In my haste I did not include the full printout of my R session. My apologies.
nd<-read.table("ex20.csv", header=TRUE, sep=",",na.strings="NA")
attach(nd)
age.frame<-data.frame(Age, Friend.Agression, Parent.Agression,
Stranger.Agression)
> age.frame
Age Friend.Agression Parent.Agression Stranger.Agression
1 y 8 7 8
2 y 5 6 8
3 y 6 3 7
4 y 5 5 7
5 m 15 13 10
6 m 13 11 9
7 m 12 12 9
8 m 18 10 7
9 o 11 11 10
10 o 10 4 12
11 o 12 9 12
12 o 9 8 14
13 y 13 7 7
14 y 9 5 10
15 y 11 4 4
16 y 15 3 4
17 m 14 12 8
18 m 10 15 11
19 m 12 11 8
20 m 10 9 9
21 o 10 8 11
22 o 13 11 13
23 o 9 8 12
24 o 7 9 16
ay<-subset(nd,Age=="y")
> ay
Gender Age Friend.Agression Parent.Agression Stranger.Agression
1 f y 8 7 8
2 f y 5 6 8
3 f y 6 3 7
4 f y 5 5 7
13 m y 13 7 7
14 m y 9 5 10
15 m y 11 4 4
16 m y 15 3 4
> am<-subset(nd,Age=="m")
> am
Gender Age Friend.Agression Parent.Agression Stranger.Agression
5 f m 15 13 10
6 f m 13 11 9
7 f m 12 12 9
8 f m 18 10 7
17 m m 14 12 8
18 m m 10 15 11
19 m m 12 11 8
20 m m 10 9 9
> ao<-subset(nd,Age=="o")
> ao
Gender Age Friend.Agression Parent.Agression Stranger.Agression
9 f o 11 11 10
10 f o 10 4 12
11 f o 12 9 12
12 f o 9 8 14
21 m o 10 8 11
22 m o 13 11 13
23 m o 9 8 12
24 m o 7 9 16
> amm<-cbind(am$Friend.Agression, am$Parent.Agression,am$Stranger.Agression)
> amm
[,1] [,2] [,3]
[1,] 15 13 10
[2,] 13 11 9
[3,] 12 12 9
[4,] 18 10 7
[5,] 14 12 8
[6,] 10 15 11
[7,] 12 11 8
[8,] 10 9 9
> aym<-cbind(ay$Friend.Agression, ay$Parent.Agression,ay$Stranger.Agression)
> aym
[,1] [,2] [,3]
[1,] 8 7 8
[2,] 5 6 8
[3,] 6 3 7
[4,] 5 5 7
[5,] 13 7 7
[6,] 9 5 10
[7,] 11 4 4
[8,] 15 3 4
> aom<-cbind(ao$Friend.Agression, ao$Parent.Agression,ao$Stranger.Agression)
> aom
[,1] [,2] [,3]
[1,] 11 11 10
[2,] 10 4 12
[3,] 12 9 12
[4,] 9 8 14
[5,] 10 8 11
[6,] 13 11 13
[7,] 9 8 12
[8,] 7 9 16
> mean(aym)
[1] 6.958333
> mean(amm)
[1] 11.16667
> mean(aom)
[1] 10.375
> ascores<-cbind(Friend.Agression, Parent.Agression, Stranger.Agression)
> ascores
Friend.Agression Parent.Agression Stranger.Agression
[1,] 8 7 8
[2,] 5 6 8
[3,] 6 3 7
[4,] 5 5 7
[5,] 15 13 10
[6,] 13 11 9
[7,] 12 12 9
[8,] 18 10 7
[9,] 11 11 10
[10,] 10 4 12
[11,] 12 9 12
[12,] 9 8 14
[13,] 13 7 7
[14,] 9 5 10
[15,] 11 4 4
[16,] 15 3 4
[17,] 14 12 8
[18,] 10 15 11
[19,] 12 11 8
[20,] 10 9 9
[21,] 10 8 11
[22,] 13 11 13
[23,] 9 8 12
[24,] 7 9 16
> mean(ascores)
[1] 9.5
meany<-colMeans(aym, na.rm = FALSE, dims = 1)
meany
meanm<-colMeans(amm, na.rm = FALSE, dims = 1)
meanm
meano<-colMeans(aom, na.rm = FALSE, dims = 1)
meano
> S<-cov(ascores)
> S
Friend.Agression Parent.Agression Stranger.Agression
Friend.Agression 10.476449 4.461957 -2.003623
Parent.Agression 4.461957 10.940217 3.489130
Stranger.Agression -2.003623 3.489130 8.427536
> meany<-colMeans(x, na.rm = FALSE, dims = 1)
Error in inherits(x, "data.frame") : object 'x' not found
> meany<-colMeans(aym, na.rm = FALSE, dims = 1)
> meany
[1] 9.000 5.000 6.875
> meanm<-colMeans(amm, na.rm = FALSE, dims = 1)
> meanm
[1] 13.000 11.625 8.875
