---
title: "predsplot_examples"
author: "Rousseeuw, P.J."
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{predsplot_examples}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
 fig.width  = 5 ,
 fig.height = 3.5,
 fig.align  = 'center'
)
oldpar <- list(mar = par()$mar, mfrow = par()$mfrow)
```


```{r}
library(classmap)
```

# TopGear data


We start by loading the data, and selecting the relevant features.
As pre-processing, we set values of accel==0 to missing because it is impossible.
We also remove the incorrect weight of the Peugeot 107.
```{r}
data(TopGear, package = "robustHD")
cars = TopGear; rm(TopGear)
dim(cars) # [1] 297  32
rownames(cars) = paste(cars[,1],cars[,2])
# Now the rownames are make and model of the cars.
colnames(cars)
colnames(cars)[4:17] = c("fuel","price","cyl","displ","drive",
                         "hp","torque","accel","topspeed",
                         "MPG","weight","length","width",
                         "height")
summary(cars$accel) # zero minimum is impossible
rownames(cars)[which(cars$accel == 0)]
# [1] "Citroen C5 Tourer" "Ford Mondeo"   "Lotus Elise"
# [4] "Renault Twizy"     "Ssangyong Rodius"
cars$accel[cars$accel == 0] = NA
summary(cars$accel) # OK

cars$weight[order(cars$weight)[1:4]]
rownames(cars)[order(cars$weight)[1:4]]
cars$weight[cars$weight == 210] = NA
summary(cars$weight) # OK
# save(cars, file = "Topgear.RData")
# load("Topgear.RData")
```

## Numerical example in the introduction


```{r}
summary(cars$topspeed) # in mph
summary(cars$displ)    # in cc
summary(cars$length)   # in mm
cars$length = cars$length/1000 # in meters

fit = lm(hp ~ topspeed + length + displ, data = cars)
summary(fit)

predsplot(fit, main = "Top Gear data")

fact = 0.51
width = fact*10; height = fact*8
maxnchar = 6
main = "Top Gear data"
pdf(file = "topgear_numerical_predsplot.pdf",
    width = width, height = height)
predsplot(fit, main=main, maxchar.level = maxnchar)
dev.off()


predscor(fit, sort.by.stdev = FALSE) 
# fact = 0.6
# width = fact*10; height = fact*8
# pdf(file = "topgear_numerical_predscor.pdf", 
#     width = height, height = height)
# predscor(fit, sort.by.stdev = FALSE)
# dev.off()

```

## Figure 2 and TopGear figures in Supplementary Material:


```{r}
cars$GPM = 1/cars$MPG

fit4 = lm(GPM ~ accel + drive + weight + fuel, data = cars)
summary(fit4)

## Figure 2:
## 
fact = 0.52
width = fact*10; height = fact*8
maxnchar = 6
main = "Top Gear data"
# pdf(file = "topgear_4_predsplot.pdf",
#     width = width, height = height)
predsplot(fit4, main=main, maxchar.level = maxnchar)
# dev.off()
```


## Three figures in the Supplementary Material:

```{r}
## 
# fact = 0.6
# width = fact*10; height = fact*8
# pdf(file = "topgear_4_predscor.pdf", 
#     width = height, height = height)
predscor(fit4, sort.by.stdev = FALSE)
# dev.off()

car = "Bentley Continental"
# pdf(file = "topgear_4_predsplot_1_dens.pdf",
#     width = width, height = height)
predsplot(fit4, main = main, casetoshow=car, 
          displaytype = "density", 
          maxchar.level = maxnchar, 
          xlab = paste0("prediction for ",car))
# dev.off()

car = "Kia Rio"
# pdf(file = "topgear_4_predsplot_2_stairs.pdf",
#     width = width, height = height)
predsplot(fit4, main = main, casetoshow=car, 
          staircase = TRUE, maxchar.level = maxnchar, 
          xlab = paste0("prediction for ",car))
# dev.off()
```

## Test combination of expressions and types

We add a logical variable (AlarmSystem) as well as a character variable (SatNav)

