Question

 # Load necessary libraries
 if (!require(forcats)) {
 install.packages("forcats", dependencies = TRUE)
 library(forcats)
      }

# Set the working directory to where your data and process.R file are located
setwd("MYPATH")

 # Check the current working directory to verify the change
 cat("Current working directory:", getwd(), "\n")
 # Load the data
 political_position_data <- read.csv("MYDATA.csv")

 # Subset the data to only include entries where CountryCode is 1
 us_data <- subset(political_position_data, CountryCode == 1)

 # Convert Rec_Cond and PO3_Ex to factors
 us_data$Rec_Cond <- as.factor(us_data$Rec_Cond)
 us_data$PO3_Ex <- as.factor(us_data$PO3_Ex)

 # Convert other covariates to numeric
 us_data$BiGender <- as.numeric(as.character(us_data$BiGender))
 us_data$BiEducation <- as.numeric(as.character(us_data$BiEducation))
 us_data$Bi_Age <- as.numeric(as.character(us_data$Bi_Age))

 # Verify the levels of categorical variables
 cat("Levels of Rec_Cond:", levels(us_data$Rec_Cond), "\n")
 cat("Levels of PO3_Ex:", levels(us_data$PO3_Ex), "\n")

# Print a summary to verify the data preparation
cat("Summary of us_data:\n")
print(summary(us_data))

# Load the PROCESS macro (make sure the file exists in the specified path)
source("process.R")

# Run the PROCESS macro with mcx and mcw options for multicategorical variables
results <- process(data = us_data, 
               y = "Conf_gov", 
               x = "Rec_Cond", 
               m = "STAXI", 
               w = "PO3_Ex", 
               cov = c("BiGender", "BiEducation", "Bi_Age"), 
               model = 58,
               mcx = list(Rec_Cond = levels(us_data$Rec_Cond)),
               mcw = list(PO3_Ex = levels(us_data$PO3_Ex)))

# Print the results
 print(results)

我认为这个问题很简单，因为将非数字值传递给MCW变量：

mcw= list（po3_ex = latver（us_data $ po3_ex）））检查列表的输出（po3_ex = laste（us_data $ po3_ex）））为），并确保其数据类型进程期望它是数字数据类型，因为它试图使用trunc（）函数.