R Run Humor Preference

Appendix E

R Run and Results As Is: Correlation and Histogram Tables at the End

R version 2.7.1 (2008-06-23)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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[Previously saved workspace restored]

> datafilename <- "RData.csv"
> person.data <- read.table(datafilename,header=TRUE,sep=",")
> names(person.data) #list the names of the variables
[1] "J.1" "J.2" "J.3" "J.4" "J.5" "J.6" "J.7" "J.8" "Age" "Gender"
> V <-person.data
> V.sub <- subset(V, select = J.1:J.8 ) # Selecting data
> Vz <- scale(V.sub,scale=T) # Extracting z-scale
> Vq <- cbind(XM=Vz[,2], NM=Vz[,1], XF=Vz[,4], NF=Vz[,3], XS=Vz[,6], NS=Vz[,5], XP=Vz[,8], NP=Vz[,7]) # Defining drives
> agec <- subset(V, select = Gender:Age )
> Vx <- cbind(Vq, agec) # Re-attaching age identifier
>
>
> Vx.Fem <- subset(Vx, Gender == 1 , select = XM:Age ) # selecting Female group
> Vx.Mal <- subset(Vx, Gender == 2 , select = XM:Age ) # selecting Male group
>
> Vx.15 <- subset(Vx, Age == 15 , select = XM:NP ) # Defining matrix with all under 15 scores
> Vx.15.Msd <- c(sd((Vx.15[,"XM"]+Vx.15[,"NM"])*0.5000), sd((Vx.15[,"XF"]+Vx.15[,"NF"])*0.5000), sd((Vx.15[,"XS"]+Vx.15[,"NS"])*0.5000), sd((Vx.15[,"XP"]+Vx.15[,"NP"])*0.5000)) # Finding motivational sd's for under 15
>
> Vx.Fem.15 <- subset(Vx.Fem, Age == 15 , select = XM:NP ) # selecting age group Under 15
> Vx.Fem.15.SDM <- c(sd((Vx.Fem.15[,"XM"]+Vx.Fem.15[,"NM"])*0.5000), sd((Vx.Fem.15[,"XF"]+Vx.Fem.15[,"NF"])*0.5000), sd((Vx.Fem.15[,"XS"]+Vx.Fem.15[,"NS"])*0.5000), sd((Vx.Fem.15[,"XP"]+Vx.Fem.15[,"NP"])*0.5000)) # Finding Motivation sd's for under 15 females
>
> Vx.Fem.15.SD <- c(sd(Vx.Fem.15[,"XM"]-Vx.Fem.15[,"NM"]), sd(Vx.Fem.15[,"XF"]-Vx.Fem.15[,"NF"]), sd(Vx.Fem.15[,"XS"]-Vx.Fem.15[,"NS"]), sd(Vx.Fem.15[,"XP"]-Vx.Fem.15[,"NP"])) # Finding cognitive sd's for under 15 Females
> Vx.Fem.15 <- mean(Vx.Fem.15)
> Vx.Fem.15 <- round(Vx.Fem.15,2)
> Vx.Mal.15 <- subset(Vx.Mal, Age == 15 , select = XM:NP ) # selecting age group Under 15
> Vx.Mal.15.SDM <- c(sd((Vx.Mal.15[,"XM"]+Vx.Mal.15[,"NM"])*0.5000), sd((Vx.Mal.15[,"XF"]+Vx.Mal.15[,"NF"])*0.5000), sd((Vx.Mal.15[,"XS"]+Vx.Mal.15[,"NS"])*0.5000), sd((Vx.Mal.15[,"XP"]+Vx.Mal.15[,"NP"])*0.5000)) # Finding Motivation sd's for under 15 males
>
> Vx.Mal.15.SD <- c(sd(Vx.Mal.15[,"XM"]-Vx.Mal.15[,"NM"]), sd(Vx.Mal.15[,"XF"]-Vx.Mal.15[,"NF"]), sd(Vx.Mal.15[,"XS"]-Vx.Mal.15[,"NS"]), sd(Vx.Mal.15[,"XP"]-Vx.Mal.15[,"NP"])) # Finding cognitive sd's for under 15 Males
> Vx.Mal.15 <- mean(Vx.Mal.15)
> Vx.Mal.15 <- round(Vx.Mal.15,2)
>
> Vx.16 <- subset(Vx, Age == 16 , select = XM:NP ) # Defining matrix with all 16-25 scores
> Vx.16.Msd <- c(sd((Vx.16[,"XM"]+Vx.16[,"NM"])*0.5000), sd((Vx.16[,"XF"]+Vx.16[,"NF"])*0.5000), sd((Vx.16[,"XS"]+Vx.16[,"NS"])*0.5000), sd((Vx.16[,"XP"]+Vx.16[,"NP"])*0.5000)) # Finding motivational sd's for 16-25
