ipsRdbs (version 1.0.0)

cheese: Testing of cheese data set

Description

Testing of cheese data set

Usage

cheese

Format

A data frame with 30 rows and 5 columns

Taste

A measure of taste quality of cheese

AceticAcid

Concentration of Acetic acid

H2S

Concentration of hydrogen sulphide

LacticAcid

Concentration lactic acid

logH2S

Logarithm of H2S

Examples

data(cheese)
summary(cheese)
pairs(cheese)
cheese.lm <- lm(Taste ~ AceticAcid +  LacticAcid + logH2S, data=cheese, subset=2:30)
# Check the diagnostics 
plot(cheese.lm$fit, cheese.lm$res, xlab="Fitted values", ylab = "Residuals")
abline(h=0)
# Should be a random scatter
qqnorm(cheese.lm$res, col=2)
qqline(cheese.lm$res, col="blue")
summary(cheese.lm)
cheese.lm2 <- lm(Taste ~ LacticAcid + logH2S, data=cheese)
# Check the diagnostics 
plot(cheese.lm2$fit, cheese.lm2$res, xlab="Fitted values", ylab = "Residuals")
abline(h=0)
qqnorm(cheese.lm2$res, col=2)
qqline(cheese.lm2$res, col="blue")
summary(cheese.lm2)
# How can we predict? 
newcheese <- data.frame(AceticAcid = 300, LacticAcid = 1.5, logH2S=4)
cheese.pred <- predict(cheese.lm2, newdata=newcheese, se.fit=TRUE)
cheese.pred
# Obtain confidence interval 
cheese.pred$fit + c(-1, 1) * qt(0.975, df=27) * cheese.pred$se.fit
# Using R to predict  
cheese.pred.conf.limits <- predict(cheese.lm2, newdata=newcheese, interval="confidence")
cheese.pred.conf.limits
# How to find prediction interval 
cheese.pred.pred.limits <- predict(cheese.lm2, newdata=newcheese, interval="prediction")
cheese.pred.pred.limits