### Example Data Frame Setup

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/conv.fivenum.html

Sets up an example data frame with mixed five-number summary values and direct mean/SD values for demonstration purposes.

```R
dat <- data.frame(case=c(1:3,NA), min=c(2,NA,2,NA), q1=c(NA,4,4,NA),
                      median=c(6,6,6,NA), q3=c(NA,10,10,NA), max=c(14,NA,14,NA),
                      mean=c(NA,NA,NA,7.0), sd=c(NA,NA,NA,4.2), n=c(20,20,20,20))
dat
```

--------------------------------

### Install metafor Package

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/00-index.md

Installs the metafor package from CRAN. Load the library to use its functions.

```r
install.packages("metafor")
library(metafor)
```

--------------------------------

### Meta-analysis Workflow Example

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/00-index.md

A comprehensive example demonstrating a typical meta-analysis workflow using the metafor package, from data loading and effect size calculation to model fitting, visualization, and post-analysis.

```r
library(metafor)

# 1. Load and examine data
data(dat.bcg)
head(dat.bcg)

# 2. Calculate effect sizes
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg,
              data=dat.bcg, slab=paste(author, year, sep=", "))

# 3. Fit model
res <- rma(yi, vi, data=dat, test="knha")
res

# 4. Visualize
forest(res, atransf=exp)
funnel(res)

# 5. Post-analysis
predict(res, transf=exp)
confint(res)
leave1out(res)
regtest(res)

# 6. Meta-regression (if moderators available)
res.mr <- rma(yi, vi, mods=~ablat, data=dat)
regplot(res.mr, mod="ablat")
```

--------------------------------

### Get Help and Load Data in Metafor

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/README.md

Provides R code to access help files for metafor functions, load example datasets, and view package vignettes.

```r
# Get help
?rma                    # Main function help
?escalc                 # Effect size calculation
help(package="metafor") # Package overview

# Load data
data(dat.bcg)           # Example dataset
data(package="metafor") # List all datasets

# Read vignettes
vignette("metafor")     # Main vignette

# Citation
citation(package="metafor")
```

--------------------------------

### Install Development Version of metafor

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/00-index.md

Installs the latest development version of the metafor package from GitHub. Requires the 'remotes' package.

```r
install.packages("remotes")
remotes::install_github("wviechtb/metafor")
```

--------------------------------

### Example: Fitting a Mixed-Effects Meta-Regression Model and Extracting Studentized Residuals

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/methods.list.rma.html

This example demonstrates how to prepare data, fit a mixed-effects meta-regression model using `rma`, and then extract and filter studentized residuals using `rstudent`. Note the special behavior for subsetting residuals with 'rma.list' objects.

```R
### copy data into 'dat' and examine data
dat <- dat.viechtbauer2021

### calculate log odds ratios and corresponding sampling variances
dat <- escalc(measure="OR", ai=xTi, n1i=nTi, ci=xCi, n2i=nCi, add=1/2, to="all", data=dat)

### fit mixed-effects meta-regression model
res <- rma(yi, vi, mods = ~ dose, data=dat)

### get studentized residuals
sav <- rstudent(res)
sav
#> 
#>      resid     se       z 
#> 1   0.5344 0.4849  1.1021 
#> 2  -0.3653 0.5163 -0.7076 
#> 3  -0.0468 0.4815 -0.0972 
#> 4  -0.3142 0.4177 -0.7522 
#> 5   0.1743 0.4262  0.4089 
#> 6  -0.9963 0.3195 -3.1187 
#> 7   0.0250 0.3723  0.0672 
#> 8   0.9197 0.7572  1.2147 
#> 9   0.7779 0.3678  2.1149 
#> 10  0.3078 0.4548  0.6768 
#> 11 -0.2261 0.3995 -0.5661 
#> 12 -0.2763 0.4113 -0.6718 
#> 13 -0.6072 0.4468 -1.3590 
#> 14  0.9350 0.7213  1.2964 
#> 15  0.2894 0.4754  0.6087 
#> 16 -0.1011 0.5738 -0.1762 
#> 17  0.2310 0.6001  0.3849 
#> 18  0.0103 0.4944  0.0207 
#> 19  0.3425 0.3941  0.8690 
#> 20 -0.3079 0.4618 -0.6667 
#> 

### studies with studentized residuals larger than +-1.96
sav[abs(sav$z) > 1.96,]
#> 
#>     resid     se       z 
#> 6 -0.9963 0.3195 -3.1187 
#> 9  0.7779 0.3678  2.1149 
#> 

### variables specified are automatically searched for within the object itself
sav[abs(z) > 1.96,]
#> 
#>     resid     se       z 
#> 6 -0.9963 0.3195 -3.1187 
#> 9  0.7779 0.3678  2.1149 
#> 

### note: this behavior is specific to 'rma.list' objects; this doesn't work for regular data frames

