# R Example for Generalized Random Block Design  

# We analyze the Task Completion data in Section 21.7.  


# Save the data file into a directory and 
# use the full path name:

taskcomp.data <- read.table(file = "z:/My Documents/teaching/stat_705/taskcompletiondata.txt", 
header=FALSE, col.names = c('time', 'gender', 'distract', 'subject'))

# attaching the data frame:

attach(taskcomp.data)

# Making "gender" and "distract" factors:

gender <- factor(gender)
distract <- factor(distract)

# The response is time required to complete a task.  

# Gender will serve as the blocking factor here.  
# In the data above, 1 = male and 2 = female.     

# Distraction level is the treatment factor of primary interest.  
# In the data, 1 = low distraction, 2 = high distraction.         

# There are d = 4 observations per trt-block combination,  
# so we use a generalized random block model.              

# The block levels are clearly fixed in this case (why?).  
# The distraction levels are also fixed.                   

# We analyze the data with the lm() and anova() functions as usual:  

# Fitting the ANOVA model and producing the ANOVA table:

taskcomp.fit <- lm(time ~ distract + gender + distract:gender);
anova(taskcomp.fit)

# We see a significant effect due to distraction  
# (F* = 20.17, P-value = .0007).                  

# Since there are only 2 levels of distraction,   
# there is no need for further comparisons.