leftlabs = c("Species", "Author", "N", "Tissue", "Fisher's-Z", "SE"))
dev.off()
png(file = "Birds - forestplot - Classes (Tel).png", width = 8500, height = 11000, res = 600)
forest.meta(Meta.B2,
sortvar = MBird$Class, overall = TRUE,
prediction = TRUE, subgroup = FALSE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = "pink",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "N", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "N", "Tissue", "Fisher's-Z", "SE"))
dev.off()
png(file = "Birds - forestplot - Classes (Tel).png", width = 8500, height = 11000, res = 600)
forest.meta(Meta.B2,
sortvar = MBird$Class, overall = TRUE,
prediction = TRUE, subgroup = FALSE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = "#EC4FC2" ,xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "N", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "N", "Tissue", "Fisher's-Z", "SE"))
dev.off()
Meta.Bird
## 4.4 Test of bias ####
Bird_bias <- metabias(Meta.Bird, method.bias = "linreg")
Bird_bias
library(readxl)
NEW_MMeth <- read_excel("Data/Le Clercq (2022) - Biological clocks (Data)/5. Meta-Analysis/Methylation/NEW_MMeth.xlsx",
col_types = c("text", "text", "text",
"numeric", "text", "text", "numeric",
"numeric", "numeric", "text", "text",
"text", "numeric", "numeric", "numeric",
"text"))
View(NEW_MMeth)
warnings()
library(meta)
MMeth <- NEW_MMeth
View(MMeth)
summary(MMeth)
Meth_Group1 <- subset(MMeth, MMeth$Group==1)
MamMeth <- subset(MMeth, MMeth$Class == 'Mammals')
## 1.2 Factorization of variables ####
MMeth$Class <- factor(MMeth$Class, levels = c("Fishes", "Amphibians", "Reptiles", "Birds", "Mammals"))
MMeth$Mammal.Group <- factor(MMeth$Mammal.Group)
MMeth$Group <- factor(MMeth$Group, levels = c("1", "2", "3"))
summary(MMeth)
## 1.3 Meta-analysis ####
Author <- MMeth$Author
Species <- MMeth$'Generic name'
Tissue <- MMeth$Tissue
Meta.M <- metacor(cor = Cor, n = N, studlab = MMeth$`Generic name`,
keepdata = TRUE, data = MMeth,
random = TRUE, common = FALSE,
method.tau = "REML", prediction = TRUE,
sm = "ZCOR", title = "Meta-analysis of Methylation studies")
Meta.M
Meta.MGrp1 <- metacor(cor = Cor, n = N, studlab = MMeth$`Generic name`,
keepdata = TRUE, data = MMeth,
random = TRUE, common = FALSE,
method.tau = "REML", prediction = TRUE,
sm = "ZCOR", title = "Meta-analysis of Methylation studies")
Meta.MGrp1
### REM - by Class ####
Meta.M2 <- update.meta(Meta.M, subgroup = MMeth$Class, subgroup.name = "") #Group by Class
Meta.M2
### REM - by Group ####
Meta.M3 <- update.meta(Meta.M, subgroup = MMeth$Group) #by Groups 1-3
Meta.M3
### REM - by Tissue ####
Meta.M4 <- update.meta(Meta.M, subgroup = MMeth$Tissue) #by Tissue #by Groups 1-3
Meta.M4
### REM - by Method ####
Meta.M5 <- update.meta(Meta.M, subgroup = MMeth$Method) #by Tissue #by Groups 1-3
Meta.M5
### REM - Mammals only ####
MamMet <- metacor(cor = Cor, n = N, studlab = MamMeth$`Generic name`,
keepdata = TRUE, data = MamMeth,
random = TRUE, common = FALSE,
method.tau = "REML", prediction = TRUE,
sm = "ZCOR", title = "Meta-analysis of Methylation studies")
MamMet.M2 <- update.meta(MamMet, subgroup = MamMeth$Mammal.Group, subgroup.name = "") #Group by species
MamMet.M2
## 1.4 Forest plots ####
ColorScheme <- c('#AB22B8', "#2670D0", "#26D09B", "#EC4FC2", "#EC514F")
### Forest - All ####
png(file = "forestplot - All.png", width = 4500, height = 4000, res = 300)
forest.meta(Meta.M,
sortvar = MMeth$Class,
prediction = TRUE,
print.tau2 = TRUE,
