Safe predictions from a linear model

Safe predictions from a linear model

# S3 method for lm
safe_predict(object, new_data, type = c("response",
  "conf_int", "pred_int"), ..., std_error = FALSE, level = 0.95)

Arguments

object	An lm object returned from a call to stats::lm().
new_data	TODO
type	What kind of predictions to return. Options are: "response" (default): Standard predictions from linear regression. "conf_int": Fitted values plus a confidence interval for the fit. "pred_int": Predictions with accompanying prediction interval.
...	Unused. safe_predict() checks that all arguments in ... are evaluated via the ellipsis package. The idea is to prevent silent errors when arguments are mispelled. This feature is experimental and feedback is welcome.
std_error	Logical indicating whether or not calculate standard errors for the fit at each point. Not available for all models, and can be computationally expensive to compute. The standard error is always the standard error for the mean, and never the standard error for predictions. Standard errors are returned in a column called .std_error. Defaults to FALSE.
level	A number strictly between 0 and 1 to use as the confidence level when calculating confidence and prediction intervals. Setting level = 0.90 correspondings to a 90 percent confidence interval. Ignored except when type = "conf_int" or type = "pred_int". Defaults to 0.95.

Details

Do not use on model objects that only subclass lm. This will result in an error.

Examples

fit <- lm(hp ~ ., mtcars)

safe_predict(fit, mtcars)
#> # A tibble: 32 x 1
#>    .pred
#>    <dbl>
#>  1 149. 
#>  2 141. 
#>  3  80.0
#>  4 126. 
#>  5 183. 
#>  6 111. 
#>  7 228. 
#>  8  77.1
#>  9  72.7
#> 10 146. 
#> # ... with 22 more rows

mt2 <- mtcars
diag(mt2) <- NA

safe_predict(fit, mt2, std_error = TRUE)
#> # A tibble: 32 x 2
#>    .pred .pred_std_error
#>    <dbl>           <dbl>
#>  1   NA             NA  
#>  2   NA             NA  
#>  3   NA             NA  
#>  4  126.            11.9
#>  5   NA             NA  
#>  6   NA             NA  
#>  7   NA             NA  
#>  8   NA             NA  
#>  9   NA             NA  
#> 10   NA             NA  
#> # ... with 22 more rows
safe_predict(fit, mt2, type = "pred_int", level = 0.9)
#> # A tibble: 32 x 3
#>    .pred .pred_lower .pred_upper
#>    <dbl>       <dbl>       <dbl>
#>  1   NA         NA           NA 
#>  2   NA         NA           NA 
#>  3   NA         NA           NA 
#>  4  126.        76.6        175.
#>  5   NA         NA           NA 
#>  6   NA         NA           NA 
#>  7   NA         NA           NA 
#>  8   NA         NA           NA 
#>  9   NA         NA           NA 
#> 10   NA         NA           NA 
#> # ... with 22 more rows