Merge pull request #62 from egouldo/29-functionalise-ms-sm3

Functionalise fit_MA_mv #29

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: ManyEcoEvo
 Title: Meta-analyse data from 'Many-Analysts' style studies
-Version: 2.0.0.9000
+Version: 2.0.0.9001
 Authors@R: c(person(given = "Elliot",
                     family = "Gould", 
                     email =  "elliot.gould@unimelb.edu.au", 
@@ -68,7 +68,7 @@ Remotes:
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 URL: https://github.com/egouldo/ManyEcoEvo,
      https://egouldo.github.io/ManyEcoEvo/
 BugReports: https://github.com/egouldo/ManyEcoEvo/issues

NAMESPACE

@@ -35,6 +35,7 @@ export(cube_back)
 export(divide_back)
 export(est_to_zr)
 export(exclude_extreme_VZ)
+export(fit_MA_mv)
 export(fit_boxcox_ratings_cat)
 export(fit_boxcox_ratings_cont)
 export(fit_boxcox_ratings_ord)
@@ -103,7 +104,6 @@ export(summarise_variable_counts)
 export(validate_predictions)
 export(validate_predictions_df_blue_tit)
 export(validate_predictions_df_euc)
-import(cli)
 import(dplyr)
 import(ggbeeswarm)
 import(ggplot2)
@@ -134,6 +134,8 @@ importFrom(dplyr,rename)
 importFrom(dplyr,right_join)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
+importFrom(pointblank,col_vals_not_null)
+importFrom(pointblank,stop_if_not)
 importFrom(forcats,fct_relevel)
 importFrom(glue,glue)
 importFrom(magrittr,"%>%")
@@ -151,6 +153,7 @@ importFrom(purrr,possibly)
 importFrom(purrr,reduce)
 importFrom(purrr,reduce2)
 importFrom(purrr,set_names)
+importFrom(rlang,enquo)
 importFrom(rlang,is_na)
 importFrom(rlang,is_null)
 importFrom(rlang,na_chr)

R/fit_MA_mv.R

@@ -0,0 +1,32 @@
+#' Fit Meta-regression with random-effects
+#' @description Fit a meta-regression model with random effects using the [metafor::rma.mv()] function from the `metafor` package to a dataframe containing the estimates and variances for the meta-analysis.
+#'
+#' @param effects_analysis A dataframe containing the estimates and variances for the meta-analysis.
+#' @param Z_colname The name of the column containing the estimates.
+#' @param VZ_colname The name of the column containing the variances.
+#' @param estimate_type The type of estimate to be used in the model. One of "Zr", "y50", "y25", "y75", or "yi".
+#'
+#' @return A fitted model of class `c("rma.mv","rma")`.
+#' @export
+#' @import dplyr
+#' @importFrom rlang enquo
+#' @import metafor
+#' @importFrom pointblank stop_if_not
+#' @details
+#' This function is a wrapper around the [metafor::rma.mv()] function from the `metafor` package. It takes a dataframe containing the estimates and variances for the meta-analysis, the name of the column containing the estimates, the name of the column containing the variances, and the type of estimate to be used in the model. It then fits a metaregression model with random-effects using the [metafor::rma.mv()] function called in [fit_metafor_mv()] and returns the fitted model.
+#' 
+#' Nested random effects are included for `TeamIdentifier` and `TeamIdentifier/study_id`.
+#' @examples
+#' ManyEcoEvo_results$effects_analysis[[1]] %>% 
+#'   fit_MA_mv(beta_estimate, beta_SE, "Zr")
+fit_MA_mv <- function(effects_analysis = data.frame(), Z_colname, VZr_colname, estimate_type = character(1L)){
+  pointblank::stop_if_not(estimate_type %in% c("Zr", "yi", "y25", "y50", "y75"))
+
+  Zr <- effects_analysis %>%  dplyr::pull({{Z_colname}})
+  VZr <- effects_analysis %>%  dplyr::pull({{VZr_colname}})
+  mod <- ManyEcoEvo::fit_metafor_mv(estimate = Zr,
+                                    variance = VZr, 
+                                    estimate_type = estimate_type, 
+                                    data = effects_analysis)
+  return(mod)
+}

