## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----eval = FALSE------------------------------------------------------------- # # Install from CRAN (once published) # install.packages("MAIVE") # # # Or install development version from GitHub # install.packages("devtools") # devtools::install_github("meta-analysis-es/maive") ## ----setup-------------------------------------------------------------------- library(MAIVE) ## ----custom-mapping, eval = FALSE--------------------------------------------- # custom_dat <- data.frame( # my_est = c(0.5, 0.6, 0.4, 0.55), # my_se = c(0.2, 0.18, 0.25, 0.22), # my_n = c(80, 120, 95, 110), # my_study = c("A", "A", "B", "C") # ) # # result <- maive( # dat = custom_dat, # estimate = "my_est", # se = "my_se", # n = "my_n", # study_id = "my_study", # method = 3, # weight = 0, # instrument = 1, # studylevel = 2, # SE = 3, # AR = 1 # ) ## ----example-data------------------------------------------------------------- # Simulated meta-analysis data set.seed(123) n_studies <- 50 data <- data.frame( bs = rnorm(n_studies, mean = 0.3, sd = 0.2), sebs = runif(n_studies, min = 0.05, max = 0.3), Ns = sample(100:1000, n_studies, replace = TRUE), study_id = rep(1:10, each = 5) ) head(data) ## ----default-maive, eval = FALSE---------------------------------------------- # # Run MAIVE with defaults # result <- maive( # dat = data, # method = 3, # PET-PEESE (default) # weight = 0, # No weights (default) # instrument = 1, # Instrument SEs (default) # studylevel = 2, # Cluster-robust (default) # SE = 3, # Wild bootstrap (default) # AR = 1 # Anderson-Rubin CI (default) # ) # # # View key results # cat("MAIVE Estimate:", round(result$Estimate, 3), "\n") # cat("MAIVE SE:", round(result$SE, 3), "\n") # cat("Standard Estimate:", round(result$StdEstimate, 3), "\n") # cat("Hausman Test:", round(result$Hausman, 3), "\n") # cat("First-stage F-test:", round(result$`F-test`, 3), "\n") ## ----pet, eval = FALSE-------------------------------------------------------- # result_pet <- maive( # dat = data, # method = 1, # FAT-PET # weight = 0, # instrument = 1, # studylevel = 2, # SE = 3, # AR = 1 # ) # # cat("PET Estimate:", round(result_pet$Estimate, 3), "\n") ## ----peese, eval = FALSE------------------------------------------------------ # result_peese <- maive( # dat = data, # method = 2, # PEESE # weight = 0, # instrument = 1, # studylevel = 2, # SE = 3, # AR = 1 # ) # # cat("PEESE Estimate:", round(result_peese$Estimate, 3), "\n") ## ----petpeese, eval = FALSE--------------------------------------------------- # result_petpeese <- maive( # dat = data, # method = 3, # PET-PEESE (default) # weight = 0, # instrument = 1, # studylevel = 2, # SE = 3, # AR = 1 # ) # # cat("PET-PEESE Estimate:", round(result_petpeese$Estimate, 3), "\n") ## ----ek, eval = FALSE--------------------------------------------------------- # result_ek <- maive( # dat = data, # method = 4, # EK # weight = 0, # instrument = 1, # studylevel = 2, # SE = 3, # AR = 0 # AR not available for EK # ) # # cat("EK Estimate:", round(result_ek$Estimate, 3), "\n") ## ----no-weights, eval = FALSE------------------------------------------------- # result_noweight <- maive( # dat = data, # method = 3, # weight = 0, # No weights # instrument = 1, # studylevel = 2, # SE = 3, # AR = 1 # ) ## ----iv-weights, eval = FALSE------------------------------------------------- # result_ivweight <- maive( # dat = data, # method = 3, # weight = 1, # Inverse-variance weights # instrument = 1, # studylevel = 2, # SE = 3, # AR = 0 # AR not available with weights # ) ## ----maive-weights, eval = FALSE---------------------------------------------- # result_maiveweight <- maive( # dat = data, # method = 3, # weight = 2, # MAIVE-adjusted weights # instrument = 1, # studylevel = 2, # SE = 3, # AR = 1 # ) ## ----studylevel, eval = FALSE------------------------------------------------- # # No study-level adjustment # result_none <- maive(data, method = 3, weight = 0, instrument = 1, # studylevel = 0, SE = 0, AR = 1) # # # Study fixed effects (demeaned) # result_fe <- maive(data, method = 3, weight = 0, instrument = 1, # studylevel = 1, SE = 1, AR = 0) # AR not available with FE # # # Cluster-robust standard errors # result_cluster <- maive(data, method = 3, weight = 0, instrument = 1, # studylevel = 2, SE = 3, AR = 1) # # # Both fixed effects and clustering # result_both <- maive(data, method = 3, weight = 0, instrument = 1, # studylevel = 3, SE = 3, AR = 0) ## ----se-options, eval = FALSE------------------------------------------------- # # CR0 (Huber-White) # result_cr0 <- maive(data, method = 3, weight = 0, instrument = 1, # studylevel = 2, SE = 0, AR = 1) # # # CR1 (Standard empirical correction) # result_cr1 <- maive(data, method = 3, weight = 0, instrument = 1, # studylevel = 2, SE = 1, AR = 1) # # # CR2 (Bias-reduced estimator) # result_cr2 <- maive(data, method = 3, weight = 0, instrument = 1, # studylevel = 2, SE = 2, AR = 1) # # # Wild bootstrap (recommended, default) # result_boot <- maive(data, method = 3, weight = 0, instrument = 1, # studylevel = 2, SE = 3, AR = 1) ## ----first-stage-levels, eval = FALSE----------------------------------------- # result_levels <- maive(data, method = 3, weight = 0, instrument = 1, # studylevel = 2, SE = 3, AR = 1, first_stage = 0) # # cat("First-stage (levels) F-test:", round(result_levels$`F-test`, 3), "\n") ## ----first-stage-log, eval = FALSE-------------------------------------------- # result_log <- maive(data, method = 3, weight = 0, instrument = 1, # studylevel = 2, SE = 3, AR = 1, first_stage = 1) # # cat("First-stage (log) F-test:", round(result_log$`F-test`, 3), "\n") ## ----waive, eval = FALSE------------------------------------------------------ # result_waive <- waive( # dat = data, # method = 3, # instrument = 1, # studylevel = 2, # SE = 3, # AR = 1 # ) # # cat("WAIVE Estimate:", round(result_waive$Estimate, 3), "\n") # cat("WAIVE SE:", round(result_waive$SE, 3), "\n")