newresponse.RdGeneric function and methods for extracting response variables from new data based on fitted model objects.
newresponse(object, ...)
# S3 method for default
newresponse(object, newdata, na.action = na.pass, ...)
# S3 method for glm
newresponse(object, newdata, na.action = na.pass, initialize = NULL, ...)
# S3 method for distribution
newresponse(object, newdata, ...)a fitted model object. For the default method this
needs to needs to be formula-based so that
model.frame can be used to extract the response from
the original data the model was fitted to or terms can
be used to set up the response on newdata.
further arguments passed to methods.
optionally, a data frame in which to look for variables with which to predict. If omitted, the original observations are used.
function determining how to handle missing
values in newdata, by default these are preserved.
logical. Should the response variable from glm
objects be initialized using the corresponding expression from the family?
If NULL (the default), the initialization is only used for binomial
and quasibinomial families.
A data.frame (model.frame) containing the response variable
(and optionally a variable with "(weights)").
newresponse is a convenience function that supports functions like
proscore or proresiduals which assess
discrepancies between predictions/forecasts on new data and the corresponding
observed response variables.
The default method takes an approach that is similar to many predict
methods which rebuild the model.frame after dropping the
response from the terms of a model object. However, here
only the response variable is preserved and all explanatory variables are dropped.
Missing values values are typically preserved (i.e., using na.pass).
If the new model.frame contains a variable "(weights)",
it is preserved along with the response variable(s).
A method for distribution objects is provided which expects that
newdata is essentially already the corresponding new response.
Thus, it needs to be a vector (or data frame) of the same length as distribution.
If it is not a data frame, yet, it is transformed to one but no further
modifications are made.
## linear regression model
m <- lm(dist ~ speed, data = cars)
## extract response variable on data used for model fitting
newresponse(m)
#> dist
#> 1 2
#> 2 10
#> 3 4
#> 4 22
#> 5 16
#> 6 10
#> 7 18
#> 8 26
#> 9 34
#> 10 17
#> 11 28
#> 12 14
#> 13 20
#> 14 24
#> 15 28
#> 16 26
#> 17 34
#> 18 34
#> 19 46
#> 20 26
#> 21 36
#> 22 60
#> 23 80
#> 24 20
#> 25 26
#> 26 54
#> 27 32
#> 28 40
#> 29 32
#> 30 40
#> 31 50
#> 32 42
#> 33 56
#> 34 76
#> 35 84
#> 36 36
#> 37 46
#> 38 68
#> 39 32
#> 40 48
#> 41 52
#> 42 56
#> 43 64
#> 44 66
#> 45 54
#> 46 70
#> 47 92
#> 48 93
#> 49 120
#> 50 85
## extract response variable on "new" data
newresponse(m, newdata = cars[1:3, ])
#> dist
#> 1 2
#> 2 10
#> 3 4