Description Usage Arguments Value Estimating uncertainty Examples
Safe predictions from a multiple linear model object
1 2  ## S3 method for class 'mlm'
safe_predict(object, new_data, type = c("response"), ...)

object 
An 
new_data 
Required. A data frame or matrix containing the necessary predictors. 
type 
What kind of predictions to return. Options are:

... 
Unused. 
A tibble::tibble()
with one row for each row of new_data
.
Predictions for observations with missing data will be NA
. Returned
tibble has different columns depending on type
:
"response"
:
univariate outcome: .pred
(numeric)
multivariate outcomes: .pred_{outcome name}
(numeric) for each
outcome
"class"
: .pred_class
(factor)
"prob"
: .pred_{level}
columns (numerics between 0 and 1)
"link"
: .pred
(numeric)
"conf_int"
: .pred
, .pred_lower
, .pred_upper
(all numeric)
"pred_int"
: .pred
, .pred_lower
, .pred_upper
(all numeric)
If you request standard errors with std_error = TRUE
, an additional
column .std_error
.
For interval predictions, the tibble has additional attributes level
and interval
. The level
is the same as the level
argument and is
between 0 and 1. interval
is either "confidence"
or "prediction"
.
Some models may also set a method
attribute to detail the method
used to calculate the intervals.
'stats::predict.mlm()“ provides neither confidence nor prediction intervals, although there is not theoretical issue with calculating these.
At some point in the future we may implement these intervals within
safepredict
. If you are interested in this, you can move intervals
for mlm
objects up the priority list by opening an issue on
Github.
1 2 3 4 5 6 7 8  fit < lm(cbind(hp, mpg) ~ ., mtcars)
safe_predict(fit, mtcars)
mt2 < mtcars
diag(mt2) < NA
safe_predict(fit, mt2)

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