```

--------------------------------

### Load Metafor Package

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/00-index.md

This is the required command to load the metafor package before running any examples. Ensure this is executed first.

```r
library(metafor)
```

--------------------------------

### Rounding Coefficients with dfround

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/10-data-manipulation.md

This example shows how to round the coefficients of a model to a specified number of decimal places using dfround.

```r
# Round results
as.data.frame(coef(res)) %>%
dfround(digits=3)
```

--------------------------------

### Example Output: Prediction Intervals

Source: https://github.com/wviechtb/metafor/blob/master/README.md

This output shows predicted values and their confidence and prediction intervals for a meta-analysis.

```text
## pred ci.lb ci.ub pi.lb pi.ub
## 0.49  0.33  0.73  0.14  1.76
```

--------------------------------

### Elaborate Example: BCG Vaccine Data Analysis

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/conv.wald.html

This example processes the BCG vaccine dataset, first using `escalc` to compute log odds ratios and their variances, and then fitting a random-effects model to the results. It showcases a typical workflow for meta-analysis with binary outcome data.

```R
dat <- dat.bcg[,c(2:7)]
dat
#>                  author year tpos  tneg cpos  cneg
#> 1               Aronson 1948    4   119   11   128
#> 2      Ferguson & Simes 1949    6   300   29   274
#> 3       Rosenthal et al 1960    3   228   11   209
#> 4     Hart & Sutherland 1977   62 13536  248 12619
#> 5  Frimodt-Moller et al 1973   33  5036   47  5761
#> 6       Stein & Aronson 1953  180  1361  372  1079
#> 7      Vandiviere et al 1973    8  2537   10   619
#> 8            TPT Madras 1980  505 87886  499 87892
#> 9      Coetzee & Berjak 1968   29  7470   45  7232
#> 10      Rosenthal et al 1961   17  1699   65  1600
#> 11       Comstock et al 1974  186 50448  141 27197
#> 12   Comstock & Webster 1969    5  2493    3  2338
#> 13       Comstock et al 1976   27 16886   29 17825

### with complete data, we can use escalc() in the usual way
dat1 <- escalc(measure="OR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat)
dat1
#> 
#>                  author year tpos  tneg cpos  cneg      yi     vi 
#> 1               Aronson 1948    4   119   11   128 -0.9387 0.3571 
#> 2      Ferguson & Simes 1949    6   300   29   274 -1.6662 0.2081 
#> 3       Rosenthal et al 1960    3   228   11   209 -1.3863 0.4334 
#> 4     Hart & Sutherland 1977   62 13536  248 12619 -1.4564 0.0203 
#> 5  Frimodt-Moller et al 1973   33  5036   47  5761 -0.2191 0.0520 
#> 6       Stein & Aronson 1953  180  1361  372  1079 -0.9581 0.0099 
#> 7      Vandiviere et al 1973    8  2537   10   619 -1.6338 0.2270 
#> 8            TPT Madras 1980  505 87886  499 87892  0.0120 0.0040 
#> 9      Coetzee & Berjak 1968   29  7470   45  7232 -0.4717 0.0570 
#> 10      Rosenthal et al 1961   17  1699   65  1600 -1.4012 0.0754 
#> 11       Comstock et al 1974  186 50448  141 27197 -0.3408 0.0125 
#> 12   Comstock & Webster 1969    5  2493    3  2338  0.4466 0.5342 
#> 13       Comstock et al 1976   27 16886   29 17825 -0.0173 0.0716 
#> 

### random-effects model fitted to these data
res1 <- rma(yi, vi, data=dat1)
res1
#> 
#> Random-Effects Model (k = 13; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.3378 (SE = 0.1784)
#> tau (square root of estimated tau^2 value):      0.5812
#> 