>
> Vx.Fem.16 <- subset(Vx.Fem, Age == 16 , select = XM:NP ) # selecting age group 16-25
> Vx.Fem.16.SDM <- c(sd((Vx.Fem.16[,"XM"]+Vx.Fem.16[,"NM"])*0.5000), sd((Vx.Fem.16[,"XF"]+Vx.Fem.16[,"NF"])*0.5000), sd((Vx.Fem.16[,"XS"]+Vx.Fem.16[,"NS"])*0.5000), sd((Vx.Fem.16[,"XP"]+Vx.Fem.16[,"NP"])*0.5000)) # Finding Motivation sd's for 16-25 females
>
> Vx.Fem.16.SD <- c(sd(Vx.Fem.16[,"XM"]-Vx.Fem.16[,"NM"]), sd(Vx.Fem.16[,"XF"]-Vx.Fem.16[,"NF"]), sd(Vx.Fem.16[,"XS"]-Vx.Fem.16[,"NS"]), sd(Vx.Fem.16[,"XP"]-Vx.Fem.16[,"NP"])) # Finding cognitive sd's for 16-25 Females
> Vx.Fem.16 <- mean(Vx.Fem.16)
> Vx.Fem.16 <- round(Vx.Fem.16,2)
> Vx.Mal.16 <- subset(Vx.Mal, Age == 16 , select = XM:NP ) # selecting age group 16-25
> Vx.Mal.16.SDM <- c(sd((Vx.Mal.16[,"XM"]+Vx.Mal.16[,"NM"])*0.5000), sd((Vx.Mal.16[,"XF"]+Vx.Mal.16[,"NF"])*0.5000), sd((Vx.Mal.16[,"XS"]+Vx.Mal.16[,"NS"])*0.5000), sd((Vx.Mal.16[,"XP"]+Vx.Mal.16[,"NP"])*0.5000)) # Finding Motivation sd's for 16-25 males
>
> Vx.Mal.16.SD <- c(sd(Vx.Mal.16[,"XM"]-Vx.Mal.16[,"NM"]), sd(Vx.Mal.16[,"XF"]-Vx.Mal.16[,"NF"]), sd(Vx.Mal.16[,"XS"]-Vx.Mal.16[,"NS"]), sd(Vx.Mal.16[,"XP"]-Vx.Mal.16[,"NP"])) # Finding cognitive sd's for 16-25 Males
> Vx.Mal.16 <- mean(Vx.Mal.16)
> Vx.Mal.16 <- round(Vx.Mal.16,2)
>
> Vx.26 <- subset(Vx, Age == 26 , select = XM:NP ) # Defining matrix with all 26-35 scores
> Vx.26.Msd <- c(sd((Vx.26[,"XM"]+Vx.26[,"NM"])*0.5000), sd((Vx.26[,"XF"]+Vx.26[,"NF"])*0.5000), sd((Vx.26[,"XS"]+Vx.26[,"NS"])*0.5000), sd((Vx.26[,"XP"]+Vx.26[,"NP"])*0.5000)) # Finding motivational sd's for 26-35
>
> Vx.Fem.26 <- subset(Vx.Fem, Age == 26 , select = XM:NP ) # selecting age group 26-35
> Vx.Fem.26.SDM <- c(sd((Vx.Fem.26[,"XM"]+Vx.Fem.26[,"NM"])*0.5000), sd((Vx.Fem.26[,"XF"]+Vx.Fem.26[,"NF"])*0.5000), sd((Vx.Fem.26[,"XS"]+Vx.Fem.26[,"NS"])*0.5000), sd((Vx.Fem.26[,"XP"]+Vx.Fem.26[,"NP"])*0.5000)) # Finding Motivation sd's for 26-35 females
>
> Vx.Fem.26.SD <- c(sd(Vx.Fem.26[,"XM"]-Vx.Fem.26[,"NM"]), sd(Vx.Fem.26[,"XF"]-Vx.Fem.26[,"NF"]), sd(Vx.Fem.26[,"XS"]-Vx.Fem.26[,"NS"]), sd(Vx.Fem.26[,"XP"]-Vx.Fem.26[,"NP"])) # Finding cognitive sd's for 26-35 Females
> Vx.Fem.26 <- mean(Vx.Fem.26)
> Vx.Fem.26 <- round(Vx.Fem.26,2)
> Vx.Mal.26 <- subset(Vx.Mal, Age == 26 , select = XM:NP ) # selecting age group 26-35
> Vx.Mal.26.SDM <- c(sd((Vx.Mal.26[,"XM"]+Vx.Mal.26[,"NM"])*0.5000), sd((Vx.Mal.26[,"XF"]+Vx.Mal.26[,"NF"])*0.5000), sd((Vx.Mal.26[,"XS"]+Vx.Mal.26[,"NS"])*0.5000), sd((Vx.Mal.26[,"XP"]+Vx.Mal.26[,"NP"])*0.5000)) # Finding Motivation sd's for 26-35 males
>
> Vx.Mal.26.SD <- c(sd(Vx.Mal.26[,"XM"]-Vx.Mal.26[,"NM"]), sd(Vx.Mal.26[,"XF"]-Vx.Mal.26[,"NF"]), sd(Vx.Mal.26[,"XS"]-Vx.Mal.26[,"NS"]), sd(Vx.Mal.26[,"XP"]-Vx.Mal.26[,"NP"])) # Finding cognitive sd's for 26-35 Males