> meano<-colMeans(aom, na.rm = FALSE, dims = 1)
> meano
[1] 10.125 8.500 12.500
> mahalanobis(meany,meanm,S)
[1] 4.233462
> mahalanobis(meano,meanm,S)
[1] 3.857911
From: [email protected]
To: [email protected]
Date: Tue, 22 Mar 2011 22:01:04 -0400
Subject: [R] Using the mahalanobis( ) function
I want to calculate the Manhalanobis D as an effect size for a follow up to a
MANOVA. I think I'm getting further but still not there. No one has weighed
in yet to lend help and I would much appreciate it, particulalry those who are
familiar with cluster analysis or MANOVA follow up/effect sizes.
According to my stats professor I know the Mahalanobis D should be 3.93 & 3.04
for the distance between the center of the y to m cluster and the center of the
m to o cluster respectively (groups under the Age variable). I get 4.233462 &
3.857911 respectively. I'm still messing it up.
So far here is what I've done:
> am
Gender Age Friend.Agression Parent.Agression Stranger.Agression
5 f m 15 13 10
6 f m 13 11 9
7 f m 12 12 9
8 f m 18 10 7
17 m m 14 12 8
18 m m 10 15 11
19 m m 12 11 8
20 m m 10 9 9
> ao<-subset(nd,Age=="o")
> ao
Gender Age Friend.Agression Parent.Agression Stranger.Agression
9 f o 11 11 10
10 f o 10 4 12
11 f o 12 9 12
12 f o 9 8 14
21 m o 10 8 11
22 m o 13 11 13
23 m o 9 8 12
24 m o 7 9 16
> amm<-cbind(am$Friend.Agression, am$Parent.Agression,am$Stranger.Agression)
> amm
[,1] [,2] [,3]
[1,] 15 13 10
[2,] 13 11 9
[3,] 12 12 9
[4,] 18 10 7
[5,] 14 12 8
[6,] 10 15 11
[7,] 12 11 8
[8,] 10 9 9
> aym<-cbind(ay$Friend.Agression, ay$Parent.Agression,ay$Stranger.Agression)
> aym
[,1] [,2] [,3]
[1,] 8 7 8
[2,] 5 6 8
[3,] 6 3 7
[4,] 5 5 7
[5,] 13 7 7
[6,] 9 5 10
[7,] 11 4 4
[8,] 15 3 4
> aom<-cbind(ao$Friend.Agression, ao$Parent.Agression,ao$Stranger.Agression)
> aom
[,1] [,2] [,3]
[1,] 11 11 10
[2,] 10 4 12
[3,] 12 9 12
[4,] 9 8 14
[5,] 10 8 11
[6,] 13 11 13
[7,] 9 8 12
[8,] 7 9 16
> mean(aym)
[1] 6.958333
> mean(amm)
[1] 11.16667
> mean(aom)
[1] 10.375
> ascores<-cbind(Friend.Agression, Parent.Agression, Stranger.Agression)
> ascores
Friend.Agression Parent.Agression Stranger.Agression
[1,] 8 7 8
[2,] 5 6 8
[3,] 6 3 7
[4,] 5 5 7
[5,] 15 13 10
[6,] 13 11 9
[7,] 12 12 9
[8,] 18 10 7
[9,] 11 11 10
[10,] 10 4 12
[11,] 12 9 12
[12,] 9 8 14
[13,] 13 7 7
[14,] 9 5 10
[15,] 11 4 4
[16,] 15 3 4
[17,] 14 12 8
[18,] 10 15 11
[19,] 12 11 8
[20,] 10 9 9
[21,] 10 8 11
[22,] 13 11 13
[23,] 9 8 12
[24,] 7 9 16
> mean(ascores)
[1] 9.5
meany<-colMeans(aym, na.rm = FALSE, dims = 1)
meany
meanm<-colMeans(amm, na.rm = FALSE, dims = 1)
meanm
meano<-colMeans(aom, na.rm = FALSE, dims = 1)
meano
> S<-cov(ascores)
> S
Friend.Agression Parent.Agression Stranger.Agression
Friend.Agression 10.476449 4.461957 -2.003623
Parent.Agression 4.461957 10.940217 3.489130
Stranger.Agression -2.003623 3.489130 8.427536
> meany<-colMeans(x, na.rm = FALSE, dims = 1)
Error in inherits(x, "data.frame") : object 'x' not found
> meany<-colMeans(aym, na.rm = FALSE, dims = 1)
> meany
[1] 9.000 5.000 6.875
