Last updated: 20160912. Kajiyama             [ 目次に戻る ]

パッケージFactoMineRのMCAを利用した多元表(hobbies)の多重対応分析



MCA {FactoMineR}	R Documentation  Multiple Correspondence Analysis (MCA)

Description

Performs Multiple Correspondence Analysis (MCA) with supplementary individuals, supplementary quantitative variables 
and supplementary categorical variables.Missing values are treated as an additional level, 
categories which are rare can be ventilated

Usage

MCA(X, ncp = 5, ind.sup = NULL, quanti.sup = NULL, 
    quali.sup = NULL, graph = TRUE, level.ventil = 0, 
    axes = c(1,2), row.w = NULL, method="Indicator",
    na.method="NA", tab.disj=NULL)
Arguments

X	a data frame with n rows (individuals) and p columns (categorical variables)

ncp	number of dimensions kept in the results (by default 5)

ind.sup	a vector indicating the indexes of the supplementary individuals

quanti.sup	a vector indicating the indexes of the quantitative supplementary variables

quali.sup	a vector indicating the indexes of the categorical supplementary variables

graph	boolean, if TRUE a graph is displayed

level.ventil	a proportion corresponding to the level under which the category is ventilated; 
by default, 0 and no ventilation is done

axes	a length 2 vector specifying the components to plot

row.w	an optional row weights (by default, a vector of 1 for uniform row weights)

method	a string corresponding to the name of the method used: "Indicator" (by default) is the CA on the Indicator matrix, 
"Burt" is the CA on the Burt table. For Burt and the Indicator, the graph of the individuals and the graph of 
the categories are given

na.method	a string corresponding to the name of the method used if there are missing values; available methods are 
"NA" or "Average" (by default, "NA")

tab.disj	optional data.frame corresponding to the disjunctive table used for the analysis;it corresponds to 
a disjunctive table obtained from imputation method (see package missMDA).

Value

Returns a list including:

eig	a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance

var	a list of matrices containing all the results for the active variables (coordinates, square cosine, 
contributions, v.test, square correlation ratio)

ind	a list of matrices containing all the results for the active individuals (coordinates, square cosine, contributions)

ind.sup	a list of matrices containing all the results for the supplementary individuals (coordinates, square cosine)

quanti.sup	a matrix containing the coordinates of the supplementary quantitative variables (the correlation between 
a variable and an axis is equal to the variable coordinate on the axis)

quali.sup	a list of matrices with all the results for the supplementary categorical variables (coordinates of each 
categories of each variables, square cosine and v.test which is a criterion with a Normal distribution, square 
correlation ratio)

call	a list with some statistics

Returns the graphs of the individuals and categories and the graph with the variables.The plots may be improved using 
the argument autolab, modifying the size of the labels or selecting some elements thanks to the plot.MCA function.

Author(s)

Francois Husson husson@agrocampus-ouest.fr, Julie Josse, Jeremy Mazet

References

Husson, F., Le, S. and Pages, J. (2010). Exploratory Multivariate Analysis by Example Using R, 
Chapman and Hall.

See Also

plotellipses, summary.MCA,print.MCA, plot.MCA, dimdesc,
Video showing how to perform MCA with FactoMineR

Examples

## Hobbies example
data(hobbies)
res.mca <- MCA(hobbies,quali.sup=19:22,quanti.sup=23)
plot(res.mca,invisible=c("ind","quali.sup"),hab="quali") 
plot(res.mca,invisible=c("var","quali.sup"),cex=.5,label="none") 
plot(res.mca,invisible=c("ind","var"),hab="quali")
dimdesc(res.mca)
plotellipses(res.mca,keepvar=1:4)


 rm(list=ls(all=TRUE)) # 以前作成したオブジェクトの削除

 library(FactoMineR)
警告メッセージ: 
 パッケージ ‘FactoMineR’ はバージョン 3.1.3 の R の下で造られました 

 ? FactoMineR
CA	Correspondence Analysis (CA)
ellipseCA	Draw confidence ellipses in CA
MCA	Multiple Correspondence Analysis (MCA)
plot.CA	Draw the Correspondence Analysis (CA) graphs
plot.CaGalt	Draw the Correspondence Analysis on Generalised Aggregated Lexical Table (CaGalt) graphs
plot.spMCA	Draw the specific Multiple Correspondence Analysis (spMCA) graphs
print.CA	Print the Correspondance Analysis (CA) results
print.CaGalt	Print the Correspondence Analysis on Generalised Aggregated Lexical Table (CaGalt) results
print.spMCA	Print the specific Multiple Correspondance Analysis (spMCA) results