> Vx.Mal.26 <- mean(Vx.Mal.26)
> Vx.Mal.26 <- round(Vx.Mal.26,2)
>
> Vx.36 <- subset(Vx, Age == 36 , select = XM:NP ) # Defining matrix with all 36-45 scores
> Vx.36.Msd <- c(sd((Vx.36[,"XM"]+Vx.36[,"NM"])*0.5000), sd((Vx.36[,"XF"]+Vx.36[,"NF"])*0.5000), sd((Vx.36[,"XS"]+Vx.36[,"NS"])*0.5000), sd((Vx.36[,"XP"]+Vx.36[,"NP"])*0.5000)) # Finding motivational sd's for 36-45
>
>
> Vx.Fem.36 <- subset(Vx.Fem, Age == 36 , select = XM:NP ) # selecting age group 36-45
> Vx.Fem.36.SDM <- c(sd((Vx.Fem.36[,"XM"]+Vx.Fem.36[,"NM"])*0.5000), sd((Vx.Fem.36[,"XF"]+Vx.Fem.36[,"NF"])*0.5000), sd((Vx.Fem.36[,"XS"]+Vx.Fem.36[,"NS"])*0.5000), sd((Vx.Fem.36[,"XP"]+Vx.Fem.36[,"NP"])*0.5000)) # Finding Motivation sd's for 36-45 females
>
> Vx.Fem.36.SD <- c(sd(Vx.Fem.36[,"XM"]-Vx.Fem.36[,"NM"]), sd(Vx.Fem.36[,"XF"]-Vx.Fem.36[,"NF"]), sd(Vx.Fem.36[,"XS"]-Vx.Fem.36[,"NS"]), sd(Vx.Fem.36[,"XP"]-Vx.Fem.36[,"NP"])) # Finding cognitive sd's for 36-45 Females
> Vx.Fem.36 <- mean(Vx.Fem.36)
> Vx.Fem.36 <- round(Vx.Fem.36,2)
> Vx.Mal.36 <- subset(Vx.Mal, Age == 36 , select = XM:NP ) # selecting age group 36-45
> Vx.Mal.36.SDM <- c(sd((Vx.Mal.36[,"XM"]+Vx.Mal.36[,"NM"])*0.5000), sd((Vx.Mal.36[,"XF"]+Vx.Mal.36[,"NF"])*0.5000), sd((Vx.Mal.36[,"XS"]+Vx.Mal.36[,"NS"])*0.5000), sd((Vx.Mal.36[,"XP"]+Vx.Mal.36[,"NP"])*0.5000)) # Finding Motivation sd's for 36-45 males
>
> Vx.Mal.36.SD <- c(sd(Vx.Mal.36[,"XM"]-Vx.Mal.36[,"NM"]), sd(Vx.Mal.36[,"XF"]-Vx.Mal.36[,"NF"]), sd(Vx.Mal.36[,"XS"]-Vx.Mal.36[,"NS"]), sd(Vx.Mal.36[,"XP"]-Vx.Mal.36[,"NP"])) # Finding cognitive sd's for 36-45 Males
> Vx.Mal.36 <- mean(Vx.Mal.36)
> Vx.Mal.36 <- round(Vx.Mal.36,2)
>
> Vx.46 <- subset(Vx, Age == 46 , select = XM:NP ) # Defining matrix with all 46-55 scores
> Vx.46.Msd <- c(sd((Vx.46[,"XM"]+Vx.46[,"NM"])*0.5000), sd((Vx.46[,"XF"]+Vx.46[,"NF"])*0.5000), sd((Vx.46[,"XS"]+Vx.46[,"NS"])*0.5000), sd((Vx.46[,"XP"]+Vx.46[,"NP"])*0.5000)) # Finding motivational sd's for 46-55
>
> Vx.Fem.46 <- subset(Vx.Fem, Age == 46 , select = XM:NP ) # selecting age group 46-55
> Vx.Fem.46.SDM <- c(sd((Vx.Fem.46[,"XM"]+Vx.Fem.46[,"NM"])*0.5000), sd((Vx.Fem.46[,"XF"]+Vx.Fem.46[,"NF"])*0.5000), sd((Vx.Fem.46[,"XS"]+Vx.Fem.46[,"NS"])*0.5000), sd((Vx.Fem.46[,"XP"]+Vx.Fem.46[,"NP"])*0.5000)) # Finding Motivation sd's for 46-55 females
>
> Vx.Fem.46.SD <- c(sd(Vx.Fem.46[,"XM"]-Vx.Fem.46[,"NM"]), sd(Vx.Fem.46[,"XF"]-Vx.Fem.46[,"NF"]), sd(Vx.Fem.46[,"XS"]-Vx.Fem.46[,"NS"]), sd(Vx.Fem.46[,"XP"]-Vx.Fem.46[,"NP"])) # Finding cognitive sd's for 46-55 Females
> Vx.Fem.46 <- mean(Vx.Fem.46)
> Vx.Fem.46 <- round(Vx.Fem.46,2)
> Vx.Mal.46 <- subset(Vx.Mal, Age == 46 , select = XM:NP ) # selecting age group 46-55