```

--------------------------------

### Reconstruct 2x2 Table with Larger Frequencies

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/conv.2x2.html

This example shows the reconstruction of a 2x2 table with cell frequencies that are 10 times larger than the previous example. It follows the same logic of calculating summary statistics and then reconstructing the table.

```R
### same example but with cell frequencies that are 10 times as large
dat <- data.frame(ai=c(360,NA,NA,NA), bi=c(860,NA,NA,NA), ci=c(200,NA,NA,NA), di=c(980,NA,NA,NA),
                      oddsratio=NA, phi=NA, chisq=NA, ni=NA, n1i=NA, n2i=NA)
dat$oddsratio[2] <- round(exp(escalc(measure="OR", ai=ai, bi=bi, ci=ci, di=di, data=dat)$yi[1]), 2)
dat$phi[3] <- round(escalc(measure="PHI", ai=ai, bi=bi, ci=ci, di=di, data=dat)$yi[1], 2)
```

--------------------------------

### Check metafor Package Version and Citation

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/00-index.md

Retrieves the installed version of the metafor package and displays its citation information.

```r
packageVersion("metafor")
citation(package="metafor")
```

--------------------------------

### Open metafor Package Diagram from R

Source: https://github.com/wviechtb/metafor/blob/master/docs/articles/pkgdown/diagram.html

Opens the metafor package diagram directly from an R console session. Requires the package to be installed.

```r
vignette("diagram")
```

--------------------------------

### Running Metafor Vignettes

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/13-configuration-reference.md

Use this command to access the comprehensive vignettes for the metafor package, which provide detailed examples and explanations of its functionalities.

```r
vignette("metafor")
```

--------------------------------

### Example Data Frame for conv.fivenum

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/conv.fivenum.html

This snippet defines an example data frame containing five number summary statistics and sample sizes, which will be used as input for the `conv.fivenum` function.

```r
# example data frame
dat <- data.frame(case=c(1:3,NA), min=c(2,NA,2,NA), q1=c(NA,4,4,NA),
                  median=c(6,6,6,NA), q3=c(NA,10,10,NA), max=c(14,NA,14,NA),
                  mean=c(NA,NA,NA,7.0), sd=c(NA,NA,NA,4.2), n=c(20,20,20,20))
dat
```

--------------------------------

### R Output for Posting

Source: https://github.com/wviechtb/metafor/blob/master/docs/ISSUE_TEMPLATE.html

Placeholder comment indicating where to insert R output for reproducible examples.

```r
# use this for posting output (if applicable)
```

--------------------------------

### Plotting with Trimmed Distributions

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/plot.permutest.rma.uni.html

This example demonstrates plotting the permutation distribution after trimming a fraction of observations from the tails using the 'trim' argument. This can help in visualizing the central part of the distribution.

```R
plot(object, trim=0.1)
```

--------------------------------

### Set and Get Metafor Space Option

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/mfopt.html

Sets the 'space' option to FALSE and then retrieves all current options to verify the change.

```R
setmfopt(space=FALSE)
getmfopt()
```

--------------------------------

### Restructure Data to Wide Format

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/contrmat.html

This example demonstrates how to restructure a dataset to wide format using the to.wide function, a prerequisite for using contrmat.

```r
dat <- dat.senn2013
dat <- dat[c(1,4,3,2,5,6)]
dat <- to.wide(dat, study="study", grp="treatment", ref="placebo", grpvars=4:6)
dat
```

--------------------------------

### Reconstruct 2x2 Table from Odds Ratio and Marginals (Example)

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/conv.2x2.html

Reconstructs a 2x2 table using a provided odds ratio and marginal counts.

```R
conv.2x2(ri=0.54, ni=400, n1i=200, n2i=248) # using the true marginal counts
```

--------------------------------

### Inspect Data Before Aggregation (Second Example)

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/aggregate.escalc.html

Displays the first few rows of a different dataset to understand its structure before performing aggregation. This is useful for checking data integrity and variable types.

```R
dat <- dat.assink2016
head(dat, 19)
```

--------------------------------

### Extracting Coefficients from Permutation Test Results

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/coef.permutest.rma.uni.html

