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

 

table 1 data frame r error lm fit zero non na cases

 

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

Do you want to learn more about errors? Then I recommend watching the following video of my YouTube channel. In the video, I’m explaining the R programming code of this article:

 

The YouTube video will be added soon.

 

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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10 Comments. Leave new

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

    Reply
  • I want to make a linear reg with y, x1 and x2

    with m1 <- lm(data=data1, y ~ x1, na.action = "na.exclude") I get a result
    with m2 <- lm(data=data1, y ~ x2, na.action = "na.exclude") I get a result
    with m3 <- lm(data=data1, y ~ x1, x2, na.action = "na.exclude") I get Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, …) :
    0 (non-NA) cases

    What happens here?
    both x1 and x2 contain some NA, but only 20 of 140 observations

    Reply
    • Hey,

      It seems like the specification of your formula is incorrect. You would have to use a + sign between x1 and x2 in your third example:

      lm(data=data1, y ~ x1 + x2, na.action = "na.exclude")

      Does this fix your error?

      Regards,
      Joachim

      Reply
  • Hello!
    I get the same error when trying to perform a two-ways anova.
    My script:
    res.aov <- anova_test(
    data = Apportal, dv = score, wid = record_id,
    within = c(time, visit))

    Error message: Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, …) :
    0 (non-NA) cases

    Output: get_anova_table(res.aov)
    ANOVA Table (type III tests)

    Effect DFn DFd F p p<.05 ges
    1 visit 1 29 1.68e-30 1 1.7e-33

    I have NO missing values in any of my variables.
    Whay am I doing wrong?

    Thank you,
    Bea

    Reply
  • Hi! I actually have the same problem as Bea, only with a Repeated Measures ANOVA. I have no missing values in my data

    res.aov <- anova_test(
    data = dat, dv = e.coli, wid = id,
    within = c(site, time)
    )
    get_anova_table(res.aov)

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

    and I don't even get an output…

    id site time weight e.coli
    1 p t1 0,2297 96968256,53
    2 ww t1 0,2354 4575876,75
    3 p t1 0,1959 35296621,32

    I would appreciate any help.
    Thanks! Lisa

    Reply
  • I’m having the exact same issue as Lisa, no NAs in my data frame but this error appears when I try to run a repeated measures ANOVA in R using the same method Lisa mentioned.

    Reply
    • Hey Jess,

      Unfortunately, I have no experience with this error message when using ANOVA myself. However, I found this thread on Stack Overflow, which seems to explain the problem.

      I’m quoting from the answer of StupidWolf in this thread:

      “For repeated measure anova, you need complete observations for each time point, before and after treatment”

      Is this maybe the reason for your error?

      Regards,
      Joachim

      Reply

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