> Vx.Mal.46.SDM <- c(sd((Vx.Mal.46[,"XM"]+Vx.Mal.46[,"NM"])*0.5000), sd((Vx.Mal.46[,"XF"]+Vx.Mal.46[,"NF"])*0.5000), sd((Vx.Mal.46[,"XS"]+Vx.Mal.46[,"NS"])*0.5000), sd((Vx.Mal.46[,"XP"]+Vx.Mal.46[,"NP"])*0.5000)) # Finding Motivation sd's for 46-55 males
>
> Vx.Mal.46.SD <- c(sd(Vx.Mal.46[,"XM"]-Vx.Mal.46[,"NM"]), sd(Vx.Mal.46[,"XF"]-Vx.Mal.46[,"NF"]), sd(Vx.Mal.46[,"XS"]-Vx.Mal.46[,"NS"]), sd(Vx.Mal.46[,"XP"]-Vx.Mal.46[,"NP"])) # Finding cognitive sd's for 46-55 Males
> Vx.Mal.46 <- mean(Vx.Mal.46)
> Vx.Mal.46 <- round(Vx.Mal.46,2)
>
> Vx.56 <- subset(Vx, Age == 56 , select = XM:NP ) # Defining matrix with all over 55 scores
>
> Vx.56.Msd <- c(sd((Vx.56[,"XM"]+Vx.56[,"NM"])*0.5000), sd((Vx.56[,"XF"]+Vx.56[,"NF"])*0.5000), sd((Vx.56[,"XS"]+Vx.56[,"NS"])*0.5000), sd((Vx.56[,"XP"]+Vx.56[,"NP"])*0.5000)) # Finding motivational sd's for over 55
>
> Vx.Fem.56 <- subset(Vx.Fem, Age == 56 , select = XM:NP ) # selecting age group Over 55
> Vx.Fem.56.SDM <- c(sd((Vx.Fem.56[,"XM"]+Vx.Fem.56[,"NM"])*0.5000), sd((Vx.Fem.56[,"XF"]+Vx.Fem.56[,"NF"])*0.5000), sd((Vx.Fem.56[,"XS"]+Vx.Fem.56[,"NS"])*0.5000), sd((Vx.Fem.56[,"XP"]+Vx.Fem.56[,"NP"])*0.5000)) # Finding Motivation sd's for over 55 females
>
> Vx.Fem.56.SD <- c(sd(Vx.Fem.56[,"XM"]-Vx.Fem.56[,"NM"]), sd(Vx.Fem.56[,"XF"]-Vx.Fem.56[,"NF"]), sd(Vx.Fem.56[,"XS"]-Vx.Fem.56[,"NS"]), sd(Vx.Fem.56[,"XP"]-Vx.Fem.56[,"NP"])) # Finding cognitive sd's for over 55 Females
> Vx.Fem.56 <- mean(Vx.Fem.56)
> Vx.Fem.56 <- round(Vx.Fem.56,2)
> Vx.Mal.56 <- subset(Vx.Mal, Age == 56 , select = XM:NP ) # selecting age group Over 55
> Vx.Mal.56.SDM <- c(sd((Vx.Mal.56[,"XM"]+Vx.Mal.56[,"NM"])*0.5000), sd((Vx.Mal.56[,"XF"]+Vx.Mal.56[,"NF"])*0.5000), sd((Vx.Mal.56[,"XS"]+Vx.Mal.56[,"NS"])*0.5000), sd((Vx.Mal.56[,"XP"]+Vx.Mal.56[,"NP"])*0.5000)) # Finding Motivation sd's for over 55 males
>
> Vx.Mal.56.SD <- c(sd(Vx.Mal.56[,"XM"]-Vx.Mal.56[,"NM"]), sd(Vx.Mal.56[,"XF"]-Vx.Mal.56[,"NF"]), sd(Vx.Mal.56[,"XS"]-Vx.Mal.56[,"NS"]), sd(Vx.Mal.56[,"XP"]-Vx.Mal.56[,"NP"])) # Finding cognitive sd's for over 55 Males
> Vx.Mal.56 <- mean(Vx.Mal.56)
> Vx.Mal.56 <- round(Vx.Mal.56,2)
>
> maleAge <- c(Vx.Mal.15, Vx.Mal.16, Vx.Mal.26, Vx.Mal.36, Vx.Mal.46, Vx.Mal.56)
> femAge <- c(Vx.Fem.15, Vx.Fem.16, Vx.Fem.26, Vx.Fem.36, Vx.Fem.46, Vx.Fem.56)
> r2 <- as.factor(rep(1:8,6))
> r1 <- as.factor(rep(rep(1:6,rep(8,6)),1))
> r3 <- as.factor(rep(1:2,24))
> r4 <- as.factor(rep(1:1,48))
> ageGroups <- data.frame(cbind(Male=as.vector(maleAge), Female=as.vector(femAge), Ages=0.5000*(as.vector(maleAge)+as.vector(femAge)), Drive=r2, Agg=r1, Cogn=r3, Dumy=r4 ))
> ageGroups
Male Female Ages Drive Agg Cogn Dumy
1 0.03 -0.64 -0.305 1 1 1 1
2 -0.21 -0.73 -0.470 2 1 2 1
3 0.00 -0.41 -0.205 3 1 1 1
4 0.01 -0.43 -0.210 4 1 2 1
5 -0.47 -0.67 -0.570 5 1 1 1
6 -0.35 -0.69 -0.520 6 1 2 1
7 -0.20 -0.80 -0.500 7 1 1 1