R/fit_metafor_mv.R

@@ -6,6 +6,7 @@
 #' @param data Dataframe containing estimates and variances with case id column \code{study_id}.
 #'
 #' @return Object of class \code{rma.mv}
+#' @import metafor
 #' @export
 #'
 #' @examples
@@ -21,7 +22,7 @@
 #'          !is.infinite(VZr)) %>%
 #' fit_metafor_mv(estimate = .$Zr, variance = .$VZr, estimate_type = "Zr", data = .)
 fit_metafor_mv <- function(estimate, variance, estimate_type = character(1L), data){
-  cli::cli_h2(glue::glue("Fitting multivariate metaregression"))
+  cli::cli_h2(glue::glue("Fitting metaregression"))
   match.arg(estimate_type, choices = c("Zr", "y50", "y25", "y75", "yi"), several.ok = FALSE) 
 
   if(estimate_type != "Zr"){ # you need to put SE^2 for y
@@ -30,24 +31,28 @@ fit_metafor_mv <- function(estimate, variance, estimate_type = character(1L), da
 
   mod <- metafor::rma.mv(yi = estimate, # of type "Zr" or "ymed", "y25", "y75"
                   V = variance, 
-                  random=list(~1|TeamIdentifier/study_id),
+                  random = list(~1|TeamIdentifier/study_id),
                   data = data,
                   sparse = TRUE,
                   # verbose = TRUE,
-                  control=list(optimizer="nloptr", maxeval=1000),
+                  control = list(optimizer="nloptr", maxeval=1000),
                   slab = data$study_id)
   return(mod)
 }
 
 
-#' Fit reduced multivariate metaregression model
+#' Fit reduced metaregression model
 #'
 #' @param estimate Numeric vector
 #' @param variance Numeric vector
 #' @param estimate_type Character vector of either "Zr", "y50", "y25", "y75".
 #' @param data Dataframe containing estimates and variances with case id column \code{study_id}.
 #'
 #' @return Object of class \code{rma.mv}
+#' @import metafor
+#' @import dplyr
+#' @importFrom glue glue
+#' @importFrom cli cli_h2
 #' @export
 #'
 #' @examples
@@ -80,8 +85,9 @@ fit_metafor_mv_reduced <- function(estimate, variance, estimate_type = character
                   data = data,
                   sparse = TRUE,
                   sigma2 =  c(0, NA))
+
 }
 
 poss_fit_metafor_mv <- purrr::possibly(fit_metafor_mv,
                                        otherwise = NA,
-                                       quiet = FALSE)
+                                       quiet = FALSE)

R/meta_analyse_datasets.R

@@ -34,16 +34,6 @@ meta_analyse_datasets <- function(MA_data){
 
   cli::cli_h1(text = "Meta-analysing Datasets")
 
-  fit_MA_mv <- function(effects_analysis, Z_colname, VZ_colname, estimate_type){
-    Zr <- effects_analysis %>%  pull({{Z_colname}})
-    VZr <- effects_analysis %>%  pull({{VZ_colname}})
-    mod <- poss_fit_metafor_mv(estimate = Zr, 
-                          variance = VZr, 
-                          estimate_type = estimate_type, 
-                          data = effects_analysis)
-    return(mod)
-  }
-
   if( any(str_detect(unique(MA_data$estimate_type), pattern = "Zr")) ){
     # Must group by cols else multiple "effects_analysis" elements
     # get passed to fit_MA_mv()

renv.lock

@@ -636,14 +636,15 @@
     },
     "cli": {
       "Package": "cli",
-      "Version": "3.6.2",
+      "Version": "3.6.3",
       "Source": "Repository",
       "Repository": "CRAN",
       "Requirements": [
         "R",
         "utils"
       ],
-      "Hash": "1216ac65ac55ec0058a6f75d7ca0fd52"
+      "Hash": "b21916dd77a27642b447374a5d30ecf3"
+
     },
     "clipr": {
       "Package": "clipr",
@@ -2143,7 +2144,7 @@
     },
     "knitr": {
       "Package": "knitr",
-      "Version": "1.46",
+      "Version": "1.48",
       "Source": "Repository",
       "Repository": "CRAN",
       "Requirements": [
@@ -2155,7 +2156,7 @@
         "xfun",
         "yaml"
       ],
-      "Hash": "6e008ab1d696a5283c79765fa7b56b47"
+      "Hash": "acf380f300c721da9fde7df115a5f86f"
     },
     "labeling": {
       "Package": "labeling",