##1 Correspondence analysis 對應分析
#讀資料
#ftbl是一個列聯表
ftbl <- read.ftable('103death.txt',skip=1,row.var.names = "性別年齡組",
col.vars = list('死因' =c('惡性腫瘤','心臟疾病','腦血管疾病','肺炎','事故傷害','糖尿病','慢性下呼吸道','慢性肝','高血壓','腎')))
#將列連表恢復成原來的資料
library(vcdExtra)
## Loading required package: vcd
## Loading required package: grid
## Loading required package: gnm
dtaraw <- expand.dft(as.data.frame(ftbl),freq='Freq')
mytable <- with(dtaraw, table(性別年齡組,死因)) # create a 2 way table
prop.table(mytable, 1) # row percentages
## 死因
## 性別年齡組 心臟疾病 事故傷害 肺炎 高血壓 惡性腫瘤
## 女_0 0.120000000 0.120000000 0.060000000 0.000000000 0.040000000
## 女_1-4 0.145833333 0.041666667 0.041666667 0.020833333 0.229166667
## 女_10-14 0.116279070 0.046511628 0.023255814 0.023255814 0.372093023
## 女_15-19 0.040404040 0.030303030 0.010101010 0.000000000 0.171717172
## 女_20-24 0.069306931 0.019801980 0.019801980 0.019801980 0.257425743
## 女_25-29 0.151515152 0.015151515 0.037878788 0.007575758 0.340909091
## 女_30-34 0.091254753 0.030418251 0.072243346 0.015209125 0.494296578
## 女_35-39 0.097065463 0.027088036 0.060948081 0.013544018 0.541760722
## 女_40-44 0.074820144 0.012949640 0.071942446 0.008633094 0.660431655
## 女_45-49 0.078202995 0.024126456 0.068219634 0.004159734 0.678036606
## 女_5-9 0.090909091 0.000000000 0.000000000 0.000000000 0.575757576
## 女_50-54 0.071355236 0.022073922 0.051848049 0.009240246 0.687885010
## 女_55-59 0.080603607 0.022451233 0.062200957 0.006624954 0.654398233
## 女_60-64 0.105590062 0.029872819 0.060041408 0.016267377 0.563738539
## 女_65-69 0.126412214 0.036030534 0.065954198 0.017099237 0.492213740
## 女_70-74 0.130092779 0.050221864 0.085316660 0.019766035 0.438079871
## 女_75-79 0.159159591 0.064901425 0.102748597 0.033386099 0.346668585
## 女_80-84 0.181883537 0.088305775 0.118619698 0.043973161 0.274622574
## 女_85-89 0.210132269 0.123034689 0.120164712 0.052283504 0.188794609
## 女_90+ 0.232502966 0.181939502 0.116251483 0.065539739 0.120996441
## 男_0 0.166666667 0.454545455 0.212121212 0.000000000 0.030303030
## 男_1-4 0.108695652 0.282608696 0.043478261 0.000000000 0.434782609
## 男_10-14 0.100000000 0.471428571 0.028571429 0.000000000 0.342857143
## 男_15-19 0.041825095 0.836501901 0.000000000 0.000000000 0.098859316
## 男_20-24 0.061728395 0.743827160 0.018518519 0.003086420 0.148148148
## 男_25-29 0.089337176 0.547550432 0.017291066 0.008645533 0.198847262
## 男_30-34 0.128032345 0.358490566 0.018867925 0.018867925 0.253369272
## 男_35-39 0.140337987 0.224834680 0.024981631 0.025716385 0.280675974
## 男_40-44 0.141713748 0.148305085 0.021657250 0.021186441 0.380885122
## 男_45-49 0.130199430 0.109116809 0.020797721 0.019658120 0.438176638
## 男_5-9 0.060000000 0.520000000 0.000000000 0.000000000 0.380000000
## 男_50-54 0.139355884 0.078387685 0.026331780 0.028964958 0.464249544