> meanm<-colMeans(amm, na.rm = FALSE, dims = 1)
> meanm
[1] 13.000 11.625 8.875
> meano<-colMeans(aom, na.rm = FALSE, dims = 1)
> meano
[1] 10.125 8.500 12.500
> mahalanobis(meany,meanm,S)
[1] 4.233462
> mahalanobis(meano,meanm,S)
[1] 3.857911
From: [email protected]
To: [email protected]
Subject: RE: [R] Using the mahalanobis( ) function
Date: Mon, 21 Mar 2011 23:55:08 -0400
This is what I've tried so far and just can't get it. I know I want a value of
3.93 (for Age= y and m) using mahalanobis d as an effect size for a follow up
to an MANOVA:
age.frame<-data.frame(Age, Friend.Agression, Parent.Agression,
Stranger.Agression)
> age.frame
Age Friend.Agression Parent.Agression Stranger.Agression
1 y 8 7 8
2 y 5 6 8
3 y 6 3 7
4 y 5 5 7
5 m 15 13 10
6 m 13 11 9
7 m 12 12 9
8 m 18 10 7
9 o 11 11 10
10 o 10 4 12
11 o 12 9 12
12 o 9 8 14
13 y 13 7 7
14 y 9 5 10
15 y 11 4 4
16 y 15 3 4
17 m 14 12 8
18 m 10 15 11
19 m 12 11 8
20 m 10 9 9
21 o 10 8 11
22 o 13 11 13
23 o 9 8 12
24 o 7 9 16
ay<-subset(nd,Age=="y")
> ay
Gender Age Friend.Agression Parent.Agression Stranger.Agression
1 f y 8 7 8
2 f y 5 6 8
3 f y 6 3 7
4 f y 5 5 7
13 m y 13 7 7
14 m y 9 5 10
15 m y 11 4 4
16 m y 15 3 4
> am<-subset(nd,Age=="m")
> am
Gender Age Friend.Agression Parent.Agression Stranger.Agression
5 f m 15 13 10
6 f m 13 11 9
7 f m 12 12 9
8 f m 18 10 7
17 m m 14 12 8
18 m m 10 15 11
19 m m 12 11 8
20 m m 10 9 9
> ao<-subset(nd,Age=="o")
> ao
Gender Age Friend.Agression Parent.Agression Stranger.Agression
9 f o 11 11 10
10 f o 10 4 12
11 f o 12 9 12
12 f o 9 8 14
21 m o 10 8 11
22 m o 13 11 13
23 m o 9 8 12
24 m o 7 9 16
> amm<-cbind(am$Friend.Agression, am$Parent.Agression,am$Stranger.Agression)
> amm
[,1] [,2] [,3]
[1,] 15 13 10
[2,] 13 11 9
[3,] 12 12 9
[4,] 18 10 7
[5,] 14 12 8
[6,] 10 15 11
[7,] 12 11 8
[8,] 10 9 9
> aym<-cbind(ay$Friend.Agression, ay$Parent.Agression,ay$Stranger.Agression)
> aym
[,1] [,2] [,3]
[1,] 8 7 8
[2,] 5 6 8
[3,] 6 3 7
[4,] 5 5 7
[5,] 13 7 7
[6,] 9 5 10
[7,] 11 4 4
[8,] 15 3 4
> aom<-cbind(ao$Friend.Agression, ao$Parent.Agression,ao$Stranger.Agression)
> aom
[,1] [,2] [,3]
[1,] 11 11 10
[2,] 10 4 12
[3,] 12 9 12
[4,] 9 8 14
[5,] 10 8 11
[6,] 13 11 13
[7,] 9 8 12
[8,] 7 9 16
> mean(aym)
[1] 6.958333
> mean(amm)
[1] 11.16667
> mean(aom)
[1] 10.375
> ascores<-cbind(Friend.Agression, Parent.Agression, Stranger.Agression)
> ascores
Friend.Agression Parent.Agression Stranger.Agression
[1,] 8 7 8
[2,] 5 6 8
[3,] 6 3 7
[4,] 5 5 7
[5,] 15 13 10
[6,] 13 11 9
[7,] 12 12 9
[8,] 18 10 7
[9,] 11 11 10
[10,] 10 4 12
[11,] 12 9 12
[12,] 9 8 14
[13,] 13 7 7
[14,] 9 5 10
[15,] 11 4 4
[16,] 15 3 4
[17,] 14 12 8
[18,] 10 15 11
[19,] 12 11 8
[20,] 10 9 9
[21,] 10 8 11
[22,] 13 11 13
[23,] 9 8 12
[24,] 7 9 16
> mean(ascores)
[1] 9.5
> S<-cov(ascores)
> S
Friend.Agression Parent.Agression Stranger.Agression
Friend.Agression 10.476449 4.461957 -2.003623
Parent.Agression 4.461957 10.940217 3.489130
Stranger.Agression -2.003623 3.489130 8.427536
> ymean<-mean(aym)
> mmean<-mean(amm)
