Package 'buildmer' reference manual

Title:	Stepwise Elimination and Term Reordering for Mixed-Effects Regression
Description:	Finds the largest possible regression model that will still converge for various types of regression analyses (including mixed models and generalized additive models) and then optionally performs stepwise elimination similar to the forward and backward effect-selection methods in SAS, based on the change in log-likelihood or its significance, Akaike's Information Criterion, the Bayesian Information Criterion, the explained deviance, or the F-test of the change in R².
Authors:	Cesko C. Voeten [aut, cre] (ORCID: <https://orcid.org/0000-0003-4687-9973>)
Maintainer:	Cesko C. Voeten <[email protected]>
License:	FreeBSD
Version:	2.12
Built:	2026-06-08 08:13:23 UTC
Source:	https://gitlab.com/cvoeten/buildmer

Construct and fit as complete a model as possible and perform stepwise elimination

Description

The buildmer package consists of a number of functions, each designed to fit specific types of models (e.g. buildmer for mixed-effects regression, buildgam for generalized additive models, buildmertree for mixed-effects-regression trees, and so forth). The common parameters shared by all (or most of) these functions are documented in buildmerControl. For function-specific details, see the documentation for each individual function.

Author(s)

Maintainer: Cesko C. Voeten [email protected] (ORCID)

Adds terms to a formula

Description

Adds terms to a formula

Usage

add.terms(formula, add)
add.terms(formula, add)

Arguments

formula

The formula to add terms to.

add

A vector of terms to add. To add terms nested in random-effect groups, use ‘(term|group)’ syntax if you want to add an independent random effect (e.g. ‘(olderterm|group) + (term|group)’), or use ‘term|group’ syntax if you want to add a dependent random effect to a pre-existing term group (if no such group exists, it will be created at the end of the formula).

Value

The updated formula.

Examples

library(buildmer)
form <- Reaction ~ Days + (1|Subject)
add.terms(form,'Days|Subject')
add.terms(form,'(0+Days|Subject)')
add.terms(form,c('many','more|terms','to|terms','(be|added)','to|test'))
library(buildmer)
form <- Reaction ~ Days + (1|Subject)
add.terms(form,'Days|Subject')
add.terms(form,'(0+Days|Subject)')
add.terms(form,c('many','more|terms','to|terms','(be|added)','to|test'))

Converts a buildmer term list into a proper model formula

Description

Converts a buildmer term list into a proper model formula

Usage

build.formula(dep, terms, env = parent.frame())
build.formula(dep, terms, env = parent.frame())

Arguments

dep

The dependent variable.

terms

The term list.

env

The environment of the formula to return.

Value

A formula.

Examples

library(buildmer)
form1 <- Reaction ~ Days + (Days|Subject)
terms <- tabulate.formula(form1)
form2 <- build.formula(dep='Reaction',terms)

# check that the two formulas give the same results
library(lme4)
check <- function(f) resid(lmer(f,sleepstudy))
all.equal(check(form1),check(form2))

# can also do double bars now
form1 <- Reaction ~ Days + (Days||Subject)
terms <- tabulate.formula(form1)
form2 <- build.formula(dep='Reaction',terms)
all.equal(check(form1),check(form2))
library(buildmer)
form1 <- Reaction ~ Days + (Days|Subject)
terms <- tabulate.formula(form1)
form2 <- build.formula(dep='Reaction',terms)

# check that the two formulas give the same results
library(lme4)
check <- function(f) resid(lmer(f,sleepstudy))
all.equal(check(form1),check(form2))

# can also do double bars now
form1 <- Reaction ~ Days + (Days||Subject)
terms <- tabulate.formula(form1)
form2 <- build.formula(dep='Reaction',terms)
all.equal(check(form1),check(form2))

Uses `buildmer` to fit big generalized additive models using `bam` from package `mgcv`

Description

Uses buildmer to fit big generalized additive models using bam from package mgcv

Usage

buildbam(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)
buildbam(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)

Arguments

formula

See the general documentation under buildmer-package.

data

See the general documentation under buildmer-package.

family

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Details

To work around an issue in bam, you must make sure that your data do not contain a variable named 'intercept'.

lme4 random effects are supported: they will be automatically converted using re2mgcv.

In the generalized case, bam only returns the correct ML/REML values as of mgcv 1.9-4.

Examples



library(buildmer)
model <- buildbam(f1 ~ s(timepoint,by=following) + s(participant,by=following,bs='re') +
       s(participant,timepoint,by=following,bs='fs'),data=vowels)

library(buildmer)
model <- buildbam(f1 ~ s(timepoint,by=following) + s(participant,by=following,bs='re') +
       s(participant,timepoint,by=following,bs='fs'),data=vowels)

Uses `buildmer` to fit cumulative link mixed models using `clmm` from package `ordinal`

Description

Uses buildmer to fit cumulative link mixed models using clmm from package ordinal

Usage

buildclmm(formula, data = NULL, buildmerControl = buildmerControl())
buildclmm(formula, data = NULL, buildmerControl = buildmerControl())

Arguments

formula

A formula specifying both fixed and random effects using lme4 syntax.

data

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Examples

if (requireNamespace('ordinal')) {
model <- buildclmm(SURENESS ~ PROD + (1|RESP),data=ordinal::soup,
buildmerControl=list(args=list(link='probit',threshold='equidistant')))
}
if (requireNamespace('ordinal')) {
model <- buildclmm(SURENESS ~ PROD + (1|RESP),data=ordinal::soup,
buildmerControl=list(args=list(link='probit',threshold='equidistant')))
}

Uses `buildmer` to perform stepwise elimination using a custom fitting function

Description

Uses buildmer to perform stepwise elimination using a custom fitting function

Usage

buildcustom(
  formula,
  data = NULL,
  fit = function(p, formula) stop("'fit' not specified"),
  crit = function(p, ref, alt) stop("'crit' not specified"),
  elim = function(x) stop("'elim' not specified"),
  REML = FALSE,
  buildmerControl = buildmerControl()
)
buildcustom(
  formula,
  data = NULL,
  fit = function(p, formula) stop("'fit' not specified"),
  crit = function(p, ref, alt) stop("'crit' not specified"),
  elim = function(x) stop("'elim' not specified"),
  REML = FALSE,
  buildmerControl = buildmerControl()
)

Arguments

formula

See the general documentation under buildmer-package.

data

See the general documentation under buildmer-package.

fit

A function taking two arguments, of which the first is the buildmer parameter list p and the second one is a formula. The function must return a single object, which is treated as a model object fitted via the provided formula. The function must return an error ('stop()') if the model does not converge.

crit

A function taking one argument and returning a single value. The argument is the return value of the function passed in fit, and the returned value must be a numeric indicating the goodness of fit, where smaller is better (like AIC or BIC).

elim

A function taking one argument and returning a single value. The argument is the return value of the function passed in crit, and the returned value must be a logical indicating if the small model must be selected (return TRUE) or the large model (return FALSE).

