【迁移】森林图展示回归分析的结果

Last updated on March 19, 2024 pm

配置环境

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conda create -n forestplot -c conda-forge r-forestplot -y
conda activate forestplot
conda install -c conda-forge r-tidyverse r-irkernel -y
Rscript -e "IRkernel::installspec(name='forestplot', displayname='r-forestplot')"

使用示例

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require(forestplot)
f_forestplot <- function(df, xlab="XR", zero=0, lineheight=unit(10,'mm'), colgap=unit(2,'mm'), graphwidth=unit(60,'mm'), title="Forestplot"){
df_labeltext <- df[,c('VarName', 'Pvalue')]
df_labeltext[[paste0(xlab,'(95%CI)')]] <- paste0(sprintf("%0.2f", df$mean),'(',sprintf("%0.2f", df$lower),'~',sprintf("%0.2f", df$upper),')')
df_labeltext[['Pvalue']] <- sprintf('%0.1e', df_labeltext[['Pvalue']])
df_labeltext <- rbind(colnames(df_labeltext), df_labeltext)
df <- rbind(rep(NaN, ncol(df)), df)
forestplot(labeltext=as.matrix(df_labeltext[,c(1,3,2)]),
mean=df$mean,
lower=df$lower,
upper=df$upper,
zero=zero,
boxsize=0.5,
lineheight=lineheight,
colgap=colgap,
graphwidth=graphwidth,
lwd.zero=2,
lwd.ci=2,
col=fpColors(box='#458B00',
summary='#8B008B',
lines = 'black',
zero = '#7AC5CD'),
xlab=xlab,
lwd.xaxis =2,
txt_gp = fpTxtGp(ticks = gpar(cex = 0.85),
xlab = gpar(cex = 0.8),
cex = 0.9),
lty.ci="solid",
title=title,
line.margin = 1,
graph.pos=2)
}
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df <- data.frame(
'VarName' = c('varA', 'varB', 'varC', 'varD', 'varE', 'varF', 'varG'),
'mean' = c(0.2, 0.1, 0.4, 0.6, -0.3, 0.2, -0.2),
'lower' = c(0.1, -0.1, 0.2, 0.3, -0.5, -0.1, -0.6),
'upper' = c(0.6, 0.3, 0.5, 0.7, -0.2, 0.4, -0.1),
'Pvalue' = c(0.6, 0.3, 0.5, 0.7, 0.2, 0.4, 0.1)
)
options(repr.plot.width=6, repr.plot.height=4)
f_forestplot(df)


【迁移】森林图展示回归分析的结果
https://hexo.limour.top/Forest-plot-displays-the-results-of-regression-analysis
Author
Limour
Posted on
July 15, 2022
Updated on
March 19, 2024
Licensed under