---
title: "Structure and functionality"
output: rmarkdown::html_vignette
date: "Last compiled on `r format(Sys.time(), '%Y-%m-%d')`"
author: "André Menezes"
vignette: >
  %\VignetteIndexEntry{Structure and functionality}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.align = "center",
  fig.width = 12,
  fig.height = 6)
```

## Introduction

The `unitquantreg` R package provide efficient tools for estimation and inference
in parametric quantile regression models for bounded data.

The current version of `unitquantreg` has 11 probability distributions available
for user choice. The Table above lists the families of distributions,
their abbreviations and the paper reference.

```{r, echo=FALSE}
fam_name <- c("unit-Weibull", "Kumaraswamy", "unit-Logistic", "unit-Chen",
              "unit-Birnbaum-Saunders", "log-extended Exponential-Geometric",
              "unit-Generalized Half-Normal-E", "unit-Generalized Half-Normal-X",
              "unit-Gompertz", "unit-Burr-XII", "Johnson-SB",
              "arc-secant hyperbolic Weibull", "unit-Gumbel")
abbrev_name <- c("uweibull", "kum", "ulogistic", "uchen", "ubs", "leeg", "ughne",
                 "ughnx", "ugompertz", "uburrxii", "johnsonsb", "ashw", "ugumbel")
refs <- c("[Mazucheli, et al. (2018)](http://japs.isoss.net/13(2)1%2011046.pdf)",
          "[Kumaraswamy, (1980)](https://www.sciencedirect.com/science/article/abs/pii/0022169480900360)",
          "[Tadikamalla and Johnson (1982)](https://www.jstor.org/stable/2335422)",
          "[Korkmaz, et al. (2020)](https://doi.org/10.1515/ms-2022-0052)",
          "[Mazucheli, et al. (2021)](https://www.mdpi.com/2073-8994/13/4/682)",
          "[Jodrá and Jiménez-Gamero (2020)](https://doi.org/10.57805/revstat.v18i4.309)",
          "[Korkmaz MÇ (2020)](https://www.scientificbulletin.upb.ro/rev_docs_arhiva/full6b9_464742.pdf)",
          "New",
          "[Mazucheli et al. (2019)](https://rivista-statistica.unibo.it/article/view/8497)",
          "[Korkmaz and Chesneau (2021)](https://link.springer.com/article/10.1007/s40314-021-01418-5)",
          "[Johnson (1949)](https://www.jstor.org/stable/2332539)",
          "[Korkmaz et al. (2021)](https://www.tandfonline.com/doi/full/10.1080/02664763.2021.1981834)",
          "New")
tab <- data.frame(fam_name, abbrev_name, refs)
knitr::kable(tab[order(tab$abbrev_name), ], col.names = c("Family", "Abbreviation", "Reference"),
             caption = "Available families of distributions their abbreviations and reference.",
             label = "distributions", row.names = FALSE)
```

The [`dpqr`]'s functions of the distributions are vectorized and implemented in `C++`.
The log likelihood, score and hessian functions are also implemented in `C++` in order
to guarantee more computational efficiency.

The parameter estimation and inference are performed under the frequentist paradigm.
Maximization of the log-likelihood function is done by optimization techniques available
in the `R` through the [`optimx`](https://CRAN.R-project.org/package=optimx) 
package, which is a general-purpose optimization wrapper function that allows the use of
several `R` tools for optimization, including the existing `stats::optim()` function.
To achieve quick and better convergence the analytical score function is use during the
maximization. Also, standard errors of parameter estimates are computed using
the analytical hessian matrix.

## Structure

The `unitquantreg` package is built around the `unitquantreg()` function which
perform the fit of parametric quantile regression models via likelihood method.
The `unitquantreg()` function has standard arguments as `stats::glm()` function,
and they are as follows:

```{r structure, echo=FALSE}
library(unitquantreg)
args(unitquantreg)
```

The `formula` argument use the concept of [`Formula`](https://CRAN.R-project.org/package=Formula)
package allows multiple parts on the right-hand side, which indicates regression structure
for the quantile and shape parameter of the distribution. For instance,
`formula = y ~ x1 | z1` means the following regression structure

$$
g_1(\mu) = \beta_0 + \beta_1\,x_1 \quad \textrm{and} \quad g_2(\theta) = \gamma_0 + \gamma_1\,z_1
$$
where $\mu$ indicates the quantile of order $\tau$ and $\theta$ is the shape parameter.

The `tau` argument indicates the quantile(s) to be estimated, being possible to specify a
vector of quantiles. `family` argument specify the distribution family using the
abbreviation of `dpqr` functions, listed in Table above.

The `control` argument controls the fitting process through the `unitquantreg.control()`
function which returned a `list` and the default values are:
```{r}
unlist(unitquantreg.control())
```

The two most important arguments are `hessian` and `gradient` which tell the `optimx::optimx()`
whether it should use the numerical hessian matrix and the analytical score vector,
respectively. That is, if `hessian = TRUE`, then the standard errors are computed using
the numerical hessian matrix. For detailed description of other arguments see the package
documentation.

### Object-oriented programming 

The `unitquanreg()` function returns an object of class `unitquanreg` if the
argument `tau` is scalar or `unitquanregs` if `tau` is a vector.
The currently methods implemented for `unitquantreg` objects are:
```{r methods-unitquantreg}
methods(class = "unitquantreg")
```

And for the `unitquantregs` objects are
```{r methods-unitquantregs}
methods(class = "unitquantregs")
```

It is important to mention that the `unitquantregs` objects consists of a list
with `unitquantreg` objects for according to the vector of `tau`.


Furthermore, the package provide functions designated for model comparison
between `uniquantreg` objects. Particularly,

- `likelihood_stats()` function computes likelihood-based statistics (Neg2LogLike, AIC, BIC and HQIC),

- `vuong.test()` function performs Vuong test between two fitted **non nested** models,

- `pairwise.vuong.test()` function performs pairwise Vuong test with adjusted p-value according to `stats::p.adjust.methods`
between fitted models 


Finally, `uniquantreg` objects also permits use the inference methods functions 
`lmtest::coeftest()`, `lmtest::coefci`, `lmtest::coefci`, `lmtest::waldtest` and `lmtest::lrtest`
implemented in [`lmtest`](https://CRAN.R-project.org/package=lmtest) to
perform hypothesis test, confidence intervals for **nested models**.

Next, a detailed account of the usage of all these functions is provided.

## Functionality

As in [Mazucheli et al. (2020)](https://www.tandfonline.com/doi/abs/10.1080/02664763.2019.1657813?journalCode=cjas20)
consider the data set related to the access of people in
households with piped water supply in the cities of Brazil from the Southeast and Northeast
regions. The response variable `phpws` is the proportion of households with piped water
supply. The covariates are:

- `mhdi`: human development index.

- `incpc`: per capita income.

- `region`: 0 for southeast, 1 for northeast.

- `pop`: population.

```{r water-data}
data(water)
head(water)
```

Assuming the following regression structure for the parameters:
$$
\textrm{logit}(\mu_i) = \beta_0 + \beta_1 \texttt{mhdi}_{i1} + \beta_2 \texttt{incp}_{i2} +
\beta_3 \texttt{region}_{i3} + \beta_4 \log\left(\texttt{pop}_{i4}\right),
$$
and
$$
\log(\theta_i) = \gamma_0.
$$
for $i = 1, \ldots, 3457$.

### Model fitting

For $\tau = 0.5$, that is, the median regression model we fitted for all families of distributions as follows:
```{r fitting}
lt_families <- list("unit-Weibull" = "uweibull",
                    "Kumaraswamy" = "kum",
                    "unit-Logistic" = "ulogistic",
                    "unit-Birnbaum-Saunders" = "ubs",
                    "log-extended Exponential-Geometric" = "leeg",
                    "unit-Chen" = "uchen",
                    "unit-Generalized Half-Normal-E" = "ughne",
                    "unit-Generalized Half-Normal-X" = "ughnx",
                    "unit-Gompertz" = "ugompertz",
                    "Johnson-SB" = "johnsonsb",
                    "unit-Burr-XII" = "uburrxii",
                    "arc-secant hyperbolic Weibull" = "ashw",
                    "unit-Gumbel" = "ugumbel")
lt_fits <- lapply(lt_families, function(fam) {
  unitquantreg(formula = phpws ~ mhdi + incpc + region + log(pop),
               data = water, tau = 0.5, family = fam, link = "logit",
               link.theta = "log")
})
t(sapply(lt_fits, coef))
```

### Model comparison

Let's check the likelihood-based statistics of fit
```{r like-stats}
likelihood_stats(lt = lt_fits)
```

According to the statistics the unit-Logistic, Johnson-SB, unit-Burr-XII and unit-Weibull
were the best models. Now, let's perform the pairwise [vuong test](https://en.wikipedia.org/wiki/Vuong%27s_closeness_test)
to check if there is statistical significant difference between the four models.

```{r, vuong-tests}
lt_chosen <- lt_fits[c("unit-Logistic", "Johnson-SB", "unit-Burr-XII", "unit-Weibull")]
pairwise.vuong.test(lt = lt_chosen)
```

The adjusted p-values of pairwise Vuong tests shows that there is a large statistical 
significance difference between the models. In particular, the pairwise comparison between
unit-Logistic and the other models provide a smaller p-values, indicating that the
unit-Logistic median regression model is the most suitable model for this data set
comparing to the others families of distributions.

### Diagnostic analysis

It is possible to check model assumptions from diagnostic plots using the `plot()`
function method for `unitquantreg` objects.
The `residuals()` method provides `quantile`, `cox-snell`, `working` and `partial`
residuals type. The randomize quantile residuals is the default choice of `plot()`
method.