REML

A logical indicating if the fitting function wishes to distinguish between fits differing in fixed effects (for which p$reml will be set to FALSE) and fits differing only in the random part (for which p$reml will be TRUE). Note that this ignores the usual semantics of buildmer's optional REML argument, because they are redundant: if you wish to force REML on or off, simply code it so in your custom fitting function.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Examples

## Use \code{buildmer} to do stepwise linear discriminant analysis
library(buildmer)
migrant[,-1] <- scale(migrant[,-1])
flipfit <- function(p,formula) {
    # The predictors must be entered as dependent variables in a MANOVA
    # (i.e. the predictors must be flipped with the dependent variable)
    Y <- model.matrix(formula,migrant)
    m <- lm(Y ~ 0+migrant$changed)
    # the model may error out when asking for the MANOVA
    test <- try(anova(m))
    if (inherits(test,'try-error')) test else m
}
crit.F <- function(p,a,b) { # use whole-model F
    pvals <- anova(b)$'Pr(>F)' # not valid for backward!
    pvals[length(pvals)-1]
}
crit.Wilks <- function(p,a,b) {
    if (is.null(a)) return(crit.F(p,a,b)) #not completely correct, but close as F approximates X2
    Lambda <- anova(b,test='Wilks')$Wilks[1]
    p <- length(coef(b))
    n <- 1
    m <- nrow(migrant)
    Bartlett <- ((p-n+1)/2-m)*log(Lambda)
    pchisq(Bartlett,n*p,lower.tail=FALSE)
}

# First, order the terms based on Wilks' Lambda
model <- buildcustom(changed ~ friends.nl+friends.be+multilingual+standard+hearing+reading+
       attention+sleep+gender+handedness+diglossic+age+years,buildmerControl=list(
       fit=flipfit,crit=crit.Wilks,direction='order'))
# Now, use the six most important terms (arbitrary choice) in the LDA
if (require('MASS')) {
model <- lda(changed ~ diglossic + age + reading + friends.be + years + 
       multilingual,data=migrant)
}
## Use \code{buildmer} to do stepwise linear discriminant analysis
library(buildmer)
migrant[,-1] <- scale(migrant[,-1])
flipfit <- function(p,formula) {
    # The predictors must be entered as dependent variables in a MANOVA
    # (i.e. the predictors must be flipped with the dependent variable)
    Y <- model.matrix(formula,migrant)
    m <- lm(Y ~ 0+migrant$changed)
    # the model may error out when asking for the MANOVA
    test <- try(anova(m))
    if (inherits(test,'try-error')) test else m
}
crit.F <- function(p,a,b) { # use whole-model F
    pvals <- anova(b)$'Pr(>F)' # not valid for backward!
    pvals[length(pvals)-1]
}
crit.Wilks <- function(p,a,b) {
    if (is.null(a)) return(crit.F(p,a,b)) #not completely correct, but close as F approximates X2
    Lambda <- anova(b,test='Wilks')$Wilks[1]
    p <- length(coef(b))
    n <- 1
    m <- nrow(migrant)
    Bartlett <- ((p-n+1)/2-m)*log(Lambda)
    pchisq(Bartlett,n*p,lower.tail=FALSE)
}

# First, order the terms based on Wilks' Lambda
model <- buildcustom(changed ~ friends.nl+friends.be+multilingual+standard+hearing+reading+
       attention+sleep+gender+handedness+diglossic+age+years,buildmerControl=list(
       fit=flipfit,crit=crit.Wilks,direction='order'))
# Now, use the six most important terms (arbitrary choice) in the LDA
if (require('MASS')) {
model <- lda(changed ~ diglossic + age + reading + friends.be + years + 
       multilingual,data=migrant)
}

Uses `buildmer` to fit generalized additive models using `gam` from package `mgcv`

Description

Uses buildmer to fit generalized additive models using gam from package mgcv

Usage

buildgam(
  formula,
  data = NULL,
  family = gaussian(),
  quickstart = 0,
  buildmerControl = buildmerControl()
)
buildgam(
  formula,
  data = NULL,
  family = gaussian(),
  quickstart = 0,
  buildmerControl = buildmerControl()
)

Arguments

formula

See the general documentation under buildmer-package.

data

See the general documentation under buildmer-package.

family

See the general documentation under buildmer-package.

quickstart

A numeric with values from 0 to 5. If set to 1, will use bam to obtain starting values for gam's outer iteration, potentially resulting in a much faster fit for each model. If set to 2, will disregard ML/REML and always use bam's fREML for the quickstart fit. 3 also sets discrete=TRUE. Values between 3 and 4 fit the quickstart model to a subset of that value (e.g.\ quickstart=3.1 fits the quickstart model to 10% of the data, which is also the default if quickstart=3. Values between 4 and 5 do the same, but also set a very sloppy convergence tolerance of 0.2.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Details

To work around an issue in gam, you must make sure that your data do not contain a variable named 'intercept'.

lme4 random effects are supported: they will be automatically converted using re2mgcv.

If gam's optimizer argument is not set to use outer iteration, gam fits using PQL. In this scenario, only crit='F' and crit='deviance' (note that the latter is not a formal test) are legitimate in the generalized case.

General families implemented in mgcv are not supported, as they are only defined in terms of a mean-variance relationship and hence always use a linearized (PQL) approximation of the model. They do not have a real likelihood.

buildmerControl's quickstart function may be used here. If you desire more control (e.g.\ discrete=FALSE but use.chol=TRUE), additional options can be provided as extra arguments and will be passed on to bam as they are applicable. Note that quickstart needs to be larger than 0 to trigger the quickstart path at all.

If scaled-t errors are used (family=scat), the quickstart path will also provide initial values for the two theta parameters (corresponding to the degrees of freedom and the scale parameter), but only if your installation of package mgcv is at least at version 1.8-32.

