R语言聚类分析因子分析t检验程序.docx
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R语言聚类分析因子分析t检验程序.docx
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R语言聚类分析因子分析t检验程序
R语言聚类分析、因子分析、t检验相关程序及程序运行结果相关程序:
#####读入数据
x=read.delim("G:
\\上机考试数据.txt",header=TRUE,row.names=1)
####作系统聚类
d=dist(scale(x))
hc1=hclust(d);hc1
hc2=hclust(d,"average")
hc3=hclust(d,"centroid")
hc4=hclust(d,"ward")
####绘出谱系图和聚类情况(最长距离法、类平均法)
opar=par(mfrow=c(2,1),mar=c(5.2,4,0,0))
plclust(hc1,hang=-1)
re1=rect.hclust(hc1,k=3,border="red")
plclust(hc2,hang=-1)
re2=rect.hclust(hc2,k=3,border="red")
par(opar)
####绘出谱系图和聚类情况(重心法和Ward法)
opar<-par(mfrow=c(2,1),mar=c(5.2,4,0,0))
plclust(hc3,hang=-1)
re3=rect.hclust(hc3,k=3,border="red")
plclust(hc4,hang=-1)
re4=rect.hclust(hc4,k=3,border="red")
par(opar)
####动态聚类法
km<-kmeans(scale(x),3,nstart=35);km
sort(km$cluster)
####因子分析
y=read.delim("G:
\\上机考试数据.txt",header=TRUE,row.names=1)
R=cov(scale(y))
fa<-factanal(factors=4,covmat=R);fa
####计算因子得分
y=read.delim("G:
\\上机考试数据.txt",header=TRUE,row.names=1)
fa<-factanal(~.,factors=4,data=y,scores="Bartlett");fa
fa$scores####输出因子得分
####画出散点图
plot(fa$scores[,1:
2],type="n")
text(fa$scores[,1],fa$scores[,2])
plot(fa$scores[,3:
4],type="n")
text(fa$scores[,3],fa$scores[,4])
####t检验
a1=fa$scores[,1]
a2=fa$scores[,2]
a3=fa$scores[,3]
a4=fa$scores[,4]
t.test(a1,a2,alternative="greater")
t.test(a1,a3,alternative="greater")
t.test(a1,a4,alternative="greater")
t.test(a2,a3,alternative="greater")
t.test(a2,a4,alternative="greater")
t.test(a3,a4,alternative="greater")
程序运行结果:
>rm(list=ls(all=TRUE))
>#####读入数据
>x=read.delim("G:
\\上机考试数据.txt",header=TRUE,row.names=1)>####作系统聚类
>d=dist(scale(x))
>hc1=hclust(d);hc1
Call:
hclust(d=d)
Clustermethod:
complete
Distance:
euclidean
Numberofobjects:
35
>hc2=hclust(d,"average")
>hc3=hclust(d,"centroid")
>hc4=hclust(d,"ward")
>####绘出谱系图和聚类情况(最长距离法、类平均法)
>opar=par(mfrow=c(2,1),mar=c(5.2,4,0,0))
>plclust(hc1,hang=-1)
>re1=rect.hclust(hc1,k=3,border="red")
>plclust(hc2,hang=-1)
>re2=rect.hclust(hc2,k=3,border="red")
>par(opar)
>####绘出谱系图和聚类情况(重心法和Ward法)
>opar<-par(mfrow=c(2,1),mar=c(5.2,4,0,0))
>plclust(hc3,hang=-1)
>re3=rect.hclust(hc3,k=3,border="red")
>plclust(hc4,hang=-1)
>re4=rect.hclust(hc4,k=3,border="red")
>par(opar)
>####动态聚类法
>km<-kmeans(scale(x),3,nstart=35);km
K-meansclusteringwith3clustersofsizes21,6,8
Clustermeans:
工业生产总值.亿元.财政收入.万元.人均财政收入社会消费品零售总额.万元.
1-0.6065578-0.6358116-0.492914324-0.38769223
20.70213300.95658061.73019078
30.06157708
31.06561460.9515700-0.0037429870.97150928
外贸出口额外资利用总额.万美元.新增固定资产投资.万元.职工平均工资.元.
