Fit multiple smooth additive quantile regression models
This function fits a smooth additive regression model to several quantiles.
mqgam( form, data, qu, lsig = NULL, err = NULL, multicore = !is.null(cluster), cluster = NULL, ncores = detectCores() - 1, paropts = list(), control = list(), argGam = NULL )
form |
A GAM formula, or a list of formulae. See ?mgcv::gam details. |
data |
A data frame or list containing the model response variable and covariates required by the formula. By default the variables are taken from environment(formula): typically the environment from which gam is called. |
qu |
A vectors of quantiles of interest. Each entry should be in (0, 1). |
lsig |
The value of the log learning rate used to create the Gibbs posterior. By defauls |
err |
An upper bound on the error of the estimated quantile curve. Should be in (0, 1). If it is a vector, it should be of the
same length of |
multicore |
If TRUE the calibration will happen in parallel. |
cluster |
An object of class |
ncores |
Number of cores used. Relevant if |
paropts |
a list of additional options passed into the foreach function when parallel computation is enabled. This is important if (for example) your code relies on external data or packages: use the .export and .packages arguments to supply them so that all cluster nodes have the correct environment set up for computing. |
control |
A list of control parameters. The only one relevant here is |
argGam |
A list of parameters to be passed to |
A list with entries:
fit = a gamObject, one for each entry of qu. Notice that the
slots model and smooth of each object has been removed to save memory.
See ?gamObject.
model = the model slot of the gamObjects in the fit slot. This is the same for every
fit, hence only one copy is stored.
smooth = the smooth slot of the gamObjects in the fit slot. This is the same for every
fit, hence only one copy is stored.
calibr = a list which is the output of an internal call to tuneLearnFast, which is used for calibrating
the learning rate. See ?tuneLearnFast for details.
Matteo Fasiolo <matteo.fasiolo@gmail.com>.
Fasiolo, M., Wood, S.N., Zaffran, M., Nedellec, R. and Goude, Y., 2020. Fast calibrated additive quantile regression. Journal of the American Statistical Association (to appear). https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1725521.
#####
# Multivariate Gaussian example
####
library(qgam)
set.seed(2)
dat <- gamSim(1, n=300, dist="normal", scale=2)
fit <- mqgam(y~s(x0)+s(x1)+s(x2)+s(x3), data=dat, qu = c(0.2, 0.8))
invisible( qdo(fit, 0.2, plot, pages = 1) )
#####
# Univariate "car" example
####
library(qgam); library(MASS)
# Fit for quantile 0.8 using the best sigma
quSeq <- c(0.2, 0.4, 0.6, 0.8)
set.seed(6436)
fit <- mqgam(accel~s(times, k=20, bs="ad"), data = mcycle, qu = quSeq)
# Plot the fit
xSeq <- data.frame(cbind("accel" = rep(0, 1e3), "times" = seq(2, 58, length.out = 1e3)))
plot(mcycle$times, mcycle$accel, xlab = "Times", ylab = "Acceleration", ylim = c(-150, 80))
for(iq in quSeq){
pred <- qdo(fit, iq, predict, newdata = xSeq)
lines(xSeq$times, pred, col = 2)
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