coef.lme.RdThe estimated coefficients at level \(i\) are obtained by adding together the fixed effects estimates and the corresponding random effects estimates at grouping levels less or equal to \(i\). The resulting estimates are returned as a data frame, with rows corresponding to groups and columns to coefficients. Optionally, the returned data frame may be augmented with covariates summarized over groups.
# S3 method for class 'lme'
coef(object, augFrame, level, data, which, FUN,
omitGroupingFactor, subset, ...)an object inheriting from class "lme", representing
a fitted linear mixed-effects model.
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.
an optional positive integer giving the level of grouping to be used in extracting the coefficients from an object with multiple nested grouping levels. Defaults to the highest or innermost level of grouping.
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.
an optional positive integer or character vector
specifying which columns of data should be used in the
augmentation of the returned data frame. Defaults to all columns in
data.
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.
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.
an optional expression specifying a subset
some methods for this generic require additional arguments. None are used in this method.
a data frame inheriting from class "coef.lme" with the estimated
coefficients at level level and, optionally, other covariates
summarized over groups. The returned object also inherits from classes
"ranef.lme" and "data.frame".
Pinheiro, J. C. and Bates, D. M. (2000), Mixed-Effects Models in S and S-PLUS, Springer, New York, esp. pp. 455-457.
fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
coef(fm1)
#> (Intercept) age
#> M16 16.57335 0.5913314
#> M05 15.58444 0.6857855
#> M02 16.03361 0.6746930
#> M11 17.65160 0.5413593
#> M07 16.15314 0.6950852
#> M08 17.62141 0.5654490
#> M03 16.58721 0.6960375
#> M12 15.76312 0.7747492
#> M13 12.63157 1.0738537
#> M14 17.66546 0.6460654
#> M09 16.31672 0.7960938
#> M15 16.22614 0.8683628
#> M06 17.97875 0.7433765
#> M04 19.76156 0.5943004
#> M01 17.81269 0.8758697
#> M10 19.41435 0.8713318
#> F10 14.47973 0.4095945
#> F09 16.47016 0.4421435
#> F06 16.14053 0.4736282
#> F01 16.27515 0.4819755
#> F05 17.27792 0.4922276
#> F07 16.57335 0.5913314
#> F02 15.74926 0.6700431
#> F08 18.01143 0.4857849
#> F03 15.98832 0.7108275
#> F04 17.83027 0.6303230
#> F11 17.97875 0.7433765
coef(fm1, augFrame = TRUE)
#> (Intercept) age distance Sex
#> M16 16.57335 0.5913314 23.000 Male
#> M05 15.58444 0.6857855 23.000 Male
#> M02 16.03361 0.6746930 23.375 Male
#> M11 17.65160 0.5413593 23.625 Male
#> M07 16.15314 0.6950852 23.750 Male
#> M08 17.62141 0.5654490 23.875 Male
#> M03 16.58721 0.6960375 24.250 Male
#> M12 15.76312 0.7747492 24.250 Male
#> M13 12.63157 1.0738537 24.250 Male
#> M14 17.66546 0.6460654 24.875 Male
#> M09 16.31672 0.7960938 25.125 Male
#> M15 16.22614 0.8683628 25.875 Male
#> M06 17.97875 0.7433765 26.375 Male
#> M04 19.76156 0.5943004 26.625 Male
#> M01 17.81269 0.8758697 27.750 Male
#> M10 19.41435 0.8713318 29.500 Male
#> F10 14.47973 0.4095945 18.500 Female
#> F09 16.47016 0.4421435 21.125 Female
#> F06 16.14053 0.4736282 21.125 Female
#> F01 16.27515 0.4819755 21.375 Female
#> F05 17.27792 0.4922276 22.625 Female
#> F07 16.57335 0.5913314 23.000 Female
#> F02 15.74926 0.6700431 23.000 Female
#> F08 18.01143 0.4857849 23.375 Female
#> F03 15.98832 0.7108275 23.750 Female
#> F04 17.83027 0.6303230 24.875 Female
#> F11 17.97875 0.7433765 26.375 Female