mixed_model_slopes: Model from a linear mixed effects model with varying slopes

In ashenoy-cmbi/grafify: Easy Graphs for Data Visualisation and Linear Models for ANOVA

Model from a linear mixed effects model with varying slopes

Description

One of four related functions for mixed effects analyses (based on lmer and as_lmerModLmerTest) to get a linear model for downstream steps, or an ANOVA table.

1. mixed_model

2. mixed_anova

3. mixed_model_slopes

4. mixed_anova_slopes.

Usage

mixed_model_slopes(
  data,
  Y_value,
  Fixed_Factor,
  Slopes_Factor,
  Random_Factor,
  AvgRF = TRUE,
  ...
)

Arguments

data	a data table object, e.g. data.frame or tibble.
Y_value	name of column containing quantitative (dependent) variable, provided within "quotes". The following transformations are permitted: "log(Y_value)", "log(Y_value + c)" where c a positive number, "logit(Y_value)" or "logit(Y_value/100)" which may be useful when Y_value are percentages (note quotes outside the log or logit calls); "sqrt(Y_value)" or "(Y_value)^2" should also work. During posthoc-comparisons, log and logit transformations will be back-transformed to the original scale. Other transformations, e.g., "sqrt(Y_value)" will not be back-transformed. Check out the regrid and ref_grid for details if you need back-transformation to the response scale.
Fixed_Factor	name(s) of categorical fixed factors (independent variables) provided within quotes (e.g., "A") or as a vector if more than one (e.g., c("A", "B"). If a numeric variable is used, transformations similar to Y_value are permitted.
Slopes_Factor	name of factor to allow varying slopes on. One one variable is allowed.
Random_Factor	name(s) of random factors to allow random intercepts; to be provided within quotes (e.g., "R") or as a vector when more than one (e.g., c("R1", "R2")). Only one variable is allowed.
AvgRF	this is a new argument since v5.0.0. The default AvgRF = TRUE will use the mean of Y_value (the response variable) grouped by levels of the Fixed_Factor and Random_Factor (using table_summary). This ensures that replicates within Random_Factor (or any other unused variable) are averaged (e.g., technical replicates nested within experimental blocks) before fitting a linear model and the denominator Df values are sensible. The name of the data frame in the model object will have (AvgRF) appended to it to indicate the averaging within levels of the Random_Factor. Using AvgRF = FALSE will lead to behaviour like versions <5.0.0.
...	any additional arguments to pass on to lmer if required.

Details

These functions require a data table, one dependent variable (Y_value), one or more independent variables (Fixed_Factor), and at least one random factor (Random_Factor). These should match names of variables in the long-format data table exactly. Since v5.0.0, if AvgRF = TRUE, the response variable is averaged over levels of the fixed and random factors (to collapse replicate observations) and reduce the number of denominator degrees of freedom. If you do not want to do this, set AvgRF = FALSE.

For more advanced models with slopes and intercept, use mixed_model or mixed_anova using the Formula argument.

Outputs of mixed_model and mixed_model_slopes can be used for post-hoc comparisons with posthoc_Pairwise, posthoc_Levelwise, posthoc_vsRef, posthoc_Trends_Pairwise, posthoc_Trends_Levelwise and posthoc_Trends_vsRefor with emmeans.

More than one fixed factors can be provided as a vector (e.g. c("A", "B")). A full model with interaction term is fitted. This means when Y_value = Y, Fixed_factor = c("A", "B"), Random_factor = "R" are entered as arguments, these are passed on as Y ~ A*B + (1|R) (which is equivalent to Y ~ A + B + A:B + (1|R)).

In mixed_model_slopes and mixed_anova_slopes, the following kind of formula is used: Y ~ A*B + (S|R) (which is equivalent to Y ~ A + B + A:B + (S|R)). In this experimental implementation, random slopes and intercepts are fitted ((Slopes_Factor|Random_Factor)). Only one term each is allowed for Slopes_Factor and Random_Factor.

Value

This function returns an S4 object of class "lmerModLmerTest".

Examples

#two fixed factors as a vector, 
#exactly one slope factor and random factor
mod <- mixed_model_slopes(data = data_2w_Tdeath,
Y_value = "PI",
Fixed_Factor = c("Genotype", "Time"),
Slopes_Factor = "Time",
Random_Factor = "Experiment")
#get summary
summary(mod)

ashenoy-cmbi/grafify documentation built on April 13, 2025, 3:25 a.m.