inst/doc/introduction_unifed.R

In unifed: The Unifed Distribution

## ----results="hide"-----------------------------------------------------------
library(unifed)
dunifed( c(0.1,0.3,0.7), 10)

x <- c(0.1,0.4,0.7,1)
punifed(x,-5)

p <- 1:9/10
qunifed(p,5)

runifed(20,-3.3)

## ----echo=F,fig.align='center'------------------------------------------------
library(unifed)

add.theta.plot <- function(theta,mu,right=F,...){
    N <- 100
    x <- seq(0,1,length.out=N)
    y <- dunifed(x,theta=theta)
    points(x,y,type="l",...)
    label <- substitute(expression(mu == val),env=list(val=mu))
    if(right)
        text(x[N],y[N],labels=eval(label),pos="2",cex=0.8)
    else
        text(x[1],y[1],labels=eval(label),pos="4",cex=0.8)
       
}

unifed.density.plots1 <- function(){
    par(mar=c(2,2,0.5,0.5))
    plot(x=c(0,1),y=c(0,10),type="n",xlab="",ylab="",ylim=c(0,10.2),xlim=c(-0.05,1))
    mu.vector <- seq(0.1,0.5,by=0.1)
    theta.vector <- unifed.kappa.prime.inverse(mu.vector)
    for(i in 1:length(theta.vector)){
        theta <- add.theta.plot(theta.vector[i],mu.vector[i],lty=i)}
    
}

unifed.density.plots2 <- function(){
    par(mar=c(2,2,0.5,0.5))
    plot(x=c(0,1),y=c(0,10),type="n",xlab="",ylab="",ylim=c(0,10.2),xlim=c(0,1.05))
    mu.vector <- seq(0.5,0.9,by=0.1)
    theta.vector <- unifed.kappa.prime.inverse(mu.vector)
    for(i in 1:length(theta.vector)){
        theta <- add.theta.plot(theta.vector[i],mu.vector[i],T,lty=length(theta.vector)+1-i )}
    
}


par(mfrow=c(1,2))
## Densities with mean less than one
unifed.density.plots1()

## Densities with mean greater than one
unifed.density.plots2()


## ----results='hide'-----------------------------------------------------------
 log( ( exp(50) - 1) / 50  ) - ( 50 - log (50) )

## ----fig.align='center'-------------------------------------------------------
x <- seq(0,1,length.out=100)
y <- unifed.varf(x)
plot(x,y,type="l",xlab=expression(mu),ylab=expression(bold(V)(mu)),main="Unifed Variance Function")

## ----results="hide",eval=TRUE-------------------------------------------------
car.insurance$agecat <- factor(car.insurance$agecat)
car.insurance$veh_age <- factor(car.insurance$veh_age)

agg.car.data <- aggregate( cbind(exposure,rep(1, dim(car.insurance)[1] )) ~ gender + agecat + area + veh_age,
                          data=car.insurance,
                          FUN=sum)

colnames(agg.car.data)[colnames(agg.car.data) == "V2"] <- "weight"
colnames(agg.car.data)[colnames(agg.car.data) == "exposure"] <- "class.exposure"
agg.car.data$class.exposure <- agg.car.data$class.exposure / agg.car.data$weight

## ----results="hide",eval=TRUE-------------------------------------------------
library(data.table)
car.insurance <- data.table(car.insurance)
car.insurance[,agecat:=factor(agecat)]
car.insurance[,veh_age:=factor(veh_age)]

agg.car.data <- car.insurance[,.(class.exposure=sum(exposure)/.N,
                              weight=.N),
                              by=.(gender,agecat,area,veh_age)]

## ----results="hide",eval=TRUE-------------------------------------------------
exposure.model <- glm(class.exposure ~ gender + agecat + area + veh_age,
                 family=unifed(),
                 weights=weight,
                 data=agg.car.data)

## ----eval=TRUE----------------------------------------------------------------
summary(exposure.model,dispersion=1)

## ----fig.align='center',eval=TRUE---------------------------------------------
plot(predict(exposure.model, type="response"),
     residuals(exposure.model, type="deviance"),
     xlab="Predicted value",
     ylab="Deviance residuals")

## ----eval=FALSE---------------------------------------------------------------
#  exposure.model.2 <- glm(exposure ~ gender + agecat + area + veh_age,
#                          family=unifed(),
#                          data=car.insurance)

## ----eval=FALSE---------------------------------------------------------------
#  exposure.model <- glm(class.exposure ~ gender + agecat + area + veh_age,
#                        family=unifed(),
#                        weights=weight,
#                        control=list(epsilon=1e-20,maxit=1e5),
#                        data=agg.car.data)
#  
#  exposure.model.2 <- glm(exposure ~ gender + agecat + area + veh_age,
#                          family=unifed(),
#                          control=list(epsilon=1e-20,maxit=1e5),
#                          data=car.insurance)

## ----eval=FALSE---------------------------------------------------------------
#  X <- model.matrix( ~ gender + agecat + area + veh_age, agg.car.data)
#  
#  model.data <- list(M=dim(X)[1],
#                     P=dim(X)[2],
#                     X=X,
#                     y= agg.car.data$class.exposure,
#                     ws= agg.car.data$weight)
#  

## ----eval=FALSE---------------------------------------------------------------
#  library(rstan)
#  model.list <- stanc_builder("unifed_example.stan",
#                              isystem=unifed.stan.folder())
#  
#  m1 <- stan(model_code=model.list$model_code,
#             data=model.data,
#             warmup=3e3,
#             iter=2e4,
#             chains=4)