Description
This shows the output of the Hierarchical Linear Regression (HLR)
function report_hlr from the package rwf. HLR builds
regression models in sequential steps (blocks), adding predictors at
each step. This shows how much additional variance each new set of
predictors explains beyond the earlier ones, measured as the change in
R² (ΔR²).
Installation instructions for rwf can be found here
The code can be found here
Report HLR
report_hlr fits a series of hierarchical regression
models and returns a structured report with model fit statistics (R²,
adjusted R², F-change, ΔR²), standardized and unstandardized regression
coefficients, and significance tests at each step.
The example uses the built-in infert dataset, which
records infertility after induced and spontaneous abortions. The
arguments specify:
-
df = infert: the dataset. -
corlist = 8: column index of the continuous predictor(s) to include. -
factorlist = 1: column index of categorical predictor(s) treated as factors. -
predictor = "case": the outcome variable (case/control status). -
random_effect = "case": the variable used as a random effect in the mixed model comparison.
The ΔR² column in the output tells you whether each block of predictors significantly improved model fit — a key quantity when building theoretically motivated regression models step by step.
result<-report_hlr(df=infert,corlist=8,factorlist=1,predictor="case",random_effect="case")
result## dv model fixed random call Model df AIC
## 1 pooled.stratum base pooled.stratum ~ 1 <NA> nlme::gls(model = formula(fbaseline), data = temp, method = "ML") 1 2 2119.941
## 2 pooled.stratum random_intercept pooled.stratum ~ 1 ~1 | case lme.formula(fixed = formula(fbaseline), data = temp, random = frandom_intercept, method = "ML") 2 3 2121.941
## 3 pooled.stratum random_intercept_predictor pooled.stratum ~ case ~1 | case lme.formula(fixed = fpredictor, data = temp, random = frandom_intercept, method = "ML") 3 4 2123.936
## 4 pooled.stratum random_intercept_slope pooled.stratum ~ case ~case | case lme.formula(fixed = fpredictor, data = temp, random = frandom_slope, method = "ML") 4 6 2127.936
## BIC logLik Test L.Ratio p.value
## 1 2126.968 -1057.971 NA NA
## 2 2132.482 -1057.971 1 vs 2 3.060691e-07 0.9995586
## 3 2137.989 -1057.968 2 vs 3 5.861154e-03 0.9389750
## 4 2149.016 -1057.968 3 vs 4 4.047206e-08 1.0000000