Barr, Levy, Scheepers, & Tily (2013) suggest that for valid statistical inference, a regression model must control for all possible confounding factors, specifically those coming from random effects such as subjects and items. Bates, Kliegl, Vasishth, & Baayen (2015) suggest that this proposed strategy leads to overfitting and that an appropriately-parsimonious model must be chosen, preferably based on theory but possibly also using stepwise elimination (Matuschek, Kliegl, Vasishth, Baayen, & Bates, 2017). Both strategies require a maximal model to be identified (for Barr et al. (2013), this is the final model; for Matuschek et al. (2017), this is the basis for backward stepwise elimination), but for many psycholinguistic experiments, the truly maximal model will fail to converge and a reasonable subset model needs to be chosen.
The buildmer package aims to automate the procedures
identifying the maximal model that can still converge & performing
backward stepwise elimination based on a variety of criteria (change in
log-likelihood, AIC, BIC, change in explained deviance). The package
does not contain any model-fitting code, but functions as an
administrative loop around other packages by simply building up a
maximal formula object and passing it along. Currently, the package
supports models that can be fitted by (g)lm,
(g)lmer (package lme4), gls,
lme (package nlme), bam,
gam, gamm (package mgcv),
gamm4 (package gamm4), glmmTMB
(package glmmTMB), multinom (package
nnet), lmertree and glmertree
(package glmertree), mixed_model (package
GLMMadaptive), clmm (package
ordinal), and any other package if you provide your own
wrapper functions.
To illustrate what buildmer can do for you, the package
comes with a particularly pathological dataset called
vowels. It looks like this:
library(buildmer)
head(vowels)## participant word vowel neighborhood timepoint f1 f2 following information stress
## 1 1 Keulen oey 36.1961 0.05118788 407.8202 1838.611 lOns 4.511475 TRUE
## 2 1 Keulen oey 36.1961 0.11941466 416.2701 1635.436 lOns 4.511475 TRUE
## 3 1 Keulen oey 36.1961 0.18764143 450.6488 1654.561 lOns 4.511475 TRUE
## 4 1 Keulen oey 36.1961 0.25586821 449.7890 1645.075 lOns 4.511475 TRUE
## 5 1 Keulen oey 36.1961 0.32409498 444.6863 1606.261 lOns 4.511475 TRUE
## 6 1 Keulen oey 36.1961 0.39232176 438.5311 1607.581 lOns 4.511475 TRUEThis is a pilot study that I conducted when I was just starting my
PhD, and attempted to analyze in probably the worst way possible. The
research question was whether vowel diphthongization in the Dutch vowels
// was affected by syllable structure, such that an // within the same
syllable would block diphthongization but an // in the onset of the next
syllable would permit it. In plain English, the question was whether
these five vowels in Dutch were pronounced like the vowel in English
‘fear’, with the tongue held constant for the duration of the vowel, or
like the vowel in English ‘fade’, which has an upward tongue movement
towards the position of the vowel in English ‘fit’. The position of the
tongue can be measured in a simple word-list reading experiment by
measuring the speech signal’s so-called ‘first formant’, labeled
f1 in this dataset, where lower F1 = higher tongue. Thus,
the research question is if the F1 either changes or remains stable for
the duration of each vowel depending on whether the following consonant
is an ‘l’ in the same syllable (coded as lCda in column
following) or in the next syllable (coded as
lOns). Additionally, I wanted to control for the factors
neighborhood (a measure of entropy: ‘if only one sound is
changed anywhere in this word, how many new words could be generated?’),
information (another measure of entropy derived from the
famous Shannon information measure), and stress (a dummy
encoding whether the vowel was stressed or unstressed).
An entirely reasonable way to analyze these data, and the approach I
ultimately pursued later in my PhD, would be to take samples from each
vowel at 75% realization and at 25% realization, subtract these two, and
use this ‘delta score’ as dependent variable: if this score is non-zero,
the vowel changes over time, if it is approximately zero, the vowel was
stable. In this dataset, however, I instead took as many samples as were
present in the part of the wave file corresponding to these vowels, and
wanted to fit a linear regression line through all of these samples as a
function of the sample number. This number, scaled from 0 to 1 per
token, is listed in column timepoint. To make the model
even more challenging to fit, only six participants were tested in this
pilot study, making it very difficult to find an optimum when including
a full random-slope structure.
