---
title: "qgam: quantile non-parametric additive models"
date: '`r format(Sys.Date(), "%B %d %Y")`'
author: "Matteo Fasiolo, Simon N. Wood, Yannig Goude, and Raphael Nedellec"
output: 
  html_document:
    toc: true
    number_sections: true
vignette: >
    %\VignetteEngine{knitr::rmarkdown}
    %\VignetteIndexEntry{quantile_mgcViz}
    %\VignetteEncoding{UTF-8}
---



```{r setup, include=FALSE}
library(knitr)
opts_chunk$set(out.extra='style="display:block; margin: auto"', fig.align="center", tidy=FALSE)
```

This R package offers methods for fitting additive quantile regression models based on splines, using the methods described in [Fasiolo et al., 2017](https://arxiv.org/abs/1707.03307).

The main fitting functions are:

   - `qgam()` fits an additive quantile regression model to a single quantile. Very similar to `mgcv::gam()`. It returns an object of class `qgam`, which inherits from `mgcv::gamObject`.
   - `mqgam()` fits the same additive quantile regression model to several quantiles. It is more efficient that calling `qgam()` several times, especially in terms of memory usage.
   - `tuneLearn()` useful for tuning the learning rate of the Gibbs posterior. It evaluates a calibration loss function on a grid of values provided by the user. 
   - `tuneLearnFast()` similar to `tuneLearn()`, but here the learning rate is selected by minimizing the calibration loss, using Brent method.
   
A first example: smoothing the motorcycle dataset
=======================

Let's start with a simple example. Here we are fitting a regression model with an adaptive spline basis to quantile 0.8 of the motorcycle dataset.
```{r 1, message = F}
library(qgam); library(MASS)
if( suppressWarnings(require(RhpcBLASctl)) ){ blas_set_num_threads(1) } # Optional

fit <- qgam(accel~s(times, k=20, bs="ad"), 
            data = mcycle, 
            qu = 0.8)

# Plot the fit
xSeq <- data.frame(cbind("accel" = rep(0, 1e3), "times" = seq(2, 58, length.out = 1e3)))
pred <- predict(fit, newdata = xSeq, se=TRUE)
plot(mcycle$times, mcycle$accel, xlab = "Times", ylab = "Acceleration", ylim = c(-150, 80))
lines(xSeq$times, pred$fit, lwd = 1)
lines(xSeq$times, pred$fit + 2*pred$se.fit, lwd = 1, col = 2)
lines(xSeq$times, pred$fit - 2*pred$se.fit, lwd = 1, col = 2)   
```
`qgam` automatically calls `tuneLearnFast` to select the learning rate. The results of the calibrations are stored in `fit$calibr`. We can check whether the optimization succeded as follows:
```{r 2}
check(fit$calibr, 2)
```
The plot suggest that the calibration criterion has a single minimum, and that the optimizer has converged to its neighbourhood. Alternatively, we could have selected the learning rate by evaluating the loss function on a grid.
```{r 3, message = F}
set.seed(6436)
cal <- tuneLearn(accel~s(times, k=20, bs="ad"), 
                 data = mcycle, 
                 qu = 0.8,
                 lsig = seq(1, 3, length.out = 20), 
                 control = list("progress" = "none")) #<- sequence of values for learning rate
                 
check(cal)
```
Here the generic `check` function produces a different output. The first plot is the calibration criterion as a function of $log(\sigma)$, which should look fairly smooth. The second plot shows how the effective degrees of freedom (EDF) vary with $log(\sigma)$. Notice that here we are using an adaptive smoother, which includes five smoothing parameters. 