8 -0.46 -0.80 -0.630 8 1 2 1
9 0.08 -0.03 0.025 1 2 1 1
10 -0.02 -0.06 -0.040 2 2 2 1
11 0.01 0.05 0.030 3 2 1 1
12 0.03 0.07 0.050 4 2 2 1
13 -0.10 0.01 -0.045 5 2 1 1
14 0.08 -0.01 0.035 6 2 2 1
15 0.01 -0.09 -0.040 7 2 1 1
16 -0.06 -0.09 -0.075 8 2 2 1
17 0.23 -0.13 0.050 1 3 1 1
18 0.21 -0.20 0.005 2 3 2 1
19 0.17 -0.01 0.080 3 3 1 1
20 0.14 -0.14 0.000 4 3 2 1
21 0.20 -0.04 0.080 5 3 1 1
22 0.36 -0.12 0.120 6 3 2 1
23 0.13 -0.10 0.015 7 3 1 1
24 0.08 -0.09 -0.005 8 3 2 1
25 0.13 -0.18 -0.025 1 4 1 1
26 0.16 -0.11 0.025 2 4 2 1
27 0.06 -0.29 -0.115 3 4 1 1
28 -0.04 -0.15 -0.095 4 4 2 1
29 -0.01 -0.17 -0.090 5 4 1 1
30 -0.04 -0.38 -0.210 6 4 2 1
31 0.12 -0.19 -0.035 7 4 1 1
32 0.22 -0.24 -0.010 8 4 2 1
33 0.17 -0.06 0.055 1 5 1 1
34 0.27 -0.12 0.075 2 5 2 1
35 0.26 -0.19 0.035 3 5 1 1
36 0.28 -0.10 0.090 4 5 2 1
37 0.29 0.31 0.300 5 5 1 1
38 0.14 -0.10 0.020 6 5 2 1
39 0.39 -0.10 0.145 7 5 1 1
40 0.45 0.22 0.335 8 5 2 1
41 0.26 -0.25 0.005 1 6 1 1
42 0.29 -0.05 0.120 2 6 2 1
43 0.30 -0.82 -0.260 3 6 1 1
44 0.15 -0.42 -0.135 4 6 2 1
45 0.35 0.06 0.205 5 6 1 1
46 0.29 -0.01 0.140 6 6 2 1
47 0.48 0.06 0.270 7 6 1 1
48 0.46 0.15 0.305 8 6 2 1
>
> ctab1 <- tapply(ageGroups[,3],list(ageGroups[,4],ageGroups[,5]),mean)
>
> # FemPref <- tapply(ageGroups[,2],list(ageGroups[,7],ageGroups[,6]),mean)
> FemPref <- tapply(ageGroups[,2],list(ageGroups[,4],ageGroups[,5]),mean)
> FemPref # Female Pref Means
1 2 3 4 5 6
1 -0.64 -0.03 -0.13 -0.18 -0.06 -0.25
2 -0.73 -0.06 -0.20 -0.11 -0.12 -0.05
3 -0.41 0.05 -0.01 -0.29 -0.19 -0.82
4 -0.43 0.07 -0.14 -0.15 -0.10 -0.42
5 -0.67 0.01 -0.04 -0.17 0.31 0.06
6 -0.69 -0.01 -0.12 -0.38 -0.10 -0.01
7 -0.80 -0.09 -0.10 -0.19 -0.10 0.06
8 -0.80 -0.09 -0.09 -0.24 0.22 0.15
> FemPref.Mot <- t(cbind(Emotn=(FemPref[1, ]+FemPref[2,]), Feedg=(FemPref[3, ]+FemPref[4, ]), SoSex=(FemPref[5, ]+FemPref[6, ]), Prntg=(FemPref[7, ]+FemPref[8, ]))) # Female Pref motivation means
>
>
> CFpm <- cbind(Emotn=(FemPref[1, ]-FemPref[2,]), Feedg=(FemPref[3, ]-FemPref[4, ]), SoSex=(FemPref[5, ]-FemPref[6, ]), Prntg=(FemPref[7, ]-FemPref[8, ]))
> CFpm # Cognitive Female mean
Emotn Feedg SoSex Prntg
1 0.09 0.02 0.02 0.00
2 0.03 -0.02 0.02 0.00
3 0.07 0.13 0.08 -0.01
4 -0.07 -0.14 0.21 0.05
5 0.06 -0.09 0.41 -0.32
6 -0.20 -0.40 0.07 -0.09
>
> #MalPref <- tapply(ageGroups[,1],list(ageGroups[,7],ageGroups[,6]),mean)
> MalPref <- tapply(ageGroups[,1],list(ageGroups[,4],ageGroups[,5]),mean)
> MalPref # Male Pref Means
1 2 3 4 5 6
1 0.03 0.08 0.23 0.13 0.17 0.26
2 -0.21 -0.02 0.21 0.16 0.27 0.29
3 0.00 0.01 0.17 0.06 0.26 0.30
4 0.01 0.03 0.14 -0.04 0.28 0.15
5 -0.47 -0.10 0.20 -0.01 0.29 0.35
6 -0.35 0.08 0.36 -0.04 0.14 0.29
7 -0.20 0.01 0.13 0.12 0.39 0.48
8 -0.46 -0.06 0.08 0.22 0.45 0.46