In lme4 syntax, the fully maximal model would be given
by the following formula:
f <- f1 ~ vowel*timepoint*following * neighborhood*information*stress +
(vowel*timepoint*following * neighborhood+information+stress | participant) +
(timepoint | word)It should go without saying that this is a completely unreasonable model that will never converge. A first step towards reducing the model structure could be to reason that effects of neighborhood, information, and stress, which are all properties of the individual words in this dataset, could be subsumed into the random effects by words. This reduces the maximal model to:
f <- f1 ~ vowel*timepoint*following +
(vowel*timepoint*following | participant) +
(timepoint | word)This model is still somewhat on the large side, so we will now use
buildmer to check: - if this model is capable of converging
at all; - if all of these terms are really necessary.
To illustrate buildmer’s modular capabilities, we’ll fit
this model in two steps. We start by identifying the maximal model that
is still capable of converging. We do this by running
buildmer, including an optional
buildmerControl argument in which we set the
direction parameter to 'order'. We also set
lme4s optimizer to bobyqa, as this manages to
get much further than the default nloptwrap. Note how
control parameters intended for lmer are specified in the
args list inside buildmerControl. For
backward-compatibility reasons, they can also be passed to
buildmer itself, but this is deprecated now.
library(lme4)
m <- buildmer(f,data=vowels,buildmerControl=buildmerControl(direction='order',
args=list(control=lmerControl(optimizer='bobyqa'))))## Determining predictor order
## Fitting via lm: f1 ~ 1
## Currently evaluating LRT for: following, timepoint, vowel
## Fitting via lm: f1 ~ 1 + following
## Fitting via lm: f1 ~ 1 + timepoint
## Fitting via lm: f1 ~ 1 + vowel
## Updating formula: f1 ~ 1 + vowel
## Currently evaluating LRT for: following, timepoint
## Fitting via lm: f1 ~ 1 + vowel + following
## Fitting via lm: f1 ~ 1 + vowel + timepoint
## Updating formula: f1 ~ 1 + vowel + timepoint
## Currently evaluating LRT for: following, vowel:timepoint
## Fitting via lm: f1 ~ 1 + vowel + timepoint + following
## Fitting via lm: f1 ~ 1 + vowel + timepoint + vowel:timepoint
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint
## Currently evaluating LRT for: following
## Fitting via lm: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following
## Currently evaluating LRT for: timepoint:following, vowel:following
## Fitting via lm: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following
## Fitting via lm: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + vowel:following
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following
## Currently evaluating LRT for: vowel:following
## Fitting via lm: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following +
## vowel:following
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following +
## vowel:following
## Currently evaluating LRT for: vowel:timepoint:following
## Fitting via lm: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following +
## vowel:following + vowel:timepoint:following
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following +
## vowel:following + vowel:timepoint:following
## Fitting via gam, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following
## Currently evaluating LRT for: 1 | participant, 1 | word
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 | participant)
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 | word)
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following +
## vowel:following + vowel:timepoint:following + (1 | participant)
## Currently evaluating LRT for: following | participant, timepoint | participant, vowel |
## participant, 1 | word
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + following |
## participant)
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + timepoint |
## participant)
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + vowel | participant)
## boundary (singular) fit: see ?isSingular
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 | participant) + (1 |
## word)
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following +
## vowel:following + vowel:timepoint:following + (1 | participant) + (1 | word)
## Currently evaluating LRT for: following | participant, timepoint | participant, vowel |
## participant, timepoint | word
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + following |
## participant) + (1 | word)
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + timepoint |
## participant) + (1 | word)
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + vowel | participant)
## + (1 | word)
## boundary (singular) fit: see ?isSingular
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 | participant) + (1 +
## timepoint | word)
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following +
## vowel:following + vowel:timepoint:following + (1 | participant) + (1 + timepoint | word)
## Currently evaluating LRT for: following | participant, timepoint | participant, vowel |
## participant
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + following |
## participant) + (1 + timepoint | word)
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + timepoint |
## participant) + (1 + timepoint | word)
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + vowel | participant)
## + (1 + timepoint | word)
## boundary (singular) fit: see ?isSingular
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following +
## vowel:following + vowel:timepoint:following + (1 + timepoint | participant) + (1 + timepoint |
## word)
## Currently evaluating LRT for: following | participant, vowel | participant
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + timepoint + following
## | participant) + (1 + timepoint | word)
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + timepoint + vowel |
## participant) + (1 + timepoint | word)
## boundary (singular) fit: see ?isSingular
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following +
