请用r构建nomogram的代码，其中我的变量名称为 `original:firstorder:RobustMeanAbsoluteDeviation` ，`log-sigma-4-0-mm-3D:firstorder:InterquartileRange` ，`log-sigma-5-0-mm-3D:glszm:SmallAreaEmphasis` ，`wavelet-LLH:firstorder:InterquartileRange` ，`wavelet-LLH:firstorder:Uniformity` ， `wavelet-HHL:firstorder:Mean` ， `wavelet-HHH:firstorder:Mean` ，`wavelet-LLL:glcm:Autocorrelation` ， `wavelet-LLL:glcm:ClusterProminence`

首先需要安装`rms`包和`Hmisc`包，这两个包提供了绘制nomogram的函数。然后按照如下代码构建nomogram：

library(rms)
library(Hmisc)

# 构建数据框 df，包含每个变量的取值
df <- data.frame(
  `original:firstorder:RobustMeanAbsoluteDeviation` = c(0, 0.5, 1),
  `log-sigma-4-0-mm-3D:firstorder:InterquartileRange` = c(0, 0.5, 1),
  `log-sigma-5-0-mm-3D:glszm:SmallAreaEmphasis` = c(0, 0.5, 1),
  `wavelet-LLH:firstorder:InterquartileRange` = c(0, 0.5, 1),
  `wavelet-LLH:firstorder:Uniformity` = c(0, 0.5, 1),
  `wavelet-HHL:firstorder:Mean` = c(0, 0.5, 1),
  `wavelet-HHH:firstorder:Mean` = c(0, 0.5, 1),
  `wavelet-LLL:glcm:Autocorrelation` = c(0, 0.5, 1),
  `wavelet-LLL:glcm:ClusterProminence` = c(0, 0.5, 1)
)

# 构建 nomogram
n <- nomogram(
  data = df,
  fun = function(x) 0.5, # 预测函数，这里假设预测为常数 0.5
  varname = c(
    "RobustMeanAbsoluteDeviation",
    "InterquartileRange(log-sigma-4-0-mm-3D)",
    "SmallAreaEmphasis(log-sigma-5-0-mm-3D)",
    "InterquartileRange(wavelet-LLH)",
    "Uniformity(wavelet-LLH)",
    "Mean(wavelet-HHL)",
    "Mean(wavelet-HHH)",
    "Autocorrelation(wavelet-LLL)",
    "ClusterProminence(wavelet-LLL)"
  ),
  funlabel = "Predicted Probability",
  xfrac = 0.5,
  yfrac = 0.5,
  lp = function(x) log(x / (1 - x)),
  xlab = "Variable values",
  ggtheme = theme_bw()
)

# 绘制 nomogram
plot(n, lpscale = TRUE)

输出结果即为所求的 nomogram。需要注意的是，变量名称中的冒号需要转义，即用反斜杠`\`进行转义。