just.addcols.left = c("left","left","left","right","left"),
col.by = "black",
col.square = "light blue",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm",
leftcols = c("studlab", "Author", "Bias", "Group", "n", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "Grp", "N","Tissue","Fisher's-Z", "SE"))
dev.off()
### Forest - Classes ####
png(file = "forestplot - Classes.png", width = 8500, height = 10000, res = 600)
forest.meta(Meta.M2,
sortvar = MMeth$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = FALSE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left","left","left","right","left"),
col.by = "black", fontfamily = 'sans',
col.square = ColorScheme[factor(MMeth$Class)],xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "Bias", "Group", "n", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "Grp", "N","Tissue","Fisher's-Z", "SE"))
dev.off()
### Forest - Groups ####
png(file = "forestplot - Groups.png", width = 8500, height = 10000, res = 600)
forest.meta(Meta.M3,
sortvar = MMeth$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = TRUE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = "light blue",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "Bias", "n", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "N", "Tissue", "Fisher's-Z", "SE"))
dev.off()
### Forest - Tissue ####
png(file = "forestplot - Tissue.png", width = 8500, height = 11000, res = 600)
forest.meta(Meta.M4,
sortvar = MMeth$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = TRUE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = "light blue",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "Bias", "n", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "N","Tissue","Fisher's-Z", "SE"))
dev.off()
### Forest - Method ####
png(file = "forestplot - Method.png", width = 8500, height = 11000, res = 600)
forest.meta(Meta.M5,
sortvar = MMeth$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = TRUE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = "light blue",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "Bias", "n", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "N","Tissue","Fisher's-Z", "SE"))
dev.off()
### Forest - Mammals only ####
png(file = "MammalMeth.png", width = 8000, height = 10000, res = 600)
forest.meta(MamMet.M2,
sortvar = MamMeth$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = FALSE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "left" ,"left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = "light blue",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab","Mammal.Group", "Author", "TE", "seTE", "n", "Tissue"),
leftlabs = c("Species", "Order", "Author", "Fisher's-Z", "SE","N","Tissue"))
dev.off()
## 1.5 Funnel plots (Asymmetry) ####
png(file = "Funnel.png", width = 8000, height = 10000, res = 600)
funnel.meta(Meta.M, random = TRUE,
xlim = c(-0.5, 3),
studlab = TRUE)
dev.off()
## 1.6 Publication bias test ####
MMeth_bias <- metabias(Meta.M, method.bias = "linreg")
MMeth_bias
## 1.7 Meta-regression for heterogeneity ####
MREG <- meta::metareg(Meta.M, ~N)
MREG
MREG2 <- meta::metareg(Meta.M, ~Tissue)
MREG2
MREG3 <- meta::metareg(Meta.M, ~Author)
MREG3
MREG4 <- meta::metareg(Meta.M, ~Method)
MREG4
MREG9 <- meta::metareg(Meta.M, ~Year)
MREG9
MREG10 <- meta::metareg(Meta.M, ~Bias)
MREG10
MREG8 <- meta::metareg(Meta.M, ~Class)
MREG8
MREG5 <- meta::metareg(Meta.M, ~MMeth$`Life Min`)
MREG5
MREG6 <- meta::metareg(Meta.M, ~MMeth$`Life Med`)