This example demonstrates how to fit a mixed-effects model, conduct a permutation test, and then extract the results using `coef()`.

```r
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### fit mixed-effects model with absolute latitude and publication year as moderators
res <- rma(yi, vi, mods = ~ ablat + year, data=dat)

### carry out permutation test
set.seed(1234) # for reproducibility
sav <- permutest(res)
#> Running 1000 iterations for an approximate permutation test.
coef(sav)
#>             estimate          se       zval  pval        ci.lb        ci.ub
#> intrcpt -3.545505079 29.09587983 -0.1218559 0.907 -60.57238164 53.481371479
#> ablat   -0.028011275  0.01023404 -2.7370689 0.016  -0.04806963 -0.007952924
#> year     0.001907557  0.01468382  0.1299088 0.901  -0.02687219  0.030687307

```

--------------------------------

### View metafor Package NEWS File

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/metafor-package.html

Displays the NEWS file of the metafor package after installation, providing details on recent updates and changes.

```r
metafor.news()
```

--------------------------------

### Example Output: Mixed-Effects Model Results

Source: https://github.com/wviechtb/metafor/blob/master/README.md

This output details the results of a mixed-effects meta-regression model, including estimates of heterogeneity, moderator effects, and model fit statistics.

```text
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity):     0.0764 (SE = 0.0591)
## tau (square root of estimated tau^2 value):             0.2763
## I^2 (residual heterogeneity / unaccounted variability): 68.39%
## H^2 (unaccounted variability / sampling variability):   3.16
## R^2 (amount of heterogeneity accounted for):            75.62%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 30.7331, p-val = 0.0012
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 11) = 12.5905, p-val = 0.0046
##
## Model Results:
##
##          estimate      se     tval  df    pval    ci.lb    ci.ub
## intrcpt    0.2515  0.2839   0.8857  11  0.3948  -0.3735   0.8764
## ablat     -0.0291  0.0082  -3.5483  11  0.0046  -0.0472  -0.0111  **
##
## ---
```

--------------------------------

### Apply Function During Splitting

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/blsplit.html

This example illustrates using the 'fun' argument of blsplit to directly apply a function (cov2cor) to each sub-matrix as it is being split from the block diagonal matrix V.

```R
blsplit(V, cluster=dat$study, cov2cor)[1:2]
```

--------------------------------

### Demonstrate 2x2 Table Reconstruction

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/conv.2x2.html

This example demonstrates the reconstruction of a 2x2 table using the conv.2x2 function. It first calculates various summary statistics (odds ratio, phi, chi-squared) from initial cell frequencies and then uses these to reconstruct the table.

```R
### demonstration that the reconstruction of the 2x2 table works
### (note: the values in rows 2, 3, and 4 correspond to those in row 1)
dat <- data.frame(ai=c(36,NA,NA,NA), bi=c(86,NA,NA,NA), ci=c(20,NA,NA,NA), di=c(98,NA,NA,NA),
                  oddsratio=NA, phi=NA, chisq=NA, ni=NA, n1i=NA, n2i=NA)
dat$oddsratio[2] <- round(exp(escalc(measure="OR", ai=ai, bi=bi, ci=ci, di=di, data=dat)$yi[1]), 2)
dat$phi[3] <- round(escalc(measure="PHI", ai=ai, bi=bi, ci=ci, di=di, data=dat)$yi[1], 2)
dat$chisq[4] <- round(chisq.test(matrix(c(t(dat[1,1:4])), nrow=2, byrow=TRUE))$statistic, 2)
dat$ni[2:4]  <- with(dat, ai[1] + bi[1] + ci[1] + di[1])
dat$n1i[2:4] <- with(dat, ai[1] + bi[1])
dat$n2i[2:4] <- with(dat, ai[1] + ci[1])
dat
#>   ai bi ci di oddsratio  phi chisq  ni n1i n2i
#> 1 36 86 20 98        NA   NA    NA  NA  NA  NA
#> 2 NA NA NA NA      2.05   NA    NA 240 122  56
#> 3 NA NA NA NA        NA 0.15    NA 240 122  56
#> 4 NA NA NA NA        NA   NA  4.61 240 122  56

### reconstruct cell frequencies for rows 2, 3, and 4
dat <- conv.2x2(ri=phi, ori=oddsratio, x2i=chisq, ni=ni, n1i=n1i, n2i=n2i, data=dat)
dat
#>   ai bi ci di oddsratio  phi chisq  ni n1i n2i
#> 1 36 86 20 98        NA   NA    NA  NA  NA  NA
#> 2 36 86 20 98      2.05   NA    NA 240 122  56
#> 3 36 86 20 98        NA 0.15    NA 240 122  56
#> 4 36 86 20 98        NA   NA  4.61 240 122  56
```

