(object, ...) ## S3 method for class 'linest_class' summary(object, ...) ## S3 method for class 'linest_class' confint(object, parm, level = 0.95, ...) ```

## Arguments

 `object` Model object `...` Additional arguments; currently not used. `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 `level` The level of the (asymptotic) confidence interval. `parm` Specification of the parameters estimates for which confidence inctervals are to be calculated.

## Value

A dataframe with results from computing the contrasts.

## Author(s)

Søren Højsgaard, [email protected]

`LSmeans`, `LE_matrix`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```## 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 ) ```