##########
##
# Reading the data into R:

my.datafile <- tempfile()
cat(file=my.datafile, "                                                                                                                         
TV 5 49 
TV 5 39 
TV 10 50 
TV 10 55 
TV 15 43 
TV 15 38 
TV 20 53 
TV 20 48 
AUDIO 5 55 
AUDIO 5 41 
AUDIO 10 67 
AUDIO 10 58 
AUDIO 15 53 
AUDIO 20 85 
WRITE 5 66 
WRITE 5 68 
WRITE 10 85 
WRITE 10 92 
WRITE 15 69 
WRITE 15 62 
WRITE 20 85
", sep=" ")

options(scipen=999) # suppressing scientific notation

recog <- read.table(my.datafile, header=FALSE, col.names=c("medium", "time", "value")) 
  
# Note we could also save the data columns into a file and use a command such as:
# recog <- read.table(file = "z:/stat_516/filename.txt", header=FALSE, col.names = c("medium", "time", "value"))

attach(recog)

medium <- factor(medium)
time <- factor(time)

# The data frame called recog is now created.
#########

## Creating dummy variables:

dummy1.A <- rep(0, times=nrow(recog))
dummy1.A[medium=="AUDIO"] <- 1
dummy1.A[medium=="WRITE"] <- -1

dummy2.A <- rep(0, times=nrow(recog))
dummy2.A[medium=="TV"] <- 1
dummy2.A[medium=="WRITE"] <- -1

dummy1.C <- rep(0, times=nrow(recog))
dummy1.C[time==5] <- 1
dummy1.C[time==20] <- -1

dummy2.C <- rep(0, times=nrow(recog))
dummy2.C[time==10] <- 1
dummy2.C[time==20] <- -1

dummy3.C <- rep(0, times=nrow(recog))
dummy3.C[time==15] <- 1
dummy3.C[time==20] <- -1


# The full model:

full.model <- lm(value ~ dummy1.A + dummy2.A + dummy1.C + dummy2.C + dummy3.C + dummy1.A:dummy1.C + dummy2.A:dummy1.C + dummy1.A:dummy2.C + dummy2.A:dummy2.C + dummy1.A:dummy3.C + dummy2.A:dummy3.C)

# Define reduced.model.AC by
# removing interaction terms

# Define reduced.model.A by
# removing just factor A's dummy variables

# Define reduced.model.C by
# removing just factor C's dummy variables