--------------------------------

### Logistic Regression (Binary)

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/11-specialized-models.md

Example of using `rma.glmm` for logistic regression to analyze binary outcomes.

```APIDOC
## Logistic Regression (Binary)

### Description
Use `rma.glmm` for standard binomial logistic regression with binary outcomes.

### Function Signature
```r
rma.glmm(measure, ai, bi, ci, di, ...)
```

### Parameters
- `measure`: Set to `"OR"` for Odds Ratios.
- `ai`: Events in the treatment group.
- `bi`: Non-events in the treatment group.
- `ci`: Events in the control group.
- `di`: Non-events in the control group.

### Example
```r
# Standard binomial GLMM
res.glmm.log <- rma.glmm(measure="OR", ai=events, bi=non.events,
                         ci=control.events, di=control.non.events)
```
```

--------------------------------

### Generate Meta-Regression Plot

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/11-specialized-models.md

Example of creating a regression plot for a specific moderator from a meta-regression model.

```r
# Regression plot
regplot(res.mr, mod="ablat")
```

--------------------------------

### R Code for Posting

Source: https://github.com/wviechtb/metafor/blob/master/docs/ISSUE_TEMPLATE.html

Placeholder comment indicating where to insert R code for reproducible examples.

```r
# use this for posting code (if applicable)
```

--------------------------------

### Simple Example: Convert Odds Ratios to Log Odds Ratios

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/conv.wald.html

This snippet demonstrates a basic conversion of odds ratios and their confidence intervals into log odds ratios with corresponding sampling variances. It uses the `log` transformation for the effect size.

```R
dat <- data.frame(or=c(1.37,1.89), or.lb=c(1.03,1.60), or.ub=c(1.82,2.23))
dat
#>     or or.lb or.ub
#> 1 1.37  1.03  1.82
#> 2 1.89  1.60  2.23

### convert the odds ratios and CIs into log odds ratios with corresponding sampling variances
dat <- conv.wald(out=or, ci.lb=or.lb, ci.ub=or.ub, data=dat, transf=log)
dat
#> 
#>     or or.lb or.ub     yi     vi 
#> 1 1.37  1.03  1.82 0.3148 0.0211 
#> 2 1.89  1.60  2.23 0.6366 0.0072 
#> 

```

--------------------------------

### Fit Meta-Regression with Categorical Moderator

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/11-specialized-models.md

Example of fitting a meta-regression model using a categorical moderator (group).

```r
# Categorical moderator
dat$group <- factor(dat$alloc)
res.mr4 <- rma(yi, vi, mods=~group, data=dat)
```

--------------------------------

### Constructing Pairwise Contrasts for RMA Objects

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/pairmat.html

This example demonstrates how to construct a pairwise contrast matrix for a meta-regression model. It first fits a mixed-effects model with a categorical moderator and then uses pairmat to generate the contrast matrix.

```R
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### mixed-effects meta-regression model with the allocation method as a moderator;
### by removing the intercept term, we obtain the estimated average effect for each
### factor level from the model
res <- rma(yi, vi, mods = ~ 0 + alloc, data=dat)
res

```

--------------------------------

### Get Specific Metafor Option

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/mfopt.html

Retrieves the value of a specific global option, in this case, the 'space' option.

```R
getmfopt(space)
```

--------------------------------

### Fit Meta-Regression with Multiple Moderators

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/11-specialized-models.md

Example of fitting a meta-regression model with multiple moderators (absolute latitude and year).

```r
# Multiple moderators
res.mr2 <- rma(yi, vi, mods=~ablat + year, data=dat)
```

--------------------------------

### Apply and Round Function During Splitting

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/blsplit.html

This example demonstrates applying a function (round) with an additional argument (3) during the splitting process of the block diagonal matrix V. The result is a list of rounded sub-matrices.

```R
blsplit(V, cluster=dat$study, round, 3)[1:2]
```

--------------------------------

### Fit Simple Meta-Regression Model

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/11-specialized-models.md

Example of fitting a simple meta-regression model with absolute latitude as a single moderator.

```r
# Simple meta-regression
res.mr <- rma(yi, vi, mods=~ablat, data=dat)
```

