# linest-matrix: Linear estimates matrix In hojsgaard/doBy: Groupwise Statistics, LSmeans, Linear Contrasts, Utilities

## Description

Generate matrix specifying linear estimate.

## Usage

 ```1 2 3 4 5 6 7 8``` ```LE_matrix(object, effect = NULL, at = NULL) ## Default S3 method: LE_matrix(object, effect = NULL, at = NULL) aggregate_linest_list(lel) get_linest_list(object, effect = NULL, at = NULL) ```

## 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 `lel` Linear estimate list (as generated by `get_linest_list`.

## Details

Check this

`LSmeans`, `linest`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64``` ```## Two way anova: data(warpbreaks) ## An additive model m0 <- lm(breaks ~ wool + tension, data=warpbreaks) ## Estimate mean for each wool type, for tension="M": K <- LE_matrix(m0, at=list(wool=c("A", "B"), tension="M")) K ## Vanilla computation: K %*% coef(m0) ## Alternative; also providing standard errors etc: linest(m0, K) esticon(m0, K) ## Estimate mean for each wool type when averaging over tension; # two ways of doing this K <- LE_matrix(m0, at=list(wool=c("A", "B"))) K K <- LE_matrix(m0, effect="wool") K linest(m0, K) ## The linear estimate is sometimes called to "least squares mean" # (LSmeans) or popupulation means. # Same as LSmeans(m0, effect="wool") ## Without mentioning 'effect' or 'at' an average across all #predictors are calculated: K <- LE_matrix(m0) K linest(m0, K) ## Because the design is balanced (9 observations per combination #of wool and tension) this is the same as computing the average. If #the design is not balanced, the two quantities are in general not #the same. mean(warpbreaks\$breaks) ## Same as LSmeans(m0) ## An interaction model m1 <- lm(breaks ~ wool * tension, data=warpbreaks) K <- LE_matrix(m1, at=list(wool=c("A", "B"), tension="M")) K linest(m1, K) K <- LE_matrix(m1, at=list(wool=c("A", "B"))) K linest(m1, K) K <- LE_matrix(m1, effect="wool") K linest(m1, K) LSmeans(m1, effect="wool") K <- LE_matrix(m1) K linest(m1, K) LSmeans(m1) ```