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

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 set.seed(9364593) # develop example datadata data.frame(y = rnorm(100), x1 = rnorm(100), x2 = NA)head(data) # Head of example data
set.seed(9364593) # develop example datadata example 1: give birth the Error in lm.fit – 0 (non-NA) cases

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

Let’s assume the we desire to estimate a linear regression model using the lm duty in R. Then, us might try to usage the complying with R syntax:

 lm(y ~ ., data) # calculation model based upon entire data set# Error in lm.fit(x, y, balance out = offset, singular.ok = singular.ok, ...) : # 0 (non-NA) cases
lm(y ~ ., data) # estimate model based upon 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 ours data framework columns contains only NA values. The lm function (and other modelling features as well) deserve to not handle such only NA predictors.

So how deserve to we fix this problem?

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

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

To stop this message, we have to remove every independent variables that contain only absent values from ours model.

In the following R code, I’m explicitly specifying the I desire to use just the shaft x1 as predictor variable:

 lm(y ~ x1, data) # calculation model based upon subset# Call:# lm(formula = y ~ x1, data = data)# # Coefficients:# (Intercept) x1 # -0.15534 0.06922
lm(y ~ x1, data) # estimate model based upon 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 kind of error messages.

## Video, further Resources & Summary

Do you desire to learn an ext about errors? Then ns recommend city hall the following video clip of my YouTube channel. In the video, ns explaining the R programming code of this article:

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

At this allude you need to 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 extr comments and/or questions, allow me understand in the comments section.

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