## 男_55-59 0.131964316 0.066150480 0.027436459 0.030129608 0.487796667
## 男_60-64 0.143467730 0.062402052 0.034762787 0.025502208 0.493232654
## 男_65-69 0.137936717 0.049110086 0.047132498 0.030158207 0.471324984
## 男_70-74 0.137606397 0.051586278 0.062677328 0.032757287 0.433711633
## 男_75-79 0.143583508 0.041229137 0.085995358 0.034376036 0.380236543
## 男_80-84 0.157827939 0.033541837 0.117577735 0.042788505 0.307769015
## 男_85-89 0.176274510 0.026470588 0.151666667 0.055686275 0.250588235
## 男_90+ 0.189805500 0.026659960 0.197350771 0.061871227 0.182092555
## 死因
## 性別年齡組 腎 腦血管疾病 慢性下呼吸道 慢性肝 糖尿病
## 女_0 0.100000000 0.000000000 0.020000000 0.540000000 0.000000000
## 女_1-4 0.041666667 0.000000000 0.020833333 0.458333333 0.000000000
## 女_10-14 0.023255814 0.000000000 0.046511628 0.325581395 0.023255814
## 女_15-19 0.010101010 0.010101010 0.000000000 0.727272727 0.000000000
## 女_20-24 0.019801980 0.049504950 0.029702970 0.514851485 0.000000000
## 女_25-29 0.015151515 0.022727273 0.045454545 0.356060606 0.007575758
## 女_30-34 0.011406844 0.030418251 0.011406844 0.231939163 0.011406844
## 女_35-39 0.015801354 0.056433409 0.018058691 0.155756208 0.013544018
## 女_40-44 0.015827338 0.025899281 0.020143885 0.093525180 0.015827338
## 女_45-49 0.013311148 0.029950083 0.015806988 0.076539101 0.011647255
## 女_5-9 0.000000000 0.000000000 0.000000000 0.333333333 0.000000000
## 女_50-54 0.012320329 0.046714579 0.021047228 0.054414784 0.023100616
## 女_55-59 0.015458226 0.058520427 0.032388664 0.047478837 0.019874862
## 女_60-64 0.017450458 0.087252292 0.038745933 0.057675244 0.023365868
## 女_65-69 0.021374046 0.109007634 0.047633588 0.050381679 0.033893130
## 女_70-74 0.021379589 0.124445341 0.050423558 0.040540541 0.039733764
## 女_75-79 0.030795798 0.124766153 0.055835372 0.031515326 0.050223054
## 女_80-84 0.030913012 0.124490774 0.052600048 0.022525761 0.062065660
## 女_85-89 0.039680559 0.114549538 0.056526079 0.018717245 0.076116796
## 女_90+ 0.044039146 0.077698695 0.048042705 0.018831554 0.094157770
## 男_0 0.030303030 0.060606061 0.030303030 0.000000000 0.015151515
## 男_1-4 0.043478261 0.021739130 0.043478261 0.000000000 0.021739130
## 男_10-14 0.000000000 0.028571429 0.000000000 0.000000000 0.028571429
## 男_15-19 0.003802281 0.007604563 0.011406844 0.000000000 0.000000000
## 男_20-24 0.003086420 0.015432099 0.003086420 0.000000000 0.003086420
## 男_25-29 0.011527378 0.054755043 0.008645533 0.043227666 0.020172911
## 男_30-34 0.013477089 0.036388140 0.006738544 0.152291105 0.013477089
## 男_35-39 0.012490816 0.070536370 0.007347539 0.176340926 0.036737693
## 男_40-44 0.012711864 0.062146893 0.006591337 0.169962335 0.034839925
## 男_45-49 0.013105413 0.074358974 0.007977208 0.149002849 0.037606838
## 男_5-9 0.000000000 0.040000000 0.000000000 0.000000000 0.000000000
## 