> MalPref.Mot <- t(cbind(Emotn=(MalPref[1, ]+MalPref[2,]), Feedg=(MalPref[3, ]+MalPref[4, ]), SoSex=(MalPref[5, ]+MalPref[6, ]), Prntg=(MalPref[7, ]+MalPref[8, ]))) # Male Pref motivation means
>
> CMpm <- cbind(Emotn=(MalPref[1, ]-MalPref[2, ]), Feedg=(MalPref[3, ]-MalPref[4, ]), SoSex=(MalPref[5, ]-MalPref[6, ]), Prntg=(MalPref[7, ]-MalPref[8, ]))
> CMpm # Cognitive Male means
Emotn Feedg SoSex Prntg
1 0.24 -0.01 -0.12 0.26
2 0.10 -0.02 -0.18 0.07
3 0.02 0.03 -0.16 0.05
4 -0.03 0.10 0.03 -0.10
5 -0.10 -0.02 0.15 -0.06
6 -0.03 0.15 0.06 0.02
>
> ctab1
1 2 3 4 5 6
1 -0.305 0.025 0.050 -0.025 0.055 0.005
2 -0.470 -0.040 0.005 0.025 0.075 0.120
3 -0.205 0.030 0.080 -0.115 0.035 -0.260
4 -0.210 0.050 0.000 -0.095 0.090 -0.135
5 -0.570 -0.045 0.080 -0.090 0.300 0.205
6 -0.520 0.035 0.120 -0.210 0.020 0.140
7 -0.500 -0.040 0.015 -0.035 0.145 0.270
8 -0.630 -0.075 -0.005 -0.010 0.335 0.305
> ctabu <- ctab1
> ctabu <- cbind(Emotn=(ctabu[1, ]+ctabu[2, ]), Feedg=(ctabu[3, ]+ctabu[4, ]), SoSex=(ctabu[5, ]+ctabu[6, ]), Prntg=(ctabu[7, ]+ctabu[8, ])) # ctabu is age-group unscaled motivation means
>
> ctab1 <- scale(ctab1)
> ctab1
1 2 3 4 5 6
1 0.7384170 0.6873457 0.1480397 0.5981825 -0.6353000 -0.3847090
2 -0.2664391 -0.6873457 -0.8209474 1.2721909 -0.4700187 0.1955079
3 1.3474207 0.7930912 0.7940311 -0.6150327 -0.8005813 -1.7217306
4 1.3169705 1.2160731 -0.9286127 -0.3454293 -0.3460577 -1.0910601
5 -0.8754428 -0.7930912 0.7940311 -0.2780285 1.3893959 0.6243639
6 -0.5709410 0.8988367 1.6553530 -1.8956487 -0.9245422 0.2964152
7 -0.4491402 -0.6873457 -0.6056169 0.4633808 0.1084658 0.9523125
8 -1.2408450 -1.4275641 -1.0362779 0.8003850 1.6786381 1.1289003
attr(,"scaled:center")
1 2 3 4 5 6
-0.426250 -0.007500 0.043125 -0.069375 0.131875 0.081250
attr(,"scaled:scale")
1 2 3 4 5 6
0.16420262 0.04728334 0.04644025 0.07418305 0.12100583 0.19820174
> ctab2 <- t(ctab1)
> ctab3 <- cbind(Emotn=0.5000*(ctab2[,1]+ctab2[,2]), Feedg=0.5000*(ctab2[,3]+ctab2[,4]), SoSex=0.5000*(ctab2[,5]+ctab2[,6]), Prntg=0.5000*(ctab2[,7]+ctab2[,8]), cntr=rep(1:6, 1))
> ctab3
Emotn Feedg SoSex Prntg cntr
1 0.23598893 1.33219556 -0.72319188 -0.8449926 1
2 0.00000000 1.00458215 0.05287274 -1.0574549 2
3 -0.33645386 -0.06729077 1.22469205 -0.8209474 3
4 0.93518667 -0.48023099 -1.08683856 0.6318829 4
5 -0.55265933 -0.57331949 0.23242682 0.8935520 5
6 -0.09460058 -1.40639534 0.46038951 1.0406064 6
> ctab4 <- t(ctab3)
> ctab4
1 2 3 4 5 6
Emotn 0.2359889 0.00000000 -0.33645386 0.9351867 -0.5526593 -0.09460058
Feedg 1.3321956 1.00458215 -0.06729077 -0.4802310 -0.5733195 -1.40639534
SoSex -0.7231919 0.05287274 1.22469205 -1.0868386 0.2324268 0.46038951
Prntg -0.8449926 -1.05745490 -0.82094742 0.6318829 0.8935520 1.04060642
cntr 1.0000000 2.00000000 3.00000000 4.0000000 5.0000000 6.00000000
>
> summary(lm(ctab4[1,] ~ ctab4[5,])) # Emotion Linear Model