We might want to fit several quantiles at once. This can be done with `mqgam`.
```{r 4}
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)
```

To save memory `mqgam` does not return one `mgcv::gamObject` for each quantile, but it avoids storing some redundant data (such as several copies of the design matrix). The output of `mqgam` can be manipulated using the `qdo` function.

```{r 5}
# Plot the data
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))

# Predict each quantile curve and plot
for(iq in quSeq){
  pred <- qdo(fit, iq, predict, newdata = xSeq)
  lines(xSeq$times, pred, col = 2)
}
```

Using `qdo` we can print out the summary for each quantile, for instance: 
```{r 6}
# Summary for quantile 0.4
qdo(fit, qu = 0.4, summary)
```
Notice that here the generic function `summary` is calling `summary.gam`, because `summary.qgam` has not been implemented yet. Hence one cannot quite rely on the p-value provided by this function, because their are calculated using result that apply to parametric, not quantile, regression.

Dealing with heteroscedasticity
=======================
  
Let us simulate some data from an heteroscedastic model.
```{r h1}
set.seed(651)
n <- 2000
x <- seq(-4, 3, length.out = n)
X <- cbind(1, x, x^2)
beta <- c(0, 1, 1)
sigma =  1.2 + sin(2*x)
f <- drop(X %*% beta)
dat <- f + rnorm(n, 0, sigma)
dataf <- data.frame(cbind(dat, x))
names(dataf) <- c("y", "x")
   
qus <- seq(0.05, 0.95, length.out = 5)
plot(x, dat, col = "grey", ylab = "y")
for(iq in qus){ lines(x, qnorm(iq, f, sigma)) }
```

We now fit ten quantiles between 0.05 and 0.95, using a quantile GAM with scalar learning rate.
```{r h2}
fit <- mqgam(y~s(x, k = 30, bs = "cr"), 
             data = dataf,
             qu = qus)
             
qus <- seq(0.05, 0.95, length.out = 5)
plot(x, dat, col = "grey", ylab = "y")
for(iq in qus){ 
 lines(x, qnorm(iq, f, sigma), col = 2)
 lines(x, qdo(fit, iq, predict))
}
legend("top", c("truth", "fitted"), col = 2:1, lty = rep(1, 2))
```

With the exception of `qu = 0.95`, the fitted quantiles are close to the true ones, but their credible intervals don't vary much with x. Indeed, let's look at intervals for quantile 0.95.
```{r h3}
plot(x, dat, col = "grey", ylab = "y")
tmp <- qdo(fit, 0.95, predict, se = TRUE)
lines(x, tmp$fit)
lines(x, tmp$fit + 3 * tmp$se.fit, col = 2)
lines(x, tmp$fit - 3 * tmp$se.fit, col = 2)
```

We can get better credible intervals, and solve the "wigglines" problem for the top quantile, by letting the learning rate vary with the covariate. In particular, we can use an additive model for quantile location and one for learning rate.
```{r h4}
fit <- qgam(list(y~s(x, k = 30, bs = "cr"), ~ s(x, k = 30, bs = "cr")), 
            data = dataf, qu = 0.95)

plot(x, dat, col = "grey", ylab = "y")
tmp <- predict(fit, se = TRUE)
lines(x, tmp$fit)
lines(x, tmp$fit + 3 * tmp$se.fit, col = 2)
lines(x, tmp$fit - 3 * tmp$se.fit, col = 2)
```
Now the credible intervals correctly represent the underlying uncertainty, and the fit has the correct amount of smoothness.

Neglecting to take the heteroscedasticity into account can lead to bias, in addition to inadequate coverage of the credible intervals. Let's go back the motorcycle data set, and to the first model we fitted:
```{r mcy2rnd, message = F}
fit <- qgam(accel~s(times, k=20, bs="ad"), 
            data = mcycle, 
            qu = 0.8)

# Plot the fit
xSeq <- data.frame(cbind("accel" = rep(0, 1e3), "times" = seq(2, 58, length.out = 1e3)))
pred <- predict(fit, newdata = xSeq, se=TRUE)
plot(mcycle$times, mcycle$accel, xlab = "Times", ylab = "Acceleration", ylim = c(-150, 80))
lines(xSeq$times, pred$fit, lwd = 1)
lines(xSeq$times, pred$fit + 2*pred$se.fit, lwd = 1, col = 2)
lines(xSeq$times, pred$fit - 2*pred$se.fit, lwd = 1, col = 2)   
```

