r2mlm_comp_manual: Compute R-squared differences between two multilevel models,...

In r2mlm: R-Squared Measures for Multilevel Models

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

Description

r2mlm_comp_manual reads in raw data and multilevel model (MLM) parameter estimates from two separate models 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 also 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_manual(
  data,
  within_covs_modA,
  between_covs_modA,
  random_covs_modA,
  gamma_w_modA,
  gamma_b_modA,
  Tau_modA,
  sigma2_modA,
  within_covs_modB,
  between_covs_modB,
  random_covs_modB,
  gamma_w_modB,
  gamma_b_modB,
  Tau_modB,
  sigma2_modB,
  bargraph = TRUE
)

Arguments

data	Dataset with rows denoting observations and columns denoting variables.
within_covs_modA, within_covs_modB	List of numbers corresponding to the columns in the dataset of the level-1 predictors used in the MLM (if none used, set to NULL).
between_covs_modA, between_covs_modB	List of numbers corresponding to the columns in the dataset of the level-2 predictors used in the MLM (if none used, set to NULL).
random_covs_modA, random_covs_modB	List of numbers corresponding to the columns in the dataset of the level-1 predictors that have random slopes in the MLM (if no random slopes, set to NULL).
gamma_w_modA, gamma_w_modB	Vector of fixed slope estimates for all level-1 predictors, to be entered in the order of the predictors listed by within_covs (if none, set to NULL).
gamma_b_modA, gamma_b_modB	Vector of fixed intercept estimate (if applicable; see has_intercept below) and fixed slope estimates for all level-2 predictors, to be entered intercept first (if applicable) followed by level-2 slopes in the order listed by between_covs (if none, set to NULL).
Tau_modA, Tau_modB	Random effect covariance matrix; note that the first row/column denotes the intercept variance and covariances (if intercept is fixed, set all to 0) and each subsequent row/column denotes a given random slope’s variance and covariances (to be entered in the order listed by random_covs).
sigma2_modA, sigma2_modB	Level-1 residual variance.
bargraph	Optional bar graph output, default is TRUE.

Value

If the inputs are valid models, then the output will be a list and associated graphical representation of R-squared decompositions.

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()

Examples

# Model A: no "salary" components included

modelA <- 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 <- 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"))

r2mlm_comp_manual(data = teachsat, within_covs_modA = c(4), between_covs_modA
= c(6, 8), random_covs_modA = c(4), gamma_w_modA = c(0.314410), gamma_b_modA
= c(6.892046, 0.059464, -0.037376), Tau_modA = matrix(c(0.559, 0.0105,
0.0105, 0.0273), 2, 2), sigma2_modA = 1.19295, within_covs_modB = c(5, 4),
between_covs_modB = c(7, 6, 8), random_covs_modB = c(5, 4), gamma_w_modB =
c(0.074485, 0.310800), gamma_b_modB = c(4.352652, 0.036759, 0.027532,
-0.035250), Tau_modB = matrix(c(0.387, 0.0000646, 0.00625, 0.0000646,
0.00277, -0.000333, 0.00625, -0.000333, 0.0285), 3, 3), sigma2_modB =
0.550311)

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