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.

You are watching: Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-na) cases

The table of contents is structured prefer this:


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:

The YouTube video will be added soon.

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.

See more: How To Open Explorer In Roblox Studio Explorer, Roblox Studio Explorer






*

June 10, 2021 3:45 pm

Reply






*



Get regular updates on the latest tutorials, supplies & news at Statistics Globe. I hate spam & you may opt out anytime: Privacy Policy.