R: Linear estimates matrix

linest-matrix {doBy} | R Documentation

## Linear estimates matrix

### Description

Generate matrix specifying linear estimate.

### Usage

    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

### See Also

`LSmeans`, `linest`

### Examples

    ## 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)

[Package doBy version 4.6.5 Index]