 data(hobbies) # データhobbiesの読み込み

 ? hobbies
A data frame with 8403 rows and 23 columns. Rows represent the individuals, columns represent the different questions. 
The first 18 questions are active ones, and the 4 following ones are supplementary categorical variables and the 23th 
is a supplementary quantitative variable (the number of activities)

 str(hobbies) # データhobbiesの構造 23変数
'data.frame':   8403 obs. of  23 variables:
 $ Reading        : Factor w/ 2 levels "0","1": 2 2 2 2 2 1 1 2 2 2 ...
 $ Listening music: Factor w/ 2 levels "0","1": 2 1 2 1 2 1 2 2 2 2 ...
 $ Cinema         : Factor w/ 2 levels "0","1": 2 1 1 1 2 1 1 2 2 2 ...
 $ Show           : Factor w/ 2 levels "0","1": 2 1 1 1 2 1 1 2 2 1 ...
 $ Exhibition     : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 2 1 2 ...
 $ Computer       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 2 2 1 ...
 $ Sport          : Factor w/ 2 levels "0","1": 2 2 1 2 1 1 2 1 2 1 ...
 $ Walking        : Factor w/ 2 levels "0","1": 2 2 1 1 2 1 1 1 2 1 ...
 $ Travelling     : Factor w/ 2 levels "0","1": 2 1 2 2 1 1 1 2 2 1 ...
 $ Playing music  : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 2 ...
 $ Collecting     : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
 $ Volunteering   : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 1 1 ...
 $ Mechanic       : Factor w/ 2 levels "0","1": 2 2 1 1 1 2 2 1 1 2 ...
 $ Gardening      : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 2 ...
 $ Knitting       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ Cooking        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ Fishing        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ TV             : Factor w/ 5 levels "0","1","2","3",..: 3 5 5 2 4 4 4 1 2 2 ...

 $ Sex            : Factor w/ 2 levels "M","F": 2 1 2 1 1 1 1 1 2 2 ...
 $ Age            : Factor w/ 8 levels "[15,25]","(25,35]",..: 5 4 2 7 5 4 3 1 3 2 ...
 $ Marital status : Factor w/ 5 levels "Single","Married",..: 2 2 5 2 2 2 2 1 1 1 ...
 $ Profession     : Factor w/ 7 levels "Unskilled worker",..: 5 NA 5 NA 6 2 6 NA 5 6 ...
 $ nb.activitees  : int  11 9 5 5 6 2 5 7 10 8 .