Examples



library(buildmer)
model <- buildgam(f1 ~ s(timepoint,by=following) + s(participant,by=following,bs='re') +
       s(participant,timepoint,by=following,bs='fs'),data=vowels)

library(buildmer)
model <- buildgam(f1 ~ s(timepoint,by=following) + s(participant,by=following,bs='re') +
       s(participant,timepoint,by=following,bs='fs'),data=vowels)

Uses `buildmer` to fit big generalized additive models using `gamm` from package `mgcv`

Description

Uses buildmer to fit big generalized additive models using gamm from package mgcv

Usage

buildgamm(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)
buildgamm(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)

Arguments

formula

See the general documentation under buildmer-package.

data

See the general documentation under buildmer-package.

family

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Details

The fixed and random effects are to be passed as a single formula in lme4 format. This is internally split up into the appropriate fixed and random parts. Only a single grouping factor is allowed. The random-effect covariance matrix is always unstructured. If you want to use pdMat covariance structures, you must (a) not specify any lme4 random-effects term in the formula, and (b) specify your own custom random argument in the args list in buildmerControl. Note that buildgamm will merely pass this through; no term reordering or stepwise elimination is done on a user-provided random argument.

Examples


library(buildmer)
model <- buildgamm(f1 ~ s(timepoint,by=following) + (following|participant) +
       s(participant,timepoint,by=following,bs='fs'),data=vowels)

library(buildmer)
model <- buildgamm(f1 ~ s(timepoint,by=following) + (following|participant) +
       s(participant,timepoint,by=following,bs='fs'),data=vowels)

Uses `buildmer` to fit generalized additive models using package `gamm4`

Description

Uses buildmer to fit generalized additive models using package gamm4

Usage

buildgamm4(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)
buildgamm4(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)

Arguments

formula

See the general documentation under buildmer-package.

data

See the general documentation under buildmer-package.

family

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Details

The fixed and random effects are to be passed as a single formula in lme4 format. This is internally split up into the appropriate fixed and random parts.

Examples



library(buildmer)
if (requireNamespace('gamm4')) model <- buildgamm4(f1 ~ s(timepoint,by=following) +
       s(participant,timepoint,by=following,bs='fs'),data=vowels)

library(buildmer)
if (requireNamespace('gamm4')) model <- buildgamm4(f1 ~ s(timepoint,by=following) +
       s(participant,timepoint,by=following,bs='fs'),data=vowels)

Uses `buildmer` to fit generalized linear mixed models using `mixed_model` from package `GLMMadaptive`

Description

Uses buildmer to fit generalized linear mixed models using mixed_model from package GLMMadaptive

Usage

buildGLMMadaptive(
  formula,
  data = NULL,
  family,
  buildmerControl = buildmerControl()
)
buildGLMMadaptive(
  formula,
  data = NULL,
  family,
  buildmerControl = buildmerControl()
)

Arguments

formula

A formula specifying both fixed and random effects using lme4 syntax. (Unlike mixed_model, buildGLMMadaptive does not use a separate random argument!).

data

See the general documentation under buildmer-package.

family

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Details

The fixed and random effects are to be passed as a single formula in lme4 format. This is internally split up into the appropriate fixed and random parts.

As GLMMadaptive can only fit models with a single random-effect grouping factor, having multiple different grouping factors will raise an error.

If multiple identical random-effect grouping factors are provided, they will be concatenated into a single grouping factor using the double-bar syntax, causing GLMMadaptive to assume a diagonal random-effects covariance matrix. In other words, (1|g) + (0+x|g) will correctly be treated as diagonal, but note the caveat: (a|g) + (b|g) will also be treated as fully diagonal, even if a and b are factors which might still have had correlations between their individual levels! This is a limitation of both GLMMadaptive and buildmer's approach to handling double bars.

Examples


if (requireNamespace('GLMMadaptive')) {
# nonsensical model given these data
model <- buildGLMMadaptive(stress ~ vowel + (vowel|participant),
       family=binomial,data=vowels,buildmerControl=list(args=list(nAGQ=1)))
# or with double-bar syntax for a diagonal r.e. cov. matrix
model <- buildGLMMadaptive(stress ~ vowel + (vowel||participant),
       family=binomial,data=vowels,buildmerControl=list(args=list(nAGQ=1)))
}

if (requireNamespace('GLMMadaptive')) {
# nonsensical model given these data
model <- buildGLMMadaptive(stress ~ vowel + (vowel|participant),
       family=binomial,data=vowels,buildmerControl=list(args=list(nAGQ=1)))
# or with double-bar syntax for a diagonal r.e. cov. matrix
model <- buildGLMMadaptive(stress ~ vowel + (vowel||participant),
       family=binomial,data=vowels,buildmerControl=list(args=list(nAGQ=1)))
}

Uses `buildmer` to perform stepwise elimination on `glmmTMB` models

Description

Uses buildmer to perform stepwise elimination on glmmTMB models

Usage

buildglmmTMB(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)
buildglmmTMB(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)

Arguments

formula

See the general documentation under buildmer-package.

data

See the general documentation under buildmer-package.

family

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Examples

library(buildmer)
if (requireNamespace('glmmTMB')) {
	model <- buildglmmTMB(Reaction ~ Days + (Days|Subject),data=lme4::sleepstudy)
}
library(buildmer)
if (requireNamespace('glmmTMB')) {
	model <- buildglmmTMB(Reaction ~ Days + (Days|Subject),data=lme4::sleepstudy)
}

Uses `buildmer` to fit generalized-least-squares models using `gls` from `nlme`

Description

Uses buildmer to fit generalized-least-squares models using gls from nlme

Usage

buildgls(formula, data = NULL, buildmerControl = buildmerControl())
buildgls(formula, data = NULL, buildmerControl = buildmerControl())

Arguments

formula

See the general documentation under buildmer-package.

data

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Details

A workaround is included to prevent an error when the model matrix is of less than full rank. The summary output of such a model will look a bit strange!

