automobile <- read.table("automobile.txt", header=TRUE,sep=",")

##  1978 Automobile Data - no missings

##  make            Make and Model
##  price           Price
##  mpg             Mileage (mpg - mile per gallon)
##  rep78           Repair Record 1978
##  headroom        Headroom (in.)
##  trunk           Trunk space (cu. ft.)
##  weight          Weight (lbs.)
##  length          Length (in.)
##  turn            Turn Circle (ft.) 
##  displacement    Displacement (cu. in.)
##  gear_ratio      Gear Ratio
##  foreign         Car type



#transformace, aby to bylo srozumitelnejsi...
# litru na 100 km <- mili na galon
automobile$spotreba <-  1/automobile$mpg * 3.78541178/1.609344 * 100
attach(automobile)

#neumi americani delat motory, nebo maji jen radi velka auta?
pairs(automobile)
stripchart(spotreba~foreign,vertical = TRUE,method = "jitter")
stripchart(weight~foreign,vertical = TRUE,method = "jitter")


##########

#!!! vykreslete zavislost spotreby na hmotnosti (x-y graf) a ziskejte regresni rovnici pro tento vztah
plot(   )

model1 <- lm(   )
summary(model1)

#jednoducha cara
abline(model1)

#slozitejsi zpusob, potreba u slozitejsich modelu
newweight <- data.frame(weight = seq(min(weight), max(weight), length.out = 100))
lines(predict(model1, newweight) ~ newweight$weight)




#################################################################
#odhad modelu -- spolecne
model1 <- lm(spotreba ~ weight,model = TRUE, x = TRUE, y = TRUE)


class(model1)
model1
summary(model1)

#z objektu lze vyndat jeho jednotlive komponenty
model1$coefficients
coef(model1)
model1$residuals
resid(model1)
model1$fitted.values
fitted(model1)

#matice planu
model1$x
#matice vysledku
model1$y




#neco malo maticove algebry

#indexace
model1$x[1,]
#transpozice
t(model1$x)
#nasobeni
t(model1$x) %*% model1$x
#inverze
solve(t(model1$x) %*% model1$x)

#OLS odhad
( solve(t(model1$x) %*% model1$x) ) %*% ( t(model1$x) %*% model1$y )
#srovnani
model1$coefficients



#rezidualni soucet ctvercu
sum(resid(model1)^2)
#pocet stupnu volnosti
model1$df.residual
#odhad rozptylu modelu
sum(resid(model1)^2) / model1$df.residual
#odhad smerodatne odchylky modelu - "residual standard error"
sqrt( sum(resid(model1)^2) / model1$df.residual )



#################################################################
# model s kategorialnim prediktorem

#!!! ziskejte regresni rovnici pro vztah mezi spotrebou a mistem vyroby (foreign)
model2 <- lm(   )
summary(model2)
#coz odpovida prumerum ve skupinach
tapply(spotreba,foreign,mean)


#nez. promenna
foreign
#matice planu
model2$x




#################################################################
# model s obema prediktory

model3 <- lm(spotreba ~ weight+foreign,model = TRUE, x = TRUE, y = TRUE)
summary(model3)
model3$x

plot(spotreba ~ weight, 
     xlab = "Hmotnost [lbs]",
     ylab = "Spotreba [l/100km]",
     type="n")
     
points(weight[foreign=="Domestic"],spotreba[foreign=="Domestic"],pch=1)
points(weight[foreign=="Foreign"],spotreba[foreign=="Foreign"],pch=2)

#slozitejsi zpusob, potreba u slozitejsich modelu
newweightDom <- data.frame(weight = seq(min(weight), max(weight), length.out = 100), foreign = rep("Domestic",100))
newweightFor <- data.frame(weight = seq(min(weight), max(weight), length.out = 100), foreign = rep("Foreign",100))

lines(predict(model3, newweightDom) ~ newweightDom$weight)
lines(predict(model3, newweightFor) ~ newweightFor$weight,lty=2)


#F test

XX <- t(model3$x) %*% model3$x
XX_inv = solve(XX)

V <- XX_inv[2:3,2:3]
V_inv <- solve(V)

beta <- as.matrix(model3$coefficient[2:3])
s2 <- sum(resid(model3)^2) / model3$df.residual

Fstat <- 1/(s2*2) * ( t(beta) %*% V_inv %*% beta )

summary(model3)
#p-hodnota pro vyznamnost modelu
1-pf(Fstat,2,66)


#################################################################
# problem multikolinearity

pairs(data.frame(weight,length,foreign,spotreba))
cor(weight,length)

model4 <- lm(spotreba ~ weight+length+foreign,model = TRUE, x = TRUE, y = TRUE)
summary(model4)
summary(model3)

#korelace odhadu parametru
summary(model4,correlation=TRUE)

#variance inflation faktor - nad 10 velky problem
#library(faraway)
vif(model4)