Introduction

This R warning usually means your prediction formula is pulling some variables from somewhere other than newdata. In other words, predict() was given a data frame with 16623 rows, but at least one variable referenced during model evaluation still points to an object of length 22488, often from the original training environment or from a badly written formula.

Why predict.glm() can see the wrong data

When you fit a GLM in R, the model stores a formula and an evaluation environment. During prediction, R tries to rebuild the model frame for newdata. If every variable in the formula is a plain column name, that usually works fine.

Problems start when the formula contains things like:

• 'df$column'
• external vectors
• objects created outside newdata
• transforms that depend on training objects in a non-portable way

Then predict() may partially use newdata and partially use objects from the old environment, causing row-count mismatches.

A common bad pattern

This is the sort of formula that creates trouble:

fit <- glm(y ~ train_df$x1 + train_df$x2, data = train_df, family = binomial())

It may appear to work during training, but at prediction time train_df$x1 and train_df$x2 are still tied to the original training object instead of the columns in newdata.

The better pattern is:

fit <- glm(y ~ x1 + x2, data = train_df, family = binomial())

Then predict(fit, newdata = test_df) can resolve x1 and x2 from test_df cleanly.

Check the formula and model frame inputs

A useful debugging step is to inspect the formula terms and ensure every predictor name exists in newdata with the expected length:

all.vars(formula(fit))
names(newdata)
nrow(newdata)

If the formula references objects that are not plain column names, that is usually the problem.

Precompute transformations into columns

Another common source of mismatch is writing formulas with awkward external transformations. Instead of relying on complex references inside the formula, create the derived columns first:

1train_df$log_income <- log(train_df$income)
2test_df$log_income  <- log(test_df$income)
3
4fit <- glm(y ~ log_income + age, data = train_df, family = binomial())
5pred <- predict(fit, newdata = test_df, type = "response")

This keeps the formula simple and keeps all prediction-time variables inside newdata.

Factor levels are a separate issue

A row-count mismatch warning is different from a factor-level mismatch, but both often appear in the same kinds of messy modeling pipelines. Once you fix the row source problem, also check that categorical predictors in newdata have compatible levels with the training data.

That does not usually cause this exact warning, but it is the next thing worth verifying in GLM prediction code.

Avoid attach() and hidden globals

If your R session used attach() or relied heavily on global variables, predict() can find objects in places you did not intend. That makes debugging much harder because the code may appear to work until a differently sized newdata frame exposes the hidden dependency.

The clean workflow is:

• explicit data= at training time
• plain column names in the formula
• all derived variables stored in the data frame

Common Pitfalls

• Writing formulas with df$column instead of plain column names.
• Letting variables come from the global environment instead of newdata.
• Using transforms that depend on objects not present in the prediction data frame.
• Relying on attach() or hidden session state.
• Debugging row counts only in newdata while ignoring where the model formula is actually sourcing variables.

Summary

• This warning usually means the prediction formula is mixing newdata columns with variables from another environment.
• Plain column names in the formula are much safer than df$column references.
• Precompute transformed predictors into the data frame before fitting and predicting.
• Keep all prediction-time variables inside newdata.
• Clean formula scoping is the real fix, not just checking nrow(newdata).