Métodos Multivariados em Administração

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Modelagem Preditiva
> library(readxl)
> DIRTYSHOPCSV <- read_excel("C:/Users/Gustavo Giusti/Desktop/DIRTYSHOPCSV.xlsx")
> View(DIRTYSHOPCSV)
> drt=DIRTYSHOPCSV
> attach(drt)
> summary(IDADE)
 Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 
 0.0 31.0 40.0 42.4 53.0 89.0 3 
> library(psych)
> describe(IDADE)
 vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 2797 42.4 14.2 40 41.5 16.31 0 89 89 0.54 -0.28 0.27
A data.frame of the relevant statistics:
item name
item number
number of valid cases
mean
standard deviation
trimmed mean (with trim defaulting to .1)
median (standard or interpolated
mad: median absolute deviation (from the median)
minimum
maximum
skew
kurtosis
standard error
> describeBy(IDADE,STATUS)
 Descriptive statistics by group 
group: bom
 vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 1946 42.55 14.25 41 41.64 16.31 18 89 71 0.53 -0.36 0.32
--------------------------------------------------------------------------------------------------------- 
group: mau
 vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 851 42.06 14.08 40 41.17 14.83 0 89 89 0.57 -0.11 0.48
Árvores de Decisão
Abrir e fixar arquivo
> library(readxl)
> TEBA <- read_excel("~/Parcial/TEBA.xlsx")
> View(TEBA)
> teba=TEBA
> attach(teba)
Packages necessários
> library(rpart)
> library(caret)
> library(partykit)
> library(rattle)
> library(rpart.plot)
> library(RColorBrewer)
> library(gmodels)
Gerando arquivos de aprendizado e teste
> set.seed(1234)
> index=createDataPartition(cancel,p=0.6,list=F)
> teba.learn=teba[index,]
> teba.test=teba[-index,]
Construindo a árvore de decisão
> ad1=rpart(data=teba.learn,cancel~idade+linhas+temp_cli+renda+fatura+temp_rsd+local+tvcabo+debaut,method="class")
Gráficos
> prp(ad1,type=2,extra=104,nn=T,fallen.leaves=T,branch.col="red",branch.lty=5,box.col=c("white",'green'))
> fancyRpartPlot(ad1)
Nó intermediário (39% dos dados)
59% de cancel=nao
41% de cancel = sim
Split: variável “temp_cli”
Nó inicial (100% dos dados)
76% de cancel=nao
24% de cancel = sim
Split: variável “local”
> fancyRpartPlot(ad1,palettes=c("Greys",'Blues'))
Regras de classificação
> ad1Tem 727 clientes
Classifica todos “nao”
86,93% de “não”
13,07% de “sim”
95 mal classificados
n= 1201 
node), split, n, loss, yval, (yprob)
 * denotes terminal node
 1) root 1201 287 nao (0.76103247 0.23896753) 
 2) local=A,C 727 95 nao (0.86932600 0.13067400) 
 4) temp_cli>=16.5 517 37 nao (0.92843327 0.07156673) *
 5) temp_cli< 16.5 210 58 nao (0.72380952 0.27619048) Nó k gera nós 2k e 2k+1
* Nó terminal (folha)
 10) fatura< 497.5 108 13 nao (0.87962963 0.12037037) *
 11) fatura>=497.5 102 45 nao (0.55882353 0.44117647) 
 22) tvcabo=nao 37 10 nao (0.72972973 0.27027027) *