男_50-54 0.019850111 0.071298359 0.008912295 0.108365404 0.054283978
## 男_55-59 0.024743309 0.074061606 0.013129103 0.083655950 0.060932503
## 男_60-64 0.030773614 0.074939450 0.021940447 0.047727597 0.065251460
## 男_65-69 0.031806196 0.086354647 0.030652604 0.036750165 0.078773896
## 男_70-74 0.032628321 0.092210472 0.048620067 0.024116585 0.084085633
## 男_75-79 0.038244722 0.104233448 0.072068089 0.022548911 0.077484249
## 男_80-84 0.042969812 0.112138519 0.101713353 0.016045689 0.067627595
## 男_85-89 0.042549020 0.105392157 0.118725490 0.009901961 0.062745098
## 男_90+ 0.045271630 0.103621730 0.135311871 0.007880617 0.050134138
prop.table(mytable, 2) # column percentages
## 死因
## 性別年齡組 心臟疾病 事故傷害 肺炎 高血壓
## 女_0 3.092784e-04 6.550934e-04 2.712477e-04 0.000000e+00
## 女_1-4 3.608247e-04 2.183645e-04 1.808318e-04 2.194908e-04
## 女_10-14 2.577320e-04 2.183645e-04 9.041591e-05 2.194908e-04
## 女_15-19 2.061856e-04 3.275467e-04 9.041591e-05 0.000000e+00
## 女_20-24 3.608247e-04 2.183645e-04 1.808318e-04 4.389816e-04
## 女_25-29 1.030928e-03 2.183645e-04 4.520796e-04 2.194908e-04
## 女_30-34 1.237113e-03 8.734578e-04 1.717902e-03 8.779631e-04
## 女_35-39 2.216495e-03 1.310187e-03 2.441230e-03 1.316945e-03
## 女_40-44 2.680412e-03 9.826400e-04 4.520796e-03 1.316945e-03
## 女_45-49 4.845361e-03 3.166285e-03 7.414105e-03 1.097454e-03
## 女_5-9 1.546392e-04 0.000000e+00 0.000000e+00 0.000000e+00
## 女_50-54 7.164948e-03 4.694836e-03 9.132007e-03 3.950834e-03
## 女_55-59 1.128866e-02 6.660116e-03 1.528029e-02 3.950834e-03
## 女_60-64 1.840206e-02 1.102740e-02 1.835443e-02 1.207199e-02
## 女_65-69 2.134021e-02 1.288350e-02 1.952984e-02 1.229148e-02
## 女_70-74 3.324742e-02 2.718637e-02 3.824593e-02 2.151010e-02
## 女_75-79 5.701031e-02 4.924118e-02 6.455696e-02 5.092186e-02
## 女_80-84 7.824742e-02 8.046730e-02 8.951175e-02 8.055312e-02
## 女_85-89 8.680412e-02 1.076537e-01 8.707052e-02 9.196664e-02
## 女_90+ 8.082474e-02 1.339666e-01 7.088608e-02 9.701493e-02
## 男_0 5.670103e-04 3.275467e-03 1.265823e-03 0.000000e+00
## 男_1-4 2.577320e-04 1.419369e-03 1.808318e-04 0.000000e+00
## 男_10-14 3.608247e-04 3.603013e-03 1.808318e-04 0.000000e+00
## 男_15-19 5.670103e-04 2.402009e-02 0.000000e+00 0.000000e+00
## 男_20-24 1.030928e-03 2.631292e-02 5.424955e-04 2.194908e-04
## 男_25-29 1.597938e-03 2.074462e-02 5.424955e-04 6.584723e-04
## 男_30-34 4.896907e-03 2.904247e-02 1.265823e-03 3.072871e-03
## 男_35-39 9.845361e-03 3.340976e-02 3.074141e-03 7.682177e-03
## 男_40-44 1.551546e-02 3.439240e-02 4.159132e-03 9.877085e-03
## 男_45-49 2.355670e-02 4.181679e-02 6.600362e-03 1.514486e-02
## 男_5-9 1.546392e-04 2.838738e-03 0.000000e+00 0.000000e+00
## 男_50-54 3.546392e-02 4.225352e-02 1.175407e-02 3.138718e-02
## 男_55-59 4.041237e-02 4.290861e-02 1.473779e-02 3.928885e-02
## 男_60-64 5.190722e-02 