qrcmNP (version 0.2.1)

predict.niqr: Prediction After Nonlinear Quantile Regression Coefficients Modeling

Description

Predictions from an object of class “niqr”.

Usage

# S3 method for niqr
predict(object, type=c("beta", "CDF", "QF", "sim"), newdata, p, ...)

Arguments

object

an object of class “niqr”, the result of a call to niqr.

type

a character string specifying the type of prediction. See ‘Details’.

newdata

an optional data frame in which to look for variables with which to predict. If omitted, the data are used. For type = "CDF", it must include the response variable. Ignored if type = "beta".

p

a numeric vector indicating the order(s) of the quantile to predict. Only used if type = "beta" or type = "QF".

...

for future methods.

Author

Gianluca Sottile

Details

Different type of prediction from the model.

See Also

niqr, for model fitting; testfit.niqr, to do goodness of fit test; summary.niqr and plot.niqr, for summarizing and plotting niqr objects.

Examples

# using simulated data

n <- 300
x <- runif(n)
fun <- function(theta, p){
  beta0 <- theta[1] + exp(theta[2]*p)
  beta1 <- theta[3] + theta[4]*p
  cbind(beta0, beta1)}
beta <- fun(c(1,1,1,1), runif(n))
y <- beta[, 1] + beta[, 2]*x
model <- niqr(fun=fun, x0=rep(0, 4), X=cbind(1,x), y=y)

# predict beta(0.25), beta(0.5), beta(0.75)
predict(model, type = "beta", p = c(0.25,0.5, 0.75))

# predict the CDF and the PDF at new values of x and y
predict(model, type = "CDF",
        newdata = data.frame(X1=runif(3), y = c(1,2,3)))

# computes the quantile function at new x, for p = (0.25,0.5,0.75)
predict(model, type = "QF", p = c(0.25,0.5,0.75),
        newdata = data.frame(X1=runif(3), y = c(1,2,3)))

# simulate data from the fitted model
ysim <- predict(model, type = "sim") # 'newdata' can be supplied

# if the model is correct, the distribution of y and that of ysim should be similar
qy <- quantile(y, prob = seq(.1,.9,.1))
qsim <- quantile(ysim, prob = seq(.1,.9,.1))
plot(qy, qsim); abline(0,1)