`R/02_sof_pc.R`

`predict_sof_pc.Rd`

Predict new observations of the scalar response variable and calculate the corresponding prediction error, with prediction interval limits, given new observations of functional covariates and a fitted scalar-on-function linear regression model

```
predict_sof_pc(
object,
y_new = NULL,
mfdobj_x_new = NULL,
alpha = 0.05,
newdata
)
```

- object
A list obtained as output from

`sof_pc`

, i.e. a fitted scalar-on-function linear regression model.- y_new
A numeric vector containing the new observations of the scalar response variable to be predicted.

- mfdobj_x_new
An object of class

`mfd`

containing new observations of the functional covariates. If NULL, it is set as the functional covariates data used for model fitting.- alpha
A numeric value indicating the Type I error for the regression control chart and such that this function returns the

`1-alpha`

prediction interval on the response. Default is 0.05.- newdata
Deprecated, use

`mfdobj_x_new`

argument.

A `data.frame`

with as many rows as the
number of functional replications in `newdata`

,
with the following columns:

* `fit`

: the predictions of the response variable
corresponding to `new_data`

,

* `lwr`

:
lower limit of the `1-alpha`

prediction interval
on the response, based on the assumption that it is normally distributed.

* `upr`

:
upper limit of the `1-alpha`

prediction interval
on the response, based on the assumption that it is normally distributed.

* `res`

:
the residuals obtained as the values of `y_new`

minus their
fitted values. If the scalar-on-function model has been fitted with
`type_residual == "studentized"`

, then the studentized residuals
are calculated.

```
library(funcharts)
data("air")
air <- lapply(air, function(x) x[1:10, , drop = FALSE])
fun_covariates <- c("CO", "temperature")
mfdobj_x <- get_mfd_list(air[fun_covariates], lambda = 1e-2)
y <- rowMeans(air$NO2)
mod <- sof_pc(y, mfdobj_x)
predict_sof_pc(mod)
#> fit lwr upr pred_err y
#> 1 7.328329 7.238309 7.418348 -0.0183013627 7.310027
#> 2 7.363446 7.270688 7.456204 0.0120441880 7.375490
#> 3 7.388644 7.305105 7.472182 -0.0204670518 7.368177
#> 4 7.359027 7.263627 7.454426 0.0143399689 7.373367
#> 5 7.519710 7.427195 7.612224 0.0052219043 7.524931
#> 6 7.453269 7.375114 7.531425 -0.0099885559 7.443281
#> 7 7.442536 7.358799 7.526274 -0.0005674598 7.441969
#> 8 7.446190 7.370298 7.522082 -0.0194755345 7.426714
#> 9 7.404424 7.326028 7.482819 0.0447843608 7.449208
#> 10 7.384681 7.304250 7.465112 -0.0075904572 7.377091
```