4.782181e-02 2.206148e-02 3.928885e-02
## 男_65-69 4.314433e-02 3.253630e-02 2.585895e-02 4.016681e-02
## 男_70-74 5.500000e-02 4.367289e-02 4.394213e-02 5.575066e-02
## 男_75-79 6.695876e-02 4.072497e-02 7.034358e-02 6.826163e-02
## 男_80-84 8.974227e-02 4.039742e-02 1.172694e-01 1.035996e-01
## 男_85-89 9.268041e-02 2.947920e-02 1.398734e-01 1.246708e-01
## 男_90+ 5.835052e-02 1.735997e-02 1.064195e-01 8.099210e-02
## 死因
## 性別年齡組 惡性腫瘤 腎 腦血管疾病 慢性下呼吸道
## 女_0 4.338865e-05 1.256597e-03 0.000000e+00 1.423285e-04
## 女_1-4 2.386376e-04 5.026389e-04 0.000000e+00 1.423285e-04
## 女_10-14 3.471092e-04 2.513194e-04 0.000000e+00 2.846570e-04
## 女_15-19 3.688036e-04 2.513194e-04 8.372405e-05 0.000000e+00
## 女_20-24 5.640525e-04 5.026389e-04 4.186202e-04 4.269855e-04
## 女_25-29 9.762447e-04 5.026389e-04 2.511721e-04 8.539710e-04
## 女_30-34 2.820263e-03 7.539583e-04 6.697924e-04 4.269855e-04
## 女_35-39 5.206638e-03 1.759236e-03 2.093101e-03 1.138628e-03
## 女_40-44 9.957696e-03 2.764514e-03 1.507033e-03 1.992599e-03
## 女_45-49 1.768088e-02 4.021111e-03 3.014066e-03 2.704241e-03
## 女_5-9 4.121922e-04 0.000000e+00 0.000000e+00 0.000000e+00
## 女_50-54 2.907040e-02 6.031666e-03 7.618888e-03 5.835468e-03
## 女_55-59 3.857251e-02 1.055542e-02 1.331212e-02 1.252491e-02
## 女_60-64 4.134939e-02 1.482785e-02 2.469859e-02 1.864503e-02
## 女_65-69 3.497126e-02 1.759236e-02 2.988948e-02 2.220325e-02
## 女_70-74 4.712008e-02 2.663986e-02 5.165774e-02 3.558212e-02
## 女_75-79 5.226163e-02 5.378236e-02 7.258875e-02 5.522346e-02
## 女_80-84 4.972340e-02 6.484041e-02 8.698928e-02 6.248221e-02
## 女_85-89 3.282352e-02 7.991958e-02 7.685867e-02 6.447481e-02
## 女_90+ 1.770257e-02 7.464187e-02 4.387140e-02 4.611443e-02
## 男_0 4.338865e-05 5.026389e-04 3.348962e-04 2.846570e-04
## 男_1-4 4.338865e-04 5.026389e-04 8.372405e-05 2.846570e-04
## 男_10-14 5.206638e-04 0.000000e+00 1.674481e-04 0.000000e+00
## 男_15-19 5.640525e-04 2.513194e-04 1.674481e-04 4.269855e-04
## 男_20-24 1.041328e-03 2.513194e-04 4.186202e-04 1.423285e-04
## 男_25-29 1.496909e-03 1.005278e-03 1.590757e-03 4.269855e-04
## 男_30-34 4.078533e-03 2.513194e-03 2.260549e-03 7.116425e-04
## 男_35-39 8.287233e-03 4.272430e-03 8.037508e-03 1.423285e-03
## 男_40-44 1.755071e-02 6.785625e-03 1.105157e-02 1.992599e-03
## 男_45-49 3.336587e-02 1.156069e-02 2.185198e-02 3.985198e-03
## 男_5-9 4.121922e-04 0.000000e+00 1.674481e-04 0.000000e+00
## 男_50-54 4.972340e-02 2.462930e-02 2.947086e-02 6.262454e-03
## 男_55-59 6.287016e-02 3.694396e-02 3.683858e-02 1.110162e-02
## 男_60-64 7.510576e-02 5.428500e-02 4.403885e-02 2.191859e-02
## 男_65-69 6.204578e-02 4.850465e-02 4.387140e-02 2.647310e-02
## 男_70-74 7.295802e-02 6.358382e-02 5.986269e-02 5.365784e-02
## 男_75-79 7.462848e-02 8.695652e-02 7.895177e-02 9.279818e-02
## 男_80-84 7.365224e-02 