doBy (version 4.6-3)

linest-matrix: Linear estimates matrix

Description

Generate matrix specifying linear estimate.

Usage

LE_matrix(object, effect = NULL, at = NULL)
# S3 method for default
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 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

See Also

LSmeans, linest

Examples

# NOT RUN {
## 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)
# }