 hobbies # データhobbies表示

 res.mca <- MCA(hobbies, quali.sup = 19:22, quanti.sup = 23) # データteaを対応分析






図1-2

図1-3

図1-4

図1-5
summary(res.mca) Call: MCA(X = hobbies, quanti.sup = 23, quali.sup = 19:22) Eigenvalues Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Variance 0.198 0.081 0.072 0.063 0.058 0.056 0.056 0.053 0.053 0.049 0.046 % of var. 16.947 6.913 6.173 5.389 5.011 4.784 4.759 4.569 4.547 4.211 3.985 Cumulative % of var. 16.947 23.859 30.033 35.422 40.433 45.217 49.976 54.545 59.092 63.303 67.288 Dim.12 Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21 Variance 0.045 0.044 0.043 0.041 0.038 0.037 0.036 0.035 0.032 0.030 % of var. 3.864 3.730 3.717 3.497 3.256 3.200 3.105 2.997 2.772 2.575 Cumulative % of var. 71.152 74.881 78.598 82.095 85.351 88.551 91.655 94.652 97.425 100.000 Individuals (the 10 first) Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2 11000210 | 0.667 0.027 0.336 | -0.191 0.005 0.027 | 0.147 0.004 0.016 | 11000410 | 0.140 0.001 0.011 | 0.434 0.028 0.108 | 0.163 0.004 0.015 | 11000610 | -0.155 0.001 0.032 | -0.244 0.009 0.079 | -0.293 0.014 0.113 | 11000710 | -0.108 0.001 0.011 | -0.285 0.012 0.073 | 0.000 0.000 0.000 | 11000810 | -0.022 0.000 0.001 | -0.268 0.011 0.087 | -0.225 0.008 0.061 | 11000910 | -0.636 0.024 0.449 | 0.019 0.000 0.000 | 0.192 0.006 0.041 | 11001010 | -0.206 0.003 0.046 | -0.239 0.008 0.063 | 0.319 0.017 0.111 | 11001110 | 0.284 0.005 0.065 | -0.611 0.055 0.304 | -0.066 0.001 0.004 | 11001210 | 0.598 0.021 0.261 | -0.577 0.049 0.243 | 0.028 0.000 0.001 | 11001310 | 0.204 0.003 0.033 | -0.015 0.000 0.000 | 0.089 0.001 0.006 | Categories (the 10 first) Dim.1 ctr cos2 v.test Dim.2 ctr cos2 v.test Dim.3 ctr cos2 v.test Reading_0 | -0.699 4.503 0.239 -44.766 | -0.051 0.058 0.001 -3.255 | 0.460 5.367 0.104 29.496 | Reading_1 | 0.341 2.199 0.239 44.766 | 0.025 0.028 0.001 3.255 | -0.225 2.621 0.104 -29.496 | Listening music_0 | -0.817 5.478 0.275 -48.111 | 0.241 1.170 0.024 14.202 | 0.231 1.207 0.022 13.630 | Listening music_1 | 0.337 2.262 0.275 48.111 | -0.100 0.483 0.024 -14.202 | -0.096 0.498 0.022 -13.630 | Cinema_0 | -0.509 4.369 0.389 -57.170 | 0.287 3.398 0.123 32.200 | -0.045 0.093 0.003 -5.030 | Cinema_1 | 0.764 6.561 0.389 57.170 | -0.430 5.103 0.123 -32.200 | 0.067 0.139 0.003 5.030 | Show_0 | -0.394 3.109 0.383 -56.753 | 0.109 0.586 0.029 15.735 | 0.032 0.056 0.002 4.582 | Show_1 | 0.972 7.663 0.383 56.753 | -0.270 1.444 0.029 -15.735 | -0.078 0.137 0.002 -4.582 | Exhibition_0 | -0.422 3.461 0.399 -57.885 | -0.005 0.001 0.000 -0.748 | 0.076 0.306 0.013 10.384 | Exhibition_1 | 0.945 7.745 0.399 57.885 | 0.012 0.003 0.000 0.748 | -0.169 0.684 0.013 -10.384 | Categorical variables (eta2) Dim.1 Dim.2 Dim.3 Reading | 0.239 0.001 0.104 | Listening music | 0.275 0.024 0.022 | Cinema | 0.389 0.123 0.003 | Show | 0.383 0.029 0.002 | Exhibition | 0.399 0.000 0.013 | Computer | 0.327 0.058 0.041 | Sport | 0.287 0.053 0.062 | Walking | 0.172 0.107 0.002 | Travelling | 0.355 0.000 0.000 | Playing music | 0.209 0.005 0.002 | Supplementary categories (the 10 first) Dim.1 cos2 v.test Dim.2 cos2 v.test Dim.3 cos2 v.test F | 0.018 0.000 1.779 | 0.042 0.002 4.253 | -0.459 0.257 -46.432 | M | -0.021 0.000 -1.779 | -0.051 0.002 -4.253 | 0.559 0.257 46.432 | (25,35] | 0.267 0.013 10.495 | -0.315 0.018 -12.358 | 0.119 0.003 4.664 | (35,45] | 0.201 0.010 9.092 | -0.020 0.000 -0.916 | 0.158 0.006 7.169 | (45,55] | 0.022 0.000 1.064 | 0.213 0.013 10.309 | 0.025 0.000 1.228 | (55,65] | -0.153 0.004 -5.885 | 0.380 0.025 14.599 | -0.115 0.002 -4.407 | (65,75] | -0.447 0.025 -14.526 | 0.301 0.011 9.789 | -0.300 0.011 -9.747 | (75,85] | -0.701 0.030 -15.862 | 0.101 0.001 2.276 | -0.413 0.010 -9.349 | (85,100] | -1.015 0.011 -9.400 | -0.214 0.000 -1.986 | -0.477 0.002 -4.420 | [15,25] | 0.370 0.016 11.416 | -0.860 0.084 -26.580 | 0.237 0.006 7.322 | Supplementary categorical variables (eta2) Dim.1 Dim.2 Dim.3 Sex | 0.000 0.002 0.257 | Age | 0.097 0.134 0.037 | Marital status | 0.046 0.099 0.030 | Profession | 0.128 0.017 0.062 | Supplementary continuous variable Dim.1 Dim.2 Dim.3 nb.activitees | 0.975 | 0.198 | 0.013 | plot(res.mca,invisible=c("ind","quali.sup"),hab="quali") #2-1 plot(res.mca,invisible=c("var","quali.sup"),cex=.5,label="none") #3-1
  #2-1

