predict() 函数使用 R 中线性模型的因子抛出错误

Question

I am using the "lung capacity" data set to try to set up a linear model:

library(tidyverse)
library(rvest)
h <- "https://docs.google.com/spreadsheets/d/0BxQfpNgXuWoIWUdZV1ZTc2ZscnM/edit?resourcekey=0-gqXT7Re2eUS2JGt_w1y4vA#gid=1055321634"
t <- rvest::read_html(h)
Nodes <- t %>% html_nodes("table")
table <- html_table(Nodes[[1]])
colnames(table) <- table[1,]
table <- table[-1,]
table <- table %>% select(LungCap, Age, Height, Smoke, Gender, Caesarean)
Lung_Capacity <- table

Lung_Capacity$LungCap <- as.numeric(Lung_Capacity$LungCap)
Lung_Capacity$Age <- as.numeric(Lung_Capacity$Age)
Lung_Capacity$Height <- as.numeric(Lung_Capacity$Height)
Lung_Capacity$Smoke <- as.numeric(Lung_Capacity$Smoke == "yes")
Lung_Capacity$Gender <- as.numeric(Lung_Capacity$Gender == "male")
Lung_Capacity$Caesarean <- as.numeric(Lung_Capacity$Caesarean == "yes")

colnames(Lung_Capacity)[4] <- "Smoker_YN"
colnames(Lung_Capacity)[5] <- "Male_YN"
colnames(Lung_Capacity)[6] <- "Caesarean_YN"
head(Lung_Capacity)
Capacity <- Lung_Capacity

I am splitting the data into a training set and a validation set:

library(caret)
set.seed(1)
y <- Capacity$LungCap
testIndex <- caret::createDataPartition(y, times = 1, p = 0.2, list = FALSE)

train <- Capacity[-testIndex,]
test <- Capacity[testIndex,]

Cross-validating to obtain my final model:

set.seed(3)
control <- trainControl(method="cv", number = 5)
LinearModel <- train(LungCap ~ ., data = train, method = "lm", trControl = control)
LM <- LinearModel$finalModel
summary(LM)

And trying to run a prediction on the held-out test set:

lmPredictions <- predict(LM, newdata = test)

However, there is an error thrown that reads:

Error in eval(predvars, data, env) : object 'Smoker_YN1' not found

Looking through this site, I thought the column names of the test and train tables may have been off, but that is not the case, they are identical. The issue seems to be that training the model has renamed the factor predictors "Smoker_YN1" as opposed to the column name "Smokey_YN" that is intended. I tried renaming the column headers in the test set and I tried re-naming the coefficient headers. Neither approach was successful.

I've run out of research and experimental approaches, can anyone please help with this issue?

Answer 1

I am not sure. Please go through and tell me: My guess (and I am not an expert, is that LungCap character and Lung numeric interfer in this code):

h <- "https://docs.google.com/spreadsheets/d/0BxQfpNgXuWoIWUdZV1ZTc2ZscnM/edit?resourcekey=0-gqXT7Re2eUS2JGt_w1y4vA#gid=1055321634"
#install.packages("textreadr")
library(textreadr)
library(rvest)
t <- read_html(h)
t
Nodes <- t %>% html_nodes("table")
table <- html_table(Nodes[[1]])
colnames(table) <- table[1,]
table <- table[-1,]
table <- table %>% select(LungCap, Age, Height, Smoke, Gender, Caesarean)
Lung_Capacity <- table


# I changed Lung_Capacity$LungCap <- as.numeric(Lung_Capacity$LungCap) to
Lung_Capacity$Lung <- as.numeric(Lung_Capacity$LungCap)

Lung_Capacity$Age <- as.numeric(Lung_Capacity$Age)
Lung_Capacity$Height <- as.numeric(Lung_Capacity$Height)
Lung_Capacity$Smoke <- as.numeric(Lung_Capacity$Smoke == "yes")
Lung_Capacity$Gender <- as.numeric(Lung_Capacity$Gender == "male")
Lung_Capacity$Caesarean <- as.numeric(Lung_Capacity$Caesarean == "yes")

colnames(Lung_Capacity)[4] <- "Smoker_YN"
colnames(Lung_Capacity)[5] <- "Male_YN"
colnames(Lung_Capacity)[6] <- "Caesarean_YN"
head(Lung_Capacity)
# I changed to
Capacity <- Lung_Capacity
Capacity

library(caret)
set.seed(1)
# I changed y <- Capacity$LungCap to 
y <- Capacity$Lung
testIndex <- caret::createDataPartition(y, times = 1, p = 0.2, list = FALSE)

train <- Capacity[-testIndex,]
test <- Capacity[testIndex,]

# I removed 
train$LungCap <- NULL
test$LungCap <- NULL

set.seed(3)
control <- trainControl(method="cv", number = 5)
# I changed LungCap to Lung 
LinearModel <- train(Lung ~ ., data = train, method = "lm", trControl = control)
LM <- LinearModel$finalModel
summary(LM)

lmPredictions <- predict(LM, newdata = test)
lmPredictions

Output:

        1         2         3         4         5         6         7 
 6.344355 10.231586  4.902900  7.500179  5.295711  9.434454  8.879997 
        8         9        10        11        12        13        14 
12.227635 11.097691  7.775063  8.085810  6.399364  7.852107  9.480219 
       15        16        17        18        19        20 
 8.982051 10.115840  7.917863 12.089960  7.838881  9.653292