broom (version 0.3.4)

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

x

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

• group
  the group within which the random effect is being estimated
• level
  level within group
• term
  term being estimated
• estimate
  estimated coefficient
• If effects="fixed", tidy returns the columns
• term
  gixed term being estimated
• estimate
  estimate of fixed effect
• std.error
  standard error
• statistic
  t-statistic
• p.value
  P-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
• .fitted
  predicted values
• .resid
  residuals
• .fixed
  predicted values with no random effects
• Also added 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
• df.residual
  residual degrees of freedom

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")
    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)
}