midq2q: Recover Ordinary Conditional Quantiles from Conditional...

In marco-geraci/Qtools: Utilities for Quantiles

Recover Ordinary Conditional Quantiles from Conditional Mid-Quantiles

Description

This function recovers ordinary conditional quantile functions based on fitted mid-quantile regression models.

Usage

midq2q(object, newdata, observed = FALSE, ...)

Arguments

object	an object of class midrq.
newdata	a required data frame in which to look for variables with which to predict.
observed	logical flag. If TRUE, ordinary quantiles are recovered from observed sample values. Otherwise, they are calcuated as rounded mid-quantiles. See details.
...	not used.

Details

If the values of the support of the random variable are equally spaced integers, then observed should ideally be set to FALSE so that the ordinary quantile is obtained by rounding the predicted mid-quantile. Otherwise, the function returns an integer observed in the sample. See Geraci and Farcomeni for more details.

Value

a vector or a matrix of estimated ordinary quantiles. The attribute Fhat provides the corresponding estimated cumulative distribution.

Author(s)

Marco Geraci

References

Geraci, M. and A. Farcomeni. Mid-quantile regression for discrete responses. arXiv:1907.01945 [stat.ME]. URL: https://arxiv.org/abs/1907.01945.

See Also

plot.midq2q, predict.midrq

Examples

## Not run: 
# Esterase data
data(esterase)

# Fit quantiles 0.1, 0.15, ..., 0.85
fit <- midrq(Count ~ Esterase, tau = 2:17/20, data = esterase, type = 3, lambda = 0)

# Recover ordinary quantile function
xx <- seq(min(esterase$Esterase), max(esterase$Esterase), length = 5)
print(Qhat <- midq2q(fit, newdata = data.frame(Esterase = xx)))

# Plot
plot(Qhat, sub = TRUE)


## End(Not run)

marco-geraci/Qtools documentation built on Sept. 4, 2023, 7:07 p.m.