This R code throws a warning

```
# Fit regression model to each cluster
y <- list()
length(y) <- k
vars <- list()
length(vars) <- k
f <- list()
length(f) <- k
for (i in 1:k) {
vars[[i]] <- names(corc[[i]][corc[[i]]!= "1"])
f[[i]] <- as.formula(paste("Death ~", paste(vars[[i]], collapse= "+")))
y[[i]] <- lm(f[[i]], data=C1[[i]]) #training set
C1[[i]] <- cbind(C1[[i]], fitted(y[[i]]))
C2[[i]] <- cbind(C2[[i]], predict(y[[i]], C2[[i]])) #test set
}
```

I have a training data set (C1) and a test data set (C2). Each one has 129 variables. I did k means cluster analysis on the C1 and then split my data set based on cluster membership and created a list of different clusters (C1[[1]], C1[[2]], …, C1[[k]]). I also assigned a cluster membership to each case in C2 and created C2[[1]],…, C2[[k]]. Then I fit a linear regression to each cluster in C1. My dependant variable is "Death". My predictors are different in each cluster and vars[[i]] (i=1,…,k) shows a list of predictors' name. I want to predict Death for each case in test data set (C2[[1]],…, C2[[k]). When I run the following code, for some of the clusters.

**I got this warning:**

```
In predict.lm(y[[i]], C2[[i]]) :
prediction from a rank-deficient fit may be misleading
```

I read a lot about this warning but I couldn't figure out what the issue is.

## Best Answer

You can inspect the predict function with

`body(predict.lm)`

. There you will see this line:This warning checks if the rank of your data matrix is at least equal to the number of parameters you want to fit. One way to invoke it is having some collinear covariates:

Notice that x3 and x4 have the same direction in

`data`

. One is the multiple of the other. This can be checked with`length(fit$coefficients) > fit$rank`

Another way is having more parameters than available variables: