Understanding the na.fail.default Error in R: Missing Value Handling and Data Preparation for lme Models

Background and Error Phenomenon

In statistical analysis with R, users often encounter the error message "Error in na.fail.default: missing values in object" when fitting linear mixed-effects models. This error indicates missing values in the data, but it can be confusing when the reported variable itself has no NA values. A specific case illustrates this issue.

Error Case Analysis

Consider the following data generation and model fitting code:

tot_nochc=runif(10,1,15)
cor_partner=factor(c(1,1,0,1,0,0,0,0,1,0))
age=runif(10,18,75)
agecu=age^3
day=factor(c(1,2,2,3,3,NA,NA,4,4,4))
dt=as.data.frame(cbind(tot_nochc,cor_partner,agecu,day))
attach(dt)

corpart.lme.1=lme(tot_nochc~cor_partner+agecu+cor_partner *agecu, 
                  random = ~cor_partner+agecu+cor_partner *agecu |day, 
                  na.exclude(day))

Running this code triggers the error: Error in na.fail.default(list(cor_partner = c(1L, 1L, 2L, 1L, 1L, 1L, : missing values in object. The user notes that the cor_partner variable has no missing values and the object appears as a factor, yet the error persists.

Root Cause Analysis

The core issue lies in the data preparation steps. Using the cbind() function to combine vectors coerces all inputs to the same type (often character or numeric), which can disrupt data structures, especially for factors. Specifically:

• cbind(tot_nochc, cor_partner, agecu, day) converts factors cor_partner and day to numeric or character, losing factor attributes.
• The resulting data frame from as.data.frame() may not retain original variable types, affecting model recognition.
• attach(dt) adds the data frame to the search path but can cause naming conflicts and is generally discouraged.
• In the lme() call, na.exclude(day) is misapplied; it should be used as the na.action parameter value for the entire data frame, not a single variable.

Thus, even if cor_partner has no missing values, missing values in other variables (e.g., day) mark entire observations as missing, triggering the na.fail.default error.

Solution and Correct Code

Based on the best answer, the correct approach for data preparation and model fitting is:

set.seed(101)
tot_nochc=runif(10,1,15)
cor_partner=factor(c(1,1,0,1,0,0,0,0,1,0))
age=runif(10,18,75)
agecu=age^3
day=factor(c(1,2,2,3,3,NA,NA,4,4,4))
## Use data.frame() directly to create data frame, avoiding cbind()
dt=data.frame(tot_nochc, cor_partner, agecu, day)
## Avoid attach(), specify data directly in lme()

library(nlme)
corpart.lme.1=lme(tot_nochc~cor_partner+agecu+cor_partner *agecu, 
              random = ~cor_partner+agecu+cor_partner *agecu |day, 
              data=dt,
              na.action=na.exclude)

Key improvements:

• Use data.frame() instead of cbind() to preserve original variable types and factor attributes.
• Specify the data frame via the data parameter in lme(), avoiding attach().
• Set na.action=na.exclude to handle missing values across the entire data frame properly.

Running this code may yield convergence warnings due to small sample size, but no missing value error occurs.

Supplementary References and Alternative Methods

Other answers mention using the na.roughfix function from the randomForest package for missing value imputation, e.g.:

fit_rf<-randomForest(store~.,
        data=store_train,
        importance=TRUE,
        prOximity=TRUE,
        na.action=na.roughfix)

This method imputes missing values with median/mode, suitable for machine learning models, but should be used cautiously in mixed-effects models to avoid bias.

Conclusion and Best Practices

To handle missing value errors in R effectively, consider:

1. Use data.frame() directly to create data frames, avoiding type coercion from cbind().
2. Explicitly specify the data parameter in model functions, rather than relying on attach().
3. Correctly set the na.action parameter (e.g., na.exclude, na.omit) to manage missing values.
4. Check data integrity using functions like is.na() or complete.cases() to identify missing observations.

By following these practices, you can prevent "missing values in object" errors and ensure accurate model fitting.