Examples

library(buildmer)
library(nlme)
vowels$event <- with(vowels,interaction(participant,word))
model <- buildgls(f1 ~ timepoint*following,data=vowels,
	buildmerControl=list(args=list(correlation=corAR1(form=~1|event))))
library(buildmer)
library(nlme)
vowels$event <- with(vowels,interaction(participant,word))
model <- buildgls(f1 ~ timepoint*following,data=vowels,
	buildmerControl=list(args=list(correlation=corAR1(form=~1|event))))

Uses `buildmer` to perform stepwise elimination of mixed-effects models fit via `lme` from `nlme`

Description

Uses buildmer to perform stepwise elimination of mixed-effects models fit via lme from nlme

Usage

buildlme(formula, data = NULL, buildmerControl = buildmerControl())
buildlme(formula, data = NULL, buildmerControl = buildmerControl())

Arguments

formula

A formula specifying both fixed and random effects using lme4 syntax. (Unlike lme, buildlme does not use a separate random argument!).

data

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Details

The fixed and random effects are to be passed as a single formula in lme4 format. This is internally split up into the appropriate fixed and random parts. Only a single grouping factor is allowed. The random-effect covariance matrix is always unstructured. If you want to use pdMat covariance structures, you must (a) not specify any lme4 random-effects term in the formula, and (b) specify your own custom random argument in the args list in buildmerControl. Note that buildlme will merely pass this through; no term reordering or stepwise elimination is done on a user-provided random argument.

Examples

library(buildmer)
model <- buildlme(Reaction ~ Days + (Days|Subject),data=lme4::sleepstudy)
library(buildmer)
model <- buildlme(Reaction ~ Days + (Days|Subject),data=lme4::sleepstudy)

Uses `buildmer` to fit mixed-effects models using `lmer`/`glmer` from `lme4`

Description

Uses buildmer to fit mixed-effects models using lmer/glmer from lme4

Usage

buildmer(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)
buildmer(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl()
)

Arguments

formula

See the general documentation under buildmer-package.

data

See the general documentation under buildmer-package.

family

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Examples

library(buildmer)
model <- buildmer(Reaction ~ Days + (Days|Subject),lme4::sleepstudy)

# Tests from github issue #2, that also show the use of the 'direction' and 'crit' parameters:
bm.test <- buildmer(cbind(incidence,size - incidence) ~ period + (1 | herd),
	family=binomial,data=lme4::cbpp)
bm.test <- buildmer(cbind(incidence,size - incidence) ~ period + (1 | herd),
	family=binomial,data=lme4::cbpp,buildmerControl=buildmerControl(direction='forward'))
bm.test <- buildmer(cbind(incidence,size - incidence) ~ period + (1 | herd),
	family=binomial,data=lme4::cbpp,buildmerControl=buildmerControl(crit='AIC'))
bm.test <- buildmer(cbind(incidence,size - incidence) ~ period + (1 | herd),
	family=binomial,data=lme4::cbpp,
	buildmerControl=buildmerControl(direction='forward',crit='AIC'))

# Example showing use of the 'include' parameter to force a particular term into the model
m1 <- buildmer(Reaction ~ Days,data=lme4::sleepstudy,buildmerControl=list(include=~(1|Subject)))
# the below are equivalent
m2 <- buildmer(Reaction ~ Days,data=lme4::sleepstudy,buildmerControl=list(include='(1|Subject)'))
m3 <- buildmer(Reaction ~ Days + (1|Subject),data=lme4::sleepstudy,buildmerControl=list(
	include=~(1|Subject)))
m4 <- buildmer(Reaction ~ Days + (1|Subject),data=lme4::sleepstudy,buildmerControl=list(
	include='(1|Subject)'))
library(buildmer)
model <- buildmer(Reaction ~ Days + (Days|Subject),lme4::sleepstudy)

# Tests from github issue #2, that also show the use of the 'direction' and 'crit' parameters:
bm.test <- buildmer(cbind(incidence,size - incidence) ~ period + (1 | herd),
	family=binomial,data=lme4::cbpp)
bm.test <- buildmer(cbind(incidence,size - incidence) ~ period + (1 | herd),
	family=binomial,data=lme4::cbpp,buildmerControl=buildmerControl(direction='forward'))
bm.test <- buildmer(cbind(incidence,size - incidence) ~ period + (1 | herd),
	family=binomial,data=lme4::cbpp,buildmerControl=buildmerControl(crit='AIC'))
bm.test <- buildmer(cbind(incidence,size - incidence) ~ period + (1 | herd),
	family=binomial,data=lme4::cbpp,
	buildmerControl=buildmerControl(direction='forward',crit='AIC'))

# Example showing use of the 'include' parameter to force a particular term into the model
m1 <- buildmer(Reaction ~ Days,data=lme4::sleepstudy,buildmerControl=list(include=~(1|Subject)))
# the below are equivalent
m2 <- buildmer(Reaction ~ Days,data=lme4::sleepstudy,buildmerControl=list(include='(1|Subject)'))
m3 <- buildmer(Reaction ~ Days + (1|Subject),data=lme4::sleepstudy,buildmerControl=list(
	include=~(1|Subject)))
m4 <- buildmer(Reaction ~ Days + (1|Subject),data=lme4::sleepstudy,buildmerControl=list(
	include='(1|Subject)'))

The buildmer class

Description

This is a simple convenience class that allows ‘anova’ and ‘summary’ calls to fall through to the underlying model object, while retaining buildmer's iteration history. If you need to use the final model for other things, such as prediction, access it through the ‘model’ slot of the buildmer class object.

Slots

model: The final model containing only the terms that survived elimination
p: Parameters used during the fitting process
anova: The model's ANOVA, if the model was built with ‘anova=TRUE’
summary: The model's summary, if the model was built with ‘summary=TRUE’

Examples

# Manually create a bare-bones buildmer object:
model <- lm(Sepal.Length ~ Petal.Length,iris)
p <- list(in.buildmer=FALSE)
library(buildmer)
bm <- mkBuildmer(model=model,p=p,anova=NULL,summary=NULL)
summary(bm)
# Manually create a bare-bones buildmer object:
model <- lm(Sepal.Length ~ Petal.Length,iris)
p <- list(in.buildmer=FALSE)
library(buildmer)
bm <- mkBuildmer(model=model,p=p,anova=NULL,summary=NULL)
summary(bm)

Uses `buildmer` to fit negative-binomial models using `glm.nb` and `glmer.nb`

Description

Uses buildmer to fit negative-binomial models using glm.nb and glmer.nb

Usage

buildmer.nb(formula, data = NULL, buildmerControl = buildmerControl())
buildmer.nb(formula, data = NULL, buildmerControl = buildmerControl())

Arguments

formula

See the general documentation under buildmer-package.

data

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Examples

library(buildmer)
if (requireNamespace('MASS')) {
model <- buildmer.nb(Days ~ Sex*Age*Eth*Lrn,MASS::quine)
}
library(buildmer)
if (requireNamespace('MASS')) {
model <- buildmer.nb(Days ~ Sex*Age*Eth*Lrn,MASS::quine)
}

Sets control options for buildmer

Description

buildmerControl provides all the knobs and levers that can be manipulated during the buildmer fitting and summary/anova process. Some of these are part of buildmer's core functionality—for instance, crit allows to specify different elimination criteria, a core buildmer feature—whereas some are only meant for internal usage, e.g. I_KNOW_WHAT_I_AM_DOING is used to turn off the PQL safeguards in buildbam/buildgam, which you really should only do if you have a very good reason to believe that the PQL check is being triggered erroneously for your problem.

