Extract lmList Random Effects

The difference between the individual lm components coefficients and their average is calculated.

# S3 method for class 'lmList'
ranef(object, augFrame, data, which, FUN, standard,
                 omitGroupingFactor, ...)

Arguments

object

an object inheriting from class "lmList", representing a list of lm objects with a common model.

augFrame

an optional logical value. If TRUE, the returned data frame is augmented with variables defined in data; else, if FALSE, only the coefficients are returned. Defaults to FALSE.

data

an optional data frame with the variables to be used for augmenting the returned data frame when augFrame = TRUE. Defaults to the data frame used to fit object.

which

an optional positive integer vector specifying which columns of data should be used in the augmentation of the returned data frame. Defaults to all columns in data.

FUN

an optional summary function or a list of summary functions to be applied to group-varying variables, when collapsing data by groups. Group-invariant variables are always summarized by the unique value that they assume within that group. If FUN is a single function it will be applied to each non-invariant variable by group to produce the summary for that variable. If FUN is a list of functions, the names in the list should designate classes of variables in the frame such as ordered, factor, or numeric. The indicated function will be applied to any group-varying variables of that class. The default functions to be used are mean for numeric factors, and Mode for both factor and ordered. The Mode function, defined internally in gsummary, returns the modal or most popular value of the variable. It is different from the mode function that returns the S-language mode of the variable.

standard

an optional logical value indicating whether the estimated random effects should be "standardized" (i.e. divided by the corresponding estimated standard error). Defaults to FALSE.

omitGroupingFactor

an optional logical value. When TRUE the grouping factor itself will be omitted from the group-wise summary of data but the levels of the grouping factor will continue to be used as the row names for the returned data frame. Defaults to FALSE.

...

some methods for this generic require additional arguments. None are used in this method.

Value

a vector with the differences between the individual lm coefficients in object and their average.

References

Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer, esp. pp. 100, 461.

Author

José Pinheiro and Douglas Bates bates@stat.wisc.edu

See also

fixed.effects.lmList, lmList, random.effects

Examples

fm1 <- lmList(distance ~ age | Subject, Orthodont)
ranef(fm1)
#>     (Intercept)         age
#> M16   0.1888889 -0.11018519
#> M05  -3.1111111  0.18981481
#> M02  -1.9111111  0.11481481
#> M11   3.2888889 -0.33518519
#> M07  -1.8111111  0.13981481
#> M08   2.9888889 -0.28518519
#> M03  -0.7611111  0.08981481
#> M12  -3.5111111  0.33981481
#> M13 -13.9611111  1.28981481
#> M14   2.3388889 -0.13518519
#> M09  -2.3611111  0.31481481
#> M15  -3.2611111  0.46481481
#> M06   2.1888889  0.01481481
#> M04   7.9388889 -0.48518519
#> M01   0.5388889  0.28981481
#> M10   4.4888889  0.08981481
#> F10  -3.2111111 -0.21018519
#> F09   1.3388889 -0.38518519
#> F06   0.2388889 -0.28518519
#> F01   0.4888889 -0.28518519
#> F05   2.8388889 -0.38518519
#> F07   0.1888889 -0.11018519
#> F02  -2.5611111  0.13981481
#> F08   4.6888889 -0.48518519
#> F03  -2.3611111  0.18981481
#> F04   2.8888889 -0.18518519
#> F11   2.1888889  0.01481481
random.effects(fm1)              # same as above
#>     (Intercept)         age
#> M16   0.1888889 -0.11018519
#> M05  -3.1111111  0.18981481
#> M02  -1.9111111  0.11481481
#> M11   3.2888889 -0.33518519
#> M07  -1.8111111  0.13981481
#> M08   2.9888889 -0.28518519
#> M03  -0.7611111  0.08981481
#> M12  -3.5111111  0.33981481
#> M13 -13.9611111  1.28981481
#> M14   2.3388889 -0.13518519
#> M09  -2.3611111  0.31481481
#> M15  -3.2611111  0.46481481
#> M06   2.1888889  0.01481481
#> M04   7.9388889 -0.48518519
#> M01   0.5388889  0.28981481
#> M10   4.4888889  0.08981481
#> F10  -3.2111111 -0.21018519
#> F09   1.3388889 -0.38518519
#> F06   0.2388889 -0.28518519
#> F01   0.4888889 -0.28518519
#> F05   2.8388889 -0.38518519
#> F07   0.1888889 -0.11018519
#> F02  -2.5611111  0.13981481
#> F08   4.6888889 -0.48518519
#> F03  -2.3611111  0.18981481
#> F04   2.8888889 -0.18518519
#> F11   2.1888889  0.01481481