How to fix this error:
Note that we can use the sapply() and lapply() functions together to count the number of unique values present in each of the predictor variables.
Example:
Here, Using lapply() function we can even print the values present in the individual predictor variables.
R
# Create a data frame dataframe <- data.frame(parameter1=c(8, 1, 13, 24, 9), parameter2=as.factor(6), parameter3=c(17, 9, 18, 13, 12), parameter4=c(12, 21, 32, 4, 19)) # Find the unique values for each variable sapply(lapply(dataframe, unique), length) |
Output:
Output
Example:
Now from the below code, we can see that parameter2 contains only one unique value. Hence, we can fix this error by simply removing parameter2 from the regression model.
R
# Create a data frame dataframe <- data.frame(parameter1=c(8, 1, 13, 24, 9), parameter2=as.factor(6), parameter3=c(17, 9, 18, 13, 12), parameter4=c(12, 21, 32, 4, 19)) # Find the unique values for each variable lapply(dataframe[c('parameter1', 'parameter2', 'parameter3', 'parameter4')], unique) |
Output:
Example:
Hence, by removing parameter2, the program compiled successfully without any error.
R
# Create a data frame dataframe <- data.frame(parameter1=c(8, 1, 13, 24, 9), parameter2=as.factor(6), parameter3=c(17, 9, 18, 13, 12), parameter4=c(12, 21, 32, 4, 19)) # Fit regression model using all the predictor variables # except parameter2 model <- lm(parameter4 ~ parameter1 + parameter3, data=dataframe) # Display model summary summary(model) |
Output:
How to Fix in R: Contrasts can be applied only to factors with 2 or more levels.
In this article, we will discuss how we can fix “contrasts can be applied only to factors with 2 or more levels” error in the R programming language.
Contrasts can be applied only to factors with 2 or more levels:
It is a common error produced by the R compiler. The complete form of this error is given below:
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
contrasts can be applied only to factors with 2 or more levels
Such an error is produced by the R compiler when we try to fit a regression model with the help of the predictor variable that is either a character or factor and contains only one unique value.
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