Usage

buildmerControl(
  formula = quote(stop("No formula specified")),
  data = NULL,
  family = gaussian(),
  args = list(),
  direction = c("order", "backward"),
  cl = NULL,
  crit = NULL,
  elim = NULL,
  fit = function(...) stop("No fitting function specified"),
  include = NULL,
  quiet = FALSE,
  calc.anova = FALSE,
  calc.summary = TRUE,
  ddf = "Wald",
  quickstart = 0,
  singular.ok = FALSE,
  grad.tol = quote(formals(buildmer::converged)$grad.tol),
  hess.tol = quote(formals(buildmer::converged)$hess.tol),
  dep = NULL,
  REML = NA,
  can.use.reml = TRUE,
  force.reml = FALSE,
  scale.est = NA,
  I_KNOW_WHAT_I_AM_DOING = FALSE
)
buildmerControl(
  formula = quote(stop("No formula specified")),
  data = NULL,
  family = gaussian(),
  args = list(),
  direction = c("order", "backward"),
  cl = NULL,
  crit = NULL,
  elim = NULL,
  fit = function(...) stop("No fitting function specified"),
  include = NULL,
  quiet = FALSE,
  calc.anova = FALSE,
  calc.summary = TRUE,
  ddf = "Wald",
  quickstart = 0,
  singular.ok = FALSE,
  grad.tol = quote(formals(buildmer::converged)$grad.tol),
  hess.tol = quote(formals(buildmer::converged)$hess.tol),
  dep = NULL,
  REML = NA,
  can.use.reml = TRUE,
  force.reml = FALSE,
  scale.est = NA,
  I_KNOW_WHAT_I_AM_DOING = FALSE
)

Arguments

formula

The model formula for the maximal model you would like to fit. Alternatively, a buildmer term list as obtained from tabulate.formula. In the latter formulation, you also need to specify a dep='...' argument specifying the dependent variable to go along with the term list. See tabulate.formula for an example of where this is useful.

data

The data to fit the model(s) to.

family

The error distribution to use.

args

Extra arguments passed to the fitting function.

direction

Character string or vector indicating the direction for stepwise elimination; possible options are 'order' (order terms by their contribution to the model), 'backward' (backward elimination), 'forward' (forward elimination, implies order). The default is the combination c('order','backward'), to first make sure that the model converges and to then perform backward elimination; other such combinations are perfectly allowed.

cl

Specifies a cluster to use for parallelizing the evaluation of terms. This can be an object as returned by function makeCluster from package parallel, or a whole number to let buildmer create, manage, and destroy a cluster for you with the specified number of parallel processes.

crit

Character string or vector determining the criterion used to test terms for their contribution to the model fit in the ordering step. Possible options are 'LRT' (likelihood-ratio test based on chi-square mixtures per Stram & Lee 1994 for random effects; this is the default), 'LL' (use the raw -2 log likelihood), 'AIC' (Akaike Information Criterion), 'BIC' (Bayesian Information Criterion), and 'deviance' (explained deviance – note that this is not a formal test). If left at its default value of NULL, the same value is used as in the elim argument; if that is also NULL, both are set to 'LRT'. If crit is a function, it may optionally have an crit.name attribute, which will be used as its name in buildmer. This is used to guide the code checking for mismatches between crit and elim arguments.

elim

Character string or vector determining the criterion used to test terms for elimination in the elimination step. Possible options are 'LRT' (likelihood-ratio test based on chi-square mixtures per Stram & Lee 1994 for random effects; this is the default), 'LL' (use the raw -2 log likelihood), 'AIC' (Akaike Information Criterion), 'BIC' (Bayesian Information Criterion), and 'deviance' (explained deviance — note that this is not a formal test). If left at its default value of NULL, the same value is used as in the crit argument; if that is also NULL, both are set to 'LRT'. If elim is a function, it may optionally have an elim.name attribute, which will be used as its name in buildmer. This is used to guide the code checking for mismatches between crit and elim arguments.

fit

Internal parameter — do not modify.

include

A one-sided formula or character vector of terms that will be included in the model at all times and are not subject to testing for elimination. These do not need to be specified separately in the formula argument. Useful for e.g. passing correlation structures in glmmTMB models.

quiet

A logical indicating whether to suppress progress messages.

calc.anova

Logical indicating whether to also calculate the ANOVA table for the final model after term elimination.

calc.summary

Logical indicating whether to also calculate the summary table for the final model after term elimination.

ddf

The method used for calculating p-values for lme4 models and calc.anova=TRUE or calc.summary=TRUE. Options are 'Wald' (default), 'Satterthwaite' (if package lmerTest is available), 'Kenward-Roger' (if packages lmerTest and pbkrtest are available), and 'lme4' (no p-values).

quickstart

For gam models only: a numeric with values from 0 to 5. If set to 1, will use bam to obtain starting values for gam's outer iteration, potentially resulting in a much faster fit for each model. If set to 2, will disregard ML/REML and always use bam's fREML for the quickstart fit. 3 also sets discrete=TRUE. Values between 3 and 4 fit the quickstart model to a subset of that value (e.g.\ quickstart=3.1 fits the quickstart model to 10% of the data, which is also the default if quickstart=3. Values between 4 and 5 do the same, but also set a very sloppy convergence tolerance of 0.2.

singular.ok

Logical indicating whether singular fits are acceptable. Only for lme4 models.

grad.tol

Tolerance for declaring gradient convergence.

hess.tol

Tolerance for declaring Hessian convergence.

dep

A character string specifying the name of the dependent variable. Only used if formula is a buildmer terms list.

