Description Usage Arguments Details Value Note Author(s) See Also Examples
Predictions from an object of class “iqr
”.
1 2 
object 
an object of class “ 
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". 
se 
logical. If TRUE (the default), standard errors of the prediction will be computed. Only used if type = "beta" or type = "QF". 
... 
for future methods. 
Using iqr
, quantile regression coefficients
β(p) are modeled as parametric functions of p, the order of the quantile.
This implies that the model parameter is not β(p) itself.
The function predict.iqr
permits computing β(p) and other
quantities of interest, as detailed below.
if type = "beta" (the default), β(p) is returned at the supplied value(s) of p. If p is missing, a default p = (0.01, ..., 0.99) is used.
if type = "CDF", the value of the fitted CDF (cumulative distribution function) and PDF (probability density function) are computed.
if type = "QF", the fitted values x'β(p), corresponding to the conditional quantile function, are computed at the supplied values of p.
if type = "sim", data are simulated from the fitted model. To simulate the data, the fitted conditional quantile function is computed at randomly generated p following a Uniform(0,1) distribution.
if type = "beta" a list with one item for each covariate in the model. Each element of the list is a data frame with columns (p, beta, se, low, up) reporting β(p), its estimated standard error, and the corresponding 95% confidence interval. If se = FALSE, the last three columns are not computed.
if type = "CDF", a twocolumns data frame (CDF,PDF).
if type = "QF" and se = FALSE, a data frame with one row for each observation, and one column for each value of p. If se = TRUE, a list of two data frames, fit (predictions) and se.fit (standard errors).
if type = "sim", a vector of simulated data.
Prediction may generate quantile crossing
if the support of the new covariates values supplied in newdata
is different from that of the observed data.
Paolo Frumento [email protected]
iqr
, for model fitting; summary.iqr
and plot.iqr
,
for summarizing and plotting iqr
objects.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  # using simulated data
n < 1000
x < runif(n)
y < rlogis(n, 1 + x, 1 + x)
# true quantile function: Q(p  x) = beta0(p) + beta1(p)*x, with
# beta0(p) = beta1(p) = 1 + log(p/(1  p))
model < iqr(y ~ x, formula.p = ~ I(log(p)) + I(log(1  p)))
# (fit asymmetric logistic distribution)
# 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(x = c(.1,.2,.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(x = 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)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.