--------------------------------

### Load and Inspect Dataset

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/vcalc.html

Loads the 'knapp2017' dataset and displays a subset of its columns. This is a common first step for data analysis.

```r
dat <- dat.knapp2017
dat[-c(1:2)]
```

--------------------------------

### Prepare Data for Meta-Analysis

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/conv.wald.html

This snippet shows how to assemble a dataset from study information, including handling missing values and rounding reported statistics. It prepares the data frame for subsequent meta-analysis calculations.

```R
dat2 <- data.frame(summary(dat1))
dat2[c("yi", "ci.lb", "ci.ub")] <- data.frame(summary(dat1, transf=exp))[c("yi", "ci.lb", "ci.ub")]
names(dat2)[which(names(dat2) == "yi")] <- "or"
dat2[,c("or","ci.lb","ci.ub","pval")] <- round(dat2[,c("or","ci.lb","ci.ub","pval")], digits=2)
dat2$vi <- dat2$sei <- dat2$zi <- NULL
dat2$ntot <- with(dat2, tpos + tneg + cpos + cneg)
dat2[c(1,12),c(3:6,9:10)] <- NA
dat2[c(4,9), c(3:6,8)] <- NA
dat2[c(2:3,5:8,10:11,13),c(7:10)] <- NA
dat2$ntot[!is.na(dat2$tpos)] <- NA
dat2
```

--------------------------------

### Recommended Analysis Pipeline in Metafor

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/13-configuration-reference.md

This snippet outlines a comprehensive analysis pipeline for meta-analysis using the metafor package, including steps for calculating effect sizes, fitting initial models, assessing publication bias, checking for outliers, examining heterogeneity, exploring moderators, and final visualization.

```r
# 1. Calculate effect sizes
dat <- escalc(measure="RR", ai=a, bi=b, ci=c, di=d, data=raw)

# 2. Initial model
res <- rma(yi, vi, data=dat, test="knha")

# 3. Assess publication bias
regtest(res)      # Egger's test
ranktest(res)     # Rank test
funnel(res)       # Visual assessment

# 4. Check for outliers/influence
leave1out(res)
baujat(res)

# 5. Examine heterogeneity
confint(res)      # CI for tau2

# 6. Explore moderators (if applicable)
res.mr <- rma(yi, vi, mods=~moderator, data=dat, test="knha")
anova(res, res.mr) # Test moderation

# 7. Final visualization
forest(res, atransf=exp)
```

--------------------------------

### Common Transformation Functions

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/05-forest.md

Provides examples of common transformation functions used in meta-analysis, such as log, logit, and z-score transformations.

```r
# Log transformation (for log-scale effects)
transf=exp, atransf=log

# Logit-based transformations
transf=transf.ilogit

# Correlation z-score transformation
transf=transf.ztor

# Fisher's z-score back-transform
transf=transf.ztor
```

--------------------------------

### Estimate Average Risk Ratio with CI/PI

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/predict.rma.html

This example demonstrates how to estimate the average risk ratio (by applying the exponential transformation) with its 95% confidence and prediction intervals, formatted to two decimal places.

```r
predict(res, transf=exp, digits=2)
```

--------------------------------

### Reset and Get Metafor Space Option

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/mfopt.html

Resets the 'space' option to TRUE and then retrieves all current options to confirm the update.

```R
setmfopt(space=TRUE)
getmfopt()
```

--------------------------------

### Specify Confidence Interval Method for Random-Effects Models

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/08-confint.md

Demonstrates how to compute confidence intervals for a random-effects model using different methods, including the default, Q-profile, and generalized methods.

```r
# Default
confint(res)

# Specify method
confint(res, method="Q-profile")
confint(res, method="generalized")
```

--------------------------------

### Fixed-Effects Model for Heterogeneity Testing

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/rma.uni.html

Demonstrates fitting a fixed-effects model to calculate the total heterogeneity (Q_Total). This is used to verify that Q_E + Q_M equals Q_Total for fixed-effects models.

```R
res <- rma(yi, vi, data=dat, method="FE")
res
```

--------------------------------

### Fit Meta-Regression Model

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/robust.html

Fit a meta-regression model with 'length' as a moderator. This is a standard starting point before applying robust inference.

```r
res <- rma(yi, vi, mods = ~ length, data=dat)
```