```{r}
summary(cars$AlarmSystem) 
cars$alarm = (cars$AlarmSystem == "standard")
summary(cars$alarm)

summary(cars$SatNav)
cars$navig = "y"
cars$navig[cars$SatNav == "no"] = "n"
summary(cars$navig)

table(cars$navig)
class(cars$navig) 
str(cars)

fitcombin = lm(1/MPG ~ accel + log(weight) + accel:torque + alarm + navig, data = cars)
summary(fitcombin)

fact = 0.45
width = fact*10; height = fact*8
main = "Example with transformation, interaction, logical, character"
# pdf(file = "topgear_logical+char_predsplot.pdf", width = width, height = height)
predsplot(fitcombin, main=main)
# dev.off()  

fact = 0.6
width = fact*10; height = fact*8
# pdf(file = "topgear_logical+char_predscor.pdf",
#     width = height, height = height)
predscor(fitcombin)
# dev.off()

```

# Titanic data


We start by loading the data, turning the pclass and sex variables into factors.

```{r}
data(data_titanic, package = "classmap")
titanic = data_titanic; rm(data_titanic)
dim(titanic) # 1309  13
# A data frame with 1309 observations on the following variables.
#   passengerId: a unique identifier for each passenger.
#   pclass: travel class of the passenger.
#   name: name of the passenger.
#   sex: sex of the passenger.
#   age: age of the passenger.
#   sibsp: number of siblings and spouses traveling with the passenger.
#   parch: number of parents and children traveling with the passenger.
#   ticket: ticket number of the passenger.
#   fare: fare paid for the ticket.
#   Cabin: cabin number of the passenger.
#   embarked: Port of embarkation. Takes the values
#             C (Cherbourg), Q (Queenstown) and
#             S (Southampton).
#   y: factor indicating casualty or survivor.
#   dataType: vector taking the values “train” or “test”
#             indicating whether the observation belongs
#             to training or test data.
#
colnames(titanic) = c("passengerId","pclass","name","sex",
                   "age", "sibsp", "parch", "ticket",
                   "fare", "cabin", "embarked", "survival",
                   "dataType")
str(titanic)

unique(titanic$pclass) # 3 1 2
titanic$pclass = factor(titanic$pclass, levels = unique(titanic$pclass))
#

unique(titanic$sex) 
titanic$sex = factor(titanic$sex, levels = unique(titanic$sex) )
titanic$sex = factor(titanic$sex, labels = c("M","F"))
head(titanic)
# save(titanic, file="Titanic.RData")
# load("Titanic.RData")
```

Now fit a logistic regression model.

```{r}
fit5 <- glm(survival ~ sex + age + sibsp + parch + pclass,
            family=binomial, data = titanic)
summary(fit5)
```

## Figure 3:

```{r} 
fact = 0.5
width = fact*10; height = fact*8
main = "Titanic data"
# pdf(file = "titanic_5_predsplot_dens.pdf",
#     width = width, height = height)
predsplot(fit5, main=main, displaytype = "density")

# The glm fit used the logit link function.
# dev.off()

# With other options for density estimation:
predsplot(fit5, main=main, displaytype = "density",
          bw = "SJ", adjust = 0.5)
```


## Three figures in Supplementary Material:

```{r} 
# pdf(file = "titanic_5_predsplot_1.pdf",
#     width = width, height = height)
predsplot(fit5, main = main, casetoshow=1)
# dev.off()

# pdf(file = "titanic_5_predsplot_2_stairs.pdf",
#     width = width, height = height)
predsplot(fit5, main = main, casetoshow=2, staircase = TRUE)
# dev.off()

# fact = 0.6
# width = fact*10; height = fact*8
# pdf(file = "titanic_5_predscor.pdf", 
#     width = height, height = height)
predscor(fit5, sort.by.stdev = FALSE)
# dev.off()
```

# German credit data


