ceresPlots.default(fit_2) ：绘制 CERES 图时跳过的因素

问题描述

每当我尝试通过函数 ceresPlots() 绘制图形时，它都会显示类似这样的错误。

> ceresPlots(fit_2)
Error in ceresPlots.default(fit_2) : No plots specified
In addition: Warning message:
In ceresPlots.default(fit_2) : Factors skipped in drawing CERES plots.

我不知道为什么会这样。我在下面附上我的代码。请帮帮我。

#### Importing the main required libraries ####

#### 1. Activate Libraries ####
library(Hmisc)
library(car)
library(readxl)
library(magrittr)
library(broom)
library(ggplot2)

#### Manipulating Data ####

#### 1. Setting a working directory ####
setwd("C:/Users/acer/Desktop/Important/M.SC/STATISTICS THIRD SEMESTER/4. PROJECT/PROJECT")

#### 2. Reading the cancer data set and eliminating the values ####
cancer<-read_excel("Main spreadsheet.xlsx",na="---")
summary(cancer)
View(cancer)
cancer$`% Ctry Some College 18_24`<-as.numeric(impute(cancer$`% Ctry Some College 18_24`,mean))
cancer$`% Ctry Employed 16_Over`<-as.numeric(impute(cancer$`% Ctry Employed 16_Over`,mean))
cancer$`% Ctry Private H Coverage Alone`<-as.numeric(impute(cancer$`% Ctry Private H Coverage Alone`,mean))
summary(cancer)
View(cancer)

#### To check if there is a relationship between the response variable and the predictors ####

#### 1. Making a set of variables for regression modelling ####
cancer.sub<-subset(cancer,select=c(`Avg Deaths per year`,`Avg Ann Count`,`Incidence Rate`,`Median Income`,`Popu Est 2015`,`Poverty Percent`,`Study per Capita`,`Binned Income`,`Median Age`,`Median Age Male`,`Median Age Female`,`Percent Married`,`% Ctry No LHS 18-24`,`% Ctry No HSD 18-24`,`% Ctry Some College 18_24`,`% Ctry Bach Deg 18_24`,`% Ctry No HSD 25_Over`,`% Ctry Bach Deg 25_Over`,`% Ctry Employed 16_Over`,`% Ctry Unemployed 16_Over`,`% Ctry Private H Coverage`,`% Ctry Private H Coverage Alone`,`% Ctry Emp Priv H Coverage`,`% Ctry Public H Coverage`,`% Ctry Public H Coverage Alone`,`% Ctry Res White`,`% Ctry Res Black`,`% Ctry Res Asian`,`% Ctry Other Race`,`% Ctry Married Households`,`Birth Rate`,`Target Death Rate`))

fit<-lm(formula = `Target Death Rate`~.,data = cancer.sub)
summary(fit)

#### To check which variables have a strong relation to Target Death Rate ####

#### 1. Linear regression using the features with significant p-values only ####
fit_1<-lm(`Target Death Rate` ~ `Avg Deaths per year` + `Avg Ann Count` + `Incidence Rate` + `Median Income` + `Popu Est 2015` +
          `Binned Income` + `Median Age Male` + `Percent Married` + `% Ctry No LHS 18-24` + `% Ctry No HSD 18-24` +  `% Ctry Bach Deg 18_24` + 
          `% Ctry No HSD 25_Over` + `% Ctry Bach Deg 25_Over` + `% Ctry Employed 16_Over` + `% Ctry Private H Coverage` + `% Ctry Emp Priv H Coverage` + 
          `% Ctry Public H Coverage` + `% Ctry Public H Coverage Alone` + `% Ctry Res White` + `% Ctry Res Black` + `% Ctry Other Race` + 
          `% Ctry Married Households` + `Birth Rate`,data = cancer.sub)

#### 2. Linear regression using mixed selection ####
fit_step<-MASS::stepAIC(fit,direction = "both",trace = FALSE)

#### 3. Comparison of the two above models ####
fit_step$call
fit_1$call

#### To check the presence of multicollinearity in the model ####

#### 1. Checking multicollinearity in fit_step model ####
vif(fit_step) %>% knitr::kable()

#### 2. Removing all the multicollinear variables ####
fit_2<-lm(`Target Death Rate` ~ `Incidence Rate` + `Median Income` + `Popu Est 2015` +
            `Median Age Male` + `% Ctry No LHS 18-24` + `% Ctry No HSD 18-24` +  `% Ctry Bach Deg 18_24` + 
            `% Ctry No HSD 25_Over` +  `% Ctry Employed 16_Over` + `% Ctry Emp Priv H Coverage` + 
            `% Ctry Public H Coverage Alone` + `% Ctry Res Black` + `% Ctry Other Race` + 
            `% Ctry Married Households` + `Birth Rate`,data = cancer.sub)
summary(fit_2)
#### 3. Checking multicollinearity of the refitted model ####
vif(fit_2) %>% knitr::kable()

#### To check if there is any linear relationship between the predictors and the outcome ####

#### 1. The residual plot of fitted values versus the residuals ####
residualPlot(fit_2,type = "rstandard")

#### 2. Exploring non-linearity by looking at Component Residual plots(CR plots) 
ceresPlots(fit_2)

我已经尝试了这个函数的每一种形式，但没有任何效果。我真的不知道自己哪里出错了。

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linear-regression model-fitting r r regression statistics