1-0.4402834-0.6033739-0.5009169-0.5949817
20.430951
31.46478750.877016
41.4748054
30.83253060.48526590.65714450.4557230
农民人均纯收入.元.城镇固定资产投资人均固定资产投资万人拥有工业企业数量
1-0.5985518-0.6641800-0.50088606-0.4006611
21.35346171.11060891.784768221.5715357
30.55610210.9105157-0.02375026-0.1269165人均科教文卫.事业费支出
1-0.21817624
20.80904765
3-0.03407311
Clusteringvector:
长丰县肥东县肥西县天长市明光市来安县全椒县定远县
33331111
凤阳县当涂县庐江县无为县含山县和县芜湖县繁昌县
12131322
南陵县宁国市郎溪县广德县泾县绩溪县旌德县铜陵县
32121112
东至县石台县青阳县桐城市怀宁县枞阳县潜山县太湖县
11131111
宿松县望江县岳西县
111
Withinclustersumofsquaresbycluster:
[1]91.9507140.3227274.39793
(between_SS/total_SS=53.2%)
Availablecomponents:
[1]"cluster""centers""totss""withinss"
[5]"tot.withinss""betweenss""size"
>sort(km$cluster)
明光市来安县全椒县定远县凤阳县庐江县含山县郎溪县
11111111
泾县绩溪县旌德县东至县石台县青阳县怀宁县枞阳县
11111111
潜山县太湖县宿松县望江县岳西县当涂县芜湖县繁昌县
11111
222
宁国市广德县铜陵县长丰县肥东县肥西县天长市无为县
222
33333
和县南陵县桐城市
333
>
>####因子分析
>y=read.delim("G:
\\上机考试数据.txt",header=TRUE,row.names=1)
>R=cov(scale(y))
>fa<-factanal(factors=4,covmat=R);fa
Call:
factanal(factors=4,covmat=R)
Uniquenesses:
工业生产总值.亿元.财政收入.万元.人均财政收入
0.1310.0050.017社会消费品零售总额.万元.外贸出口额外资利用总额.万美元.
0.1350.7570.466新增固定资产投资.万元.职工平均工资.元.农民人均纯收入.元.
0.2780.2490.228
城镇固定资产投资人均固定资产投资万人拥有工业企业数量
0.0050.0140.213人均科教文卫.事业费支出
0.518
Loadings:
Factor1Factor2Factor3Factor4
工业生产总值.亿元.0.1640.8630.306
财政收入.万元.0.2700.9040.2520.204
人均财政收入0.9110.2570.2360.177
社会消费品零售总额.万元.-0.3340.6620.5290.188
外贸出口额0.1440.2550.396
外资利用总额.万美元.0.4180.4410.405
新增固定资产投资.万元.0.1710.4850.675
职工平均工资.元.0.6690.4460.322
农民人均纯收入.元.0.5280.4530.5010.192
城镇固定资产投资0.3040.8930.293-0.142
人均固定资产投资0.8880.2720.263-0.231
万人拥有工业企业数量0.8380.271
人均科教文卫.事业费支出0.659-0.197
Factor1Factor2Factor3Factor4
SSloadings4.0093.8381.8910.247
ProportionVar0.3080.2950.1450.019
CumulativeVar0.3080.6040.7490.768
Thedegreesoffreedomforthemodelis32andthefitwas1.9244
>
>####计算因子得分
>y=read.delim("G:
\\上机考试数据.txt",header=TRUE,row.names=1)
>fa<-factanal(~.,factors=4,data=y,scores="Bartlett");fa
Call:
factanal(x=~.,factors=4,data=y,scores="Bartlett")
Uniquenesses:
工业生产总值.亿元.财政收入.万元.人均财政收入
0.1310.0050.017社会消费品零售总额.万元.外贸出口额外资利用总额.万美元.
0.1350.7570.466新增固定资产投资.万元.职工平均工资.元.农民人均纯收入.元.
0.2780.2490.228
城镇固定资产投资人均固定资产投资万人拥有工业企业数量
0.0050.0140.213人均科教文卫.事业费支出
0.518
Loadings:
Factor1Factor2Factor3Factor4
工业生产总值.亿元.0.1640.8630.306
财政收入.万元.0.2700.9040.2520.204
人均财政收入0.9110.2570.2360.177
社会消费品零售总额.万元.-0.3340.6620.5290.188
外贸出口额0.1440.2550.396
外资利用总额.万美元.0.4180.4410.405
新增固定资产投资.万元.0.1710.4850.675
职工平均工资.元.0.6690.4460.322
农民人均纯收入.元.0.5280.4530.5010.192
城镇固定资产投资0.3040.8930.293-0.142
人均固定资产投资0.8880.2720.263-0.231
万人拥有工业企业数量0.8380.271
人均科教文卫.事业费支出0.659-0.197
Factor1Factor2Factor3Factor4
SSloadings4.0093.8381.8910.247
ProportionVar0.3080.2950.1450.019
CumulativeVar0.3080.6040.7490.768
Testofthehypothesisthat4factorsaresufficient.
Thechisquarestatisticis50.36on32degreesoffreedom.
