Check if mtrx() will give matching results to model.matrix()

When the following are true, mtrx() returns a matrix that matches that returned by model.matrix() :

There are no non-default factor contrasts.
Every column is one of a numeric, integer, character, or factor.
The na.action option is "na.pass".

In that case, in code where model.matrix() results in slowdowns, one can write:

if (mtrx_compatible(data)) {
  res <- mtrx(formula, data)
} else {
  res <- model.matrix(formula, data)
}

Usage

mtrx_compatible(data, na_action = getOption("na.action"))

Arguments

data: A data frame.
na_action: What to do about missing values–see na.pass(). Can be either a function or character, just as the option can be.

Value

A logical value. Returns TRUE if mtrx() will return a similar matrix to model.matrix(), and FALSE otherwise.

Details

In this case, "matching" output means that dummy variables will be encoded in the same way, missing values will be handled in the same way (returned as-is), and that mtrx() won't error due to the presence of unsupported column types.

Examples

# Compatible data frame
df1 <- data.frame(a = 1:3, b = letters[1:3], c = factor(letters[1:3]))
mtrx_compatible(df1)
#> [1] TRUE

# Incompatible due to non-default contrasts
df2 <- df1
contrasts(df2$c) <- "contr.sum"
mtrx_compatible(df2)
#> [1] FALSE

# Incompatible due to unsupported column type
df3 <- data.frame(a = 1:3, b = as.Date(c("2023-01-01", "2023-01-02", "2023-01-03")))
mtrx_compatible(df3)
#> [1] FALSE

# Missing values are problematic only when na_action is not `na.pass`
df4 <- data.frame(a = c(1, NA, 3), b = letters[1:3])
mtrx_compatible(df4, na_action = "na.omit")
#> [1] FALSE
mtrx_compatible(df4, na_action = "na.pass")
#> [1] TRUE