Prediction from fitted frfast model — predict.frfast • npregfast

Takes a fitted frfast object and produces predictions (with their 95% confidence intervals) from a fitted model with interactions or without interactions.

Usage

# S3 method for frfast
predict(object = model, newdata, fac = NULL, der = NULL, seed = NULL, ...)

Arguments

object: A fitted frfast object as produced by frfast().
newdata: A data frame containing the values of the model covariates at which predictions are required. If newdata is provided, then it should contain all the variables needed for prediction: a warning is generated if not.
fac: Factor's level to take into account. By default is NULL.
der: Number which determines any inference process. By default der is NULL. If this term is 0, the function returns the initial estimate. If it is 1 or 2, it is designed for the first or second derivative, respectively.
seed: Seed to be used in the bootstrap procedure.
...: Other options.

Value

predict.frfast computes and returns a list containing predictions of the estimates, first and second derivative, with their 95% confidence intervals.

Author

Marta Sestelo, Nora M. Villanueva and Javier Roca-Pardinas.

Examples

library(npregfast)
data(barnacle)

# Nonparametric regression without interactions
fit <- frfast(DW ~ RC, data = barnacle, nboot = 100)
nd <- data.frame(RC = c(10, 14, 18))
predict(fit, newdata = nd)
#> $Estimation
#>           Pred       Lwr       Upr
#> [1,] 0.2170758 0.2127310 0.2246697
#> [2,] 0.5761761 0.5683398 0.5836086
#> [3,] 1.2611420 1.2445001 1.2785451
#> 
#> $First_deriv
#>            Pred        Lwr        Upr
#> [1,] 0.06050908 0.05847311 0.06255974
#> [2,] 0.12648909 0.12199960 0.13158479
#> [3,] 0.20630283 0.19366221 0.21788740
#> 
#> $Second_deriv
#>             Pred          Lwr         Upr
#> [1,] 0.005990312  0.005517764 0.006306726
#> [2,] 0.009904393  0.008934122 0.010635841
#> [3,] 0.002984966 -0.000233695 0.006756137
#> 
#> attr(,"class")
#> [1] "predict.frfast"

# Nonparametric regression with interactions
# fit2 <- frfast(DW ~ RC : F, data = barnacle, nboot = 100)
# nd2 <- data.frame(RC = c(10, 15, 20))
# predict(fit2, newdata = nd2)
# predict(fit2, newdata = nd2, der = 0, fac = "barca")