## vowel:following + vowel:timepoint:following + (1 + timepoint + following | participant) + (1 +
## timepoint | word)
## Currently evaluating LRT for: timepoint:following | participant, vowel | participant
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + timepoint + following
## + timepoint:following | participant) + (1 + timepoint | word)
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + timepoint + following
## + vowel | participant) + (1 + timepoint | word)
## boundary (singular) fit: see ?isSingular
## Updating formula: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following + timepoint:following +
## vowel:following + vowel:timepoint:following + (1 + timepoint + following + timepoint:following
## | participant) + (1 + timepoint | word)
## Currently evaluating LRT for: vowel | participant
## Fitting via lmer, with REML: f1 ~ 1 + vowel + timepoint + vowel:timepoint + following +
## timepoint:following + vowel:following + vowel:timepoint:following + (1 + timepoint + following
## + timepoint:following + vowel | participant) + (1 + timepoint | word)
## boundary (singular) fit: see ?isSingular
## Ending the ordering procedure due to having reached the maximal feasible model - all higher models
## failed to converge. The types of convergence failure are: Singular fit
## Finalizing by converting the model to lmerTestThe order step is useful if the maximal model includes
random effects: buildmer will start out with an empty model
and keeps adding terms to this model until convergence can no longer be
achieved. The order step adds terms in order of their
contribution to a certain criterion, such that the most important random
slopes will be included first; this criterion is controlled by the
crit argument. The default criterion is the significance of
the change in log-likelihood (LRT: terms which provide
lower chi-square \(p\) values are
considered more important), but other options are also supported. These
are the raw log-likelihood (LL: terms which provide the
largest increase in the log-likelihood; this measure will favor
categorical predictors with many levels), AIC (AIC), BIC
(BIC), explained deviance (deviance), and for
GAMMs fitted using package mgcv the change in R-squared
(F). You can select among them by passing
e.g. crit='LRT' within the buildmerControl
argument. The default direction is
c('order','backward'), i.e. proceeding directly to backward
stepwise elimination, but for illustration purposes we separate
those steps here. (The crit argument also accepts vectors,
such that
e.g. direction=c('order','backward'),crit=c('LL','LRT') is
allowed.)
After a lot of model fits, the model converges onto the following maximal model:
(f <- formula(m@model))## f1 ~ following + vowel + timepoint + vowel:timepoint + following:timepoint +
## following:vowel + following:vowel:timepoint + (1 + timepoint +
## following + timepoint:following | participant) + (1 + timepoint |
## word)The maximal feasible model, i.e. the maximal model that is
actually capable of converging, is one excluding random slopes for
vowels by participants. This is not optimal for inference purposes, but
for now it will do; we will see below that taking out the correlation
parameters in the random effects makes it possible to include random
slopes for vowels as well. We now proceed to the next step: stepwise
elimination. This could also be done using e.g. lmerTest,
but since the machinery was needed for direction='order'
anyway it came at very little cost to also implement stepwise
elimination in buildmer (both forward and backward are
supported). This uses the same elimination criterion as could be
specified previously; if left unspecified, it defaults to
crit='LRT', for the likelihood-ratio test. This is the
preferred test for mixed models in Matuschek et
al. (2017).
m <- buildmer(f,data=vowels,buildmerControl=list(direction='backward',
args=list(control=lmerControl(optimizer='bobyqa'))))## Fitting ML and REML reference models
## Fitting via lmer, with REML: f1 ~ following + vowel + timepoint + vowel:timepoint +
## following:timepoint + following:vowel + following:vowel:timepoint + (1 + timepoint + following
## + timepoint:following | participant) + (1 + timepoint | word)
##
## Fitting via lmer, with REML: f1 ~ following + vowel + timepoint + vowel:timepoint +
## following:timepoint + following:vowel + following:vowel:timepoint + (1 + timepoint + following
## + timepoint:following | participant) + (1 + timepoint | word)
## Testing terms
## Fitting via lmer, with ML: f1 ~ 1 + following + vowel + timepoint + vowel:timepoint +
## following:timepoint + following:vowel + (1 + timepoint + following + timepoint:following |
## participant) + (1 + timepoint | word)
## Fitting via lmer, with REML: f1 ~ 1 + following + vowel + timepoint + vowel:timepoint +
## following:timepoint + following:vowel + following:vowel:timepoint + (1 + timepoint + following
## | participant) + (1 + timepoint | word)
## Fitting via lmer, with REML: f1 ~ 1 + following + vowel + timepoint + vowel:timepoint +
## following:timepoint + following:vowel + following:vowel:timepoint + (1 + timepoint + following
## + timepoint:following | participant) + (1 | word)
## grouping term block Iteration LRT
## 1 <NA> 1 NA NA 1 1 NA
## 2 <NA> following NA NA following 1 NA
## 3 <NA> vowel NA NA vowel 1 NA
## 4 <NA> timepoint NA NA timepoint 1 NA
## 5 <NA> vowel:timepoint NA NA vowel:timepoint 1 NA
## 6 <NA> following:timepoint NA NA following:timepoint 1 NA
## 7 <NA> following:vowel NA NA following:vowel 1 NA
## 8 <NA> following:vowel:timepoint NA NA following:vowel:timepoint 1 3.609316e-30
## 9 participant 1 NA participant 1 1 NA
## 10 participant timepoint NA participant timepoint 1 NA
## 11 participant following NA participant following 1 NA
## 12 participant timepoint:following NA participant timepoint:following 1 1.013211e-10
## 13 word 1 NA word 1 1 NA
## 14 word timepoint NA word timepoint 1 2.198802e-153
## All terms are significant
## Finalizing by converting the model to lmerTestIt appears that in this example, all terms were significant in backward-stepwise elimination.