REML

In some situations, the user may want to force REML on or off, rather than using buildmer's autodetection. If REML=TRUE (or more precisely, if isTRUE(REML) evaluates to true), then buildmer will always use REML. This results in invalid results if formal model-comparison criteria are used with models differing in fixed effects (and the user is not guarded against this), but is useful with the 'deviance-explained' criterion, where it is actually the default (you can disable this and use the 'normal' REML/ML-differentiating behavior by passing REML=NA).

can.use.reml

Internal option specifying whether the fitting engine should distinguish between fixed-effects and random-effects model comparisons. Do not set this option yourself unless you are programming a new fitting function for buildcustom.

force.reml

Internal option specifying whether, if not differentiating between fixed-effects and random-effects model comparisons, these comparisons should be based on ML or on REML (if possible). Do not set this option yourself unless you are programming a new fitting function for buildcustom. Enabling this option only makes sense for criteria that do not compare likelihoods, in which case this is an optimization; it is applied automatically for the 'deviance-explained' criterion.

scale.est

Internal option specifying whether the model estimates an unknown scale parameter. Used only in crit.F. Possible values are TRUE (scale is estimated), FALSE (scale is known), and NA (unknown, needs to be inferred from the fitted model; this is the default). There is limited support for modifying this parameter.

I_KNOW_WHAT_I_AM_DOING

An internal option that you should not modify unless you know what you are doing.

Details

With the default options, all buildmer functions will do two things:

Determine the order of the effects in your model, based on their importance as measured by the likelihood-ratio test statistic. This identifies the ‘maximal model’, which is the model containing either all effects specified by the user, or subset of those effects that still allow the model to converge, ordered such that the most information-rich effects have made it in.
Perform backward stepwise elimination based on the significance of the change in log-likelihood.

The final model is returned in the model slot of the returned buildmer object. All functions in the buildmer package are aware of the distinction between (f)REML and ML, and know to correct chi-square p-values using the Stram & Lee (1994) correction when comparing nlme/lme4-style models differing only in random effects (see Pinheiro & Bates 2000). The steps executed above can be changed using the direction argument, allowing for arbitrary chains of, for instance, forward-backward-forward stepwise elimination (although using more than one elimination method on the same data is not recommended). The criterion for determining the importance of terms in the ordering stage and the elimination of terms in the elimination stage can also be changed, using the crit argument.

Uses `buildmer` to perform stepwise elimination for `lmertree` and `glmertree` models from package `glmertree`

Description

Uses buildmer to perform stepwise elimination for lmertree and glmertree models from package glmertree

Usage

buildmertree(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl(crit = "AIC")
)
buildmertree(
  formula,
  data = NULL,
  family = gaussian(),
  buildmerControl = buildmerControl(crit = "AIC")
)

Arguments

formula

Either a glmertree formula, looking like dep ~ left | middle | right where the middle part is an lme4-style random-effects specification, or an ordinary formula (or buildmer term list thereof) specifying only the dependent variable and the fixed and random effects for the regression part. In the latter case, the additional argument partitioning must be specified as a one-sided formula containing the partitioning part of the model.

data

See the general documentation under buildmer-package.

family

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Details

Note that the likelihood-ratio test is not available for glmertree models, as it cannot be assured that the models being compared are nested. The default is thus to use AIC. In the generalized case or when testing many partitioning variables, it is recommended to pass joint=FALSE, as this results in a dramatic speed gain and reduces the odds of the final glmer model failing to converge or converging singularly.

Examples

if (requireNamespace('glmertree')) {
	model <- buildmertree(Reaction ~ 1 | (Days|Subject) | Days,
		buildmerControl=buildmerControl(crit='LL',direction='order',args=list(joint=FALSE)),
        data=lme4::sleepstudy)

	model <- buildmertree(Reaction ~ 1 | (Days|Subject) | Days,
		buildmerControl=buildmerControl(crit='LL',direction='order',args=list(joint=FALSE)),
	        data=lme4::sleepstudy,family=Gamma(link=identity))

}
if (requireNamespace('glmertree')) {
	model <- buildmertree(Reaction ~ 1 | (Days|Subject) | Days,
		buildmerControl=buildmerControl(crit='LL',direction='order',args=list(joint=FALSE)),
        data=lme4::sleepstudy)

	model <- buildmertree(Reaction ~ 1 | (Days|Subject) | Days,
		buildmerControl=buildmerControl(crit='LL',direction='order',args=list(joint=FALSE)),
	        data=lme4::sleepstudy,family=Gamma(link=identity))

}

Uses `buildmer` to perform stepwise elimination for `multinom` models from package `nnet`

Description

Uses buildmer to perform stepwise elimination for multinom models from package nnet

Usage

buildmultinom(formula, data = NULL, buildmerControl = buildmerControl())
buildmultinom(formula, data = NULL, buildmerControl = buildmerControl())

Arguments

formula

See the general documentation under buildmer-package.

data

See the general documentation under buildmer-package.

buildmerControl

Control arguments for buildmer — see the general documentation under buildmerControl.

Examples

if (requireNamespace('nnet') && require('MASS')) {
	options(contrasts = c("contr.treatment", "contr.poly"))
	example(birthwt)
	bwt.mu <- buildmultinom(low ~ age*lwt*race*smoke,bwt)
}
if (requireNamespace('nnet') && require('MASS')) {
	options(contrasts = c("contr.treatment", "contr.poly"))
	example(birthwt)
	bwt.mu <- buildmultinom(low ~ age*lwt*race*smoke,bwt)
}

Sets up deviation contrasts

Description

Deviation-coding is a contrast-coding scheme that compares each level to the average of the other levels. It is often confused with sum-coding, which compares each level to the average of all levels. In the binary case, deviation coding works out to $ (+0.5,-0.5) $; in the ternary case and beyond, the coding is a little bit more involved.

Usage

contr.deviation(n, contrasts = TRUE, sparse = FALSE)
contr.deviation(n, contrasts = TRUE, sparse = FALSE)

Arguments

n

See contr.sum.

contrasts

See contr.sum.

sparse

See contr.sum.

Details

contr.deviation starts by calling contr.sum and hence accepts the same arguments as it does.

Value

A matrix of deviation-coded contrasts.

Examples

iris$Species <- contr.deviation(iris$Species)
iris$Species <- contr.deviation(iris$Species)

Tests a model for convergence

Description

Tests a model for convergence

Usage

converged(
  model,
  singular.ok = FALSE,
  grad.tol = if (inherits(model, "bam")) 10 else 0.1,
  hess.tol = if (inherits(model, "bam")) 10 else 0.01
)
converged(
  model,
  singular.ok = FALSE,
  grad.tol = if (inherits(model, "bam")) 10 else 0.1,
  hess.tol = if (inherits(model, "bam")) 10 else 0.01
)

Arguments

model

The model object to test.

singular.ok

A logical indicating whether singular fits are accepted as ‘converged’ or not. Relevant only for lme4 models.

grad.tol

The tolerance to use for checking the gradient. This is currently only used by mgcv, glmmTMB, and clm(m) models.

hess.tol

The tolerance to use for checking the Hessian for negative eigenvalues. This is currently only used by mgcv, glmmTMB, and cl(m)m models.