The slightly disturbing thing about this quantile fit is that for `Times < 10` the fit is clearly above all the responses. But we are fitting quantile 0.8, hence we should expect around 20$\%$ of the responses to be above the fit. The problem here is that the variance of the response (`accel`) varies wildly with `Times`, so that the bias induced by the smoothed pinball loss used by `qgam` is not constant (see Fasiolo et al. 2017 for details). This issue is solved by letting the learning rate change with `Times`: 
```{r mcy2rnd2, message = F}
fit <- qgam(list(accel ~ s(times, k=20, bs="ad"), ~ s(times)),
            data = mcycle, 
            qu = 0.8)

pred <- predict(fit, newdata = xSeq, se=TRUE)
plot(mcycle$times, mcycle$accel, xlab = "Times", ylab = "Acceleration", ylim = c(-150, 80))
lines(xSeq$times, pred$fit, lwd = 1)
lines(xSeq$times, pred$fit + 2*pred$se.fit, lwd = 1, col = 2)
lines(xSeq$times, pred$fit - 2*pred$se.fit, lwd = 1, col = 2)  
```


Model checking
=======================

The `qgam` package provides some functions that can be useful for model checking, but a more complete set of visualisation and checking tools can be found in the `mgcViz` R package (Fasiolo et al., 2018). In `qgam` we have:

   - `cqcheck` if we are fitting, say, quantile 0.2 we expect roughly $20\%$ of the observations to fall below the fitted quantile. This function produces some plots to verify this.
   - `cqcheckI` interactive version of `cqcheckI`. Implemented using the `shiny` package. Not demonstrated here, 
                but see `?cqcheckI`.
   - `check.qgam` provides some diagnostics regarding the optimization. Mainly based to `gam.check`.
   - `check.learn` diagnostic checks to verify that the learning rate selection went well. It can be used
                   on the output of `tuneLearn`.
   - `check.tuneLearn` similar to `check.learn`, but it can be used on the output of `tuneLearn` or on the                              `$calibr` slot of a `qgam` object.

We start by illustrating the `cqcheck` function. In particular, let us consider the additive model:
$$
y \sim x+x^2+z+xz/2+e,\;\;\; e \sim N(0, 1)
$$
We start by simulating some data from it.
```{r c1}
library(qgam)
set.seed(15560)
n <- 1000
x <- rnorm(n, 0, 1); z <- rnorm(n)
X <- cbind(1, x, x^2, z, x*z)
beta <- c(0, 1, 1, 1, 0.5)
y <- drop(X %*% beta) + rnorm(n) 
dataf <- data.frame(cbind(y, x, z))
names(dataf) <- c("y", "x", "z")
```

We fit a linear model to the median and we use `cqcheck` produce a diagnostic plot. 
```{r c2}
qu <- 0.5
fit <- qgam(y~x, qu = qu, data = dataf)
cqcheck(obj = fit, v = c("x"), X = dataf, y = y) 
```

The `cqcheck` function takes a `qgam` object as input and it predicts the conditional quantile using the data in `X`. Then it bins the responses `y` using the corresponding values of `v` and it calculates, for every bin, what fraction of responses falls below the fitted quantile. Given that we are fitting the median, we would expect that around $50\%$ of the point falls below the fit. But, as the plot shows, this fraction varies widely along `x`. There is clearly a non-linear relation between the quantile location and `x`, hence we add a smooth for `x`.
```{r c3, message = F}
fit <- qgam(y~s(x), qu = qu, data = dataf)
cqcheck(obj = fit, v = c("x"), X = dataf, y = y)
```