 23) tvcabo=sim 65 30 sim (0.46153846 0.53846154) 
 46) fatura>=809.5 26 8 nao (0.69230769 0.30769231) *
 47) fatura< 809.5 39 12 sim (0.30769231 0.69230769) *
 3) local=B,D 474 192 nao (0.59493671 0.40506329) 
 6) temp_cli>=18.5 221 52 nao (0.76470588 0.23529412) 
 12) fatura< 1075 157 20 nao (0.87261146 0.12738854) *
 13) fatura>=1075 64 32 nao (0.50000000 0.50000000) 
 26) temp_cli>=31.5 25 5 nao (0.80000000 0.20000000) *
 27) temp_cli< 31.5 39 12 sim (0.30769231 0.69230769) *
 7) temp_cli< 18.5 253 113 sim (0.44664032 0.55335968) 
 14) fatura< 466.5 108 34 nao (0.68518519 0.31481481) 
 28) idade>=28.5 81 19 nao (0.76543210 0.23456790) *
 29) idade< 28.5 27 12 sim (0.44444444 0.55555556) 
 58) linhas< 1.5 17 6 nao (0.64705882 0.35294118) *
 59) linhas>=1.5 10 1 sim (0.10000000 0.90000000) *
 15) fatura>=466.5 145 39 sim (0.26896552 0.73103448) *
Estimação das probabilidades
> phat.test=predict(ad1,newdata=teba.test,type="prob")
Data frame com probabilidades de não e sim (ordem alfabética)
> yhat.test=predict(ad1,newdata=teba.test,type="class")
Onde foi classificado (nao ou sim)
> phat.test
 nao sim
1 0.8796296 0.12037037
2 0.7654321 0.23456790
3 0.2689655 0.73103448
4 0.9284333 0.07156673
5 0.8726115 0.12738854
6 0.9284333 0.07156673
7 0.8796296 0.12037037
8 0.3076923 0.69230769
9 0.9284333 0.07156673
10 0.9284333 0.07156673
Matriz de classificação
> CrossTable(teba.test$cancel,yhat.test)
 
 Cell Contents
|-------------------------|
| N |
| Chi-square contribution |
| N / Row Total |
| N / Col Total |
| N / Table Total |
|-------------------------|
 
Total Observations in Table: 799 
 
 | yhat.test 
teba.test$cancel | nao | sim | Row Total | 
-----------------|-----------|-----------|-----------|
 nao | 534 | 75 | 609 | 
 | 6.397 | 23.494 | | 
 | 0.877 | 0.123 | 0.762 | 
 | 0.850 | 0.439 | | 
 | 0.668 | 0.094 | | 
-----------------|-----------|-----------|-----------|
 sim | 94 | 96 | 190 | 
 | 20.505 | 75.305 | | 
 | 0.495 | 0.505 | 0.238 | 
 | 0.150 | 0.561 | | 
 | 0.118 | 0.120 | | 
-----------------|-----------|-----------|-----------|
 Column Total | 628 | 171 | 799 | 
 | 0.786 | 0.214 | | 
-----------------|-----------|-----------|-----------|
Podando a árvore
> printcp(ad1)
Classification tree:
rpart(formula = cancel ~ idade + linhas + temp_cli + renda + 
 fatura + temp_rsd + local + tvcabo + debaut, data = teba.learn, 
 method = "class")
Variables actually used in tree construction:
[1] fatura idade linhas local temp_cli tvcabo 
Última linha
Resubs erro r= 0,239 * 0,634
Cv error = 0,819 * 0,239
Root node error: 287/1201 = 0.23897
n= 1201 
 CP nsplit rel error xerror xstdComplexity Parameter (CP) é obtido via cross validation
Cross-over error (xerror): média dos erros
Podar utilizando CP que corresponde ao menor erro (xerror) calculado via cross validation
