RunPoolEffect: Calcutate effect sizes via linear (Mixed-Effects) models

In xiangpin/MetaMicrobiome: An R package for meta-analysis and visualization of Microbiome.

Description

The function can be used to calculate various effect sizes. See [metafor]{rma} and [metafor]{escalc} details.

Usage

RunPoolEffect(var_of_interest, dataset, measure = "OR",
  methodtype = "REML")

Arguments

var_of_interest	vector, the interesting variables.
dataset	dataframe, the results of multiRunRR.
measure	character, a character string indicating which effect size or outcome measure should be calculated. See [metafor]{rma} and [metafor]{escalc} details.
methodtype	character, the specifying whether a fixed- or a random/mixed-effects model should be fitted, See [metafor]{rma} details.

Details

TODO

Value

a results dataframe of the pooled data with the random-effect model ot fixed-effect model. See the metafor{rma} details.

Author(s)

Shuangbin Xu

Examples

library("MetaMicrobiome") 
study <- c("Baxter_16", 
	"Deng_18", 
	"Flemer_17",
      	"Flemer_18",
      	"Hale_17", 
	"Mori_18",
      	"Zeller_15")
data <- lapply(study, 
        function(x){read.csv(system.file("data", 
                                          package="MetaMicrobiome", 
                                          paste(x, "_alpha_data.csv.gz", sep="")))})
names(data) <- study
threholds <- mapply(getthresholds, data, 
                    MoreArgs=list(var_of_interest=c("Observe", "Shannon", "J"),
                    type="median"), SIMPLIFY=FALSE)
multiHighLowVector <- mapply(MultiHighLow, 
                         data,
                         threholds,
                         MoreArgs=list(var_of_interest=c("Observe", 
					"Shannon", 
					"J")),SIMPLIFY=FALSE)

multiRRresult <- mapply(multiRunRR, 
                    multiHighLowVector, 
                    data, 
                    MoreArgs=list(prefix="Group", grouptype="CRC"), 
                    SIMPLIFY=FALSE)

multistudyRRresult <- mapply(multiVarRRTab, 
                            multiRRresult, 
                            MoreArgs=list(var_of_interest=c("Observe", 
							"Shannon", "J")), 
                            SIMPLIFY=FALSE)
multistudyRRresult2 <- dplyr::bind_rows(lapply(study,                                                                     
                                              function(x) 
			dplyr::mutate(multistudyRRresult[[x]], study=x)))

multistudyRRresult2
pooledREML <- dplyr::bind_rows(mapply(RunPoolEffect,
					c("Observe", "Shannon", "J"),
                                      MoreArgs=list(dataset=multistudyRRresult2,
                                      methodtype="REML"),
                                      SIMPLIFY=FALSE)) 
head(pooledREML)

xiangpin/MetaMicrobiome documentation built on May 26, 2019, 2:34 a.m.