```{r}
data(german.credit, package = "fairml")
credit = german.credit; rm(german.credit)
dim(credit) # 1000 21
str(credit)
#
# Shorten variable names for plotting:
colnames(credit) <- c("checking","months","history","purpose","amount","savings","employment","rate","guarantors","residence","property","age","inst_plans","housing","nloans","job","nclients","phone","foreign","credit","sex")
#
# Give factors sex and purpose shorter labels for plotting:
credit$sex = factor(as.numeric(credit$sex), labels = c("F","M"))
levels(credit$purpose)
# [10] "retrainin"
levels(credit$purpose) = c("business","n.car","u.car","appliances","education","furniture","other","TV","repairs","retraining")
# save(credit, file = "German_credit.RData")
# load("German_credit.RData")
```

Now fit a logistic regression model

```{r}
fit7 <- glm(credit ~ months + purpose + amount + rate + age +
            nclients + sex, family=binomial, data = credit)
```

 
## Figure in Supplementary Material:

```{r} 
main = "German credit data"
# fact = 0.48
# width = fact*10; height = fact*8
# pdf(file = "germancredit_7_predsplot.pdf",
#     width = width, height = height)
predsplot(fit7, main = main)       
# dev.off()
```


## Figure 4:
```{r} 
# fact = 0.48
# width = fact*10; height = fact*8
# pdf(file = "germancredit_7_predsplot_1.pdf",
#     width = width, height = height)
predsplot(fit7, main = main, casetoshow=1, 
          displaytype = "density")
# dev.off()
```

## Figure 5:
```{r} 
# pdf(file = "germancredit_7_predsplot_2_stairs.pdf",
#     width = width, height = height)
predsplot(fit7, main = main, casetoshow=2, staircase = TRUE) 
# dev.off()
```

## Figure 6:

```{r}
# fact = 0.6
# width = fact*10; height = fact*8
# pdf(file = "germancredit_7_predscor_equalsizes.pdf", 
#     width = height, height = height)
predscor(fit7, sort.by.stdev = FALSE, cell.length = "equal")
# dev.off()
#
# pdf(file = "germancredit_7_predscor.pdf", 
#     width = height, height = height)
predscor(fit7, sort.by.stdev = FALSE)
# dev.off()
```

## Two figures in Supplementary Material:
```{r} 
## Prediction for a new case:
newc = list("u.car", 36, 2, 6000, 55, "F", 1)
names(newc) = c("purpose","months","rate","amount",
                "age","sex","nclients")
# fact = 0.48
# width = fact*10; height = fact*8
# pdf(file = "germancredit_7_predsplot_new_stairs.pdf",
#     width = width, height = height)
predsplot(fit7, main = main, casetoshow = newc, 
          staircase = TRUE)
# dev.off()

## Figure with profile:
# pdf(file = "germancredit_7_predsplot_1_dens_profile.pdf",
#     width = width, height = height)
predsplot(fit7, main = main, casetoshow=1, 
               displaytype = "density", profile = TRUE)
# dev.off()

```

# Artificial example to illustrate high correlation:


```{r}
credit$x1 = credit$months + credit$nclients
credit$x2 = credit$months - credit$nclients
cor(credit$x1,credit$x2) # 0.998199
# But minus that for prediction terms!

fitart <- glm(credit ~ x1 + x2 + purpose + amount + rate + age +
           sex, family=binomial, data = credit)

## Figure 7:
## 
# fact = 0.48
# width = fact*10; height = fact*8
# main = "German credit data with artificial variables x1 and x2"
# pdf(file = "germancredit_7_artif_predsplot.pdf", 
#     width = width, height = height)
predsplot(fitart, main = main)
# dev.off()

## Figure 8:
## 
# fact = 0.6
# width = fact*10; height = fact*8
# pdf(file = "germancredit_7_artif_predscor.pdf", 
#     width = height, height = height)
predscor(fitart, sort.by.stdev = FALSE)
# dev.off()

```


.