  #3-1

plot(res.mca,invisible=c("ind","var"),hab="quali") #4-1 plotellipses(res.mca,keepvar=1:4) #5-1
  #4-1

  #5-1

dimdesc(res.mca) #5-1
$`Dim 1`
$`Dim 1`$quanti
              correlation p.value
nb.activitees   0.9753459       0

$`Dim 1`$quali
                        R2       p.value
Reading         0.23851813  0.000000e+00
Listening music 0.27548544  0.000000e+00
Cinema          0.38900068  0.000000e+00
Show            0.38335191  0.000000e+00
Exhibition      0.39878925  0.000000e+00
Computer        0.32739645  0.000000e+00
Sport           0.28683998  0.000000e+00
Walking         0.17212148  0.000000e+00
Travelling      0.35491399  0.000000e+00
Playing music   0.20922813  0.000000e+00
Mechanic        0.13493609 8.816716e-267
Cooking         0.12539365 9.423346e-247
Profession      0.12836813 7.201742e-245
Volunteering    0.10877078 2.247113e-212
Age             0.09747901 1.104310e-181
TV              0.05192240  1.282203e-95
Gardening       0.04696289  7.138377e-90
Marital status  0.04566170  1.135400e-83
Collecting      0.04356310  2.322542e-83
Knitting        0.01143504  8.427145e-23

$`Dim 1`$category
                     Estimate       p.value
Playing music_1    0.26839870  0.000000e+00
Travelling_1       0.27033560  0.000000e+00
Walking_1          0.18447699  0.000000e+00
Sport_1            0.24685643  0.000000e+00
Computer_1         0.26265070  0.000000e+00
Exhibition_1       0.30388560  0.000000e+00
Show_1             0.30379833  0.000000e+00
Cinema_1           0.28307598  0.000000e+00
Listening music_1  0.25657041  0.000000e+00
Reading_1          0.23125560  0.000000e+00
Mechanic_1         0.16540452 8.816716e-267
Cooking_1          0.15865273 9.423346e-247
Volunteering_1     0.20372577 2.247113e-212
Management         0.29445799 4.929397e-132
Gardening_1        0.09837170  7.138377e-90
Collecting_1       0.15293668  2.322542e-83
Single             0.15105015  1.207987e-54
[15,25]            0.24528149  2.108266e-30
(25,35]            0.19984888  6.389909e-26
Foreman            0.14904669  2.149018e-25
TV_1               0.13159677  3.341439e-25
TV_2               0.09449946  1.114616e-24
Knitting_1         0.06356668  8.427145e-23
(35,45]            0.17031375  7.998317e-20
(55,65]            0.01289136  3.849963e-09
Technician         0.05991778  6.892575e-04
Profession.NA     -0.05116908  3.088722e-04
Married           -0.00169137  1.912935e-07
TV_4              -0.05681694  9.650520e-17
(85,100]          -0.37013968  4.325646e-21
Knitting_0        -0.06356668  8.427145e-23
Manual labourer   -0.18364686  2.978737e-45
Widower           -0.20369965  7.479622e-48
(65,75]           -0.11794860  2.179375e-48
TV_0              -0.19424004  7.701514e-56
(75,85]           -0.23095998  1.734900e-57
Unskilled worker  -0.27848868  1.030645e-70
Collecting_0      -0.15293668  2.322542e-83
Gardening_0       -0.09837170  7.138377e-90
Volunteering_0    -0.20372577 2.247113e-212
Cooking_0         -0.15865273 9.423346e-247
Mechanic_0        -0.16540452 8.816716e-267
Playing music_0   -0.26839870  0.000000e+00
Travelling_0      -0.27033560  0.000000e+00
Walking_0         -0.18447699  0.000000e+00
Sport_0           -0.24685643  0.000000e+00
Computer_0        -0.26265070  0.000000e+00
Exhibition_0      -0.30388560  0.000000e+00
Show_0            -0.30379833  0.000000e+00
Cinema_0          -0.28307598  0.000000e+00
Listening music_0 -0.25657041  0.000000e+00
Reading_0         -0.23125560  0.000000e+00
$`Dim 2`
$`Dim 2`$quanti
              correlation      p.value
nb.activitees   0.1980007 4.826398e-75