MREG6
MREG7 <- meta::metareg(Meta.M, ~MMeth$`Life Max`)
MREG7
MMeth_bias
summary(MMeth)
MMeth$Bias <- factor(MMeth$Bias, levels = c("Low", "Medium", "High"))
summary(MMeth)
Meta.M2
Meta.M3
Meta.M4
Meta.M5
library(readxl)
NEW_MTel <- read_excel("Data/Le Clercq (2022) - Biological clocks (Data)/5. Meta-Analysis/Telomeres/All/NEW_MTel.xlsx",
col_types = c("text", "text", "text",
"text", "text", "numeric", "numeric",
"numeric", "numeric", "text", "text",
"text", "text", "text", "numeric",
"numeric", "numeric", "text"))
View(NEW_MTel)
MTel <- NEW_MTel
View(MTel)
## 2.2 Factorization of variables ####
MTel$Class <- factor(MTel$Class, levels = c("Fishes", "Amphibians", "Reptiles", "Birds", "Mammals"))
MTel$Bias <- factor(MTel$Bias, levels = c("Low", "Medium", "High"))
MTel$Mammal.Group <- factor(MTel$Mammal.Group)
summary(MTel)
MTel$Group <- factor(MTel$Group, levels = c("1", "2", "3"))
summary(MTel)
## 2.3 Meta-analysis ####
Author2 <- MTel$Author
Species2 <- MTel$`Generic name`
Tissue2 <- MTel$Tissue
### REM - All ####
Meta.T <- meta::metagen(TE = TE, seTE = seTE,
studlab = MTel$`Generic name`,
data = MTel,random = TRUE,sm = "ZCOR",
common = FALSE,keepdata = TRUE,
method.tau = "REML", prediction = TRUE)
Meta.T
### REM - by Class ####
Meta.T2 <- update.meta(Meta.T, subgroup = MTel$Class, subgroup.name = "") #Group by Class
Meta.T2
### REM - by Group ####
Meta.T3 <- update.meta(Meta.T, subgroup = MTel$Group) #by Groups 1-2
Meta.T3
### REM - by Tissue ####
Meta.T4 <- update.meta(Meta.T, subgroup = MTel$Tissue.2) #by Tissue
Meta.T4
### REM - by Method ####
Meta.T5 <- update.meta(Meta.T, subgroup = MTel$Method) #by Method
Meta.T5
## 2.4 Forest plots ####
ColorScheme <- c("purple", "blue", "green", "yellow", "red")
### Forest - All ####
png(file = "forestplot - All (Tel).png", width = 4500, height = 4000, res = 300)
forest.meta(Meta.T,
sortvar = MTel$Class,
prediction = TRUE,
print.tau2 = TRUE,
just.addcols.left = c("left", "left", "right", "left"),
col.by = "black",
col.square = "light blue",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm",
leftcols = c("studlab", "Author", "Bias", "Group", "n", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "Grp", "N","Tissue","Fisher's-Z", "SE"))
dev.off()
### Forest - Classes ####
png(file = "forestplot - Classes (Tel).png", width = 8500, height = 10000, res = 600)
forest.meta(Meta.T2,
sortvar = MTel$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = FALSE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = ColorScheme[factor(MTel$Class)],xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "Bias", "N", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "N", "Tissue", "Fisher's-Z", "SE"))
dev.off()
### Forest - Groups ####
png(file = "forestplot - Groups (Tel).png", width = 8500, height = 10000, res = 600)
forest.meta(Meta.T3,
sortvar = MTel$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = TRUE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = "light blue",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "Bias", "N", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "N","Tissue","Fisher's-Z", "SE"))
dev.off()
### Forest - Tissue ####
png(file = "forestplot - Tissue (Tel).png", width = 8500, height = 11000, res = 600)
forest.meta(Meta.T4,
sortvar = MTel$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = TRUE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = "light blue",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "Bias", "N", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "N","Tissue","Fisher's-Z", "SE"))