By default, buildmer automatically calculates summary
and ANOVA statistics based on Wald \(z\)-scores (summary) or Wald \(\chi^2\) tests (ANOVA). For answering our
research question, we look at the summary:
summary(m)## Linear mixed model fit by REML
## (p-values based on Wald z-scores) ['lmerMod']
## Formula: f1 ~ following + vowel + timepoint + vowel:timepoint + following:timepoint +
## (1 + timepoint | word) + (1 + timepoint + following + timepoint:following | participant)
## Data: vowels
##
## REML criterion at convergence: 149448.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2347 -0.4168 0.0102 0.3877 21.6815
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## word (Intercept) 2539.3 50.39
## timepoint 11089.5 105.31 -0.78
## participant (Intercept) 2199.7 46.90
## timepoint 3423.6 58.51 -0.50
## followinglOns 747.3 27.34 0.11 0.65
## timepoint:followinglOns 3053.3 55.26 0.64 -0.88 -0.67
## Residual 10018.1 100.09
## Number of obs: 12351, groups: word, 148; participant, 6
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 551.733 20.075 27.484 < 2e-16 ***
## followinglOns 49.106 14.504 3.386 0.00071 ***
## vowel1 114.254 8.951 12.765 < 2e-16 ***
## vowel2 142.083 8.910 15.946 < 2e-16 ***
## vowel3 -125.955 8.626 -14.601 < 2e-16 ***
## vowel4 -79.211 9.970 -7.945 1.94e-15 ***
## timepoint -15.445 26.856 -0.575 0.56524
## vowel1:timepoint -39.663 18.239 -2.175 0.02966 *
## vowel2:timepoint -91.032 18.171 -5.010 5.45e-07 ***
## vowel3:timepoint 105.886 17.554 6.032 1.62e-09 ***
## vowel4:timepoint 38.924 20.271 1.920 0.05483 .
## followinglOns:timepoint -136.888 29.411 -4.654 3.25e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fllwnO vowel1 vowel2 vowel3 vowel4 timpnt vwl1:t vwl2:t vwl3:t vwl4:t
## follwnglOns -0.043
## vowel1 -0.014 0.020
## vowel2 -0.017 0.025 -0.225
## vowel3 -0.029 0.031 -0.210 -0.207
## vowel4 0.034 -0.031 -0.278 -0.276 -0.266
## timepoint -0.533 0.596 0.017 0.020 0.034 -0.041
## vowl1:tmpnt 0.011 -0.016 -0.787 0.177 0.165 0.218 -0.021
## vowl2:tmpnt 0.013 -0.020 0.177 -0.787 0.163 0.217 -0.025 -0.226
## vowl3:tmpnt 0.023 -0.024 0.166 0.164 -0.788 0.210 -0.044 -0.210 -0.208
## vowl4:tmpnt -0.027 0.024 0.219 0.218 0.210 -0.788 0.051 -0.277 -0.276 -0.265
## fllwnglOns: 0.567 -0.720 -0.016 -0.020 -0.024 0.024 -0.788 0.020 0.024 0.031 -0.030The significant effect for followinglOns:timepoint shows
that if the following /l/ is in the onset of the next syllable, there is
a much larger change in F1 compared to the reference condition of having
the following /l/ in the coda of the same syllable.
