This function back-fits an initial LMER model either on upper- or lower-bound p-values obtained from function pamer.fnc, log-likelihood ratio testing (LLRT), AIC, BIC, relLik.AIC, or relLik.BIC. Note that this function CANNOT be used with generalized linear mixed-effects models (glmers).
bfFixefLMER_F.fnc(model, item = FALSE,
method = c("F", "llrt", "AIC", "BIC", "relLik.AIC",
"relLik.BIC"), threshold = NULL, alpha = NULL,
alphaitem = NULL, prune.ranefs = TRUE,
p.value = "upper", set.REML.FALSE = TRUE,
keep.single.factors=FALSE, reset.REML.TRUE = TRUE,
log.file = NULL)A mer object (fitted by function lmer). Note that
this function cannot be used with generalized linear mixed-effects models
(glmers).
Whether or not to evaluate the addition of by-item random
intercepts to the model, evaluated by way of log-likelihood ratio test.
Either FALSE (the default) or the column name (quoted) of the item
identifier (e.g., "Item", or "Word").
Backfitting method. One of "F" (p-value), "llrt", "AIC",
"BIC", "relLik.AIC", or "relLik.BIC" (relative likelihood, see function
relLik). Defaults to F. You can find information regarding
differences between AIC and BIC from
http://methodology.psu.edu/eresources/ask/sp07.
Method-specific threshold for parameter selection. It refers
to alpha in the case of "F" and "llrt", to the minimum reduction in
likelihood in the case of "AIC" and "BIC", or to the minimum difference
in probability in the case of "relLik.AIC" and "relLik.BIC". Defaults
NULL, which means 0.05 for "F" and "llrt", 5 for "AIC" and
"BIC", and 4 for "relLik.AIC" and "relLik.BIC".
If the
method is F, it is the p-value (from pamer.fnc)
above which a model term is dropped. In this case, it defaults to the
value passed to argument threshold, i.e., 0.05. Otherwise it is
the p-value threshold above which a test (see method) is
performed between a model with the term under consideration and a simpler
model without it (in this case, defaults to 0, i.e. all terms will
be tested).
Alpha value for the evaluation of by-item
random intercepts. Defaults to 0.05 or to the specified
threshold.
Logical. Whether to remove any random
effect for which its variable is not also present in the fixed effects
structure (with the exception of the grouping variables such as
"Subjects" and "Items"). Defaults to TRUE. For
example, if the random effects structure contains the terms
Condition + ROI + Group, and the random effects structure contains
the terms (1 | Subject) + (0 + TrialNum | Subject), the random
effect (0 + TrialNum | Subject) will be pruned from the model
given that it is not in the model's fixed effects structure.
If method = "F", whether to use upper-bound (``upper'';
the default) or lower-bound (``lower'') p-values during
backfitting.
Logical. Whether or not to set
REML to FALSE. Defaults to TRUE.
Logical. Whether or not main effects are kept (not
subjected to testing and reduction). Defaults to FALSE.
Logical. Whether or not to re-set the back-fitted
model to REML = TRUE.
Whether a back-fitting log
should be saved. Defaults to NULL, which means that a log is saved
in a temporary folder with the file name file.path(tempdir(),
paste("bfFixefLMER_F_log_", gsub(":", "-", gsub(" ", "_", date())),
".txt", sep = "")). The path and file name of the log can be changed
to whatever the use wishes. Set to FALSE to disable.
A mer model
with back-fitted fixed effects is returned and a log of the back-fitting
process is printed on screen and (by default) in a log file in a temporary
file.
Upper-bound p-values can be anti-conservative, while
lower-bound p-values can be conservative. See function
pamer.fnc.
The back-fitting process works as follows:
If
argument method is not set to F, REML is set to
FALSE;
First consider only highest-order interaction model terms:
If method is F, the model term
with the highest ANOVA p-value is identified. If this
p-value is higher than alpha,the model term is
removed and a new model is fitted. This is repeated for each model
term that has a p-value higher than the alpha value.
The algorithm then moves on to step (b). If method is not
F, the model term with the lowest p-value is
identified and the following is evaluated:
A new model without this model term is fitted;
The more complex
and simpler models are compared by way of a log-likelihood
ratio test in case method is "llrt", by way of AIC or
BIC values in case method is "AIC" or "BIC", or by
calculating the relLik based on AIC or BIC in case
method is "relLik.AIC" or "relLik.BIC". If the result
determines that the term under consideration does not increase
model fit, it is removed; otherwise it is kept.
Move on
to the next model term with the smallest p-value smaller
than alpha and repeat steps (i)--(iii).
Once
all highest-order interaction terms have been evaluated, go down to
the second highest order interactions: Repeat steps (ai)--(aiii)
with the following addition: If a term would be removed from the
model, but it is part of a high-order interaction, keep it. Once
all terms of the interaction level have been evaluated, move down
to the next lower-order level until main effects have been
evaluated, after which the process stops. If keep.single
factors = TRUE, the process stops after the evaluation of all
interaction terms.
If argument method is set to
something else other than "F", set reset.REML.TRUE to
TRUE (default) unless otherwise specified.
In brief, if method is set to "F", a term remains in the model if its
p-value is equal to or greater than alpha; if method is
set to something else, a term remains in the model if
its
p-value from the ANOVA is equal to or smaller than alpha;
it significantly increases model fit as determined by the specified method;
it is part of a significant higher-order interaction term.
This backfitting method was used in Newman, Tremblay, Nichols, Neville, and Ullman (2012). If factorial terms are included in the initial model, back-fitting on F is recommended.
Newman, A.J., Tremblay, A., Nichols, E.S., Neville, H.J., and Ullman, M.T. (2012). The Influence of Language Proficiency on Lexical Semantic Processing in Native and Late Learners of English. Journal of Cognitive Neuroscience, 25, 1205--1223.
bfFixefLMER_t.fnc;
ffRanefLMER.fnc;
fitLMER.fnc;
mcposthoc.fnc;
pamer.fnc;
mcp.fnc;
relLik;
romr.fnc;
perSubjectTrim.fnc.
# NOT RUN {
# see example in LMERConvenienceFunctions help page.
# }
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