Adding supp to readme

.Rhistory

@@ -0,0 +1,158 @@
+set.seed(123)
+# Parameters
+no.paper = 30   # Numbers of unique papers
+n.effects = 5    # Number of effects per paper. We will keep simple and balanced
+rho.e = 0.8  # Correlation in sampling variances
+study_id = rep(1:no.paper, each = n.effects)  # Study ID's
+var_paper = 0.5  # Between-study variance (i.e., tau2)
+var_effect = 0.2  # Effect size (or within study) variance
+mu = 0.4  # Average, or overall, effect size
+rho = c(0.2, 0.4, 0.6, 0.8)
+# Add sampling variance
+# First, sample
+n.sample <- rnbinom(no.paper, mu = 40, size = 0.5) + 4
+# Assume logRR. So, sampling variance approximated by the CV.
+cv <- sample(seq(0.3, 0.35,by = 0.001), no.paper, replace = TRUE)
+# Assuming sample size is the same for all effects within study (pretty sensible)
+rep_sample <- rep(n.sample, each = n.effects)
+# Sampling variances
+vi <- (2*cv^2)/rep_sample
+# Create the sampling variance matrix, which accounts for the correlation between effect sizes within study
+sigma_vi <- as.matrix(Matrix::bdiag(impute_covariance_matrix(vi = vi, cluster = study_id, r = rho.e)))
+# Now create the effect size level covariance matrix. Thsi would simulate a situation where, for example, you have effect sizes on reading and maths that are clearly correlated, but to varying extents across studies
+# Create list of matrices, 1 for each study
+matrices <- list()
+for(i in 1:no.paper){
+r <- sample(rho, size = 1, replace = TRUE)
+tmp <- matrix(r, nrow = n.effects, ncol = n.effects)
+diag(tmp) <- 1
+matrices[[i]] <- tmp
+}
+cor_yi <- as.matrix(Matrix::bdiag(matrices))
+cov_yi <- cor_yi * sqrt(var_effect) * sqrt(var_effect)
+# Derive data from simulation
+yi <- mu + rep(rnorm(no.paper, 0, sqrt(var_paper)), each = n.effects) + mvrnorm(n = 1, mu = rep(0,n.effects*no.paper), cov_yi) + mvrnorm(n = 1, mu = rep(0,n.effects*no.paper), sigma_vi)
+# Create the data. We'll just assume that meta-analysts have already derived their effect size and sampling variance
+data <- data.frame(study_id = study_id,
+yi = yi,
+vi = vi,
+obs = c(1:(n.effects*no.paper)))
+pacman::p_load(tidyverse, MASS, kableExtra, gridExtra,  MCMCglmm, brms,  metafor, robumeta, clubSandwich, pander, tidyverse)
+# Simulate a dataset composed of 30 papers, each having 5 effects from the same study.
+set.seed(123)
+# Parameters
+no.paper = 30   # Numbers of unique papers
+n.effects = 5    # Number of effects per paper. We will keep simple and balanced
+rho.e = 0.8  # Correlation in sampling variances
+study_id = rep(1:no.paper, each = n.effects)  # Study ID's
+var_paper = 0.5  # Between-study variance (i.e., tau2)
+var_effect = 0.2  # Effect size (or within study) variance
+mu = 0.4  # Average, or overall, effect size
+rho = c(0.2, 0.4, 0.6, 0.8)
+# Add sampling variance
+# First, sample
+n.sample <- rnbinom(no.paper, mu = 40, size = 0.5) + 4
+# Assume logRR. So, sampling variance approximated by the CV.
+cv <- sample(seq(0.3, 0.35,by = 0.001), no.paper, replace = TRUE)
+# Assuming sample size is the same for all effects within study (pretty sensible)
+rep_sample <- rep(n.sample, each = n.effects)
+# Sampling variances
+vi <- (2*cv^2)/rep_sample
+# Create the sampling variance matrix, which accounts for the correlation between effect sizes within study
+sigma_vi <- as.matrix(Matrix::bdiag(impute_covariance_matrix(vi = vi, cluster = study_id, r = rho.e)))
+# Now create the effect size level covariance matrix. Thsi would simulate a situation where, for example, you have effect sizes on reading and maths that are clearly correlated, but to varying extents across studies
+# Create list of matrices, 1 for each study
+matrices <- list()
+for(i in 1:no.paper){
+r <- sample(rho, size = 1, replace = TRUE)
+tmp <- matrix(r, nrow = n.effects, ncol = n.effects)
+diag(tmp) <- 1
+matrices[[i]] <- tmp
+}
+cor_yi <- as.matrix(Matrix::bdiag(matrices))
+cov_yi <- cor_yi * sqrt(var_effect) * sqrt(var_effect)
+# Derive data from simulation
+yi <- mu + rep(rnorm(no.paper, 0, sqrt(var_paper)), each = n.effects) + mvrnorm(n = 1, mu = rep(0,n.effects*no.paper), cov_yi) + mvrnorm(n = 1, mu = rep(0,n.effects*no.paper), sigma_vi)
+# Create the data. We'll just assume that meta-analysts have already derived their effect size and sampling variance
+data <- data.frame(study_id = study_id,
+yi = yi,
+vi = vi,
+obs = c(1:(n.effects*no.paper)))
+mod_multilevel = metafor::rma.mv(yi = yi, V = vi, mods = ~1,
+random=list(~1|study_id,~1|obs),
+data=data, test="t")
+summary(mod_multilevel)
+mod_multilevel_pdf = metafor::rma.mv(yi = yi, V = vi, mods = ~1,
+random=list(~1|study_id,~1|obs),
+data=data, test="t", dfs = "contain")
+summary(mod_multilevel_pdf)
+remotes::install_github("wviechtb/metafor")
+mod_multilevel_pdf = metafor::rma.mv(yi = yi, V = vi, mods = ~1,
+random=list(~1|study_id,~1|obs),
+data=data, test="t", dfs = "contain")
+summary(mod_multilevel_pdf)
+library(metafor)
+mod_multilevel_pdf = metafor::rma.mv(yi = yi, V = vi, mods = ~1,
+random=list(~1|study_id,~1|obs),
+data=data, test="t", dfs = "contain")
+?metafor::rma.mv
+library(metafor)
+mod_multilevel_pdf = rma.mv(yi = yi, V = vi, mods = ~1,
+random=list(~1|study_id,~1|obs),
+data=data, test="t", dfs = "contain")
+summary(mod_multilevel_pdf)
+# Clean workspace
+rm(list=ls())
+# Loading packages & Functions
+remotes::install_github("wviechtb/metafor"); library(metafor)
+pacman::p_load(tidyverse, MASS, kableExtra, gridExtra,  MCMCglmm, brms, robumeta, clubSandwich, pander, tidyverse)
+# Simulate a dataset composed of 30 papers, each having 5 effects from the same study.
+set.seed(123)
+# Parameters
+no.paper = 30   # Numbers of unique papers
+n.effects = 5    # Number of effects per paper. We will keep simple and balanced
+rho.e = 0.8  # Correlation in sampling variances
+study_id = rep(1:no.paper, each = n.effects)  # Study ID's
+var_paper = 0.5  # Between-study variance (i.e., tau2)
+var_effect = 0.2  # Effect size (or within study) variance
+mu = 0.4  # Average, or overall, effect size
+rho = c(0.2, 0.4, 0.6, 0.8)
+# Add sampling variance
+# First, sample
+n.sample <- rnbinom(no.paper, mu = 40, size = 0.5) + 4
+# Assume logRR. So, sampling variance approximated by the CV.
+cv <- sample(seq(0.3, 0.35,by = 0.001), no.paper, replace = TRUE)
+# Assuming sample size is the same for all effects within study (pretty sensible)
+rep_sample <- rep(n.sample, each = n.effects)
+# Sampling variances
+vi <- (2*cv^2)/rep_sample
+# Create the sampling variance matrix, which accounts for the correlation between effect sizes within study
+sigma_vi <- as.matrix(Matrix::bdiag(impute_covariance_matrix(vi = vi, cluster = study_id, r = rho.e)))
+# Now create the effect size level covariance matrix. Thsi would simulate a situation where, for example, you have effect sizes on reading and maths that are clearly correlated, but to varying extents across studies
+# Create list of matrices, 1 for each study
+matrices <- list()
+for(i in 1:no.paper){
+r <- sample(rho, size = 1, replace = TRUE)
+tmp <- matrix(r, nrow = n.effects, ncol = n.effects)
+diag(tmp) <- 1
+matrices[[i]] <- tmp
+}
+cor_yi <- as.matrix(Matrix::bdiag(matrices))
+cov_yi <- cor_yi * sqrt(var_effect) * sqrt(var_effect)
+# Derive data from simulation
+yi <- mu + rep(rnorm(no.paper, 0, sqrt(var_paper)), each = n.effects) + mvrnorm(n = 1, mu = rep(0,n.effects*no.paper), cov_yi) + mvrnorm(n = 1, mu = rep(0,n.effects*no.paper), sigma_vi)
+# Create the data. We'll just assume that meta-analysts have already derived their effect size and sampling variance
+data <- data.frame(study_id = study_id,
+yi = yi,
+vi = vi,
+obs = c(1:(n.effects*no.paper)))
+mod_multilevel = metafor::rma.mv(yi = yi, V = vi, mods = ~1,
+random=list(~1|study_id,~1|obs),
+data=data, test="t")
+summary(mod_multilevel)
+mod_multilevel_pdf = rma.mv(yi = yi, V = vi, mods = ~1,
+random=list(~1|study_id,~1|obs),
+data=data, test="t", dfs = "contain")
+summary(mod_multilevel_pdf)
+remotes::install_github("rlesur/klippy")
+klippy::klippy(tooltip_message = 'Click to Copy Code', tooltip_success = 'Done', position = 'right', color = "red")

