inst/doc/getting-started.R

In ltgsmd: Latent True-Score and Target-Population Anchored Geometric SMD

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 4
)
library(ltgsmd)
set.seed(20240501)

## ----single-study-example, eval = FALSE---------------------------------------
#  # Simulated example
#  n <- 60; k <- 4
#  study_df <- data.frame(condition = rep(c("control", "treatment"), each = n))
#  for (i in 1:k) {
#    eff <- ifelse(study_df$condition == "treatment", 0.5, 0)
#    study_df[[paste0("item", i)]] <- rnorm(2 * n, mean = eff)
#  }
#  study_df$outcome <- rowMeans(study_df[, paste0("item", 1:k)])
#  
#  # Holdout reference
#  ref_df <- data.frame(condition = rep(c("control", "treatment"), each = n))
#  for (i in 1:k) {
#    eff <- ifelse(ref_df$condition == "treatment", 0.5, 0)
#    ref_df[[paste0("item", i)]] <- rnorm(2 * n, mean = eff)
#  }
#  ref_df$outcome <- rowMeans(ref_df[, paste0("item", 1:k)])
#  
#  result <- compute_ltg_smd(
#    study_data     = study_df,
#    reference_data = ref_df,
#    group_var      = "condition",
#    score_var      = "outcome",
#    items          = paste0("item", 1:k),
#    group_levels   = c(reference = "control", focal = "treatment")
#  )
#  
#  print(result)

## ----single-study-ci, eval = FALSE--------------------------------------------
#  ci <- ltg_smd_ci(
#    result,
#    method         = c("analytic", "bootstrap"),
#    boot_type      = "bc",
#    B              = 2000,
#    seed           = 20240501,
#    study_data     = study_df,
#    reference_data = ref_df,
#    group_var      = "condition",
#    score_var      = "outcome",
#    items          = paste0("item", 1:k),
#    group_levels   = c(reference = "control", focal = "treatment")
#  )
#  print(ci)

## ----single-study-diag, eval = FALSE------------------------------------------
#  diag <- denominator_diagnostics(result)
#  print(diag)

## ----single-study-sens, eval = FALSE------------------------------------------
#  sens <- denominator_sensitivity(result)
#  print(sens)

## ----external-ref-example, eval = FALSE---------------------------------------
#  # Suppose 'study_df' is a focused US 18-30 subsample and 'norm_df' is
#  # the rest of an Open Psychometrics dataset serving as the broader
#  # reference distribution.
#  
#  result_external <- compute_ltg_smd(
#    study_data     = study_df,
#    reference_data = norm_df,
#    group_var      = "gender",
#    score_var      = "neuroticism_score",
#    items          = c("N1", "N2", "N3", "N4"),
#    group_levels   = c(reference = "Male", focal = "Female")
#  )
#  print(result_external)

## ----multisite-example, eval = FALSE------------------------------------------
#  ml2_result <- multisite_ltg_smd(
#    data               = ml2_data,
#    site_var           = "Source.Global",
#    group_var          = "condition",
#    score_var          = "SWB",
#    items              = paste0("and_item_", 1:25),
#    reference_strategy = "pooled_across_sites",
#    min_n_per_group    = 50
#  )
#  print(ml2_result)

## ----ref-sensitivity, eval = FALSE--------------------------------------------
#  # Compare three plausible references for an analysis
#  sens_ref <- sensitivity_reference(
#    object                  = result,
#    alternative_references  = list(
#      norm_alt1 = ref_df_alt1,
#      norm_alt2 = ref_df_alt2
#    ),
#    study_data   = study_df,
#    group_var    = "condition",
#    score_var    = "outcome",
#    items        = paste0("item", 1:k),
#    group_levels = c(reference = "control", focal = "treatment")
#  )
#  print(sens_ref)

## ----supplementary, eval = FALSE----------------------------------------------
#  export_supplementary(result, ci = ci, file = "supplementary_appendix.md")