--------------------------------

### Displaying V Matrix as a List of Matrices

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/vcalc.html

This example demonstrates how to display the constructed variance-covariance matrix (V) as a list of matrices, where each matrix corresponds to a study cluster. It uses the blsplit function to split the matrix and shows the first 5 study matrices.

```R
blsplit(V, dat$study)[1:5]
```

--------------------------------

### Construct Variance-Covariance Matrix with Clustering

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/vcalc.html

Example of constructing a variance-covariance matrix where effect sizes are clustered by study. This is a common scenario for meta-analyses.

```R
dat <- escalc(measure="ROM", ri=yi, ni=ni, data=dat, addx=TRUE)
# Construct the V matrix for the random-effects model
v <- vcalc(vi=dat$vi, cluster=dat$study)
# Fit the random-effects model
res <- rma.mv(yi, v, data=dat)
print(res)
```

--------------------------------

### Fit Meta-Regression with Interactions

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/11-specialized-models.md

Example of fitting a meta-regression model including an interaction term between absolute latitude and a categorical allocation variable.

```r
# Interactions
res.mr3 <- rma(yi, vi, mods=~ablat * factor(alloc), data=dat)
```

--------------------------------

### Example Interpretation of Prediction Output

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/07-predict.md

Illustrates the interpretation of prediction output, distinguishing between confidence intervals (CI) and prediction intervals (PI).

```r
# Prediction output
# pred = 0.5, ci: [0.3, 0.7], pi: [-0.5, 1.5]

# Interpretation:
# - Average pooled effect estimate: 0.5
# - We're 95% confident the true average falls between 0.3 and 0.7
# - A new study would likely yield an effect between -0.5 and 1.5
```

--------------------------------

### S3 Method for class 'rma.uni'

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/weights.rma.html

Extracts weights for 'rma.uni' objects. Specify 'type' to get diagonal elements or the full matrix.

```R
weights(object, type="diagonal", ...)
```

--------------------------------

### Fit Beta Selection Model (Citkowicz & Vevea, 2017)

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/selmodel.html

This snippet demonstrates fitting a beta selection model as described in Citkowicz and Vevea (2017). It requires loading the 'dat.baskerville2012' dataset and then fitting the model.

```R
# copy data into 'dat' and examine data
dat <- dat.baskerville2012
dat
#>              author year score design alloconc blind itt fumonths retention country outcomes duration
#> 1     Kottke et al. 1992     6    cct        0     1   1       19      83.0      US        2       18
#> 2    McBride et al. 2000     6    rct        0     0   0       18     100.0      US        4       12
#> 3     Stange et al. 2000     6    rct        0     0   0       24        NA      US       35       12
#> 4       Lobo et al. 2004     6    rct        1     0   0       21      57.0      NL       16       21
#> 5  Roetzhiem et al. 2005     6   crct        0     1   0       24     100.0      US        3       24
#> 6       Hogg et al. 2008     6    cct        0     0   1        6      87.0     Can       26       12
#> 7       Aspy et al. 2008     6    cct        0     1   0       18      89.0      US        4       18
#> 8       Jaen et al. 2010     6    rct        0     1   0       26      86.0      US       11       26
#> 9   Cockburn et al. 1992     7    rct        0     0   0        3      79.0     Aus        2        2

```

--------------------------------

### Prepare Data for Meta-Analysis of Correlation Coefficients

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/conv.delta.html

An elaborate example showing how conv.delta() can be used in data preparation steps for a meta-analysis of correlation coefficients. This involves creating a data frame with various potential input variables and then applying the conversion function.

```r
dat <- data.frame(study=1:6,
             ri=c(0.42,NA,NA,NA,NA,NA),
             tval=c(NA,2.85,NA,NA,NA,NA),
             phi=c(NA,NA,NA,0.27,NA,NA),
             ni=c(93,182,NA,112,NA,NA),
             ai=c(NA,NA,NA,NA,61,NA), bi=c(NA,NA,NA,NA,36,NA),
             ci=c(NA,NA,NA,NA,39,NA), di=c(NA,NA,NA,NA,57,NA),
             or=c(NA,NA,NA,NA,NA,1.86), lower=c(NA,NA,NA,NA,NA,1.12), upper=c(NA,NA,NA,NA,NA,3.10),
             m1i=c(NA,NA,54.1,NA,NA,NA), sd1i=c(NA,NA,5.79,NA,NA,NA), n1i=c(NA,NA,66,75,NA,NA),
             m2i=c(NA,NA,51.7,NA,NA,NA), sd2i=c(NA,NA,6.23,NA,NA,NA), n2i=c(NA,NA,65,88,NA,NA))
dat
```