The deviations from the theoretical quantile ($0.5$) are much reduced, but let's look across both `x` and `z`. 
```{r c4, message = F}
cqcheck(obj = fit, v = c("x", "z"), X = dataf, y = y, nbin = c(5, 5))
```

This plot uses binning as before, if a bin is red (green) this means that the fraction of responses falling below the fit is smaller (larger) than 0.5. Bright colours means that the deviation is statistically significant. As we move along `z` (`x2` in the plot) the colour changes from green to red, so it make sense drawing a marginal plot for `z`:
```{r c5, message = F}
cqcheck(obj = fit, v = c("z"), X = dataf, y = y, nbin = c(10))
```

We are clearly missing an effect here. Given that effect looks pretty linear, we simply add a parametric term to the fit, which seems to solve the problem:
```{r c6, message = F}
fit <- qgam(y~s(x)+z, qu = qu, data = dataf)
cqcheck(obj = fit, v = c("z"))
```

But if we look again across both `x` and `z` we see that green prevails on the top-left to bottom-right
diagonal, while the other diagonal is mainly red.
```{r c7, message = F}
cqcheck(obj = fit, v = c("x", "z"), nbin = c(5, 5))
```

This suggests that adding an interaction between `x` and `z` might be a good idea. Indeed, now `cqcheck` does not signal any problem:
```{r c8, message = F}
fit <- qgam(y~s(x)+z+I(x*z), qu = qu, data = dataf)
cqcheck(obj = fit, v = c("x", "z"), nbin = c(5, 5))
```

Now that we are fairly satisfied with the model structure, we can, for instance, fit several quantiles
by doing: 
```{r c9, message = F}
fit <- mqgam(y~s(x)+z+I(x*z), qu = c(0.2, 0.4, 0.6, 0.8), data = dataf)
```

We can then check whether the learning rate was selected correctly. Recall that the `qgam` function calls internally `tuneLearnFast`, hence we can look at how the calibration went by doing:
```{r c10, message = F}
check.learnFast(fit$calibr, 2:5)
```

For each quantile, the calibration loss seems to have a unique minimum, which is what one would hope. Objects of class `qgam` can also be checked using the generic function `check`, which defaults to `check.qgam`. To use this function on the output of `mqgam`, we must use the `qdo` function:
```{r c11, message = F}
qdo(fit, 0.2, check)
```

The printed output gives some information about the optimizer used to estimate the smoothing parameters, for fixed learning rate. See `?check.qgam` for more information. The plot has been obtained using `cqcheck`, where each data point has been binned using the fitted values. On the right side of the plot there seems to be some large deviations, but the rug shows that there are very few data points there.  


Setting the loss-smoothing parameter and checking convergence
=======================

Let's simulate some data:
```{r check1, message = F}
set.seed(5235)
n <- 1000
x <- seq(-3, 3, length.out = n)
X <- cbind(1, x, x^2)
beta <- c(0, 1, 1)
f <- drop(X %*% beta)
dat <- f + rgamma(n, 4, 1)
dataf <- data.frame(cbind(dat, x))
names(dataf) <- c("y", "x")
```
Assume that we want to estimate quantiles 0.05, 0.5 and 0.95:
```{r check2, message = F}
qus <- c(0.05, 0.5, 0.95)
fit <- mqgam(y ~ s(x), data = dataf, qu = qus)

plot(x, dat, col = "grey", ylab = "y")
lines(x, f + qgamma(0.95, 4, 1), lty = 2)
lines(x, f + qgamma(0.5, 4, 1), lty = 2)
lines(x, f + qgamma(0.05, 4, 1), lty = 2)
lines(x, qdo(fit, qus[1], predict), col = 2)
lines(x, qdo(fit, qus[2], predict), col = 2)
lines(x, qdo(fit, qus[3], predict), col = 2)
```