$`Dim 2`$quali
                         R2       p.value
Gardening       0.453046316  0.000000e+00
Knitting        0.166169732  0.000000e+00
Mechanic        0.140401965 2.363989e-278
Cooking         0.135420874 8.354586e-268
Age             0.133583530 9.125004e-256
Cinema          0.123401798 1.341384e-242
Walking         0.106756626 2.977082e-208
Marital status  0.098904206 5.042868e-188
Fishing         0.084751244 8.323766e-164
Computer        0.058202284 1.463850e-111
Sport           0.053442000 2.406308e-102
TV              0.034425908  1.895739e-62
Show            0.029467916  1.375541e-56
Collecting      0.025914681  6.803897e-50
Listening music 0.024006349  2.627936e-46
Profession      0.017395750  1.450270e-28
Volunteering    0.009426749  4.666146e-19
Playing music   0.005420573  1.407100e-11
Sex             0.002152884  2.090738e-05
Reading         0.001260776  1.132204e-03

$`Dim 2`$category
                      Estimate       p.value
Knitting_1         0.154764175  0.000000e+00
Gardening_1        0.195140682  0.000000e+00
Mechanic_1         0.107759030 2.363989e-278
Cooking_1          0.105301934 8.354586e-268
Cinema_0           0.101829204 1.341384e-242
Walking_1          0.092790996 2.977082e-208
Fishing_1          0.130842880 8.323766e-164
Computer_0         0.070728647 1.463850e-111
Sport_0            0.068053382 2.406308e-102
Married            0.058392178  7.920392e-93
Show_0             0.053795386  1.375541e-56
Collecting_1       0.075337113  6.803897e-50
(55,65]            0.122588737  7.341769e-49
Listening music_0  0.048373115  2.627936e-46
TV_3               0.082847534  1.990737e-33
(45,55]            0.075130020  4.614026e-25
(65,75]            0.100358876  9.566953e-23
Volunteering_1     0.038304920  4.666146e-19
Manual labourer    0.062446063  2.315841e-16
Playing music_0    0.027591591  1.407100e-11
Widower            0.059301096  7.755244e-10
TV_2               0.039534257  6.282529e-08
F                  0.013241401  2.090738e-05
Remarried          0.048925026  2.061549e-04
Unskilled worker   0.031284323  7.973605e-04
Reading_1          0.010738269  1.132204e-03
(75,85]            0.043334701  2.283013e-02
(85,100]          -0.046130519  4.698967e-02
Reading_0         -0.010738269  1.132204e-03
TV_4              -0.007916798  2.354582e-04
M                 -0.013241401  2.090738e-05
TV_1              -0.027119018  3.688650e-07
Management        -0.053914699  9.259268e-11
Playing music_1   -0.027591591  1.407100e-11
Profession.NA     -0.046307708  8.336614e-12
Volunteering_0    -0.038304920  4.666146e-19
TV_0              -0.087345976  2.743645e-32
(25,35]           -0.074668353  2.201473e-35
Listening music_1 -0.048373115  2.627936e-46
Collecting_0      -0.075337113  6.803897e-50
Show_1            -0.053795386  1.375541e-56
Sport_1           -0.068053382 2.406308e-102
Computer_1        -0.070728647 1.463850e-111
[15,25]           -0.229609518 1.782463e-162
Fishing_0         -0.130842880 8.323766e-164
Single            -0.150611195 6.342662e-181
Walking_0         -0.092790996 2.977082e-208
Cinema_1          -0.101829204 1.341384e-242
Cooking_0         -0.105301934 8.354586e-268
Mechanic_0        -0.107759030 2.363989e-278
Knitting_0        -0.154764175  0.000000e+00
Gardening_0       -0.195140682  0.000000e+00
$`Dim 3`
$`Dim 3`$quali
                         R2       p.value
Mechanic        0.209469073  0.000000e+00
Knitting        0.326760742  0.000000e+00
Fishing         0.331040836  0.000000e+00
Sex             0.256598886  0.000000e+00
Reading         0.103546767 1.057035e-201
TV              0.075506229 2.154507e-141
Cooking         0.068174772 5.148288e-131
Sport           0.062202668 2.431680e-119
Profession      0.062120420 4.080525e-112
Computer        0.041225998  6.762202e-79
Age             0.037096503  1.135166e-64
Gardening       0.030314206  3.475006e-58
Marital status  0.030381795  7.010460e-55
Listening music 0.022109556  9.532570e-43
Exhibition      0.012834600  2.072824e-25
Volunteering    0.003760979  1.842666e-08
Cinema          0.003011654  4.812422e-07
Show            0.002498472  4.557774e-06
Playing music   0.002317306  1.011746e-05
Walking         0.001577370  2.709621e-04