--------------------------------

### Illustrate Shrinkage of BLUPs

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/blup.html

This example visualizes the shrinkage of BLUPs towards the estimated population average. It plots the observed study effects against the BLUPs, showing how the BLUPs are pulled closer to the overall mean, especially for studies with smaller variances.

```r
res <- rma(yi, vi, data=dat)
blups <- blup(res)$pred
plot(NA, NA, xlim=c(0.8,2.4), ylim=c(-2,0.5), pch=19,
     xaxt="n", bty="n", xlab="", ylab="Log Risk Ratio")
segments(rep(1,13), dat$yi, rep(2,13), blups, col="darkgray")
points(rep(1,13), dat$yi, pch=19)
points(rep(2,13), blups, pch=19)
axis(side=1, at=c(1,2), labels=c("Observed\nValues", "BLUPs"), lwd=0)
segments(0, res$beta, 2.15, res$beta, lty="dotted")
text(2.3, res$beta, substitute(hat(mu)==muhat, list(muhat=round(res$beta[[1]], 2))), cex=1)
```

--------------------------------

### Convert Five Number Summary with 'qe' Method

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/conv.fivenum.html

This snippet shows how to use `conv.fivenum` with the 'qe' method for estimation, including setting a random seed for reproducibility.

```r
set.seed(1234)
conv.fivenum(min=min, q1=q1, median=median, q3=q3, max=max, n=n, data=dat, method="qe")
```

--------------------------------

### Generate Analysis Report

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/reporter.html

This snippet demonstrates how to generate an analysis report using the reporter function. It first prepares data, fits a random-effects model, and then calls reporter on the fitted model object. The output shows the process of report generation, including directory creation, file copying, and rendering.

```r
### copy BCG vaccine data into 'dat'
dat <- dat.bcg

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat,
              slab=paste(author, ", ", year, sep=""))

### fit random-effects model
res <- rma(yi, vi, data=dat)

### generate report
reporter(res)
#> 
#> Directory for generating the report is: /tmp/RtmpoAPsbL
#> Copying references.bib and apa.csl to report directory ...
#> Saving model object to report_res.rdata ...
#> Creating report_res.rmd file ...
#> Rendering report_res.rmd file ...
#> Generated /tmp/RtmpoAPsbL/report_res.html ...
#> Opening report ...

```

--------------------------------

### Poisson Regression (Count Data)

Source: https://github.com/wviechtb/metafor/blob/master/_autodocs/11-specialized-models.md

Example of using `rma.glmm` for Poisson regression to analyze count data, specifically for incidence rate ratios.

```APIDOC
## Poisson Regression (Count Data)

### Description
Use `rma.glmm` with `measure="IRR"` for Poisson regression on count data to obtain incidence rate ratios.

### Function Signature
```r
rma.glmm(measure, ai, ti, data, ...)
```

### Parameters
- `measure`: Set to `"IRR"` for Incidence Rate Ratios.
- `ai`: Event counts.
- `ti`: Time at risk.
- `data`: Data frame containing the variables.

### Example
```r
# For incidence rate ratios
res.glmm.irr <- rma.glmm(measure="IRR", ai=events, ti=time, 
                         data=count.data)
```
```

--------------------------------

### Get Citation Information for Metafor

Source: https://github.com/wviechtb/metafor/blob/master/README.md

Use this command in R to retrieve citation details for the Metafor package. This is important for academic and research use.

```r
citation(package='metafor')
```

--------------------------------

### Fit Random-Effects Model

Source: https://github.com/wviechtb/metafor/blob/master/docs/reference/plot.rma.uni.selmodel.html

This snippet demonstrates fitting a random-effects model using the ML estimator for heterogeneity. It then prints the model results, including heterogeneity statistics and model coefficients.

```R
dat <- dat.hackshaw1998

### fit random-effects model using the log odds ratios
res <- rma(yi, vi, data=dat, method="ML")
res
```