Since `qgam` version 1.3 the parameter `err`, which determines the smoothness of the loss function used by `qgam`, is determined automatically. But there might be scenarios where you might want to chose is manually, so let's try to use several values of `err`:
```{r check2b, message = F}
lfit <- lapply(c(0.01, 0.05, 0.1, 0.2, 0.3, 0.5),
               function(.inp){
                 mqgam(y ~ s(x), data = dataf, qu = qus, err = .inp,
                       control = list("progress" = F))
               })

plot(x, dat, col = "grey", ylab = "y", ylim = c(-2, 20))
colss <- rainbow(length(lfit))
for(ii in 1:length(lfit)){
  lines(x, qdo(lfit[[ii]], qus[1], predict), col = colss[ii])
  lines(x, qdo(lfit[[ii]], qus[2], predict), col = colss[ii])
  lines(x, qdo(lfit[[ii]], qus[3], predict), col = colss[ii])
}
lines(x, f + qgamma(0.95, 4, 1), lty = 2)
lines(x, f + qgamma(0.5, 4, 1), lty = 2)
lines(x, f + qgamma(0.05, 4, 1), lty = 2)
```
The bias increases with `err`, and it is upward (downward) for high (low) quantiles. The median fit is not much affected by `err`. The bias really starts appearing for `err > 0.1`. Decreasing `err` tends to slow down computation:
```{r check3, message = F}
system.time( fit1 <- qgam(y ~ s(x), data = dataf, qu = 0.95, err = 0.05,
                           control = list("progress" = F)) )[[3]]
system.time( fit2 <- qgam(y ~ s(x), data = dataf, qu = 0.95, err = 0.001,
                           control = list("progress" = F)) )[[3]]
```
Even worse, it can lead to numeric problems. Here we check that we have found the minimum of the calibration loss:
```{r check4, message = F}
check(fit1$calibr, sel = 2)
check(fit2$calibr, sel = 2)
```
In the first case the loss looks smooth and with as single minimum, in the second case we have some instabilities. If the calibration loss looks like this, you generally have to increase `err`.

We can use `check` to have an estimate of the bias and to have information regarding the convergence of the smoothing parameter estimation routine:
```{r check5, message = F}
check(fit1)
```
The second plot suggest that the actual bias is much lower than the bound `err = 0.05`. This is also supported by the first two lines of text, which say that 95.1\% of the residuals are negative, which is very close to the theoretical 95\%. The text says that full convergence in smoothing parameter estimation has been achieved, it is important to check this.

In summary, practical experience suggests that:

   - the automatic procedure for selecting `err` offer a good compromise between bias and stability;
   - the old default (`qgam` version < 1.3) was `err = 0.05`, which generally does not imply too much bias;
   - if the calibration loss plotted by `check(fit$learn)` is irregular, try to increase `err`;
   - same if the text printed by `check(fit)` does not say that `full convergence` was achieved;
   - you can estimate the bias using `check(fit)`;
   - if you have to increase `err` to 0.2 or higher, there might be something wrong with your model;
   - you might get messages saying that `outer Newton did not converge fully` during estimation. This might
     not be problematic as long as the calibration loss is smooth and `full convergence` was achieved;
   - in preliminary studies do not decrease `err` too much, as it slows down computation;
   - setting `err` too low is not a good idea: it is much better to have some bias than
     numerical problems.
     

Application to probabilistic electricity load forecasting
=======================

Here we consider a UK electricity demand dataset, taken from the national grid [website](https://www.nationalgrid.com/). The dataset covers the period January 2011 to June 2016 and it contains the following variables:

   - `NetDemand` net electricity demand between 11:30am and 12am.
   - `wM` instantaneous temperature, averaged over several English cities.
   - `wM_s95` exponential smooth of `wM`, that is `wM_s95[i] = a*wM[i] + (1-a)*wM_s95[i]` with `a=0.95`.
   - `Posan` periodic index in `[0, 1]` indicating the position along the year.
   - `Dow` factor variable indicating the day of the week.
   - `Trend` progressive counter, useful for defining the long term trend.
   - `NetDemand.48` lagged version of `NetDemand`, that is `NetDemand.48[i] = NetDemand[i-2]`.
   - `Holy` binary variable indicating holidays.
   - `Year` and `Date` should obvious, and partially redundant.