Call:
lm(formula = ctab4[1, ] ~ ctab4[5, ])

Residuals:
1 2 3 4 5 6
0.05908 -0.11864 -0.39683 0.93308 -0.49651 0.01982

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.23517 0.52921 0.444 0.68
ctab4[5, ] -0.05827 0.13589 -0.429 0.69

Residual standard error: 0.5685 on 4 degrees of freedom
Multiple R-squared: 0.04394, Adjusted R-squared: -0.1951
F-statistic: 0.1838 on 1 and 4 DF, p-value: 0.6902

>
> summary(lm(ctab4[2,] ~ ctab4[5,])) # Feeding Linear Model

Call:
lm(formula = ctab4[2, ] ~ ctab4[5, ])

Residuals:
1 2 3 4 5 6
0.01825 0.22891 -0.30468 -0.17935 0.26584 -0.02897

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.85222 0.23238 7.970 0.001343 **
ctab4[5, ] -0.53827 0.05967 -9.021 0.000836 ***
---
Signif. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1

Residual standard error: 0.2496 on 4 degrees of freedom
Multiple R-squared: 0.9531, Adjusted R-squared: 0.9414
F-statistic: 81.37 on 1 and 4 DF, p-value: 0.0008364

>
> summary(lm(ctab4[4,] ~ ctab4[5,])) # Parenting Linear Model

Call:
lm(formula = ctab4[4, ] ~ ctab4[5, ])

Residuals:
1 2 3 4 5 6
0.3765 -0.3141 -0.5557 0.4191 0.2026 -0.1284

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.6996 0.4117 -4.128 0.0145 *
ctab4[5, ] 0.4781 0.1057 4.523 0.0106 *
---
Signif. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1

Residual standard error: 0.4422 on 4 degrees of freedom
Multiple R-squared: 0.8364, Adjusted R-squared: 0.7955
F-statistic: 20.45 on 1 and 4 DF, p-value: 0.01064

>
> summary(lm(ctab4[3,1:3] ~ ctab4[5,1:3])) # SoSex Young Linear Model

Call:
lm(formula = ctab4[3, 1:3] ~ ctab4[5, 1:3])

Residuals:
1 2 3
0.06596 -0.13192 0.06596

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.7631 0.2468 -7.144 0.0885 .
ctab4[5, 1:3] 0.9739 0.1142 8.525 0.0743 .
---
Signif. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1

Residual standard error: 0.1616 on 1 degrees of freedom
Multiple R-squared: 0.9864, Adjusted R-squared: 0.9729
F-statistic: 72.68 on 1 and 1 DF, p-value: 0.07434

>
> summary(lm(ctab4[3,4:6] ~ ctab4[5,1:3])) # SoSex Old Linear Model

Call:
lm(formula = ctab4[3, 4:6] ~ ctab4[5, 1:3])

Residuals:
4 5 6
-0.1819 0.3638 -0.1819

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.6786 0.6805 -2.467 0.245
ctab4[5, 1:3] 0.7736 0.3150 2.456 0.246

Residual standard error: 0.4455 on 1 degrees of freedom
Multiple R-squared: 0.8578, Adjusted R-squared: 0.7155
F-statistic: 6.03 on 1 and 1 DF, p-value: 0.2462