1 0.077816 0 1.00000 1.00000 0.051494
2 0.026132 3 0.76655 0.82927 0.048134
3 0.013937 5 0.71429 0.81882 0.047904
4 0.013066 7 0.68641 0.81882 0.047904
5 0.010000 11 0.63415 0.81882 0.047904
> ad2=prune(ad1,cp=ad1$cptable[which.min(ad1$cptable[,"xerror"]),"CP"])
> ad2
n= 1201 
node), split, n, loss, yval, (yprob)
 * denotes terminal node
 1) root 1201 287 nao (0.7610325 0.2389675) 
 2) local=A,C 727 95 nao (0.8693260 0.1306740) *
 3) local=B,D 474 192 nao (0.5949367 0.4050633) 
 6) temp_cli>=18.5 221 52 nao (0.7647059 0.2352941) 
 12) fatura< 1075 157 20 nao (0.8726115 0.1273885) *
 13) fatura>=1075 64 32 nao (0.5000000 0.5000000) 
 26) temp_cli>=31.5 25 5 nao (0.8000000 0.2000000) *
 27) temp_cli< 31.5 39 12 sim (0.3076923 0.6923077) *
 7) temp_cli< 18.5 253 113 sim (0.4466403 0.5533597) 
 14) fatura< 466.5 108 34 nao (0.6851852 0.3148148) *
 15) fatura>=466.5 145 39 sim (0.2689655 0.7310345) *
> prp(ad2,type=2,extra=104,nn=T,fallen.leaves=T,branch.col="red",branch.lty=5,box.col=c("white",'green'))
Regressão Logística
Leitura do arquivo
> library(readxl)
> VENDEDORES <- read_excel("C:/Users/Gustavo Giusti/OneDrive/FGV/2017.2/Métodos Multivariados em Administração/Regressão Logística/VENDEDORES.xlsx")
> View(VENDEDORES)
> vendedorescsv=VENDEDORES
> attach(vendedorescsv)
Box-plots
> par(mfrow=c(1,3))> boxplot(IDADE~DESEMP,xlab="IDADE")
> boxplot(ENTREV~DESEMP,xlab="ENTREV")
> boxplot(EXPER~DESEMP,xlab="EXPER")
Discretização de variáveis em classes de mesma frequência
> library(arules)
> kidade=discretize(IDADE,method='frequency',categories=3)
Tabelas de contingência
> library(gmodels)
> CrossTable(SEX,DESEMP)
 
 Cell Contents
|-------------------------|
| N |
| Chi-square contribution |
| N / Row Total |
| N / Col Total |
| N / Table Total |
|-------------------------|
 
Total Observations in Table: 59 
 
 | DESEMP 
 SEX | B | M | Row Total | 
-------------|-----------|-----------|-----------|
 FEM | 12 | 10 | 22 | 
 | 0.036 | 0.049 | | 
 | 0.545 | 0.455 | 0.373 | 
 | 0.353 | 0.400 | | 
 | 0.203 | 0.169 | | 
-------------|-----------|-----------|-----------|
 MASC | 22 | 15 | 37 | 
 | 0.022 | 0.029 | | 
 | 0.595 | 0.405 | 0.627 | 
 | 0.647 | 0.600 | | 
 | 0.373 | 0.254 | | 
-------------|-----------|-----------|-----------|
Column Total | 34 | 25 | 59 | 
 | 0.576 | 0.424 | | 
-------------|-----------|-----------|-----------|
Verificação de multicolinearidade
> library(car)
> fit=lm(DESEMP.B~IDADE+ENTREV+EXPER,data=vendedorescsv)
> vif(fit)
 IDADE ENTREV EXPER 
1.336334 1.066761 1.261515 
Geração de variáveis dummy
> DESEMP.B=ifelse(DESEMP=="B",1,0)
Geração do modelo de regressão logística
> mod1=glm(DESEMP.B~ENTREV+EXPER+IDADE+SEX,family=binomial())
> summary(mod1)
Call:
glm(formula = DESEMP.B ~ ENTREV + EXPER + IDADE + SEX, family = binomial())