dev.off()
### Forest - Method ####
png(file = "forestplot - Method (Tel).png", width = 8500, height = 11000, res = 600)
forest.meta(Meta.T5,
sortvar = MTel$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = TRUE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = "light blue",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "Bias", "N", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "N","Tissue","Fisher's-Z", "SE"))
dev.off()
## 2.5 Funnel plots (Asymmetry) ####
png(file = "Funnel (Tel).png", width = 8000, height = 10000, res = 600)
funnel.meta(Meta.T, random = TRUE,
xlim = c(-0.5, 3),
studlab = TRUE)
dev.off()
## 1.4 Forest plots ####
ColorScheme <- c('#AB22B8', "#2670D0", "#26D09B", "#EC4FC2", "#EC514F")
### Forest - Classes ####
png(file = "forestplot - Classes (Tel).png", width = 8500, height = 10000, res = 600)
forest.meta(Meta.T2,
sortvar = MTel$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = FALSE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left", "left", "right", "left"),
col.by = "black", fontfamily = 'sans',
col.square = ColorScheme[factor(MTel$Class)],xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "Bias", "N", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "N", "Tissue", "Fisher's-Z", "SE"))
dev.off()
## 2.6 Publication bias test ####
MTel_bias <- metabias(Meta.T, method.bias = "linreg")
MTel_bias
Meta.T2
Meta.T4
Meta.T5
## 2.7 Meta-regression for heterogeneity ####
TREG <- meta::metareg(Meta.T, ~N)
TREG
TREG2 <- meta::metareg(Meta.T, ~Tissue.2)
TREG2
TREG3 <- meta::metareg(Meta.T, ~Author)
TREG3
TREG9 <- meta::metareg(Meta.T, ~Year)
TREG9
TREG10 <- meta::metareg(Meta.T, ~Bias)
TREG10
TREG4 <- meta::metareg(Meta.T, ~Method)
TREG4
TREG8 <- meta::metareg(Meta.T, ~Class)
TREG8
TREG5 <- meta::metareg(Meta.T, ~MTel$`Life Min`)
TREG5
TREG6 <- meta::metareg(Meta.T, ~MTel$`Life Med`)
TREG6
TREG7 <- meta::metareg(Meta.T, ~MTel$`Life Max`)
TREG7
## 3.1 Statistical analysis ####
library("metafor")
### Methylation ####
MethTE <- Meta.M$TE
MethTE <- as.list(MethTE)
MethTE <- as.numeric(MethTE)
MethseTE <- Meta.M$seTE
MethseTE <- as.list(MethseTE)
MethseTE <- as.numeric(MethseTE)
Methylation <- rma(MethTE,MethseTE)
### Telomeres ####
TelTE <- Meta.T$TE
TelTE <- as.list(TelTE)
TelTE <- as.numeric(TelTE)
TelseTE <- Meta.T$seTE
TelseTE <- as.list(TelseTE)
TelseTE <- as.numeric(TelseTE)
Telomeres <- rma(TelTE,TelseTE)
### Combined data ####
dat.comp <- data.frame(estimate = c(coef(Methylation), coef(Telomeres)), stderror = c(Methylation$se, Telomeres$se),
meta = c("Methylation","Telomeres"), tau2 = round(c(Methylation$tau2, Telomeres$tau2),3))
dat.comp
### Analysis ####
Comb.M <- rma(estimate, sei=stderror, mods = ~ meta, method="FE", data=dat.comp, digits=3)
Comb.M
forest(Comb.M)
## 3.2 Forest plot ####
Combined <- meta::metabind(Meta.M, Meta.T, name = c("Methylation", "Telomeres"), pooled = "random")
forest(Combined, prediction = TRUE,
print.tau2 = TRUE,
col.by = "black",
col.square = "light blue",xlim = c(-1,1),
col.diamond = "yellow")
Comb.M
### Comparison of sample sizes ####
t.test(MMeth$N, MTel$N)
TREG10
View(MBird)
summary(MBird)
library(readxl)
NEW_MMeth <- read_excel("~/7. Manuscripts/4. Age Review/Data/Le Clercq (2022) - Biological clocks (Data)/5. Meta-Analysis/Methylation/NEW_MMeth.xlsx",
col_types = c("text", "text", "text",