1.191254e-01 1.035666e-01 1.596926e-01
## 男_85-89 5.545070e-02 1.090726e-01 9.000335e-02 1.723598e-01
## 男_90+ 2.356004e-02 6.785625e-02 5.174146e-02 1.148591e-01
## 死因
## 性別年齡組 慢性肝 糖尿病
## 女_0 4.847397e-03 0.000000e+00
## 女_1-4 3.949731e-03 0.000000e+00
## 女_10-14 2.513465e-03 1.330495e-04
## 女_15-19 1.292639e-02 0.000000e+00
## 女_20-24 9.335727e-03 0.000000e+00
## 女_25-29 8.438061e-03 1.330495e-04
## 女_30-34 1.095153e-02 3.991485e-04
## 女_35-39 1.238779e-02 7.982970e-04
## 女_40-44 1.166966e-02 1.463544e-03
## 女_45-49 1.651706e-02 1.862693e-03
## 女_5-9 1.974865e-03 0.000000e+00
## 女_50-54 1.903052e-02 5.987227e-03
## 女_55-59 2.315978e-02 7.184673e-03
## 女_60-64 3.500898e-02 1.051091e-02
## 女_65-69 2.962298e-02 1.476849e-02
## 女_70-74 3.608618e-02 2.621075e-02
## 女_75-79 3.931777e-02 4.643427e-02
## 女_80-84 3.375224e-02 6.891964e-02
## 女_85-89 2.692998e-02 8.116019e-02
## 女_90+ 2.280072e-02 8.448643e-02
## 男_0 0.000000e+00 1.330495e-04
## 男_1-4 0.000000e+00 1.330495e-04
## 男_10-14 0.000000e+00 2.660990e-04
## 男_15-19 0.000000e+00 0.000000e+00
## 男_20-24 0.000000e+00 1.330495e-04
## 男_25-29 2.692998e-03 9.313465e-04
## 男_30-34 2.028725e-02 1.330495e-03
## 男_35-39 4.308797e-02 6.652475e-03
## 男_40-44 6.481149e-02 9.845663e-03
## 男_45-49 9.389587e-02 1.756253e-02
## 男_5-9 0.000000e+00 0.000000e+00
## 男_50-54 9.605027e-02 3.565726e-02
## 男_55-59 8.922801e-02 4.816392e-02
## 男_60-64 6.014363e-02 6.093667e-02
## 男_65-69 4.003591e-02 6.359766e-02
## 男_70-74 3.357271e-02 8.674827e-02
## 男_75-79 3.662478e-02 9.326770e-02
## 男_80-84 3.177738e-02 9.925492e-02
## 男_85-89 1.813285e-02 8.515168e-02
## 男_90+ 8.438061e-03 3.978180e-02
library(ca)
fit <- ca(mytable)
print(fit) # basic results
##
## Principal inertias (eigenvalues):
## 1 2 3 4 5 6 7
## Value 0.123221 0.094676 0.031679 0.011286 0.003661 0.002747 0.000473
## Percentage 45.96% 35.31% 11.82% 4.21% 1.37% 1.02% 0.18%
## 8 9
## Value 0.000268 1e-04
## Percentage 0.1% 0.04%
##
##
## Rows:
## 女_0 女_1-4 女_10-14 女_15-19 女_20-24 女_25-29
## Mass 0.000396 0.000380 0.000340 0.000784 0.000800 0.001045
## ChiDist 2.504946 2.040735 1.409708 3.332185 2.301867 1.553946
## Inertia 0.002484 0.001583 0.000677 0.008703 0.004237 0.002524
## Dim. 1 -3.289476 -3.211897 -2.611178 -5.493784 -3.816755 -2.688154
## Dim. 2 2.258891 0.924272 0.366967 1.446763 0.663577 0.146916
## 女_30-34 女_35-39 女_40-44 女_45-49 女_5-9 女_50-54
## Mass 0.002082 0.003507 0.005503 0.009517 0.000261 0.015423
## ChiDist 1.020190 0.723786 0.721094 0.718269 1.575338 0.693888
## Inertia 0.002167 0.001837 0.002861 0.004910 0.000648 0.007426
## Dim. 1 -2.256676 -1.784596 -1.649861 -1.628539 -3.629794 -1.489358
## Dim. 2 -0.249601 -0.558622 -1.089443 -1.030588 -0.452935 -1.128415
## 女_55-59 女_60-64 女_65-69 女_70-74 女_75-79 女_80-84
## Mass 0.021511 0.026769 0.025929 