ろ 一危磯 "Excel 譟一覦覯 覦 糾覿, 誤讌, " .
tmp <- textConnection(
"豌蟆一 蠍一螳 襷 豺
6 4 7 6 5
5 7 5 6 6
5 3 4 5 6
3 3 2 3 4
4 3 3 3 2
2 6 2 4 3
1 3 3 3 2
3 5 3 4 2
7 3 6 5 5
6 4 3 4 4
6 6 3 6 4
3 2 2 4 2
5 7 2 5 2
6 3 6 5 7
3 4 5 3 2
2 7 5 5 4
3 5 2 7 2
6 4 5 5 7
7 4 6 3 5
5 6 6 3 4
2 3 3 4 3
3 4 2 3 4
3 6 3 5 3
6 5 7 5 5
7 6 5 4 6")
x <- read.table(tmp, header=TRUE)
close.connection(tmp)
#head(x)
襾殊 譯殊焔覿螻 碁 谿企ゼ 覲伎.
#install.packages("psych")
#library("psych")
fa.parallel(x)
蟆郁骸
> fa.parallel(x)
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
語 2螳手 豢豌伎が.
- 譯殊焔 覿 5譯殊焔蟾讌 覩
- 覿 2瑚讌襦 豢 --> 蟇碁 .
碁 企慨.
fit <- factanal(x, 2, rotation="promax")
print(fit)
#print(fit, cutoff = 1e-05, digits = 2)
蟆郁骸
> print(fit)
Call:
factanal(x = x, factors = 2, rotation = "promax")
Uniquenesses:
豌蟆一 蠍一螳 襷 豺
0.339 0.869 0.419 0.005 0.287
Loadings:
Factor1 Factor2
豌蟆一 0.801
0.371
蠍一螳 0.775
襷 0.988
豺 0.833
Factor1 Factor2
SS loadings 1.939 1.122
Proportion Var 0.388 0.224
Cumulative Var 0.388 0.612
Factor Correlations:
Factor1 Factor2
Factor1 1.000 0.239
Factor2 0.239 1.000
Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 0.22 on 1 degree of freedom.
The p-value is 0.639
- Uniquenesses 煙 螳朱 旧 0.5 危企 . 豌 覲 譴 1螳 覲襯 誤螻 覈 0.5 危 螳 覲伎願 .
- 2螳 瑚讌 . (factanal 2覯讌 襷り覲襯 3朱 覃 3螳 螻牛旧語 谿場朱朱 蟇企, 襷 覲螳 り )
- Loadings螳 覿碁..
- Factor1 覲企 豌蟆一, 蠍一螳, 豺 覲螳 螳 0.5伎(覲危 0.3 伎企 り 覺)朱 .
- Factor2襯 覲企 襷 0.99襦 蟲ロ . 0.37朱 螳 .
- Cumulative Var(る 豐覿一 螳) - Factor2蟾讌 0.612襷 る.
- Factor Correlations - Factor1 Factor2 覲襦 蟯 .
- p-value 0.639襦 覿 蠏覓願れ 蠍郁讌 覈詩覩襦, 碁 覩
- 蠏覓願: 豢豢 語 豸′ 覲 伎 蟯螻襯 る(谿 )
- 襴所: 豢豢 碁慨 襷 語 (谿 )
- 蠏覓願れ 蠍郁覃, 碁 覓伎覩誤.
#誤 る 谿瑚 譟郁 るゴ蟆
#fit <- factanal(x, 2, rotation="none")
load <- fit$loadings[,1:2]
plot(load,type="n") # set up plot
text(load,labels=names(x),cex=.7) # add variable names
factanal()螳 覃..
library(psych)
library(GPArotation)
fit <- fa(r=cor(x1), nfactors=3, rotate="promax")
summary(fit)
load <- fit$loadings[,1:2]
plot(load,type="n") # set up plot
text(load,labels=names(x),cex=.7) # add variable names
螳 覃 螻牛旧瑚 蟲 .
> factanal(x, 2, rotation="promax", scores="regression")$scores
Factor1 Factor2
[1,] 0.8266176 1.15483124
[2,] 0.5335642 1.24017288
[3,] 0.5908294 0.35575418
[4,] -0.3220569 -1.12270001
[5,] -0.5015774 -1.07468932
[6,] -1.0200758 -0.06511692
[7,] -1.1099463 -0.90967064
[8,] -0.9146350 -0.09589597
[9,] 1.0607151 0.22680194
[10,] 0.2550042 -0.41469562
[11,] -0.1482268 1.42528154
[12,] -1.0589853 -0.06032610
[13,] -0.8896313 0.76484419
[14,] 1.4105432 0.13182575
[15,] -0.3724041 -1.10981555
[16,] -0.4344594 0.63898395
[17,] -1.6638319 2.69963963
[18,] 1.2315831 0.18258979
[19,] 1.4379888 -1.60177959
[20,] 0.7387958 -1.40917884
[21,] -0.8238109 -0.12347804
[22,] -0.3307093 -1.11890147
[23,] -0.8399416 0.74910139
[24,] 1.0109283 0.24243975
[25,] 1.3337221 -0.70601818