One hidden feature that is present in buildmer but that
has not yet been discussed is the ability to group terms together in
blocks for ordering and stepwise-elimination purposes. While the first
argument to buildmer functions is normally a formula, it is
also possible to pass a ‘buildmer terms list’. This is a data frame as
generated by tabulate.formula:
tabulate.formula(f)## index grouping term code
## 1 <NA> <NA> 1 1
## 2 <NA> <NA> following following
## 3 <NA> <NA> vowel vowel
## 4 <NA> <NA> timepoint timepoint
## 5 <NA> <NA> vowel:timepoint vowel:timepoint
## 6 <NA> <NA> following:timepoint following:timepoint
## 7 <NA> <NA> following:vowel following:vowel
## 8 <NA> <NA> following:vowel:timepoint following:vowel:timepoint
## 9 9 1 participant 1 9 1 participant 1
## 10 9 1 participant timepoint 9 1 participant timepoint
## 11 9 1 participant following 9 1 participant following
## 12 9 1 participant timepoint:following 9 1 participant timepoint:following
## 13 10 1 word 1 10 1 word 1
## 14 10 1 word timepoint 10 1 word timepoint
## block
## 1 NA NA 1
## 2 NA NA following
## 3 NA NA vowel
## 4 NA NA timepoint
## 5 NA NA vowel:timepoint
## 6 NA NA following:timepoint
## 7 NA NA following:vowel
## 8 NA NA following:vowel:timepoint
## 9 NA participant 1
## 10 NA participant timepoint
## 11 NA participant following
## 12 NA participant timepoint:following
## 13 NA word 1
## 14 NA word timepointThis is an internal buildmer data structure, but it is
rather self-explanatory in how it is used. It is possible to modify the
block column to force terms to be evaluated as a single
group, rather than separately, by giving these terms the same
block value. These values are not used in any other way
than this purpose of selecting terms to be grouped together, which can
be exploited to fit models with diagonal random-effect structures. The
first step is to create explicit columns for the factor
vowel; if this is not done, only random-effect correlations
between vowels and other random slopes will be eliminated and
those between the vowels themselves will remain.
vowels <- cbind(vowels,model.matrix(~vowel,vowels))We next create a formula for this modified dataset. To make it easier
to type, we do not explicitly diagonalize the formula ourselves, but use
buildmer’s diag() method for
formula objects. We then call
tabulate.formula() on the new formula, providing a regular
expression that matches terms belonging to the same vowel. Note that we
cannot use the simple vowel factor in the
fixed-effects part of the formula, as this will break
buildmer’s marginality checks when considering which terms
are eligible for inclusion or removal.
form <- diag(f1 ~ (vowel1+vowel2+vowel3+vowel4)*timepoint*following +
((vowel1+vowel2+vowel3+vowel4)*timepoint*following | participant) +
(timepoint | word))
terms <- tabulate.formula(form,group='vowel[^:]')Finally, we can instruct buildmer to use this
specially-crafted terms object by simply passing it along
instead of a regular formula. buildmer will recognize what
is going on, and use the variable name specified in the dep
control argument as the dependent variable in the data frame; this
variable name should be provided as a character string.
m <- buildmer(terms,data=vowels,buildmerControl=buildmerControl(dep='f1',
args=list(control=lmerControl(optimizer='bobyqa'))))
## (output not shown)This approach allows random slopes for vowel and for
vowel:timepoint to make it in, both of which significantly
improve model fit. This model seems much more adequate for statistical
inference.
Because buildmer does not do any model fitting by itself
but is only an administrative formula processor around pre-existing
modeling fuctions, it was straightforward to extend it beyond its
original purpose of mixed-effects models. The logical extension of
buildmer to GAMMs is fully supported, with appropriate
safeguards against using likelihood-based tests for bam and
gamm models in the generalized case, which use PQL
(penalized quasi-likelihood). Relevant functions are available as
buildbam, buildgam, buildgamm,
and buildgamm4; for buildbam and
buildgam, random effects in lme4 form are
converted to s(...,bs='re') form automatically.
glmmTMB models are also supported via function
buildglmmTMB, although their syntax for covariance
structures (e.g. diag(timepoint | participant)) is not;
these models are still useful for their ability to handle
autocorrelation, zero-inflation, and to use REML for GLMMs. From package
nlme, gls models are supported via
buildgls, lme models are supported via
buildlme. At the request of Willemijn Heeren,
buildmer was also extended to handle
multinomial-logistic-regression models fitted by function
multinom from package nnet; see function
buildmultinom. buildmertree makes it possible
to do term ordering and backward elimination of the random-effects part
of glmertree models. buildGLMMadaptive works
with function mixed_model from package
GLMMadaptive. buildclmm uses functions
clm and clmm from package
ordinal. Finally, buildcustom allows you to
write your own wrapper functions, making it possible to use the buildmer
machinery with anything that accepts a formula.