Loading required packages
Loading Data
C1Mice <- read.csv("P:/SUNY-Brockport/SUNY Brockport_1/teaching/Staitstical Methods/Fall2019/Chapter1RLab/C1Mice.csv")
View(C1Mice)
Descriptive Stats on the four groups
names(C1Mice)
## [1] "Female.Trt" "Female.Ctl" "Male.Trt" "Male.Ctl"
a<-favstats(~Female.Trt,data=C1Mice)
b<-favstats(~Female.Ctl,data=C1Mice)
c<-favstats(~Male.Trt,data=C1Mice)
d<-favstats(~Male.Ctl,data=C1Mice)
data_descriptive<-rbind(a,b,c,d)
kable(data_descriptive)
|
1 |
2 |
2 |
7 |
10 |
4.4 |
3.911521 |
5 |
0 |
| 1 |
7 |
10 |
10 |
16 |
17 |
12.0 |
4.301163 |
5 |
0 |
| 2 |
3 |
5 |
6 |
9 |
10 |
6.6 |
2.880972 |
5 |
0 |
| 3 |
13 |
26 |
28 |
31 |
47 |
29.0 |
12.186058 |
5 |
0 |
Create Dotplot
stripchart(C1Mice,vertical=TRUE,method="jitter",col=c("red","blue","green","purple"))
Randomisation Test via simulation
reps <- 10
# ctl.fem <- c(16,10,10,7,17) #Literally typing data
ctl.fem<-C1Mice$Female.Ctl
#trt.fem <- c(1,2,2,10,7)
trt.fem<-C1Mice$Female.Trt
results <- numeric(reps) # establish a vector of the right length
# all 0s initially
x <- c(trt.fem, ctl.fem)
for (i in 1:reps) {
temp <- sample(x)
results[i] <- mean(temp[1:5])-mean(temp[6:10])
}
p.value <- sum(results >= 7.6) / reps
p.value
## [1] 0
hist(results,xlab="Control Mean - Treatment Mean")
p.value <- (sum(results <= -7.6)+sum(results >= 7.6)) / reps
p.value
## [1] 0
#Number of simulations resulting in a difference greater than or equal to 7.6:
sum(results >= 7.6)
## [1] 0
#Number of simulations resulting in a difference less than or equal to -7.6:
sum(results <= -7.6)
## [1] 0