Error in nomogram(data = df, fun = function(x) 0.5, varname = c("original.firstorder.RobustMeanAbsoluteDeviation", : argument "fit" is missing, with no default

非常抱歉，我之前的回答有误。在使用`nomogram()`函数时，需要指定一个已经构建好的模型作为参数`fit`，而不是使用一个预测函数。如果您有一个已经构建好的模型，可以按照如下代码构建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)
)

# 构建一个简单的逻辑回归模型作为示例
data <- read.csv("your_data.csv") # 读取数据
fit <- glm(y ~ ., data = data, family = binomial) # 构建逻辑回归模型

# 构建 nomogram
n <- nomogram(
  fit = fit,
  data = df,
  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,
  ylab = "Predicted Probability",
  ggtheme = theme_bw()
)

# 绘制 nomogram
plot(n, lpscale = TRUE)

如果您没有一个已经构建好的模型，可以使用`lrm()`函数进行逻辑回归模型的构建。具体代码如下：

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)
)

# 构建一个简单的逻辑回归模型作为示例
data <- read.csv("your_data.csv") # 读取数据
fit <- lrm(y ~ ., data = data) # 构建逻辑回归模型

# 构建 nomogram
n <- nomogram(
  fit = fit,
  data = df,
  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,
  ylab = "Predicted Probability",
  ggtheme = theme_bw()
)

# 绘制 nomogram
plot(n, lpscale = TRUE)

其中，`lrm()`函数与`glm()`函数类似，用于构建逻辑回归模型。