OLStrajr (version 0.1.0)

cbc_lm: Case-by-Case Linear Regression (cbc_lm)

Description

Implements the case-by-case ordinary least squares (OLS) regression method, as detailed in Rogosa & Saner (1995). The cbc_lm function provides unbiased estimators of the mean population intercept and slope by calculating the mean values of the OLS intercepts and slopes for each case (Carrig et al, 2004). The standard errors reported are the standard deviations across bootstrap replicates. Additionally, 95% confidence intervals are calculated using the empirical distributions from the resampling.

Usage

cbc_lm(
  data,
  formula,
  .case,
  n_bootstrap = 4000,
  lm_options = list(),
  boot_options = list(),
  boot.ci_options = list(),
  na.rm = FALSE
)

Value

An object of class cbc_lm, which contains the results of the case-by-case OLS regression, including the mean, standard error, and confidence intervals for each coefficient.

Arguments

data

A data frame containing the variables in the model

formula

An object of class formula (or a string that can be converted to a formula object) detailing the model's specifications.

.case

A quoted variable name used to subset data into cases.

n_bootstrap

The number of bootstrap replicates for standard errors and confidence intervals of mean coefficients. Default is 4000, as in Rogosa & Saner (1995).

lm_options

Pass additional arguments to the lm function.

boot_options

Pass additional arguments to the boot function.

boot.ci_options

Pass additional arguments to the boot.ci function.

na.rm

Pass na.rm to: the mean function used to obtain mean_coef and bm_coef; the sd function used to obtain se_coef; the mean function used in the statistic parameter of boot.

References

Carrig, M. M., Wirth, R. J., & Curran, P. J. (2004). A SAS Macro for Estimating and Visualizing Individual Growth Curves. Structural Equation Modeling: A Multidisciplinary Journal, 11(1), 132-149. tools:::Rd_expr_doi("10.1207/S15328007SEM1101_9")

Rogosa, D., & Saner, H. (1995). Longitudinal Data Analysis Examples with Random Coefficient Models. Journal of Educational and Behavioral Statistics, 20(2), 149-170. tools:::Rd_expr_doi("10.3102/10769986020002149")

Examples

df <- data.frame(ids = rep(1:5, 5),
                 vals = stats::rnorm(25),
                 outs = stats::rnorm(25, 10, 25))

cbc_lm(data = df, formula = outs ~ vals, .case = "ids")