Use a scalar-on-function linear regression model for prediction
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, newdata = NULL, alpha = 0.05)
object |
A list obtained as output from |
newdata |
An object of class |
alpha |
A numeric value indicating the Type I error
for the regression control chart
and such that this function returns the |
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,
* upr:
upper limit of the 1-alpha prediction interval
on the response.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.