Deviance Residuals: 
 Min 1Q Median 3Q Max 
-2.0127 -0.7034 0.1472 0.5926 2.1905 Estimate: o quanto aumenta Z para um aumento unitário de Xi
Coefficients:
 Estimate Std. Error z value Pr(>|z|) 
(Intercept) -14.33466 3.86803 -3.706 0.000211 ***
ENTREV 1.18507 0.34739 3.411 0.000646 ***
EXPER 0.02626 0.10904 0.241 0.809702 
IDADE 0.13983 0.06183 2.261 0.023734 * 
SEXMASC 0.56778 0.72330 0.785 0.432465 
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
 Null deviance: 80.413 on 58 degrees of freedom
Residual deviance: 52.081 on 54 degrees of freedom
AIC: 62.081
Number of Fisher Scoring iterations: 5
Estatísticas para cálculo do LRT
> mod2=glm(I(DESEMP=="B")~1,family=binomial())
> logLik(mod2)
'log Lik.' -40.20656 (df=1)
> logLik(mod1)
'log Lik.' -26.04046 (df=5)
Estimativa das probabilidades
> pbom=predict(mod1,type="response")
Teste de ajuste do modelo
> kp=discretize(pbom,method='frequency',categories=5)
> table(kp,DESEMP)
 DESEMP
kp B M
 [0.0623,0.223) 2 10
 [0.2233,0.493) 4 8
 [0.4934,0.799) 7 5
 [0.7985,0.925) 10 2
 [0.9251,0.997] 11 0
Teste de Hosmer & Lemeshow
> library(ResourceSelection)
> h1=hoslem.test(mod1$y,fitted(mod1),g=10)
Teste de Spiegelhalter
Cluster Analysis
Leitura do arquivo
> library(readxl)
> GRAMPERS <- read_excel("C:/Users/Gustavo Giusti/OneDrive/FGV/2017.2/Métodos Multivariados em Administração/Cluster Analysis/GRAMPERS.xlsx")
> View(GRAMPERS)
Eliminar casos com missing values
> GRAMPERS=na.omit(GRAMPERS)
Selecionar colunas numéricas do arquivo e gerar arquivo gnum
> nums<-sapply(GRAMPERS,is.numeric)
> nums
 CLIENTE DESPESAS MAX_INT ITENS FREQUÊNCIA IDADE SEXO DEPENDENTES REGIAO OCUPAÇÃO 
 TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE 
 EST_CIVIL 
 FALSE 
> gnum=GRAMPERS[,nums]
> colnames(gnum)
[1] "CLIENTE" "DESPESAS" "MAX_INT" "ITENS" "FREQUÊNCIA" "IDADE" "DEPENDENTES"
Gerar arquivo gdrive somente com as drivers
> gdrive=gnum[,-c(1,6,7)]
> colnames(gdrive)
[1] "DESPESAS" "MAX_INT" "ITENS" "FREQUÊNCIA"
Padronizar as variáveis
> gdrive=scale(gdrive)
> gdrive=as.data.frame(gdrive)
Obter a matriz de distâncias
> gdist=dist(gdrive,method="euclidean")
> gg=as.matrix(gdist)
> gg
 1 2 3 4 5 6 7 8 9 10 11 12 13
1 0.0000000 3.315140 3.1906675 2.794454 2.244746 2.0869780 3.3442770 3.793171 3.9408053 5.215251 2.8878759 4.2864815 0.6677304
2 3.3151398 0.000000 3.7569818 2.232808 1.416499 1.4308177 2.4397616 2.018264 1.8481713 2.354323 2.0850804 2.3437745 2.9935199
3 3.1906675 3.756982 0.0000000 2.762449 2.591702 3.2768254 1.9960095 4.365486 2.8327340 5.311192 4.9256301 2.8798425 3.5661716
4 2.7944543 2.232808 2.7624492 0.000000 1.270377 2.4015510 2.8520687 1.705414 2.7624492 4.506708 3.3826726 3.2030928 2.6019549
5 2.2447464 1.416499 2.5917022 1.270377 0.000000 1.1901080 1.9802160 2.172556 2.0105040 3.591555 2.5362685 2.4720551 2.0855550
