r2mlm reads in a multilevel model (MLM) object generated using
nlme, and outputs all
relevant R-squared measures from the Rights and Sterba (2019) framework of
multilevel model R-squared measures, which can be visualized together as a
set using the outputted bar chart decompositions of outcome variance. That is,
when predictors are cluster-mean-centered, all R-squared measures from Rights
& Sterba (2019) Table 1 and decompositions from Rights & Sterba (2019) Figure
1 are outputted. When predictors are not cluster-mean-centered, the total
R-squared measures from Rights & Sterba (2019) Table 5, as well as bar chart
decompositions are outputted. Any number of level-1 and/or level-2 predictors
is supported. Any of the level-1 predictors can have random slopes.
A model generated using
Optional bar graph output, default is TRUE.
r2mlm first determines whether a given model was generated using
nlme, then passes the model
to helper functions that pull the raw data and parameter estimates from the
model, and pass that information to
If the input is a valid model, then the output will be a list and associated graphical representation of R-squared decompositions. If the model is not valid, it will return an error prompting the user to input a valid model.
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>
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# Using lme4 for your model # The "bobyqa" optimizer is required for this particular model to converge model_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")) r2mlm(model_lme4) # Using nlme for your model model_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")) r2mlm(model_nlme)
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