Displaying 5 results from an estimated 5 matches for "pca2".
Did you mean:
pca
2011 Nov 28
2
Principal componet plot from lower triangular matrix file
...2) compute first and second principal
components and 3) and plot first vs second PC's ?
In the past, I could do this by :
b <- read.csv("distance.csv", sep=",", head=F) # distance.csv file is
complete data matrix, so this command worked !!
my_matrix <- data.matrix(b)
pca2 <- princomp(my_matrix)
plot(pca2$scores[,1],pca2$scores[,2])
text(pca2$scores[,1],pca2$scores[,2],rownames(nba_matrix), cex=0.5, pos=1)
This time, I don't have a complete file. So, I was wondering, how to do this
?
Any help is much appreciated
TIA
M
--
View this message in context: http:...
2013 Oct 01
5
Análisis de componentes principales con ade4 y FactoMineR
....57
4 -14.12 -1.71
5 -16.32 4.22
6 -17.03 5.94
7 -16.90 3.68
8 -17.75 5.86
9 13.86 -13.33
10 13.16 -12.71
11 13.24 -14.18
12 12.68 -13.07
13 18.43 11.67
14 17.49 10.86
15 17.82 12.43
16 19.02 11.83
Función PCA:
Comando:
PCA2 <- PCA(DATOS[,(1:ncol(DATOS))])
Individuo Comp1 Comp2
1 -14.18 4.47
2 -14.63 4.53
3 -14.77 2.57
4 -14.12 1.71
5 -16.32 -4.22
6 -17.03 -5.94
7 -16.90 -3.68
8 -17.75 -5.86
9 13.86 13.33
10 13.16 12.71
11 13.24 14.18
12 12....
2013 Oct 01
3
Análisis de componentes principales con ade4 y FactoMineR
...-1.71
5 -16.32 4.22
6 -17.03 5.94
7 -16.90 3.68
8 -17.75 5.86
9 13.86 -13.33
10 13.16 -12.71
11 13.24 -14.18
12 12.68 -13.07
13 18.43 11.67
14 17.49 10.86
15 17.82 12.43
16 19.02 11.83
Función PCA:
Comando:
PCA2 <- PCA(DATOS[,(1:ncol(DATOS))])
Individuo Comp1 Comp2
1 -14.18 4.47
2 -14.63 4.53
3 -14.77 2.57
4 -14.12 1.71
5 -16.32 -4.22
6 -17.03 -5.94
7 -16.90 -3.68
8 -17.75 -5.86
9 13.86 13.33
10 13.16 12.71
11 13.24 14....
2013 Oct 01
0
Análisis de componentes principales con ade4 y FactoMineR
...-16.90 3.68
> 8 -17.75 5.86
> 9 13.86 -13.33
> 10 13.16 -12.71
> 11 13.24 -14.18
> 12 12.68 -13.07
> 13 18.43 11.67
> 14 17.49 10.86
> 15 17.82 12.43
> 16 19.02 11.83
>
> Función PCA:
>
> Comando:
> PCA2 <- PCA(DATOS[,(1:ncol(DATOS))])
>
> Individuo Comp1 Comp2
> 1 -14.18 4.47
> 2 -14.63 4.53
> 3 -14.77 2.57
> 4 -14.12 1.71
> 5 -16.32 -4.22
> 6 -17.03 -5.94
> 7 -16.90 -3.68
> 8 -17.75 -5.86
> 9 13.86 13....
2013 Oct 02
0
Análisis de componentes principales con ade4 y FactoMineR
...-12.71
>>> 11 13.24 -14.18
>>> 12 12.68 -13.07
>>> 13 18.43 11.67
>>> 14 17.49 10.86
>>> 15 17.82 12.43
>>> 16 19.02 11.83
>>>
>>> Función PCA:
>>>
>>> Comando:
>>> PCA2 <- PCA(DATOS[,(1:ncol(DATOS))])
>>>
>>> Individuo Comp1 Comp2
>>> 1 -14.18 4.47
>>> 2 -14.63 4.53
>>> 3 -14.77 2.57
>>> 4 -14.12 1.71
>>> 5 -16.32 -4.22
>>> 6 -17.03 -5.94
>>&g...