6 2.0869780 1.430818 3.2768254 2.401551 1.190108 0.0000000 2.2002611 2.847638 2.3212060 3.133717 1.7036997 2.7307382 1.8828033
7 3.3442770 2.439762 1.9960095 2.852069 1.980216 2.2002611 0.0000000 3.866735 1.1442290 3.404015 3.8379817 1.1563952 3.5108024
8 3.7931706 2.018264 4.3654860 1.705414 2.172556 2.8476383 3.8667354 0.000000 3.3216090 4.084229 3.0038360 3.8221769 3.3340052
9 3.9408053 1.848171 2.8327340 2.762449 2.010504 2.3212060 1.1442290 3.321609 0.0000000 2.609676 3.6827512 0.5187595 3.9233116
10 5.2152508 2.354323 5.3111920 4.506708 3.591555 3.1337165 3.4040155 4.084229 2.6096760 0.000000 3.3419531 2.7522153 4.9702212
11 2.8878759 2.085080 4.9256301 3.382673 2.536269 1.7036997 3.8379817 3.003836 3.6827512 3.341953 0.0000000 4.1211174 2.3739764
12 4.2864815 2.343775 2.8798425 3.203093 2.472055 2.7307382 1.1563952 3.822177 0.5187595 2.752215 4.1211174 0.0000000 4.3245086
13 0.6677304 2.993520 3.5661716 2.601955 2.085555 1.8828033 3.5108024 3.334005 3.9233116 4.970221 2.3739764 4.3245086 0.0000000
14 3.3361082 2.118810 2.5401883 2.999178 1.951871 1.8863378 0.5983850 3.789617 1.0502231 2.886855 3.4311939 1.1377958 3.4325908
15 2.4303801 1.535127 4.2095593 2.591344 1.754774 1.1288724 3.2478972 2.415307 3.1138414 3.312619 0.8019294 3.5816854 1.9296998
16 2.9184525 3.049863 1.2324506 2.863859 2.145502 2.4561684 0.9638671 4.199277 2.0478595 4.301069 4.1584917 2.0755323 3.2446945
17 5.9203264 3.346544 5.8019976 5.237742 4.386841 3.9939783 3.9848629 4.904809 3.2969202 1.847526 4.2670713 3.3303805 5.7260934
18 2.7575657 1.233479 3.3631174 2.726487 1.488499 0.7378547 1.8643823 3.068412 1.8127816 2.489945 2.0281451 2.1849096 2.6122974
19 3.7833911 2.311095 2.4104830 2.975050 2.152996 2.3917646 0.5645801 3.798252 0.6788524 3.032959 3.9213354 0.5924899 3.8788836
20 2.7684097 2.307061 3.1684116 3.415208 2.165935 1.4058609 1.6832825 4.079629 2.1654663 3.023732 2.7449528 2.3315885 2.8587154
21 4.3121322 1.854434 3.3065694 3.046474 2.323487 2.5512178 1.5905381 3.373205 0.4839687 2.247768 3.7441016 0.6752578 4.2454055
22 1.5732675 3.321555 2.1707770 3.073767 2.248138 2.1088173 2.2421383 4.345228 3.1720436 4.827254 3.5454036 3.3576696 2.0786052
23 3.2984318 2.784995 1.5180288 2.813003 2.093145 2.4640554 0.4999512 4.014804 1.5180288 3.895826 4.1475538 1.5030643 3.5335664
24 3.6924459 2.968369 1.5069820 2.870955 2.327483 2.8427724 0.8097700 4.074409 1.5069820 4.030140 4.5013590 1.4164000 3.9112507
25 3.4241138 3.841794 0.2612237 2.809171 2.711324 3.4478280 2.0699772 4.404391 2.8447530 5.371991 5.0958588 2.8714933 3.7804899
 14 15 16 17 18 19 20 21 22 23 24 251 3.3361082 2.4303801 2.9184525 5.920326 2.7575657 3.7833911 2.7684097 4.3121322 1.5732675 3.2984318 3.6924459 3.4241138
2 2.1188100 1.5351273 3.0498633 3.346544 1.2334785 2.3110946 2.3070611 1.8544337 3.3215545 2.7849953 2.9683695 3.8417939