>
> # Presentation processing
> ctabu <- t(ctabu)
> Vx.Fem.Mot <- cbind(Vx.Fem.15.SDM, Vx.Fem.16.SDM, Vx.Fem.26.SDM, Vx.Fem.36.SDM, Vx.Fem.46.SDM, Vx.Fem.56.SDM) # Female Motivation SD
> Vx.Mal.Mot <- cbind(Vx.Mal.15.SDM, Vx.Mal.16.SDM, Vx.Mal.26.SDM, Vx.Mal.36.SDM, Vx.Mal.46.SDM, Vx.Mal.56.SDM) # Male Motivation SD
>
> Vx.Msd <- cbind(Vx.15.Msd, Vx.16.Msd, Vx.26.Msd, Vx.36.Msd, Vx.46.Msd, Vx.56.Msd)
> Vx.CsdF <- cbind(Vx.Fem.15.SD, Vx.Fem.16.SD, Vx.Fem.26.SD, Vx.Fem.36.SD, Vx.Fem.46.SD, Vx.Fem.56.SD)
> Vx.CsdM <- cbind(Vx.Mal.15.SD, Vx.Mal.16.SD, Vx.Mal.26.SD, Vx.Mal.36.SD, Vx.Mal.46.SD, Vx.Mal.56.SD)
> CFM <- t(CFpm)
> CMM <- t(CMpm)
>
> ctab4 # Motivation humor age Mean values Scaled for age groups
1 2 3 4 5 6
Emotn 0.2359889 0.00000000 -0.33645386 0.9351867 -0.5526593 -0.09460058
Feedg 1.3321956 1.00458215 -0.06729077 -0.4802310 -0.5733195 -1.40639534
SoSex -0.7231919 0.05287274 1.22469205 -1.0868386 0.2324268 0.46038951
Prntg -0.8449926 -1.05745490 -0.82094742 0.6318829 0.8935520 1.04060642
cntr 1.0000000 2.00000000 3.00000000 4.0000000 5.0000000 6.00000000
>
> ctabu # Motivation humor age Mean values unscaled for age groups SD computed
1 2 3 4 5 6
Emotn -0.775 -0.015 0.055 6.938894e-18 0.130 0.125
Feedg -0.415 0.080 0.080 -2.100000e-01 0.125 -0.395
SoSex -1.090 -0.010 0.200 -3.000000e-01 0.320 0.345
Prntg -1.130 -0.115 0.010 -4.500000e-02 0.480 0.575
>
> Vx.Msd # Motivation humor types age group Standard Deviations
Vx.15.Msd Vx.16.Msd Vx.26.Msd Vx.36.Msd Vx.46.Msd Vx.56.Msd
[1,] 1.0107257 0.8441173 0.9713069 0.9645704 0.8608266 0.7323595
[2,] 0.8473756 0.9016989 0.9533032 0.9833829 0.8901610 0.9192562
[3,] 0.8109170 0.9417230 0.9301491 0.9966990 0.8106675 0.7556924
[4,] 0.8211666 0.8770109 0.9653394 0.9489153 0.9763407 0.8369785
>
> MalPref.Mot # Male Motivation Means
1 2 3 4 5 6
Emotn -0.18 0.06 0.44 0.29 0.44 0.55
Feedg 0.01 0.04 0.31 0.02 0.54 0.45
SoSex -0.82 -0.02 0.56 -0.05 0.43 0.64
Prntg -0.66 -0.05 0.21 0.34 0.84 0.94
> Vx.Mal.Mot # Male Motivation SD
Vx.Mal.15.SDM Vx.Mal.16.SDM Vx.Mal.26.SDM Vx.Mal.36.SDM Vx.Mal.46.SDM Vx.Mal.56.SDM
[1,] 1.1859272 0.8116202 1.0045298 0.8890683 0.8603701 0.6769898
[2,] 0.8743244 0.9709170 0.9208781 0.9415729 0.9349828 0.8994713
[3,] 0.9363035 0.9716868 0.9117006 0.9447914 0.8430817 0.7433659
[4,] 0.8887736 0.8405511 0.9440040 0.8198398 0.9638612 0.7770082
>
> FemPref.Mot # Female Motivation Means
1 2 3 4 5 6
Emotn -1.37 -0.09 -0.33 -0.29 -0.18 -0.30
Feedg -0.84 0.12 -0.15 -0.44 -0.29 -1.24
SoSex -1.36 0.00 -0.16 -0.55 0.21 0.05
Prntg -1.60 -0.18 -0.19 -0.43 0.12 0.21
> Vx.Fem.Mot # Female Motivation SD
Vx.Fem.15.SDM Vx.Fem.16.SDM Vx.Fem.26.SDM Vx.Fem.36.SDM Vx.Fem.46.SDM Vx.Fem.56.SDM
[1,] 0.8657435 0.8670724 0.9005360 1.034395 0.8529769 0.7606967
[2,] 0.8206227 0.8574180 0.9850330 1.027738 0.8084088 0.7096871
[3,] 0.7458324 0.9259143 0.9238660 1.049632 0.7920221 0.7660661
[4,] 0.7566053 0.9028007 0.9893694 1.050609 0.9798562 0.9019347
>
> CFM # Cognitive divides Female Means
1 2 3 4 5 6
Emotn 0.09 0.03 0.07 -0.07 0.06 -0.20
Feedg 0.02 -0.02 0.13 -0.14 -0.09 -0.40
SoSex 0.02 0.02 0.08 0.21 0.41 0.07
Prntg 0.00 0.00 -0.01 0.05 -0.32 -0.09
>
> Vx.CsdF # Cognitive divides Female Standard deviations
Vx.Fem.15.SD Vx.Fem.16.SD Vx.Fem.26.SD Vx.Fem.36.SD Vx.Fem.46.SD Vx.Fem.56.SD
[1,] 0.9335612 0.8222998 0.8046618 0.9176226 1.0428590 0.7418870
[2,] 0.5231201 0.7844552 0.8190234 0.7815483 0.9068011 0.6184870
[3,] 0.6615734 0.7713364 0.7145555 0.6700317 0.6290835 0.4786062
[4,] 0.5979262 0.8042974 0.8112000 0.6798709 0.5971244 0.6822790
>
> CMM # Cognitive divides Male Means
1 2 3 4 5 6
Emotn 0.24 0.10 0.02 -0.03 -0.10 -0.03
Feedg -0.01 -0.02 0.03 0.10 -0.02 0.15
SoSex -0.12 -0.18 -0.16 0.03 0.15 0.06
Prntg 0.26 0.07 0.05 -0.10 -0.06 0.02
>
> Vx.CsdM # Cognitive divides Male Standard deviations
Vx.Mal.15.SD Vx.Mal.16.SD Vx.Mal.26.SD Vx.Mal.36.SD Vx.Mal.46.SD Vx.Mal.56.SD
[1,] 0.9744725 1.0138135 0.9335796 0.5903462 0.8438384 1.0000008
[2,] 1.0739823 0.7435882 0.7352012 0.6281206 0.6094126 0.6688316
[3,] 0.7361448 0.6903084 0.8728658 0.7399339 0.7012244 0.7581183
[4,] 0.6035127 0.8330666 0.8956322 0.5830584 0.8712646 0.5729923
>
> matplot(t(ctab4[1:4,]))
>