grafify (version 4.0.1)

mixed_model: Model from a linear mixed effects model

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(data, Y_value, Fixed_Factor, Random_Factor, ...)

Value

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

Arguments

data

a data table object, e.g. data.frame or tibble.

Y_value

name of column containing quantitative (dependent) variable, provided within "quotes".

Fixed_Factor

name(s) of categorical fixed factors (independent variables) provided as a vector if more than one or within "quotes".

Random_Factor

name(s) of random factors to allow random intercepts; to be provided as a vector when more than one or within "quotes".

...

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.

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.

Examples

#one fixed factor and random factor
mixed_model(data = data_doubling_time, 
Y_value = "Doubling_time", 
Fixed_Factor = "Student", 
Random_Factor = "Experiment")

#two fixed factors as a vector, one random factor
mixed_model(data = data_cholesterol, 
Y_value = "Cholesterol", 
Fixed_Factor = c("Treatment", "Hospital"), 
Random_Factor = "Subject")

#save model
model <- mixed_model(data = data_doubling_time, 
Y_value =  "Doubling_time", 
Fixed_Factor = "Student", 
Random_Factor = "Experiment")

#get model summary
summary(model)