3 2.5401883 4.2095593 1.2324506 5.801998 3.3631174 2.4104830 3.1684116 3.3065694 2.1707770 1.5180288 1.5069820 0.2612237
4 2.9991784 2.5913439 2.8638595 5.237742 2.7264869 2.9750495 3.4152081 3.0464736 3.0737673 2.8130033 2.8709549 2.8091707
5 1.9518707 1.7547737 2.1455020 4.386841 1.4884986 2.1529961 2.1659349 2.3234870 2.2481385 2.0931449 2.3274833 2.7113242
6 1.8863378 1.1288724 2.4561684 3.993978 0.7378547 2.3917646 1.4058609 2.5512178 2.1088173 2.4640554 2.8427724 3.4478280
7 0.5983850 3.2478972 0.9638671 3.984863 1.8643823 0.5645801 1.6832825 1.5905381 2.2421383 0.4999512 0.8097700 2.0699772
8 3.7896171 2.4153066 4.1992767 4.904809 3.0684125 3.7982524 4.0796290 3.3732054 4.3452283 4.0148042 4.0744092 4.4043906
9 1.0502231 3.1138414 2.0478595 3.296920 1.8127816 0.6788524 2.1654663 0.4839687 3.1720436 1.5180288 1.5069820 2.8447530
10 2.8868547 3.3126191 4.3010694 1.847526 2.4899445 3.0329585 3.0237320 2.2477677 4.8272536 3.8958262 4.0301399 5.3719906
11 3.4311939 0.8019294 4.1584917 4.267071 2.0281451 3.9213354 2.7449528 3.7441016 3.5454036 4.1475538 4.5013590 5.0958588
12 1.1377958 3.5816854 2.0755323 3.330380 2.1849096 0.5924899 2.3315885 0.6752578 3.3576696 1.5030643 1.4164000 2.8714933
13 3.4325908 1.9296998 3.2446945 5.726093 2.6122974 3.8788836 2.8587154 4.2454055 2.0786052 3.5335664 3.9112507 3.7804899
14 0.0000000 2.9179202 1.4270330 3.544567 1.4078367 0.6772465 1.2480686 1.3985616 2.3434870 1.0838598 1.3940373 2.6354098
15 2.9179202 0.0000000 3.5314372 4.236057 1.5865093 3.3526148 2.4277527 3.2341111 3.0366373 3.5159501 3.8447680 4.3689442
16 1.4270330 3.5314372 0.0000000 4.831761 2.3838397 1.5030643 2.0071867 2.5193380 1.6228634 0.5924899 0.9735369 1.3859218
17 3.5445666 4.2360575 4.8317613 0.000000 3.3928637 3.6213738 3.7353150 2.9714956 5.4321489 4.4300582 4.5233657 5.8473402
18 1.4078367 1.5865093 2.3838397 3.392864 0.0000000 1.9266080 1.0941339 1.9731033 2.4345938 2.2465787 2.5965031 3.5062357
19 0.6772465 3.3526148 1.5030643 3.621374 1.9266080 0.0000000 1.9253151 1.0815246 2.7784389 0.9553319 1.0064341 2.4391456
20 1.2480686 2.4277527 2.0071867 3.735315 1.0941339 1.9253151 0.0000000 2.4099586 1.9466269 2.0151123 2.4618927 3.3442055
21 1.3985616 3.2341111 2.5193380 2.971496 1.9731033 1.0815246 2.4099586 0.0000000 3.6022310 1.9838735 1.9486694 3.3084797
22 2.3434870 3.0366373 1.6228634 5.432149 2.4345938 2.7784389 1.9466269 3.6022310 0.0000000 2.1190377 2.5588758 2.4264129
23 1.0838598 3.5159501 0.5924899 4.430058 2.2465787 0.9553319 2.0151123 1.9838735 2.1190377 0.0000000 0.4934225 1.5872096
24 1.3940373 3.8447680 0.9735369 4.523366 2.5965031 1.0064341 2.4618927 1.9486694 2.5588758 0.4934225 0.0000000 1.4926587
25 2.6354098 4.3689442 1.3859218 5.847340 3.5062357 2.4391456 3.3442055 3.3084797 2.4264129 1.5872096 1.4926587 0.0000000
