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

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

## Introduction of Example Data

```data(iris) # Constructing example data iris_new <- iris iris_new\$Species <- NA head(iris_new) # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 NA # 2 4.9 3.0 1.4 0.2 NA # 3 4.7 3.2 1.3 0.2 NA # 4 4.6 3.1 1.5 0.2 NA # 5 5.0 3.6 1.4 0.2 NA # 6 5.4 3.9 1.7 0.4 NA```

## Example 1: Replicating the Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, …) : 0 (non-NA) cases

```lm(Sepal.Length ~ ., iris_new) # lm estimation leads to error # Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : # 0 (non-NA) cases```

## Example 2: Solving the Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, …) : 0 (non-NA) cases

```lm(Sepal.Length ~ # Exclude only-NA column from model Sepal.Width + Petal.Length + Petal.Width, iris_new) # Call: # lm(formula = Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, # data = iris_new) # # Coefficients: # (Intercept) Sepal.Width Petal.Length Petal.Width # 1.8560 0.6508 0.7091 -0.5565```