Thep-valueis0.0206
>fa$scores####输出因子得分
Factor1Factor2Factor3Factor4长丰县-0.1839626451.07142140-0.80273922-0.55908770肥东县-0.1780912072.78806120-1.869996990.68363597肥西县0.3486745953.12457544-1.98272073-0.50108034天长市-0.3286629390.111595891.527366470.46407338明光市-0.963771018-0.863549660.470027760.81176717来安县-0.396419372-0.490653540.31428314-0.67509321全椒县-0.127042257-0.419889530.37755024-0.37704456定远县-1.024098347-0.26034805-0.17002711-0.38265932
凤阳县-0.688874840-0.11971576-0.149323670.36608860当涂县0.7037171531.670270290.790349650.31755372庐江县-1.347580892-0.102963821.402392140.73197698无为县-1.8088349901.385048182.96739685-0.89328453含山县-0.097182092-0.62906939-0.017257820.69586421和县-0.633409518-0.226670391.11273582-0.23828640芜湖县1.4658235810.209259370.70918377-0.98730471繁昌县2.663194026-0.03934722-0.096996981.76758867南陵县-0.121028895-0.240599561.317465061.09467594宁国市2.1760513040.261465912.182125960.98305592郎溪县0.322546258-0.51458155-0.18277513-2.09016770
广德县0.4589008880.384439021.34637112-1.52375887泾县0.066399254-0.786674840.12591414-0.17464409绩溪县1.630369791-1.376421080.12958686-1.09105223旌德县0.015581604-1.38966324-0.22472503-0.02326432
铜陵县2.3526855060.08194171-0.90854462-1.71218909东至县-0.420839170-0.45254138-0.451577590.05270322石台县0.004431299-1.12401413-1.869384040.42232027青阳县0.395060735-0.98715620-0.516652482.04123624桐城市-0.3598737840.448872710.569349520.74475186怀宁县-0.0229618630.38101100-0.979917222.45423594枞阳县-0.6667594010.68399096-1.01032266-0.08880833潜山县-0.659551399-0.59777412-0.595834070.51402884太湖县-0.654810156-0.58593199-0.98967268-0.43277938
宿松县-0.920198188-0.28228760-0.27058925-0.98831067望江县-0.825113048-0.70444337-0.56674157-0.10014261岳西县-0.174369973-0.40765667-1.68629963-1.30659888>####画出散点图
>plot(fa$scores[,1:
2],type="n")
>text(fa$scores[,1],fa$scores[,2])
>plot(fa$scores[,3:
4],type="n")
>text(fa$scores[,3],fa$scores[,4])
>
>####t检验
>a1=fa$scores[,1]
>a2=fa$scores[,2]
>a3=fa$scores[,3]
>a4=fa$scores[,4]
>t.test(a1,a2,alternative="greater")
>t.test(a11,b11,alternative="greater")
WelchTwoSamplet-test
data:
a11andb11
t=1.9772,df=9.704,p-value=0.03855
alternativehypothesis:
truedifferenceinmeansisgreaterthan095percentconfidenceinterval:
0.06604808Inf
sampleestimates:
meanofxmeanofy
0.99583130.1749454
>t.test(a11,c11,alternative="greater")
WelchTwoSamplet-test
data:
a11andc11
t=4.3788,df=9.086,p-value=0.0008671
alternativehypothesis:
truedifferenceinmeansisgreaterthan095percentconfidenceinterval:
1.039104Inf
sampleestimates:
meanofxmeanofy
0.9958313-0.7901305
>t.test(b11,c11,alternative="greater")
WelchTwoSamplet-test
data:
b11andc11
t=5.8762,df=21.925,p-value=3.299e-06
alternativehypothesis:
truedifferenceinmeansisgreaterthan095percentconfidenceinterval:
0.6830173Inf
sampleestimates:
meanofxmeanofy
0.1749454-0.7901305
>t.test(a12,b12,alternative="greater")
WelchTwoSamplet-test
data:
a12andb12
t=2.994,df=10.814,p-value=0.006212
alternativehypothesis:
truedifferenceinmeansisgreaterthan095percentconfidenceinterval:
0.5557607Inf
sampleestimates:
meanofxmeanofy
0.9931852-0.3988946
>t.test(a12,c12,alternative="greater")
WelchTwoSamplet-test
data:
a12andc12
t=2.5917,df=10.145,p-value=0.01329
alternativehypothesis:
truedifferenceinmeansisgreaterthan095percentconfidenceinterval:
0.356743Inf
sampleestimates:
meanofxmeanofy
0.9931852-0.1893416
>t.test(b12,c12,alternative="greater")
WelchTwoSamplet-test
data:
b12andc12
t=-0.8809,df=22.84,p-value=0.8062
alternativehypothesis:
truedifferenceinmeansisgreaterthan095percentconfidenceinterval:
-0.6173701Infsampleestimates:
meanofxmeanofy-0.3988946-0.1893416
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