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 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 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 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