README.md

@@ -6,12 +6,15 @@ This paper has now been accepted for publication in *Ecology*:
 
 Shinichi Nakagawa, Alistair M. Senior, Wolfgang Viechtbauer, and Daniel W. A. Noble. An assessment of statistical methods for non-independent data in ecological meta-analyses. Ecology, accepted 20 May 2021.
 
+To access the Supplemental Material for implementing corrections click [here](https://daniel1noble.github.io/ecology_comment/)
+
 ## Introduction
 This repository houses the code and supplementary tutorial used to demonstrate how multi-level meta-analytic models from `metafor` can be corrected to avoid infated Type I error in the presence of non-independent effect sizes. The commentary is a response to Song et al. (2020), to show how a few simple corrections can provide some resolution to problems they identify in their very thorough simulations.
 
+*Just want to know how to apply corrections?* Users who are interested in learning more about how they can correct for non-independence can read the [Supplemental Material](https://daniel1noble.github.io/ecology_comment/)
+
 *Reproducing the simulations?* Users wanting to reproduce Song et al's (2020) simulations, along with the correlations implemented by us can find all the R code in tge `R/` directory.
 
-*Just want to know how to apply corrections?* Users who are interested in learning more about how they can correct for non-independence can read the [Supplemental Example](https://daniel1noble.github.io/ecology_comment/)
 
 ## References
 Song, C., S. D. Peacor, C. W. Osenberg, and J. R. Bence. 2020. An assessment of statistical methods for nonindependent data in ecological meta-analyses. Ecology online: e03184.