Value

Logical indicating whether the model converged.

Examples

library(buildmer)
library(lme4)
good1 <- lm(Reaction ~ Days,sleepstudy)
good2 <- lmer(Reaction ~ Days + (Days|Subject),sleepstudy)
bad <- lmer(Reaction ~ Days + (Days|Subject),sleepstudy,control=lmerControl(
            optimizer='bobyqa',optCtrl=list(maxfun=1)))
sapply(list(good1,good2,bad),converged)
library(buildmer)
library(lme4)
good1 <- lm(Reaction ~ Days,sleepstudy)
good2 <- lmer(Reaction ~ Days + (Days|Subject),sleepstudy)
bad <- lmer(Reaction ~ Days + (Days|Subject),sleepstudy,control=lmerControl(
            optimizer='bobyqa',optCtrl=list(maxfun=1)))
sapply(list(good1,good2,bad),converged)

Sets up deviation contrasts

Description

This function is an alias to contr.deviation intended to allow the C command to work for deviation-coding the same way as it does for other, base-R, contrasts.

Usage

deviation(n, contrasts = TRUE, sparse = FALSE)
deviation(n, contrasts = TRUE, sparse = FALSE)

Arguments

n

See contr.sum.

contrasts

See contr.sum.

sparse

See contr.sum.

Value

A matrix of deviation-coded contrasts.

Examples

iris$Species <- C(iris$Species,deviation)
iris$Species <- C(iris$Species,deviation)

Diagonalize the random-effect covariance structure, possibly assisting convergence

Description

Diagonalize the random-effect covariance structure, possibly assisting convergence

Usage

## S4 method for signature 'formula'
diag(x)
## S4 method for signature 'formula'
diag(x)

Arguments

x

A model formula.

Value

The formula with all random-effect correlations forced to zero, per Pinheiro & Bates (2000)

Examples

# 1. Create explicit columns for factor variables
library(buildmer)
vowels <- cbind(vowels,model.matrix(~vowel,vowels))
# 2. Create formula with diagonal covariance structure
form <- diag(f1 ~ (vowel1+vowel2+vowel3+vowel4)*timepoint*following + 
	     ((vowel1+vowel2+vowel3+vowel4)*timepoint*following | participant) +
	     (timepoint | word))
# 3. Convert formula to buildmer term list, grouping terms starting with 'vowel'
terms <- tabulate.formula(form,group='vowel[^:]')
# 4. Directly pass the terms object to buildmer, using the 'dep' argument to specify the
# dependent variable
model <- buildmer(terms,data=vowels,buildmerControl=list(dep='f1'))
# 1. Create explicit columns for factor variables
library(buildmer)
vowels <- cbind(vowels,model.matrix(~vowel,vowels))
# 2. Create formula with diagonal covariance structure
form <- diag(f1 ~ (vowel1+vowel2+vowel3+vowel4)*timepoint*following + 
	     ((vowel1+vowel2+vowel3+vowel4)*timepoint*following | participant) +
	     (timepoint | word))
# 3. Convert formula to buildmer term list, grouping terms starting with 'vowel'
terms <- tabulate.formula(form,group='vowel[^:]')
# 4. Directly pass the terms object to buildmer, using the 'dep' argument to specify the
# dependent variable
model <- buildmer(terms,data=vowels,buildmerControl=list(dep='f1'))

Generates an LRT elimination function with custom alpha level

Description

The elim argument in buildmerControl can take any user-specified elimination function. LRTalpha generates such a function that uses the likelihood-ratio test, based on a user-specified alpha level. (For the default alpha of .05, one can also simply specify the string 'LRT' or the function buildmer:::elim.LRT).

Usage

LRTalpha(alpha)
LRTalpha(alpha)

Arguments

alpha

The alpha level for the likelihood-ratio test.

A very small dataset from a pilot study on sound change.

Description

A very small dataset from a pilot study on sound change.

Usage

data(migrant)
data(migrant)

Format

A standard data frame.

Converts lme4 random-effect terms to mgcv 're' smooths

Description

Converts lme4 random-effect terms to mgcv 're' smooths

Usage

re2mgcv(formula, data, drop = TRUE)
re2mgcv(formula, data, drop = TRUE)

Arguments

formula

The lme4 formula.

data

The data.

drop

Logical indicating whether constant, non-intercept columns should be dropped. Default TRUE. A warning is issued if a column needed to be dropped. Note that repeated intercept columns are silently merged without a warning.

Details

To ensure that the various buildmer functions (e.g. buildgam, buildbam) correctly apply marginality constraints between parametric terms and random-effect terms, the parametric terms are renamed using the same name-mangling scheme that is automatically applied to the random effects. To ensure that the mangled names will be grouped together, instead of passing the returned formula, use the returned termlist with a dep argument. See the description of the dep argument in buildmerControl.

Value

A list containing a new formula (has a dependent variable but loses track of what terms belong together in a single block), a buildmer term list (lacks the dependent variable but represents what terms belong together), and a new data set.

Examples

library(buildmer)
re <- re2mgcv(temp ~ angle + (1|replicate) + (1|recipe),lme4::cake)
model <- buildgam(re$formula,re$data)
# note: the below does NOT work, as the dependent variable is looked up in the data by name!

re <- re2mgcv(log(Reaction) ~ Days + (Days|Subject),lme4::sleepstudy)
library(buildmer)
re <- re2mgcv(temp ~ angle + (1|replicate) + (1|recipe),lme4::cake)
model <- buildgam(re$formula,re$data)
# note: the below does NOT work, as the dependent variable is looked up in the data by name!

re <- re2mgcv(log(Reaction) ~ Days + (Days|Subject),lme4::sleepstudy)

Converts lme4 random-effect terms to uncorrelated lme4 random-effect terms, including factor random slopes

Description

lme4 and similar packages (e.g. glmmTMB) have an issue where correlations between factor random slopes are not dropped, even if using the double-bar syntax or diag to diagonalize the random-effects formula. re2uncorr works around this issue by explicitly converting factor random slopes to numerics, which do not suffer from this problem.

