lme4_tidiers: Tidying methods for mixed effects models

Description

These methods tidy the coefficients of mixed effects models, particularly responses of the merMod class

Usage

## S3 method for class 'merMod':
tidy(x, effects = "random", ...)
## S3 method for class 'merMod':
augment(x, data = model.frame(x), newdata, ...)
## S3 method for class 'merMod':
glance(x, ...)

Arguments

An object of class merMod, such as those from lmer, glmer, or nlmer

effects

Either "random" (default) or "fixed"

...

extra arguments (not used)

data

original data this was fitted on; if not given this will attempt to be reconstructed

newdata

new data to be used for prediction; optional

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.
tidy returns one row for each estimated effect, either random or fixed depending on the effects parameter. If effects = "random", it contains the columns
groupthe group within which the random effect is being estimated
levellevel within group
termterm being estimated
estimateestimated coefficient
If effects="fixed", tidy returns the columns
termgixed term being estimated
estimateestimate of fixed effect
std.errorstandard error
statistict-statistic
p.valueP-value computed from t-statistic (depending on the model, this may or may not be calculated and included)
augment returns one row for each original observation, with columns (each prepended by a .) added. Included are the columns
.fittedpredicted values
.residresiduals
.fixedpredicted values with no random effects
Also added for "merMod" objects, but not for "mer" objects, are values from the response object within the model (of type lmResp, glmResp, nlsResp, etc). These include ".mu", ".offset", ".sqrtXwt", ".sqrtrwt", ".eta".
glance returns one row with the columns
sigmathe square root of the estimated residual variance
logLikthe data's log-likelihood under the model
AICthe Akaike Information Criterion
BICthe Bayesian Information Criterion
deviancedeviance

Details

When the modeling was performed with na.action = "na.omit" (as is the typical default), rows with NA in the initial data are omitted entirely from the augmented data frame. When the modeling was performed with na.action = "na.exclude", one should provide the original data as a second argument, at which point the augmented data will contain those rows (typically with NAs in place of the new columns). If the original data is not provided to augment and na.action = "na.exclude", a warning is raised and the incomplete rows are dropped.

Examples

Run this code

if (require("lme4")) {
    # example regressions are from lme4 documentation
    lmm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
    tidy(lmm1)
    tidy(lmm1, effects = "fixed")
    head(augment(lmm1, sleepstudy))
    glance(lmm1)

    glmm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
                  data = cbpp, family = binomial)
    tidy(glmm1)
    tidy(glmm1, effects = "fixed")
    head(augment(glmm1, cbpp))
    glance(glmm1)

    startvec <- c(Asym = 200, xmid = 725, scal = 350)
    nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree,
                  Orange, start = startvec)
    tidy(nm1)
    tidy(nm1, effects = "fixed")
    head(augment(nm1, Orange))
    glance(nm1)
}