"numeric", "text", "text", "numeric",
"numeric", "numeric", "text", "text",
"text", "numeric", "numeric", "numeric",
"text"))
View(NEW_MMeth)
MMeth <- NEW_MMeth
View(MMeth)
## 1.2 Factorization of variables ####
MMeth$Class <- factor(MMeth$Class, levels = c("Fishes", "Amphibians", "Reptiles", "Birds", "Mammals"))
MMeth$Bias <- factor(MMeth$Bias, levels = c("Low", "Medium", "High"))
MMeth$Mammal.Group <- factor(MMeth$Mammal.Group)
MMeth$Group <- factor(MMeth$Group, levels = c("1", "2", "3"))
## 1.3 Meta-analysis ####
Author <- MMeth$Author
Species <- MMeth$'Generic name'
Tissue <- MMeth$Tissue
Meta.M <- metacor(cor = Cor, n = N, studlab = MMeth$`Generic name`,
keepdata = TRUE, data = MMeth,
random = TRUE, common = FALSE,
method.tau = "REML", prediction = TRUE,
sm = "ZCOR", title = "Meta-analysis of Methylation studies")
library(meta)
Meta.M <- metacor(cor = Cor, n = N, studlab = MMeth$`Generic name`,
keepdata = TRUE, data = MMeth,
random = TRUE, common = FALSE,
method.tau = "REML", prediction = TRUE,
sm = "ZCOR", title = "Meta-analysis of Methylation studies")
Meta.M
Meta.MGrp1 <- metacor(cor = Cor, n = N, studlab = MMeth$`Generic name`,
keepdata = TRUE, data = MMeth,
random = TRUE, common = FALSE,
method.tau = "REML", prediction = TRUE,
sm = "ZCOR", title = "Meta-analysis of Methylation studies")
Meta.MGrp1
### REM - by Class ####
Meta.M2 <- update.meta(Meta.M, subgroup = MMeth$Class, subgroup.name = "") #Group by Class
Meta.M2
### REM - by Group ####
Meta.M3 <- update.meta(Meta.M, subgroup = MMeth$Group) #by Groups 1-3
Meta.M3
### REM - by Tissue ####
Meta.M4 <- update.meta(Meta.M, subgroup = MMeth$Tissue) #by Tissue #by Groups 1-3
Meta.M4
### REM - by Method ####
Meta.M5 <- update.meta(Meta.M, subgroup = MMeth$Method) #by Tissue #by Groups 1-3
Meta.M5
### REM - Mammals only ####
MamMet <- metacor(cor = Cor, n = N, studlab = MamMeth$`Generic name`,
keepdata = TRUE, data = MamMeth,
random = TRUE, common = FALSE,
method.tau = "REML", prediction = TRUE,
sm = "ZCOR", title = "Meta-analysis of Methylation studies")
MamMet.M2 <- update.meta(MamMet, subgroup = MamMeth$Mammal.Group, subgroup.name = "") #Group by species
MamMet.M2
## 1.4 Forest plots ####
ColorScheme <- c('#AB22B8', "#2670D0", "#26D09B", "#EC4FC2", "#EC514F")
### Forest - All ####
png(file = "forestplot - All.png", width = 4500, height = 4000, res = 300)
forest.meta(Meta.M,
sortvar = MMeth$Class,
prediction = TRUE,
print.tau2 = TRUE,
just.addcols.left = c("left","left","left","right","left"),
col.by = "black",
col.square = "light blue",xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm",
leftcols = c("studlab", "Author", "Bias", "Group", "n", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "Grp", "N","Tissue","Fisher's-Z", "SE"))
dev.off()
### Forest - Classes ####
png(file = "forestplot - Classes.png", width = 8500, height = 10000, res = 600)
forest.meta(Meta.M2,
sortvar = MMeth$Mammal.Group, overall = TRUE,
prediction = TRUE, subgroup = FALSE, fs.heading = 14,
addrow.subgroups = TRUE, fs.test.overall = 20,
print.tau2 = TRUE,
just.addcols.left = c("left","left","left","right","left"),
col.by = "black", fontfamily = 'sans',
col.square = ColorScheme[factor(MMeth$Class)],xlim = c(-2,2),
col.diamond = "yellow",
colgap = "4mm", digits.se = 2,
leftcols = c("studlab", "Author", "Bias", "Group", "n", "Tissue", "TE", "seTE"),
leftlabs = c("Species", "Author", "Bias", "Grp", "N","Tissue","Fisher's-Z", "SE"))
dev.off()
