Per-group linear (or generalized linear) model fits

mm_lmlist() is mixeff's counterpart of lme4::lmList(): it fits lm() (or glm() when family is given) separately within each level of a grouping factor, using lme4's response ~ predictors | group formula. It is named mm_lmlist() so attaching mixeff does not mask lme4::lmList() / nlme::lmList().

Usage

mm_lmlist(
  formula,
  data,
  family = NULL,
  subset = NULL,
  weights = NULL,
  na.action = stats::na.omit,
  pool = is.null(family)
)

Arguments

formula

A formula y ~ x | g.

data

A data frame.

family

Optional GLM family; NULL fits lm().

subset, weights, na.action

Passed to lm()/glm() within each group (subset is applied before splitting). weights may name a column of data.

pool

Pool the residual standard deviation across groups for inference. Defaults to TRUE for lm fits and FALSE for GLMs.

Value

An object of class mm_lmlist: a named list of lm/glm fits (one per group) with attributes call, pool, groups, and failed.

Details

coef() returns one row per group (lme4's shape); confint() returns a group x (lower, upper) x coefficient array. With pool = TRUE (the default for lm fits, as in lme4) confint() uses the residual standard deviation pooled across groups, with the pooled residual degrees of freedom, and sigma() returns that pooled value; confint(x, pool = FALSE) gives each group's own lm t interval (Wald z for GLMs), which is what confint() on an lme4::lmList() fit returns. A group whose fit fails is kept as NULL and reported, never silently dropped.

Examples

set.seed(1)
d <- data.frame(g = factor(rep(1:6, each = 8)), x = rep(0:7, 6))
d$y <- 2 + rnorm(6)[d$g] + (0.5 + rnorm(6, sd = 0.2)[d$g]) * d$x +
  rnorm(48)
ml <- mm_lmlist(y ~ x | g, d)
coef(ml)
#>   (Intercept)         x
#> 1   0.4814386 0.8733317
#> 2   2.8241058 0.4190855
#> 3   1.6432213 0.4689668
#> 4   3.6488567 0.5038056
#> 5   2.0853955 0.8825206
#> 6   1.5779637 0.5230055
confint(ml)[, , "x"]
#>       2.5 %    97.5 %
#> 1 0.5990784 1.1475851
#> 2 0.1448322 0.6933388
#> 3 0.1947135 0.7432202
#> 4 0.2295523 0.7780589
#> 5 0.6082673 1.1567739
#> 6 0.2487522 0.7972588