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

"tidy"(x, effects = c("ran_pars", "fixed"), scales = NULL, ran_prefix = NULL, conf.int = FALSE, conf.level = 0.95, conf.method = "Wald", ...)
"augment"(x, data = stats::model.frame(x), newdata, ...)
"glance"(x, ...)

Arguments

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

effects

A character vector including one or more of "fixed" (fixed-effect parameters), "ran_pars" (variances and covariances or standard deviations and correlations of random effect terms) or "ran_modes" (conditional modes/BLUPs/latent variable estimates)

scales

scales on which to report the variables: for random effects, the choices are ‘"sdcor"’ (standard deviations and correlations: the default if scales is NULL) or ‘"vcov"’ (variances and covariances). NA means no transformation, appropriate e.g. for fixed effects; inverse-link transformations (exponentiation or logistic) are not yet implemented, but may be in the future.

ran_prefix

a length-2 character vector specifying the strings to use as prefixes for self- (variance/standard deviation) and cross- (covariance/correlation) random effects terms

conf.int

whether to include a confidence interval

conf.level

confidence level for CI

conf.method

method for computing confidence intervals (see lme4::confint.merMod)

...

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

group: the group within which the random effect is being estimated: "fixed" for fixed effects
level: level within group (NA except for modes)
term: term being estimated
estimate: estimated coefficient
std.error: standard error
statistic: t- or Z-statistic (NA for modes)
p.value: P-value computed from t-statistic (may be missing/NA)
.fitted: predicted values
.resid: residuals
.fixed: predicted values with no random effects
sigma: the square root of the estimated residual variance
logLik: the data's log-likelihood under the model
AIC: the Akaike Information Criterion
BIC: the Bayesian Information Criterion
deviance: deviance

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")
    tidy(lmm1, effects = "fixed", conf.int=TRUE)
    tidy(lmm1, effects = "fixed", conf.int=TRUE, conf.method="profile")
    tidy(lmm1, effects = "ran_modes", conf.int=TRUE)
    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)
}