Description Usage Arguments Details Value See Also Examples
Fits a linear mixed model separately for each feature. Returns all relevant
statistics.
CITATION: When using this function, cite lme4
and lmerTest
packages
1 2 3 | perform_lmer(object, formula_char, all_features = FALSE,
ci_level = 0.95, ci_method = c("boot", "profile", "Wald"),
test_random = FALSE, ...)
|
object |
a MetaboSet object |
formula_char |
character, the formula to be used in the linear model (see Details) |
all_features |
should all features be included in FDR correction? |
ci_level |
the confidence level used in constructing the confidence intervals for regression coefficients |
ci_method |
The method for calculating the confidence intervals, see documentation of confint below |
test_random |
logical, whether tests for the significance of the random effects should be performed |
... |
additional parameters passed to lmer |
The model is fit on combined_data(object). Thus, column names in pData(object) can be specified. To make the formulas flexible, the word "Feature" must be used to signal the role of the features in the formula. "Feature" will be replaced by the actual Feature IDs during model fitting, see the example
a data frame with one row per feature, with all the relevant statistics of the linear mixed model as columns
lmer
for model specification and
confint.merMod
for the computation of confidence intervals
1 2 3 4 | # A simple example without QC samples
# Features predicted by Group and Time as fixed effects with Subject ID as a random effect
lmer_results <- perform_lmer(drop_qcs(example_set), formula_char = "Feature ~ Group + Time + (1 | Subject_ID)",
ci_method = "Wald")
|
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