基本上我试图自动化评分建模工作流程,并遇到输入从循环产生的结果从smbinning()
的问题,因此记录在名单。结果本身就是一个列表,所以我列出了一堆列表。当我尝试将结果(连续变量的存储区)添加到数据框中时出现问题。我发现无法提供进入列表级别所需的语法。我尝试通过引用列号来解决此问题,并试图从循环中传递相应的列表名称。我得到的错误是:在循环中访问与smbinning.gen()列表中的列表
[.data.frame(df,,col_id)中的错误:选择了未定义的列。
我的代码如下:
colcnt <- ncol(e_mod)
bucket_resultlist <- list()
for (i in 2:colcnt) {
#curvar = paste0('z', i)
curresult = smbinning(df = e_mod, y = "Bankrupt", x = colnames(e_mod)[i], p = 0.05)
bucket_resultlist[[paste0('Bin_Result_', colnames(e_mod)[i])]] = curresult #paste0('binresult', colnames(e)[i]) = curresult
}
e_mod2 = e_mod
for (i in 1:length(bucket_resultlist_trunc)) {
e_mod2 = smbinning.genCUSTOM(e_mod, bucket_resultlist_trunc[[i]] , chrname = i)
}
我甚至试图定义客户版本smbinning.gen()
功能,考虑到这一点,在标准的形式,它只是试图串连$ivtable
到列表引用,但我需要能够从此生成的列表中跳过一个级别,然后为该列表中的每个相应列表运行smbinning.gen()
。这里是自定义代码和原定义注释:
smbinning.genCUSTOM = function(df, ivout, chrname = "NewChar") {
df = cbind(df, tmpname = NA)
ncol = ncol(df)
col_id = paste0(ivout, '[[6]]', collapse = NULL) # Original: ivout$col_id
# Updated 20160130
b = paste0(ivout, '[[4]]', collapse = NULL) # Original: ivout$bands
df[, ncol][is.na(df[, col_id])] = 0 # Missing
df[, ncol][df[, col_id] <= b[2]] = 1 # First valid
# Loop goes from 2 to length(b)-2 if more than 1 cutpoint
if (length(b) > 3) {
for (i in 2:(length(b) - 2)) {
df[, ncol][df[, col_id] > b[i] & df[, col_id] <= b[i + 1]] = i
}
}
df[, ncol][df[, col_id] > b[length(b) - 1]] = length(b) - 1 # Last
df[, ncol] = as.factor(df[, ncol]) # Convert to factor for modeling
blab = c(paste("01 <=", b[2]))
if (length(b) > 3) {
for (i in 3:(length(b) - 1)) {
blab = c(blab, paste(sprintf("%02d", i - 1), "<=", b[i]))
}
} else { i = 2 }
blab = c(blab, paste(sprintf("%02d", i), ">", b[length(b) - 1]))
# Are there ANY missing values
# any(is.na(df[,col_id]))
if (any(is.na(df[, col_id]))) {
blab = c("00 Miss", blab)
}
df[, ncol] = factor(df[, ncol], labels = blab)
names(df)[names(df) == "tmpname"] = chrname
return(df)
}
所有帮助非常感谢!
这里的表结构 http://i.stack.imgur.com/iYau2.png
这也张贴在数据科学部分,但整个今天
我认为,问题的关键在于最有可能正确地传递参数进入'smbinning.genCUSTOM()'函数 –