Basic_corr_Meta-Analysis.Rmd

---
title: "Basic Meta-Analysis for Correlation Coefficients"
nocite: |
  @*
output:
  html_document:
    code_folding: show
  pdf_document: default
  word_document: default
bibliography: R-Pckgs.bib
---

## Setup details
```{r}

## Specify the file containing your data
filename <- "corr_dat.csv"

### Specify the columns containing the Study ID, rs and Ns within the file

col.corr  <- "r"
col.n     <- "n"
col.study.id      <- "Study.ID"


### Specify the effect size measure

## Options in this template are ZCOR (Fisher's r-to-z transformed correlation
## coefficient), COR (raw correlations), and UCOR (Raw correlations corrected
## for negative bias). In most cases ZCOR is most appropriate.

measure <- "ZCOR"

### Specify the model type. 
## In most cases REML should be the default

## Options in this template are 

# method <- "FE" # Fixed effect meta-analysis
# method <- "REML" # Default random effects meta-analysis
# method <- "DL" #  DerSimonian-Laird estimator
# method <- "HE" # Hedges estimator
# method <- "HS" # Hunter-Schmidt estimator
# method <- "SJ" # Sidik-Jonkman estimator
# method <- "ML" # maximum-likelihood estimator
# method <- "REML" # restricted maximum-likelihood estimator
# method <- "EB" # empirical Bayes estimator
# method <- "PM" # Paule-Mandel estimator
# method <- "GENQ" # generalized Q-statistic estimator

method <- "REML"

```


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(metafor)
library(DT)
library(knitr)
library(RCurl)
library(dplyr)

library(rmarkdown)

## These lines of code download and run the metafor_tidiers functions that implement broom type tidy data functions for rma objects
source("metafor_tidiers.R")

# Set so that long lines in R will be wrapped:
opts_chunk$set(tidy.opts = list(width.cutoff = 80), tidy = TRUE)

```

```{r read_data}
dat <- read.csv(filename, stringsAsFactors = FALSE)
```

## Caluclate effect sizes
```{r calculate_ES}
  dat_ES <-
    escalc(
    measure = measure,
    ri = get(col.corr),
    ni = get(col.n),  
    data = dat
    )
```

```{r dat_es_html, echo = FALSE, warning = FALSE}
datatable(dat_ES %>% 
            select(-one_of(c("X", "Timestamp"))), rownames = FALSE)  %>% 
                formatRound('yi', 3) %>% 
                  formatRound('vi', 3)
```


## Run meta-analysis
```{r run_MA}
dat_MA <- rma(yi, vi, data = dat_ES, slab = get(col.study.id), method=method)
dat_MA
```

```{r convenience, echo=FALSE}
##These are some convenience functions that help put things into tables for easier interpretation.

model <- tidy.rma(dat_MA)

het.small <- glance.rma(dat_MA) %>% 
  select(one_of(c("k", "tau2", "se.tau2", "QE", "QEp", "I2")))

```


### Summary

`r ifelse(dat_MA$method=="FE", "A fixed effect meta-analysis", "A random-effects meta-analysis")` (k = `r dat_MA$k`) was conducted `r ifelse(dat_MA$method=="FE","",paste("using the", dat_MA$method, "estimator"))`.


```{r summary_table, echo=FALSE}
kable(model, col.names=c("*r*", "se", "z", "*p*", "95% CI LB", "95% CI UB"), row.names=FALSE, digits = 3, caption="Effect Size")
```


```{r het_table, eval=dat_MA$method!="FE", echo=FALSE}
kable(het.small, col.names=c("k", "$\\tau$^2^", "se", "Q", "*p*", "I^2^"), digits = 3, caption="Heterogeneity")
```

## Plots
###Forest plot
```{r forest, warning = FALSE}
forest(dat_MA, slab= attr(dat_MA$yi, "slab"))

```

###Funnel plot
```{r funnel}
funnel(dat_MA, back="white")
```

## Packages used in this document
```{r include=FALSE}
citPkgs <- names(sessionInfo()$otherPkgs)
write_bib(citPkgs, file="R-Pckgs.bib")
```