[edit]
1 碁 螳 #
語 譬襯
- 螻牛旧(common factor) - 蟯豸″ 蟆朱 螳
- (unique factor)
- 覲螳 蟯蟯螻螳 伎 .
- 豕豐 豢豢 螻 詞 螻豺襯 ろ襴 谿碁 蟲磯 伎 蟶曙企 螻褐 伎 .
- 覈蟯 企朱 螳れ 蠍郁伎 . (KMO and Bartleet's 蟆)
- 覿: 覲 豢 覈(譯殊焔覿, 譯殊碁, 豕磯碁 ...)
- 語 覿: 企 螳れ企 覈語 蟆讀 覈
- 覲螳 蟯襦覿 螻牛旧語 豢
- 豢 螻牛旧語 伎伎 覲螳 蟯蟯螻襯 る
- 碁(factor loading) 賊0.3 伎企 り 覺.
- 螻牛旧瑚骸 蟯螻 伎 螳 覲 煙 螳蟆壱 襦 蠍一
- 碁 蟆郁骸襯 覲 蟯豸° 覿襯襯 伎
語 伎
願唄 觜讀 譬朱 碁 る蟇 覲願 譯手朱 伎.
願唄 觜讀 譬朱 碁 る蟇 覲願 譯手朱 伎.
- 覲伎 (譯殊焔 覿螻 觜)
- 覲 企 譟伎 蟲譟 覦蟆
- 語朱 覓苦伎 譴螳 覲 蟇
- 螳 螳 豸′ 覲れ 狩 語朱 覓苦企讌
- 蠏覿企 覲覿 る覲
- 瑚骸 覲 伎 蟯螻襯 伎蠍 企れ 蟆曙 語螳 螳 0 1 螳蟾讌襦 碁れ 豢 る 蟆.
- 讌螳(orthogonal rotation)
- 碁り 蟯螻螳 襴(蟯蟯螻 0)朱 螳譯狩 (蠏覿企 覲覿 れ螻旧煙 狩蠍 伎 )
- varimax, quartimax, equimax
- 碁り 蟯螻螳 襴(蟯蟯螻 0)朱 螳譯狩 (蠏覿企 覲覿 れ螻旧煙 狩蠍 伎 )
- 螳(oblique rotation)
- 螻狩 覿覿 願姥 . 覃 碁り 蟯螻螳 襴曙 蟆 覲襦 り 覲願鍵 覓.
- 讌螳 覦 觜 碁 讌螻, 覿 讌
- oblimin, covarimin, quartimin, biquartimin, promax
- 螻狩 覿覿 願姥 . 覃 碁り 蟯螻螳 襴曙 蟆 覲襦 り 覲願鍵 覓.
- R factanal varimax, promax襷 讌, 襷 rotation 讌る GPArotation れ れ覃 り .
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2 語 level 譟一 #
> x <- c("5豌", "5襷")
> x <- factor(x)
> x
[1] 5豌 5襷
Levels: 5襷 5豌
> levels(x) <- c("5豌", "5襷")
> x
[1] 5襷 5豌
Levels: 5豌 5襷
[edit]
3 R #
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 .
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 .
- 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 .
- 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.
- 蠏覓願: 豢豢 語 豸′ 覲 伎 蟯螻襯 る(谿 )
#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
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