broom (version 0.4.0)

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 = c("ran_pars", "fixed"), scales = NULL,
  ran_prefix = NULL, conf.int = FALSE, conf.level = 0.95,
  conf.method = "Wald", ...)
## S3 method for class 'merMod':
augment(x, data = stats::model.frame(x), newdata, ...)
## S3 method for class 'merMod':
glance(x, ...)

Arguments

x

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 "varcov" (variances and covariances).

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

• 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 with groups depending on the effects parameter. It contains the columns

• 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)
• augment returns one row for each original observation, with columns (each prepended by a .) added. Included are the columns
• .fitted
  predicted values
• .resid
  residuals
• .fixed
  predicted 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

• 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.

See Also

na.action

Examples

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)
}