limour-blog.github.io/source/_posts/2022-07-15-【迁移】森林图展示回归分析的结果.md

---

title: 【迁移】森林图展示回归分析的结果 urlname: Forest-plot-displays-the-results-of-regression-analysis date: 2022-07-15 20:42:25 index_img: https://api.limour.top/randomImg?d=2022-07-15 20:42:25

tags: ['forestplot', 'regression']

配置环境

• 基础编程环境
• GitHub 下载加速

• 可能需要用到的加速服务

  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

  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)
  }
  

  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)