0.039254 0.055018 0.066078
## ChiDist 0.623279 0.453840 0.318418 0.229285 0.139465 0.253098
## Inertia 0.008357 0.005514 0.002629 0.002064 0.001070 0.004233
## Dim. 1 -1.242479 -0.964462 -0.591298 -0.266964 0.226172 0.580965
## Dim. 2 -1.104255 -0.825777 -0.649742 -0.430157 -0.099947 0.301067
## 女_85-89 女_90+ 男_0 男_1-4 男_10-14 男_15-19 男_20-24
## Mass 0.063450 0.053395 0.000523 0.000364 0.000554 0.002082 0.002565
## ChiDist 0.431349 0.667748 1.623205 0.908190 1.575975 2.948069 2.592709
## Inertia 0.011806 0.023808 0.001377 0.000300 0.001376 0.018097 0.017244
## Dim. 1 0.911793 1.041583 1.024314 -0.536373 -0.798522 -0.673776 -0.639992
## Dim. 2 0.864463 1.707669 4.692106 2.078709 4.409334 8.817604 7.730789
## 男_25-29 男_30-34 男_35-39 男_40-44 男_45-49 男_5-9
## Mass 0.002747 0.005875 0.010776 0.016816 0.027790 0.000396
## ChiDist 1.840312 1.272153 0.923583 0.756023 0.639210 1.779021
## Inertia 0.009304 0.009507 0.009192 0.009612 0.011355 0.001253
## Dim. 1 -0.756362 -1.481631 -1.480206 -1.665907 -1.623855 -1.121608
## Dim. 2 5.579168 3.693687 2.230065 1.193004 0.590918 4.839300
## 男_50-54 男_55-59 男_60-64 男_65-69 男_70-74 男_75-79
## Mass 0.039088 0.047037 0.055572 0.048042 0.061391 0.071628
## ChiDist 0.465202 0.401378 0.327797 0.281403 0.219608 0.195325
## Inertia 0.008459 0.007578 0.005971 0.003804 0.002961 0.002733
## Dim. 1 -1.268316 -1.097919 -0.757520 -0.483423 -0.135506 0.244038
## Dim. 2 0.130875 -0.116165 -0.260510 -0.415045 -0.385756 -0.459806
## 男_80-84 男_85-89 男_90+
## Mass 0.087336 0.080757 0.047219
## ChiDist 0.325783 0.478949 0.661439
## Inertia 0.009269 0.018525 0.020658
## Dim. 1 0.784307 1.232642 1.691900
## Dim. 2 -0.481470 -0.485582 -0.399698
##
##
## Columns:
## 心臟疾病 事故傷害 肺炎 高血壓 惡性腫瘤 腎
## Mass 0.153596 0.072515 0.087566 0.036071 0.364950 0.031503
## ChiDist 0.234746 1.043904 0.531680 0.438420 0.377182 0.337467
## Inertia 0.008464 0.079022 0.024753 0.006933 0.051920 0.003588
## Dim. 1 0.525620 -0.239405 1.388717 1.103231 -0.843032 0.847455
## Dim. 2 0.208532 3.318775 -0.214588 0.185023 -0.633609 -0.121695
## 腦血管疾病 慢性下呼吸道 慢性肝 糖尿病
## Mass 0.094565 0.055627 0.044100 0.059507
## ChiDist 0.252391 0.657024 1.129905 0.345255
## Inertia 0.006024 0.024013 0.056301 0.007093
## Dim. 1 0.489725 1.591974 -2.509691 0.537811
## Dim. 2 -0.211844 -0.616906 0.619446 0.025676
summary(fit) # extended results
## Warning in abbreviate(rnames.temp, 4): abbreviate 不適用於非 ASCII 字元
## Warning in abbreviate(cnames.temp, 4): abbreviate 不適用於非 ASCII 字元
##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.123221 46.0 46.0 ***********
