metaAnalysis {MetaCyto} | R Documentation

## Performs meta-analysis

### Description

A function that performs meta-analysis

### Usage

    metaAnalysis(value, variableOfInterst, otherVariables, studyID, data, CILevel,
      main, ifScale = c(TRUE, FALSE), cex = 1)

### Arguments

`value` | A string to specify the column name of the dependent variable (y)
`variableOfInterst` | A string to specify the column name of the independent variable of interest (x1)
`otherVariables` | A string vector to specify the column names of independent variables included in the regression model other than the variableOfInterst.
`studyID` | A string to specify the column name of study ID.
`data` | A data frame containing the data
`CILevel` | A number between 0 to 1, used to specify the confidence interval to be plotted in the forest plot.
`main` | A string to specify the title of the forest plot
`ifScale` | A vector of two logic values, specifying if the dependent variable and the variableOfInterst should be scaled when calculating the effect size.
`cex` | A number specifying the amount by which plotting text and symbols should be scaled relative to the default in the forest plot.

### Value

Returns data frame describing the effect size of variableOfInterst on value in each individual studies, as well as the over all effect size. In addition, it returns the random effect model and the leave one out analysis result.

### Examples

    library(dplyr)
    #collect all summary statistics
    fn=system.file("extdata","",package="MetaCyto")
    files=list.files(fn,pattern="cluster_stats_in_each_sample",recursive=TRUE,
                     full.names=TRUE)
    fcs_stats=collectData(files,longform=TRUE)
    # Collect sample information
    files=list.files(fn,pattern="sample_info",recursive=TRUE,full.names=TRUE)
    sample_info=collectData(files,longform=FALSE)
    # join the cluster summary statistics with sample information
    all_data=inner_join(fcs_stats,sample_info,by="fcs_files")

    # plot forrest plot to see if the proportion of CCR7+ CD8 T cell
    # is affected by age (while controlling for Gender)
    L="CD3+|CD4-|CD8+|CCR7+"
    dat=subset(all_data,all_data$parameter_name=="fraction"&
                all_data$label==L)
    MA=metaAnalysis(value="value",variableOfInterst="Subject Age",main=L,
                    otherVariables=c("Gender"),studyID="study_id",
                    data=dat,CILevel=0.95,ifScale=c(TRUE,FALSE))

[Package MetaCyto version 1.12.0 Index]