$`Dim 3`$category
                      Estimate       p.value
M                  0.136610232  0.000000e+00
Fishing_1          0.244371238  0.000000e+00
Knitting_0         0.205088669  0.000000e+00
Mechanic_1         0.124382502  0.000000e+00
Reading_0          0.091963576 1.057035e-201
Cooking_0          0.070605355 5.148288e-131
Sport_1            0.069381729 2.431680e-119
Computer_1         0.056252675  6.762202e-79
Gardening_1        0.047701482  3.475006e-58
Listening music_0  0.043869558  9.532570e-43
Manual labourer    0.074927886  2.425621e-41
TV_2               0.051269543  9.688900e-39
TV_0               0.074320988  7.695837e-29
Exhibition_0       0.032903764  2.072824e-25
Technician         0.106451165  3.134871e-23
TV_1               0.046966483  1.721087e-17
[15,25]            0.089295409  2.250144e-13
(35,45]            0.068211010  6.999672e-13
Single             0.054243734  1.428671e-10
Volunteering_1     0.022864214  1.842666e-08
Cinema_1           0.015033026  4.812422e-07
Management         0.014062563  1.621277e-06
(25,35]            0.057570669  3.068950e-06
Show_0             0.014802663  4.557774e-06
Foreman            0.018757669  9.818621e-06
Playing music_0    0.017048181  1.011746e-05
Married            0.034417811  1.547920e-05
Walking_0          0.010658779  2.709621e-04
Divorcee          -0.010341038  3.409293e-04
Walking_1         -0.010658779  2.709621e-04
(55,65]           -0.005079741  1.038252e-05
Playing music_1   -0.017048181  1.011746e-05
(85,100]          -0.102325499  9.780901e-06
Show_1            -0.014802663  4.557774e-06
Cinema_0          -0.015033026  4.812422e-07
Volunteering_0    -0.022864214  1.842666e-08
TV_3              -0.065496551  2.227645e-20
(75,85]           -0.085282226  7.049703e-21
(65,75]           -0.054868462  1.460573e-22
Exhibition_1      -0.032903764  2.072824e-25
Listening music_1 -0.043869558  9.532570e-43
Widower           -0.118694340  9.651417e-51
Gardening_0       -0.047701482  3.475006e-58
Employee          -0.103790767  1.329776e-75
Computer_0        -0.056252675  6.762202e-79
TV_4              -0.107060462  1.674088e-84
Sport_0           -0.069381729 2.431680e-119
Cooking_1         -0.070605355 5.148288e-131
Reading_1         -0.091963576 1.057035e-201
F                 -0.136610232  0.000000e+00
Fishing_0         -0.244371238  0.000000e+00
Knitting_1        -0.205088669  0.000000e+00
Mechanic_0        -0.124382502  0.000000e+00

  FactoMineRのMCAを利用した多元表(hobbies)の多重対応分析  [ 目次に戻る ]