# R Error in lm.fit(x, y, offset, singular.ok , …) : 0 (non-NA) cases (2 Examples)

In this article, I’ll illustrate how to debug the “Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, …) : 0 (non-NA) cases” in the R programming language.

The table of content is structured like this:

Let’s get started…

## Example Data

Let’s first construct some example data:

```set.seed(9364593) # Create example data data <- data.frame(y = rnorm(100), x1 = rnorm(100), x2 = NA) head(data) # Head of example data``` Have a look at the previous table. It reveals that our example data is constructed of 100 rows and three columns.

## Example 1: Reproduce the Error in lm.fit – 0 (non-NA) cases

The following R syntax illustrates how to replicate the “Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, …) : 0 (non-NA) cases” in the R programming language.

Let’s assume that we want to estimate a linear regression model using the lm function in R. Then, we might try to use the following R syntax:

```lm(y ~ ., data) # Estimate model based on entire data set # Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : # 0 (non-NA) cases```

Unfortunately, the RStudio console returns the error message “0 (non-NA) cases”.

The reason for this is that one (or multiple) of our data frame columns contains only NA values. The lm function (and other modelling functions as well) can not handle such only NA predictors.

So how can we solve this problem?

## Example 2: Fix the Error in lm.fit – 0 (non-NA) cases

The following R code explains how to deal with the “Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, …) : 0 (non-NA) cases”.

To avoid this message, we have to remove all independent variables that contain only missing values from our model.

In the following R code, I’m explicitly specifying that I want to use only the column x1 as predictor variable:

```lm(y ~ x1, data) # Estimate model based on subset # Call: # lm(formula = y ~ x1, data = data) # # Coefficients: # (Intercept) x1 # -0.15534 0.06922```

As you can see, the lm function has returned a valid output without any error messages.

## Video, Further Resources & Summary

Furthermore, you might want to read the other posts on this homepage.

At this point you should know how to handle the “Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, …) : 0 (non-NA) cases” in R. If you have any additional comments and/or questions, let me know in the comments section.

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• Rakshathi
June 10, 2021 3:45 pm

how to handle when I still get this error despite having independent variables which have no NAs whatsoever?