## 2 0.094676 35.3 81.3 *********
## 3 0.031679 11.8 93.1 ***
## 4 0.011286 4.2 97.3 *
## 5 0.003661 1.4 98.7
## 6 0.002747 1.0 99.7
## 7 0.000473 0.2 99.9
## 8 0.000268 0.1 100.0
## 9 1e-04000 0.0 100.0
## -------- -----
## Total: 0.268112 100.0
##
##
## Rows:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | 女_0 | 0 289 9 | -1155 212 4 | 695 77 2 |
## 2 | 女_14 | 0 325 6 | -1127 305 4 | 284 19 0 |
## 3 | 女_10 | 0 429 3 | -917 423 2 | 113 6 0 |
## 4 | 女_15 | 1 353 32 | -1928 335 24 | 445 18 2 |
## 5 | 女_20 | 1 347 16 | -1340 339 12 | 204 8 0 |
## 6 | 女_25 | 1 370 9 | -944 369 8 | 45 1 0 |
## 7 | 女_30 | 2 609 8 | -792 603 11 | -77 6 0 |
## 8 | 女_35 | 4 806 7 | -626 749 11 | -172 56 1 |
## 9 | 女_40 | 6 861 11 | -579 645 15 | -335 216 7 |
## 10 | 女_45 | 10 828 18 | -572 633 25 | -317 195 10 |
## 11 | 女_59 | 0 662 2 | -1274 654 3 | -139 8 0 |
## 12 | 女_50 | 15 818 28 | -523 568 34 | -347 250 20 |
## 13 | 女_55 | 22 787 31 | -436 490 33 | -340 297 26 |
## 14 | 女_60 | 27 870 21 | -339 556 25 | -254 313 18 |
## 15 | 女_65 | 26 819 10 | -208 425 9 | -200 394 11 |
## 16 | 女_70 | 39 500 8 | -94 167 3 | -132 333 7 |
## 17 | 女_75 | 55 373 4 | 79 324 3 | -31 49 1 |
## 18 | 女_80 | 66 783 16 | 204 649 22 | 93 134 6 |
## 19 | 女_85 | 63 931 44 | 320 551 53 | 266 380 47 |
## 20 | 女_90 | 53 919 89 | 366 300 58 | 525 619 156 |
## 21 | 男_0 | 1 840 5 | 360 49 1 | 1444 791 12 |
## 22 | 男_14 | 0 539 1 | -188 43 0 | 640 496 2 |
## 23 | 男_10 | 1 773 5 | -280 32 0 | 1357 741 11 |
## 24 | 男_15 | 2 853 67 | -237 6 1 | 2713 847 162 |
## 25 | 男_20 | 3 849 64 | -225 8 1 | 2379 842 153 |
## 26 | 男_25 | 3 891 35 | -266 21 2 | 1717 870 86 |
## 27 | 男_30 | 6 965 35 | -520 167 13 | 1137 798 80 |
## 28 | 男_35 | 11 868 34 | -520 317 24 | 686 552 54 |
## 29 | 男_40 | 17 834 36 | -585 598 47 | 367 236 24 |
## 30 | 男_45 | 28 876 42 | -570 795 73 | 182 81 10 |
## 31 | 男_59 | 0 750 5 | -394 49 0 | 1489 701 9 |
## 32 | 男_50 | 39 923 32 | -445 916 63 | 40 7 1 |
## 33 | 男_55 | 47 930 28 | -385 922 57 | -36 8 1 |
## 34 | 男_60 | 56 718 22 | -266 658 32 | -80 60 4 |
## 35 | 男_65 | 48 570 14 | -170 364 11 | -128 206 8 |
## 36 | 男_70 | 61 339 11 | -48 47 1 | -119 292 9 |
## 37 | 男_75 | 72 717 10 | 86 192 4 | -141 525 15 |
## 38 | 男_80 | 87 921 35 | 275 714 54 | -148 207 20 |
## 39 | 男_85 | 81 913 69 | 433 816 123 | -149 97 19 |
## 40 | 男_90 | 47 841 77 | 594 806 135 | -123 35 8 |
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | 心臟疾病 | 154 692 32 | 185 618 42 | 64 75 7 |
## 2 | 事故傷害 | 73 963 295 | -84 6 4 | 1021 957 799 |
## 3 | 肺炎 | 88 856 92 | 487 841 169 | -66 15 4 |
## 4 | 高血壓 | 36 797 26 | 387 780 44 | 57 17 1 |
## 5 | 惡性腫瘤 | 365 883 194 | -296 616 259 | -195 267 147 |
## 6 | 腎 | 32 789 13 | 297 777 23 | -37 12 0 |
## 7 | 腦血管疾 | 95 531 22 | 172 464 23 | -65 67 4 |
## 8 | 慢性下呼 | 56 807 90 | 559 723 141 | -190 83 21 |
## 9 | 慢性肝 | 44 636 210 | -881 608 278 | 191 28 17 |
## 10 | 糖尿病 | 60 300 26 | 189 299 17 | 8 1 0 |
plot(fit) # symmetric map
##2 典型相關分析
#讀資料
dta <- read.table("PsyEdu.txt",head=T)
str(dta)
