r2mlm_comp: Compute R-squared differences between two multilevel models,...

In r2mlm: R-Squared Measures for Multilevel Models

Compute R-squared differences between two multilevel models, automatically inputting parameter estimates.

Description

r2mlm_comp reads in two multilevel models (MLMs) (generated using lmer or nlme) under comparison (designated Model A and Model B), and outputs all R-squared measures in the Rights and Sterba (2019) framework for both models, as well as R-squared differences between the two models. Definitions of these R-squared difference measures are provided in Rights & Sterba (2020) Table 1; importantly, to detect the impact of a specific kind of term (e.g., the kind of term added to Model A to form Model B), a particular target single-source R-squared difference measure from this framework is used. For instructions on how to identify which target single-source R-squared difference measure to interpret to detect the impact of which kind of term that distinguishes Model A from B, see Rights and Sterba (2020) Table 2. Additionally, this function produces side-by-side graphical comparisons of the R-squared measures for Model A vs. Model B that can be used to visualize changes in each measure across models. This function assumes all level-1 predictors are cluster-mean-centered, for reasons described in Rights & Sterba (2020). Any number of level-1 and/or level-2 predictors is supported and any of the level-1 predictors can have random slopes. This function can be used with either the hierarchical or the simultaneous model-building approach described in Rights and Sterba (2020). This function can be used with either nested or non-nested model comparisons (in which R-squared estimates for Model A are subtracted from those for Model B).

Usage

r2mlm_comp(modelA, modelB, data = NULL, bargraph = TRUE)

Arguments

modelA, modelB	Models generated using lmer or nlme. Note that models using lmer must specify random effects at the end of the model, like so: outcome ~ 1 + fixed_effects + (random_effects | cluster_variable). Anything else (e.g., outcome ~ 1 + (random_effects | cluster_variable) + fixed_effects) will not work.
data	Optional argument, only needed if models are not hierarchical. Dataset with rows denoting observations and columns denoting variables.
bargraph	Optional bar graph output, default is TRUE.

Details

Assumes that both models are fit with lmer or both models are fit with nlme.

Value

If the inputs are valid models, then the output will be a list and associated graphical representation of R-squared decompositions. If the models are not valid, the function will return an error prompting the user to input valid models.

See Also

Rights, J. D., & Sterba, S. K. (2019). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods, 24(3), 309–338. <doi:10.1037/met0000184>

Rights, J. D., & Sterba, S. K. (2020). New recommendations on the use of R-squared differences in multilevel model comparisons. Multivariate Behavioral Research. <doi:10.1080/00273171.2019.1660605>

Other r2mlm model comparison functions: r2mlm_comp_manual()

Examples

# Using lme4 for your model
# The "bobyqa" optimizer is required for these particular models to converge

## Not run:  # Model A, no "salary" components included

modelA_lme4 <- lmer(satisfaction ~ 1 + control_c + control_m + s_t_ratio + (1
+ control_c | schoolID), data = teachsat, REML = TRUE, control =
lmerControl(optimizer = "bobyqa"))

# Model B, full model with "salary" components included

modelB_lme4 <- lmer(satisfaction ~ 1 + salary_c + control_c + salary_m +
control_m + s_t_ratio + (1 + salary_c + control_c | schoolID), data =
teachsat, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))

# Compare models
r2mlm_comp(modelA_lme4, modelB_lme4)

# Compare models, optional data argument specified
r2mlm_comp(modelA_lme4, modelB_lme4, teachsat)

# Using nlme for your model

# Model A, no "salary" components included

modelA_nlme <- lme(satisfaction ~ 1 + control_c + control_m + s_t_ratio,
                  random = ~ 1 + control_c | schoolID,
                  data = teachsat,
                  method = "REML",
                  control = lmeControl(opt = "optim"))

# Model B, full model with "salary" components included

modelB_nlme <- lme(satisfaction ~ 1 + salary_c + control_c + salary_m +
                  control_m + s_t_ratio,
                  random = ~ 1 + salary_c + control_c | schoolID,
                  data = teachsat,
                  method = "REML",
                  control = lmeControl(opt = "optim"))

# Compare models
r2mlm_comp(modelA_nlme, modelB_nlme)

# Compare models, optional data argument specified
r2mlm_comp(modelA_nlme, modelB_nlme, teachsat)

## End(Not run)

r2mlm documentation built on July 26, 2023, 5:25 p.m.