I am getting the following error when calculating VIF on a small dataset in Rstudio. Could anyone help? I can provide more information on the dataset if needed.
"Error in as.vector(y) - mean(y) non-numeric argument to binary operator".
Dataset: 80 obs. and 15 variables (all variables are numeric)
Steps Followed:
# 1. Determine correlation library(corrplot) cor.data <- cor(train) corrplot(cor.data, method = 'color') cor.data # 2. Build Model model2 <- lm(Volume~., train) summary(model2) # 3. Calculate VIF library(VIF) vif(model2) Here is a sample dataset with 20 obs.
train <- structure(list(Price = c(949, 2249.99, 399, 409.99, 1079.99, 114.22, 379.99, 65.29, 119.99, 16.99, 6.55, 15, 52.5, 21.08, 18.98, 3.6, 3.6, 174.99, 9.99, 670), X.5.Star.Reviews. = c(3, 2, 3, 49, 58, 83, 11, 33, 16, 10, 21, 75, 10, 313, 349, 8, 11, 170, 15, 20), X.4.Star.Reviews. = c(3, 1, 0, 19, 31, 30, 3, 19, 9, 1, 2, 25, 8, 62, 118, 6, 5, 100, 12, 2), X.3.Star.Reviews. = c(2, 0, 0, 8, 11, 10, 0, 12, 2, 1, 2, 6, 5, 13, 27, 3, 2, 23, 4, 4 ), X.2.Star.Reviews. = c(0, 0, 0, 3, 7, 9, 0, 5, 0, 0, 4, 3, 0, 8, 7, 2, 2, 20, 0, 2), X.1.Star.Reviews. = c(0, 0, 0, 9, 36, 40, 1, 9, 2, 0, 15, 3, 1, 16, 5, 1, 1, 20, 4, 4), X.Positive.Service.Review. = c(2, 1, 1, 7, 7, 12, 3, 5, 2, 2, 2, 9, 2, 44, 57, 0, 0, 310, 3, 4), X.Negative.Service.Review. = c(0, 0, 0, 8, 20, 5, 0, 3, 1, 0, 1, 2, 0, 3, 3, 0, 0, 6, 1, 3), X.Would.consumer.recommend.product. = c(0.9, 0.9, 0.9, 0.8, 0.7, 0.3, 0.9, 0.7, 0.8, 0.9, 0.5, 0.2, 0.8, 0.9, 0.9, 0.8, 0.8, 0.8, 0.8, 0.7), X.Shipping.Weight..lbs.. = c(25.8, 50, 17.4, 5.7, 7, 1.6, 7.3, 12, 1.8, 0.75, 1, 2.2, 1.1, 0.35, 0.6, 0.01, 0.01, 1.4, 0.4, 0.25), X.Product.Depth. = c(23.94, 35, 10.5, 15, 12.9, 5.8, 6.7, 7.9, 10.6, 10.7, 7.3, 21.3, 15.6, 5.7, 1.7, 11.5, 11.5, 13.8, 11.1, 5.8), X.Product.Width. = c(6.62, 31.75, 8.3, 9.9, 0.3, 4, 10.3, 6.7, 9.4, 13.1, 7, 1.8, 3, 3.5, 13.5, 8.5, 8.5, 8.2, 7.6, 1.4), X.Product.Height. = c(16.89, 19, 10.2, 1.3, 8.9, 1, 11.5, 2.2, 4.7, 0.6, 1.6, 7.8, 15, 8.3, 10.2, 0.4, 0.4, 0.4, 0.5, 7.8), X.Profit.margin. = c(0.15, 0.25, 0.08, 0.08, 0.09, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.15), Volume = c(12, 8, 12, 196, 232, 332, 44, 132, 64, 40, 84, 300, 40, 1252, 1396, 32, 44, 680, 60, 80)), .Names = c("Price", "X.5.Star.Reviews.", "X.4.Star.Reviews.", "X.3.Star.Reviews.", "X.2.Star.Reviews.", "X.1.Star.Reviews.", "X.Positive.Service.Review.", "X.Negative.Service.Review.", "X.Would.consumer.recommend.product.", "X.Shipping.Weight..lbs..", "X.Product.Depth.", "X.Product.Width.", "X.Product.Height.", "X.Profit.margin.", "Volume"), row.names = c(NA, 20L), class = "data.frame") 3 Answers
The vif function from the VIF package does not estimates the Variance Inflation Factor(VIF). "It selects variables for a linear model" and "returns a subset of variables for building a linear model."; see here for the description.
What you want is the vif function from the car package.
install.packages("car") library(car) vif(model2) # This should do it Edit: I won't comment specifically on the statistics side, but it seems like you have a perfect fit, something quite unusual, suggesting some problem in your data.
1You're giving vif the wrong input. It wants the response y and predictor variables x:
vif(train$Volume,subset(train,select=-Volume),subsize=19) I had to set the subsize argument to a value <= the number of observations (the default is 200).
There are 2 R libraries "car" and "VIF" which have the same function vif() defined differently. Your result/error depends on which package you have loaded in your current session.
If you use "VIF" library in the session and pass the linear model as parameter to the vif() function then you will get the error given in the initial query, as shown below:
> model1 = lm(Satisfaction~., data1) > library(VIF) Attaching package: ‘VIF’ The following object is masked from ‘package:car’: vif > vif(model1) Error in as.vector(y) - mean(y) : non-numeric argument to binary operator In addition: Warning message: In mean.default(y) : argument is not numeric or logical: returning NA If you load "car" library in R session and not "VIF", then you will get the vif numbers as expected for a linear model as shown below:
> model1 = lm(Satisfaction~., data1) > library(car) Loading required package: carData Attaching package: ‘car’ The following object is masked from ‘package:psych’: logit > vif(model1) ProdQual Ecom TechSup CompRes Advertising ProdLine SalesFImage ComPricing 1.635797 2.756694 2.976796 4.730448 1.508933 3.488185 3.439420 1.635000 WartyClaim OrdBilling DelSpeed 3.198337 2.902999 6.516014 All the columns in data1 are numeric. Hope that helps
