prt-utilities {lme4} | R Documentation

## Print and Summary Method Utilities for Mixed Effects

### Description

The `print`, `summary` methods (including the `print` for the `summary()` result) in lme4 are modular, using about ten small utility functions. Other packages, building on lme4 can use the same utilities for ease of programming and consistency of output.

Notably see the Examples.

`llikAIC()` extracts the log likelihood, AIC, and related statics from a Fitted LMM.

`formatVC()` “format()”s the `VarCorr` matrix of the random effects – for `print()`ing and `show()`ing; it is also the “workhorse” of `.prt.VC()`, and returns a `character` matrix.

`.prt.*()` all use `cat` and `print` to produce output.

### Usage

    llikAIC(object, devianceFUN = devCrit, chkREML = TRUE,
            devcomp = object@devcomp)

    methTitle(dims)

    .prt.methTit(mtit, class)
    .prt.family (famL)
    .prt.resids (resids, digits, title = "Scaled residuals:", ...)
    .prt.call   (call, long = TRUE)
    .prt.aictab (aictab, digits = 1)
    .prt.grps   (ngrps, nobs)
    .prt.warn   (optinfo, summary = FALSE, ...)

    .prt.VC (varcor, digits, comp, formatter = format, ...)
    formatVC(varcor, digits = max(3, getOption("digits") - 2),
             comp = "Std.Dev.", formatter = format,
             useScale = attr(varcor, "useSc"), ...)

### Arguments

`object` | a LMM model fit
`devianceFUN` | the function to be used for computing the deviance; should not be changed for lme4 created objects.
`chkREML` | optional logical indicating if `object` maybe a REML fit.

`devcomp` | for lme4 always the equivalent of `object@devcomp`; here a `list`

`dims` | for lme4 always the equivalent of `object@devcomp$dims`, a named vector or list with components `"GLMM"`, `"NLMM"`, `"REML"`, and `"nAGQ"` of which the first two are `logical` scalars, and the latter two typically are `FALSE` or `numeric`.

`mtit` | the result of `methTitle(object)`
`class` | typically `class(object)`.

`famL` | a `list` with components `family` and `link`, each a `character` string; note that standard R `family` objects can be used directly, as well.

`resids` | numeric vector of model `residuals`.
`digits` | non-negative integer of (significant) digits to print minimally.
`title` | `character` string.
`...` | optional arguments passed on, e.g., to `residuals()`.

`call` | the `call` of the model fit; e.g., available via (generic) function `getCall()`.
`long` | logical indicating if the output may be long, e.g., printing the `control` part of the call if there is one.

`aictab` | typically the `AICtab` component of the result of `llikAIC()`.

`varcor` | typically the result of `VarCorr()`.
`comp` | optional ...

`formatter` | a `function` used for formatting the numbers.

`ngrps` | integer (vector), typically the result of `ngrps(object)`.
`nobs` | integer; the number of observations, e.g., the result of `nobs`.

`optinfo` | typically `object @ optinfo`, the optimization infos, including warnings if there were.
`summary` | logical

`useScale` | (logical) whether the parent model estimates a scale parameter

### Value

`llikAIC()` returns a `list` with components

`logLik` | which is `logLik(object)`, and
`AICtab` | a “table” of `AIC`, `BIC`, `logLik`, deviance and `df.residual()` values.

### Examples

    ## Create a few "lme4 standard" models ------------------------------
    fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
    fmM <- update(fm1, REML=FALSE) # -> Maximum Likelihood

    gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
                 data = cbpp, family = binomial)
    gmA <- update(gm1, nAGQ = 5)

    (lA1 <- llikAIC(fm1))
    (lAM <- llikAIC(fmM))
    (lAg <- llikAIC(gmA))

    (m1 <- methTitle(fm1 @ devcomp $ dims))
    (mM <- methTitle(fmM @ devcomp $ dims))
    (mG <- methTitle(gm1 @ devcomp $ dims))
    (mA <- methTitle(gmA @ devcomp $ dims))

    .prt.methTit(m1, class(fm1))
    .prt.methTit(mA, class(gmA))

    .prt.family(gaussian())
    .prt.family(binomial())
    .prt.family( poisson())

    .prt.resids(residuals(fm1), digits = 4)
    .prt.resids(residuals(fmM), digits = 2)

    .prt.call(getCall(fm1))
    .prt.call(getCall(gm1))

    .prt.aictab ( lA1 $ AICtab ) # REML
    .prt.aictab ( lAM $ AICtab ) # ML --> AIC, BIC, ...

    V1 <- VarCorr(fm1)
    m <- formatVC(V1)
    stopifnot(is.matrix(m), is.character(m), ncol(m) == 4)
    print(m, quote = FALSE) ## prints all but the first line of .prt.VC() below:
    .prt.VC( V1, digits = 4, formatter = format)
    ## Random effects:
    ##  Groups   Name        Std.Dev. Corr
    ##  Subject  (Intercept) 24.740
    ##           Days         5.922   0.07
    ##  Residual             25.592

    p1 <- capture.output(V1)
    p2 <- capture.output( print(m, quote=FALSE) )
    pX <- capture.output( .prt.VC(V1, digits = max(3, getOption("digits")-2)) )
    stopifnot(identical(p1, p2),
              identical(p1, pX[-1])) # [-1] : dropping 1st line

    .prt.grps(ngrps = ngrps(fm1),
              nobs  = nobs (fm1))
    ## --> Number of obs: 180, groups:  Subject, 18

    .prt.warn(fm1 @ optinfo) # nothing .. had no warnings

[Package lme4 version 1.1-21 Index]