Usage

re2uncorr(formula, data, drop = TRUE)
re2uncorr(formula, data, drop = TRUE)

Arguments

formula

The lme4 formula.

data

The data.

drop

Details

To ensure that the various buildmer functions (e.g. buildmer, buildglmmTMB) correctly apply marginality constraints between fixed effects and random-effect terms, the fixed effects are renamed using the same name-mangling scheme that is automatically applied to the random effects. To ensure that the mangled names will be grouped together, instead of passing the returned formula, use the returned termlist with a dep argument. See the description of the dep argument in buildmerControl.

Value

Examples

library(buildmer)
re <- re2uncorr(f1 ~ vowel*timepoint*following +
	(vowel*timepoint*following|participant) + (timepoint|word),vowels)

model <- buildmer(re$formula,re$data)
library(buildmer)
re <- re2uncorr(f1 ~ vowel*timepoint*following +
	(vowel*timepoint*following|participant) + (timepoint|word),vowels)

model <- buildmer(re$formula,re$data)

Removes terms from a formula

Description

Removes terms from a formula

Usage

remove.terms(formula, remove, check = TRUE)
remove.terms(formula, remove, check = TRUE)

Arguments

formula

The formula.

remove

A vector of terms to remove. To remove terms nested inside random-effect groups, use ‘(term|group)’ syntax. Note that marginality is respected, i.e. no effects will be removed if they participate in a higher-order interaction, and no fixed effects will be removed if a random slope is included over that fixed effect.

check

A logical indicating whether effects should be checked for marginality. If TRUE (default), effects will not be removed if doing so would violate marginality. Setting check to FALSE will remove terms unconditionally.

Examples

library(buildmer)
remove.terms(Reaction ~ Days + (Days|Subject),'(Days|Subject)')
# illustration of the marginality checking mechanism:
# this refuses to remove the term:
remove.terms(Reaction ~ Days + (Days|Subject),'(1|Subject)')
# so does this, because marginality is checked before removal:
remove.terms(Reaction ~ Days + (Days|Subject),c('(Days|Subject)','(1|Subject)'))
# but it works with check=FALSE
remove.terms(Reaction ~ Days + (Days|Subject),'(1|Subject)',check=FALSE)
library(buildmer)
remove.terms(Reaction ~ Days + (Days|Subject),'(Days|Subject)')
# illustration of the marginality checking mechanism:
# this refuses to remove the term:
remove.terms(Reaction ~ Days + (Days|Subject),'(1|Subject)')
# so does this, because marginality is checked before removal:
remove.terms(Reaction ~ Days + (Days|Subject),c('(Days|Subject)','(1|Subject)'))
# but it works with check=FALSE
remove.terms(Reaction ~ Days + (Days|Subject),'(1|Subject)',check=FALSE)

Parses a formula into a buildmer term list

Description

Parses a formula into a buildmer term list

Usage

tabulate.formula(formula, group = NULL)
tabulate.formula(formula, group = NULL)

Arguments

formula

A formula.

group

A character vector of regular expressions. Terms matching the same regular expression are assigned the same block, and will be evaluated together in buildmer functions.

Value

A buildmer term list, which is just a normal data frame.

Examples

form <- diag(f1 ~ (vowel1+vowel2+vowel3+vowel4)*timepoint*following +
             ((vowel1+vowel2+vowel3+vowel4)*timepoint*following|participant) + (timepoint|word))
tabulate.formula(form)
tabulate.formula(form,group='vowel[1-4]')
form <- diag(f1 ~ (vowel1+vowel2+vowel3+vowel4)*timepoint*following +
             ((vowel1+vowel2+vowel3+vowel4)*timepoint*following|participant) + (timepoint|word))
tabulate.formula(form)
tabulate.formula(form,group='vowel[1-4]')

Vowel data from a pilot study.

Description

Vowel data from a pilot study.

Usage

data(vowels)
data(vowels)

Format

A standard data frame.

Package 'buildmer'

Help Index

Construct and fit as complete a model as possible and perform stepwise elimination

Description

Author(s)

See Also

Adds terms to a formula

Description

Usage

Arguments

Value

Examples

Converts a buildmer term list into a proper model formula

Description

Usage

Arguments

Value

Examples

Uses buildmer to fit big generalized additive models using bam from package mgcv

Description

Usage

Arguments

Details

See Also

Examples

Uses buildmer to fit cumulative link mixed models using clmm from package ordinal

Description

Usage

Arguments

See Also

Examples

Uses buildmer to perform stepwise elimination using a custom fitting function

Description

Usage

Arguments

See Also

Examples

Uses buildmer to fit generalized additive models using gam from package mgcv

Description

Usage

Arguments

Details

See Also

Examples

Uses buildmer to fit big generalized additive models using gamm from package mgcv

Description

Usage

Arguments

Details

See Also

Examples

Uses buildmer to fit generalized additive models using package gamm4

Description

Usage

Arguments

Details

See Also

Examples

Uses buildmer to fit generalized linear mixed models using mixed_model from package GLMMadaptive

Description

Usage

Arguments

Details

See Also

Examples

Uses buildmer to perform stepwise elimination on glmmTMB models

Description

Usage

Arguments

See Also

Examples

Uses buildmer to fit generalized-least-squares models using gls from nlme

Description

Usage

Arguments

Details

See Also

Examples

Uses buildmer to perform stepwise elimination of mixed-effects models fit via lme from nlme

Description

Uses `buildmer` to fit big generalized additive models using `bam` from package `mgcv`

Uses `buildmer` to fit cumulative link mixed models using `clmm` from package `ordinal`

Uses `buildmer` to perform stepwise elimination using a custom fitting function

Uses `buildmer` to fit generalized additive models using `gam` from package `mgcv`

Uses `buildmer` to fit big generalized additive models using `gamm` from package `mgcv`

Uses `buildmer` to fit generalized additive models using package `gamm4`

Uses `buildmer` to fit generalized linear mixed models using `mixed_model` from package `GLMMadaptive`

Uses `buildmer` to perform stepwise elimination on `glmmTMB` models

Uses `buildmer` to fit generalized-least-squares models using `gls` from `nlme`

Uses `buildmer` to perform stepwise elimination of mixed-effects models fit via `lme` from `nlme`

Uses `buildmer` to fit mixed-effects models using `lmer`/`glmer` from `lme4`

Uses `buildmer` to fit negative-binomial models using `glm.nb` and `glmer.nb`

Uses `buildmer` to perform stepwise elimination for `lmertree` and `glmertree` models from package `glmertree`

Uses `buildmer` to perform stepwise elimination for `multinom` models from package `nnet`