One error you may encounter in R is:

Error in model.frame.default(formula = y ~ x, drop.unused.levels = TRUE) : invalid type (list) for variable 'x'

This error usually occurs when you attempt to fit a regression model or an ANOVA model in R and use a **list** for one of the variables instead of a **vector**.

This tutorial shares how to fix this error in practice.

**How to Reproduce the Error **

Suppose I attempt to fit a simple linear regression model in R:

**#define variables
x <- list(1, 4, 4, 5, 7, 8, 9, 10, 13, 14)
y <- c(10, 13, 13, 14, 18, 20, 22, 24, 29, 31)
#attempt to fit regression model
model <- lm(y ~ x)
Error in model.frame.default(formula = y ~ x, drop.unused.levels = TRUE) :
invalid type (list) for variable 'x'
**

I receive an error because the **lm()** function can only take vectors as input and the x variable is currently a list.

**How to Avoid the Error**

The easiest way to avoid this error is to simply use the unlist() function to convert the list variable to a vector:

**#define variables
x <- list(1, 4, 4, 5, 7, 8, 9, 10, 13, 14)
y <- c(10, 13, 13, 14, 18, 20, 22, 24, 29, 31)
#attempt to fit regression model
model <- lm(y ~ unlist(x))
#view the model output
summary(model)
Call:
lm(formula = y ~ unlist(x))
Residuals:
Min 1Q Median 3Q Max
-1.1282 -0.4194 -0.1087 0.2966 1.7068
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.58447 0.55413 11.88 2.31e-06 ***
unlist(x) 1.70874 0.06544 26.11 4.97e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8134 on 8 degrees of freedom
Multiple R-squared: 0.9884, Adjusted R-squared: 0.987
F-statistic: 681.8 on 1 and 8 DF, p-value: 4.97e-09
**

Notice that we’re able to fit the simple linear regression model without any errors this time because we used **unlist()** to convert variable x to a vector.

Note that if you’re fitting a multiple linear regression model and you have multiple predictor variables that are currently list objects, you can use **unlist()** to convert each of them to vectors before fitting the regression model:

**#define variables
x1 <- list(1, 4, 4, 5, 7, 8, 9, 10, 13, 14)
x2 <- list(20, 16, 16, 15, 16, 12, 10, 8, 8, 4)
y <- c(10, 13, 13, 14, 18, 20, 22, 24, 29, 31)
#fit multiple linear regression model
model <- lm(y ~ unlist(x1) + unlist(x2))
#view the model output
summary(model)
Call:
lm(formula = y ~ unlist(x1) + unlist(x2))
Residuals:
Min 1Q Median 3Q Max
-1.1579 -0.4211 -0.1386 0.3108 1.7130
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.34282 4.44971 1.875 0.102932
unlist(x1) 1.61339 0.24899 6.480 0.000341 ***
unlist(x2) -0.08346 0.20937 -0.399 0.702044
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8599 on 7 degrees of freedom
Multiple R-squared: 0.9887, Adjusted R-squared: 0.9854
F-statistic: 305.1 on 2 and 7 DF, p-value: 1.553e-07**

Once again we don’t receive any errors since we converted each of the list objects to vectors.

**Additional Resources**

The following tutorials explain how to perform other common operations in R:

How to Interpret glm Output in R

How to Interpret ANOVA Results in R

How to Handle R Warning: glm.fit: algorithm did not converge