```{r plots-diagnostic, cache=TRUE}
oldpar <- par(no.readonly = TRUE)
par(mfrow = c(2, 2))
plot(lt_fits[["unit-Logistic"]])
par(oldpar)
```

Plots of the residuals against the fitted linear predictor and
the residuals against indices of observations are the tools for diagnostic analysis to
check the structural form of the model.
Two features of the plots are important:

- Trends: Any trends appearing in these plots indicate that the systematic component
can be improved. This could mean changing the link function, adding extra explanatory
variables, or transforming the explanatory variables.

- Constant variation: If the random component is correct then the variance of the points
is approximately constant.

Working residuals versus linear predictor is used to check possible misspecification of
link function and Half-normal plot of residuals to check distribution assumption.

Another best practice in diagnostic analysis is to inspect the (Half)-Normal plots with
simulated envelope for several quantile value. This is done to obtain a more robust 
evaluation of the model assumptions.
Thus, let's fit the unit-Logistic quantile regression model for various quantiles.

```{r fits-ulogistic, cache=TRUE}
system.time(
  fits_ulogistic <- unitquantreg(formula = phpws ~ mhdi + incpc + region + log(pop),
                                 data = water, tau = 1:49/50,
                                 family = "ulogistic", link = "logit",
                                 link.theta = "log"))
```

Now, we can check the (Half)-Normal plots using the output of `hnp()` method.
```{r plots-hnp, cache=TRUE}
library(ggplot2)
get_data <- function(obj) {
  tmp <- hnp(obj, halfnormal = FALSE, plot = FALSE, nsim = 10)
  tmp <- as.data.frame(do.call("cbind", tmp))
  tmp$tau <- as.character(obj$tau)
  tmp
}
chosen_taus <- c("0.02", "0.5", "0.98")
df_plot <- do.call("rbind", lapply(fits_ulogistic[chosen_taus], get_data))
df_plot$tau <- paste0(expression(tau), " == ", df_plot$tau)

ggplot(df_plot, aes(x = teo, y = obs)) +
  facet_wrap(~tau, labeller = label_parsed) +
  geom_point(shape = 3, size = 1.4) +
  geom_line(aes(y = median), linetype = "dashed") +
  geom_line(aes(y = lower), col = "#0080ff") +
  geom_line(aes(y = upper), col = "#0080ff") +
  theme_bw() +
  labs(x = "Theoretical quantiles", y = "Randomized quantile residuals") +
  scale_x_continuous(breaks = seq(-3, 3, by = 1)) +
  scale_y_continuous(breaks = seq(-3, 3, by = 1)) +
  theme_bw() +
  theme(text = element_text(size = 16, family = "Palatino"), 
        panel.grid.minor = element_blank())
```


### Inference results

Inference results about the parameter estimates can be accessed through the
`summary` method. For instance,

```{r summary-fits}
summary(lt_fits[["unit-Logistic"]])
```

For `unitquantregs` objects the `plot` method provide a convenience to check the
significance as well as the effect of estimate along the specify quantile value.

```{r plot-ulogistic}
plot(fits_ulogistic, which = "coef")
```

Curiously, the unit-Logistic quantile regression models capture constant effect for all
covariates along the different quantiles. In contrast, the unit-Weibull model
(the fourth best model) found a decrease effect of `mhdi` covaraite on the response as
the quantile increases and increase effects of `incpc` and `region` on the response
variable as the quantile increases.

```{r fits-uweibull, cache=TRUE}
system.time(
  fits_uweibull <- unitquantreg(formula = phpws ~ mhdi + incpc + region + log(pop),
                                 data = water, tau = 1:49/50,
                                 family = "uweibull", link = "logit",
                                 link.theta = "log"))
plot(fits_uweibull, which = "coef")
```


Using the `plot()` method with argument `which = "conddist"` for `unitquantregs` objects
it is possible to 
estimate and visualize the conditional distribution of a response variable at
different values of covariates. For instance,

```{r plot-conddis}
lt_data <- list(mhdi = c(0.5, 0.7), incpc = round(mean(water$incpc)),
                region = c(1, 0), pop = round(mean(water$pop)))
plot(fits_ulogistic, which = "conddist", at_obs = lt_data, at_avg = FALSE,
     dist_type = "density")
plot(fits_ulogistic, which = "conddist", at_obs = lt_data, at_avg = FALSE,
     dist_type = "cdf")
```


## Session info

```{r seesion-info}
sessionInfo()
```




