Extract lmList Coefficients

The coefficients of each lm object in the object list are extracted and organized into a data frame, with rows corresponding to the lm components and columns corresponding to the coefficients. Optionally, the returned data frame may be augmented with covariates summarized over the groups associated with the lm components.

# S3 method for class 'lmList'
coef(object, augFrame, data, which, FUN,
   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 the data frame used to produce object; 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 or character vector specifying which columns of the data frame used to produce object should be used in the augmentation of the returned data frame. Defaults to all variables in the data.

FUN

an optional summary function or a list of summary functions to be applied to group-varying variables, when collapsing the 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.

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 data frame inheriting from class "coef.lmList" with the estimated coefficients for each "lm" component of object and, optionally, other covariates summarized over the groups corresponding to the "lm" components. The returned object also inherits from classes "ranef.lmList" and "data.frame".

References

Pinheiro, J. C. and Bates, D. M. (2000), Mixed-Effects Models in S and S-PLUS, Springer, New York, esp. pp. 457-458.

Author

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

Examples

fm1 <- lmList(distance ~ age|Subject, data = Orthodont)
coef(fm1)
#>     (Intercept)   age
#> M16       16.95 0.550
#> M05       13.65 0.850
#> M02       14.85 0.775
#> M11       20.05 0.325
#> M07       14.95 0.800
#> M08       19.75 0.375
#> M03       16.00 0.750
#> M12       13.25 1.000
#> M13        2.80 1.950
#> M14       19.10 0.525
#> M09       14.40 0.975
#> M15       13.50 1.125
#> M06       18.95 0.675
#> M04       24.70 0.175
#> M01       17.30 0.950
#> M10       21.25 0.750
#> F10       13.55 0.450
#> F09       18.10 0.275
#> F06       17.00 0.375
#> F01       17.25 0.375
#> F05       19.60 0.275
#> F07       16.95 0.550
#> F02       14.20 0.800
#> F08       21.45 0.175
#> F03       14.40 0.850
#> F04       19.65 0.475
#> F11       18.95 0.675
coef(fm1, augFrame = TRUE)
#>     (Intercept)   age distance    Sex
#> M16       16.95 0.550   23.000   Male
#> M05       13.65 0.850   23.000   Male
#> M02       14.85 0.775   23.375   Male
#> M11       20.05 0.325   23.625   Male
#> M07       14.95 0.800   23.750   Male
#> M08       19.75 0.375   23.875   Male
#> M03       16.00 0.750   24.250   Male
#> M12       13.25 1.000   24.250   Male
#> M13        2.80 1.950   24.250   Male
#> M14       19.10 0.525   24.875   Male
#> M09       14.40 0.975   25.125   Male
#> M15       13.50 1.125   25.875   Male
#> M06       18.95 0.675   26.375   Male
#> M04       24.70 0.175   26.625   Male
#> M01       17.30 0.950   27.750   Male
#> M10       21.25 0.750   29.500   Male
#> F10       13.55 0.450   18.500 Female
#> F09       18.10 0.275   21.125 Female
#> F06       17.00 0.375   21.125 Female
#> F01       17.25 0.375   21.375 Female
#> F05       19.60 0.275   22.625 Female
#> F07       16.95 0.550   23.000 Female
#> F02       14.20 0.800   23.000 Female
#> F08       21.45 0.175   23.375 Female
#> F03       14.40 0.850   23.750 Female
#> F04       19.65 0.475   24.875 Female
#> F11       18.95 0.675   26.375 Female