See [Fasiolo et al., 2017](https://arxiv.org/abs/1707.03307) for more details. This is how the demand over the period looks like:
```{r edf1}
data("UKload")
tmpx <- seq(UKload$Year[1], tail(UKload$Year, 1), length.out = nrow(UKload)) 
plot(tmpx, UKload$NetDemand, type = 'l', xlab = 'Year', ylab = 'Load')
```

To estimate the median demand, we consider the following model
```{r edf2}
qu <- 0.5
form <- NetDemand~s(wM,k=20,bs='cr') + s(wM_s95,k=20,bs='cr') + 
        s(Posan,bs='ad',k=30,xt=list("bs"="cc")) + Dow + s(Trend,k=4) + NetDemand.48 + Holy
```

Notice that we use very few knots for the long term trend, this is because we don't want to end up interpolating the data. We use an adaptive cyclic smooth for `Posan`, we'll explain later why adaptivity is needed here. 

Now we tune the learning rate on a grid, on two cores. As the first plot shows, the calibrations loss is minimized at $\log (\sigma)\approx 6$, the second plot shows how the effective degrees of freedom of each smooth term changes with $\log (\sigma)$.
```{r edf3, message=FALSE}
set.seed(41241)
sigSeq <- seq(4, 8, length.out = 16)
closs <- tuneLearn(form = form, data = UKload, 
                   lsig = sigSeq, qu = qu, control = list("K" = 20), 
                   multicore = TRUE, ncores = 2)

check(closs)
```

Now let's fit the model with the learning rate corresponding to the lowest loss and let's look at the resulting smooth effects. 
```{r edf4}
lsig <- closs$lsig
fit <- qgam(form = form, data = UKload, lsig = lsig, qu = qu)
plot(fit, scale = F, page = 1)
```

The effect of temperature (`wM`) is minimized around 18 degrees, which is reasonable. The cyclic effect of `Posan` has a very sharp drop corresponding to the winter holidays, we used an adaptive smooth in order to have more flexibility during this period. Now we can have a look as some diagnostic plot:
```{r edf5}
par(mfrow = c(2, 2))
cqcheck(fit, v = c("wM"), main = "wM")
cqcheck(fit, v = c("wM_s95"), main = "wM_s95")
cqcheck(fit, v = c("Posan"), main = "Posan")
cqcheck(fit, v = c("Trend"), main = "Trend", xaxt='n')
axis(1, at = UKload$Trend[c(1, 500, 1000, 1500, 2000)], 
             UKload$Year[c(1, 500, 1000, 1500, 2000)] )
```

The plots for `wM_s95` and `Posan` don't show any important deviation from 0.5, the target quantile. Along `wM` we see a large deviation, but we have essentially no data for very high temperatures. If we look at deviations along the `Trend` variable, which is just a time counter, we see several important deviations. It would be interesting verifying why these occur (we have no answer currently).

Finally, recall that we can produce 2D versions of these diagnostic plots, for instance:
```{r edf6}
par(mfrow = c(1, 1))
cqcheck(fit, v = c("wM", "Posan"), scatter = T)
```


References
=======================
  
  * Fasiolo, M., Goude, Y., Nedellec, R. and Wood, S. N. (2017). Fast calibrated additive quantile regression. Available at https://arxiv.org/abs/1707.03307
  
  * Fasiolo, M., Nedellec, R., Goude, Y. and Wood, S.N. (2018). Scalable visualisation methods for modern Generalized Additive Models. Available at https://arxiv.org/abs/1809.10632















