# Linear estimates matrix Generate matrix specifying linear estimate. ```r LE_matrix(object, effect = NULL, at = NULL) ## Default S3 method: LE_matrix(object, effect = NULL, at = NULL) aggregate_linest_list(linest_list) 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`: Either NULL, a list or a dataframe. 1) If a list, then the list must consist of covariates (including levels of some factors) to be used in the calculations. 2) If a dataframe, the dataframe is split rowwise and the function is invoked on each row. - `linest_list`: Linear estimate list (as generated by `get_linest_list`). ## Details Check this ## Examples ```r ## 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) ``` ## See Also `LSmeans`, `linest`

linest-matrix function