R: Tidying methods for mixed effects models

nlme_tidiers {broom} | R Documentation

## Tidying methods for mixed effects models

### Description

These methods tidy the coefficients of mixed effects models of the `lme` class from functions of the `nlme` package.

### Usage

    ## S3 method for class 'lme'
    tidy(x, effects = "random", ...)

    ## S3 method for class 'lme'
    augment(x, data = x$data, newdata, ...)

    ## S3 method for class 'lme'
    glance(x, ...)

### Arguments

`x` | An object of class `lme`, such as those from `lme` or `nlme`
`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

### 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.

### 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` | fixed term being estimated
`estimate` | estimate of fixed effect
`std.error` | standard error
`statistic` | t-statistic
`p.value` | P-value computed from t-statistic

`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

`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` | returned as NA. To quote Brian Ripley on R-help: McCullagh & Nelder (1989) would be the authorative reference, but the 1982 first edition manages to use 'deviance' in three separate senses on one page.

### See Also

na.action

### Examples

    if (require("nlme") & require("lme4")) {
        # example regressions are from lme4 documentation, but used for nlme
        lmm1 <- lme(Reaction ~ Days, random=~ Days|Subject, sleepstudy)
        tidy(lmm1)
        tidy(lmm1, effects = "fixed")
        head(augment(lmm1, sleepstudy))
        glance(lmm1)

        startvec <- c(Asym = 200, xmid = 725, scal = 350)
        nm1 <- nlme(circumference ~ SSlogis(age, Asym, xmid, scal),
                      data = Orange,
                      fixed = Asym + xmid + scal ~1,
                      random = Asym ~1,
                      start = startvec)
        tidy(nm1)
        tidy(nm1, effects = "fixed")
        head(augment(nm1, Orange))
        glance(nm1)
    }

[Package broom version 0.4.2 Index]