> omean<-(aom)
> mahalanobis(aym,amm,S)
[1] 8.223093 18.633617 9.838251 6.301238 7.958673 4.707784 2.547173
6.671211
Warning message:
In sweep(x, 2, center) :
STATS is longer than the extent of 'dim(x)[MARGIN]'
From: [email protected]
To: [email protected]
Date: Mon, 21 Mar 2011 20:33:58 -0400
Subject: [R] Using the mahalanobis( ) function
Hello all,
I am a 2 month newbie to R and am stumped. I have a data set that I've run
multivariate stats on using the manova function (I included the data set). Now
it comes time for a table of effect sizes with significance. The univariate
tests are easy. Where I run into trouble filling in the table of effect sizes
is the Mahalanobis D as an effect size. I've included the table so you can see
what group's I'm comparing. I know there's a great function for filling in ?1
and ?2 : mahalanobis(x, center, cov, inverted=FALSE, ...) I need to turn the
sub groups scores for y (young), m (middle) and o (old) into clusters.
The problem is I lack the knowledge around cluster analysis of what goes into
the function for [x, center, & cov.] I have only a basic understanding of this
topic (a picture of a measured distance between two clusters on a graph). I
think I have to turn the data into a matrix but lack direction. Could someone
please use my data set or a similar one (a multivariate with at least 3 outcome
variables) and actually run this function (mahalanobis). Then please send me
your output from [R] starting from the data set all the way to the statistic.
PS I know the Mahalanobis D should be ?1=3.93 & ?2=3.04.
Ive read and reread the manual around mahalanobis() and have searched through
the list serve for information. The info is for people who already have a
grasp of how to implement this concept.
I am running the latest version of R on a windows 7 machine.
Effect Sizes
Contrasts| Dependent Variables
Friends Parents Strangers All
Young-Middle -1.8768797* -3.2842941* -1.1094004* ?1
Middle-Old 1.34900725* 1.54919532* -2.0107882* ?2
(sorry the column names and values dont line up)
Age Friend Agression Parent Agression Stranger Agression
y 8 7 8
y 5 6 8
y 6 3 7
y 5 5 7
m 15 13 10
m 13 11 9
m 12 12 9
m 18 10 7
o 11 11 10
o 10 4 12
o 12 9 12
o 9 8 14
y 13 7 7
y 9 5 10
y 11 4 4
y 15 3 4
m 14 12 8
m 10 15 11
m 12 11 8
m 10 9 9
o 10 8 11
o 13 11 13
o 9 8 12
o 7 9 16
[[alternative HTML version deleted]]
______________________________________________ [email protected] mailing
list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting
guide http://www.R-project.org/posting-guide.html and provide commented,
minimal, self-contained, reproducible code.
[[alternative HTML version deleted]]
______________________________________________ [email protected] mailing
list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting
guide http://www.R-project.org/posting-guide.html and provide commented,
minimal, self-contained, reproducible code.
[[alternative HTML version deleted]]
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