## 'data.frame': 600 obs. of 8 variables:
## $ locus_of_control: num -0.84 -0.38 0.89 0.71 -0.64 1.11 0.06 -0.91 0.45 0 ...
## $ self_concept : num -0.24 -0.47 0.59 0.28 0.03 0.9 0.03 -0.59 0.03 0.03 ...
## $ motivation : num 1 0.67 0.67 0.67 1 0.33 0.67 0.67 1 0.67 ...
## $ read : num 54.8 62.7 60.6 62.7 41.6 62.7 41.6 44.2 62.7 62.7 ...
## $ write : num 64.5 43.7 56.7 56.7 46.3 64.5 39.1 39.1 51.5 64.5 ...
## $ math : num 44.5 44.7 70.5 54.7 38.4 61.4 56.3 46.3 54.4 38.3 ...
## $ science : num 52.6 52.6 58 58 36.3 58 45 36.3 49.8 55.8 ...
## $ female : int 1 1 0 0 1 1 0 0 1 1 ...
#一般的迴歸
summary(m1<-lm(read~locus_of_control+self_concept+motivation,data=dta))
##
## Call:
## lm(formula = read ~ locus_of_control + self_concept + motivation,
## data = dta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.6920 -6.8603 -0.4631 6.5138 24.3699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.7383 0.8605 56.637 < 2e-16 ***
## locus_of_control 5.2241 0.5885 8.876 < 2e-16 ***
## self_concept -0.5457 0.5662 -0.964 0.335498
## motivation 4.0281 1.1844 3.401 0.000717 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.305 on 596 degrees of freedom
## Multiple R-squared: 0.1559, Adjusted R-squared: 0.1517
## F-statistic: 36.7 on 3 and 596 DF, p-value: < 2.2e-16
summary(m2<-lm(write~locus_of_control+self_concept+motivation,data=dta))
##
## Call:
## lm(formula = write ~ locus_of_control + self_concept + motivation,
## data = dta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.575 -6.388 1.222 7.073 19.442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.1524 0.8234 58.482 < 2e-16 ***
## locus_of_control 4.7249 0.5631 8.391 3.51e-16 ***
## self_concept -1.3028 0.5417 -2.405 0.0165 *
## motivation 5.7241 1.1332 5.051 5.85e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.903 on 596 degrees of freedom
## Multiple R-squared: 0.1663, Adjusted R-squared: 0.1621
## F-statistic: 39.63 on 3 and 596 DF, p-value: < 2.2e-16
summary(m3<-lm(math~locus_of_control+self_concept+motivation,data=dta))
##
## Call:
## lm(formula = math ~ locus_of_control + self_concept + motivation,
## data = dta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.9608 -6.4511 -0.5789 6.0455 25.9800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.0821 0.8149 60.232 < 2e-16 ***
## locus_of_control 4.3814 0.5573 7.862 1.78e-14 ***
## self_concept -0.4951 0.5361 -0.923 0.35614
## motivation 3.5506 1.1216 3.166 0.00163 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.812 on 596 degrees of freedom
## Multiple R-squared: 0.1284, Adjusted R-squared: 0.124
## F-statistic: 29.27 on 3 and 596 DF, p-value: < 2.2e-16
summary(m4<-lm(science~locus_of_control+self_concept+motivation,data=dta))
##
## Call:
## lm(formula = science ~ locus_of_control + self_concept + motivation,
## data = dta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.4977 -6.2059 0.2064 6.9990 22.3416
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 50.63040 0.85048 59.532 < 2e-16 ***
## locus_of_control 4.55637 0.58165 7.834 2.18e-14 ***
## self_concept 0.07265 0.55954 0.130 0.897
## motivation 1.04827 1.17054 0.896 0.371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.196 on 596 degrees of freedom
## Multiple R-squared: 0.1068, Adjusted R-squared: 0.1023
## F-statistic: 23.75 on 3 and 596 DF, p-value: 1.563e-14
p <- dta[, 1:3]
s <- dta[, 4:7]
#cor plot
require(ggplot2)
## Loading required package: ggplot2
require(GGally)
## Loading required package: GGally
ggpairs(p)
ggpairs(s)
# correlations
cor(p, s)
## read write math science
## locus_of_control 0.37356505 0.35887684 0.3372690 0.32462694
## self_concept 0.06065584 0.01944856 0.0535977 0.06982633
## motivation 0.21060992 0.25424818 0.1950135 0.11566948
#can corr analysis
require(candisc)
## Loading required package: candisc
## Loading required package: car
##
## Attaching package: 'car'
## The following object is masked from 'package:vcdExtra':
##
## Burt
## Loading required package: heplots
##
## Attaching package: 'candisc'
## The following object is masked from 'package:stats':
##
## cancor
cc1 <- cancor(p, s, set.names=c("p", "s"))
cc1
##
## Canonical correlation analysis of:
## 3 p variables: locus_of_control, self_concept, motivation
## with 4 s variables: read, write, math, science
##
## CanR CanRSQ Eigen percent cum scree
## 1 0.4464 0.1993055 0.2489158 91.0086 91.01 ******************************
## 2 0.1534 0.0235190 0.0240855 8.8061 99.81 ***
## 3 0.0225 0.0005064 0.0005067 0.1852 100.00
##
## Test of H0: The canonical correlations in the
## current row and all that follow are zero
##
## CanR LR test stat approx F numDF denDF Pr(> F)
## 1 0.44644 0.78147 12.774 12 1569.2 < 2e-16 ***
## 2 0.15336 0.97599 2.421 6 1188.0 0.02488 *
## 3 0.02250 0.99949 2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#standarized coefficients 典型相關分析係數
coef(cc1, type="both", standardize=TRUE)
## [[1]]
## Xcan1 Xcan2 Xcan3
## locus_of_control -0.8379311 0.5134098 0.3328964
## self_concept 0.1670182 0.5941199 -0.8502290
## motivation -0.4281181 -0.9034217 -0.3747775
##
## [[2]]
## Ycan1 Ycan2 Ycan3
## read -0.44500741 -0.01609314 -0.8924138
## write -0.53581915 -0.87941356 0.9349929
## math -0.18265622 -0.02782487 -0.8268251
## science 0.03686176 1.20559459 0.8589501
#redundancy
redundancy(cc1)
##
## Redundancies for the p variables & total X canonical redundancy
##
## Xcan1 Xcan2 Xcan3 total X|Y
## 0.0789593 0.0054866 0.0001876 0.0846336
##
## Redundancies for the s variables & total Y canonical redundancy
##
## Ycan1 Ycan2 Ycan3 total Y|X
## 1.358e-01 3.533e-03 4.139e-05 1.394e-01
#plot
plot(cc1, smooth=TRUE)