 26 27 28 29 30 31 32 33 34 35 36 37 38
1 2.0418719 2.2177677 2.7985698 3.9546999 3.0849435 3.9304270 3.0216257 3.1402454 5.486003 0.9633734 4.966544 4.625804 3.6078128
2 3.1966572 2.1335270 1.9014194 1.8597836 1.5169662 2.4032431 2.1188100 2.1082947 2.688574 3.0031844 2.851546 1.701016 2.5596094
3 2.4099586 2.6489685 3.6444563 2.8328000 3.1697137 2.4076271 2.7808395 3.4678089 4.764877 2.7695034 5.060940 4.300696 1.9477085
4 3.3714466 2.8165036 3.3470582 2.7650909 2.9339525 2.9977580 3.1382711 3.5211480 4.390057 1.9291794 4.651768 3.187319 2.8647908
5 2.3620517 1.6048119 2.0809907 2.0209986 1.7270249 2.2498775 1.9576170 2.2686018 3.623451 1.7422046 3.677448 2.640187 2.1228673
6 1.9979508 1.0707859 1.0727510 2.3384818 1.2334785 2.5589249 1.5604532 1.4883111 3.520186 2.1100598 3.161659 2.785683 2.4764868
7 1.9640184 1.4882445 2.1274361 1.1518931 1.4373774 0.6970186 1.0591878 1.7721315 2.926712 3.1404426 3.299602 2.817192 0.3579732
8 4.5083945 3.6652886 3.7275812 3.3247074 3.4014294 3.8242982 3.8775187 4.0095832 4.277158 3.1117710 4.556339 2.883427 3.8903986
9 2.9125065 2.0838043 2.2929632 0.0193499 1.4222177 0.6686409 1.5438525 2.0003291 2.031260 3.6082872 2.858183 1.892447 1.0577214
10 4.4030183 3.3877806 2.6392611 2.6179842 2.3808273 3.1043821 2.8849086 2.5464450 1.506946 5.1538006 1.975007 2.080133 3.4919066
11 3.4638705 2.6740562 2.1082947 3.6992354 2.5824317 4.0788336 3.0682656 2.6700017 4.248579 3.0577634 3.580548 3.401806 4.0968138
12 3.0458363 2.3319666 2.5587634 0.5107585 1.7101957 0.4968524 1.6832825 2.1671889 1.982449 3.9910271 2.940238 2.189391 0.9937048
13 2.4499886 2.3359909 2.7254740 3.9375532 3.0354877 4.0253614 3.1170320 3.1522981 5.352163 1.0161133 4.819727 4.383707 3.7682115
14 1.9597823 1.2386682 1.6228634 1.0652672 0.9022256 0.8804205 0.5804970 1.2264802 2.577419 3.2290436 2.836207 2.544640 0.8656938
15 3.0262505 2.1910251 1.8843979 3.1293143 2.1554883 3.4980902 2.6283079 2.4048719 3.983398 2.4136268 3.522081 3.007018 3.4840385
16 1.5286422 1.6116018 2.5540322 2.0539753 2.0976479 1.5826922 1.6048633 2.2961570 3.871752 2.7424989 4.043520 3.614087 1.0934896
17 4.9943155 4.1065848 3.4770418 3.3021401 3.2230555 3.6685617 3.5808241 3.3177650 1.623936 5.8703816 1.103375 2.209982 4.0396759
18 2.1489172 1.0594898 0.7163371 1.8314806 0.5725396 2.0999146 1.1087933 0.9569850 2.838079 2.7573094 2.609950 2.341128 2.1305344
19 2.4767037 1.8461485 2.2577335 0.6829765 1.4485396 0.2031740 1.2577436 1.8698024 2.433585 3.5314372 3.060660 2.455532 0.4745941
20 1.4203597 0.6955385 0.7045640 2.1838887 0.9220824 2.1284891 0.6675716 0.4861878 3.220911 3.0103567 2.829814 3.114357 2.0293721
21 3.3108004 2.4214562 2.4453958 0.4800849 1.5998895 1.0548413 1.8330425 2.1562612 1.609019 3.9881217 2.672528 1.642588 1.5043709
22 0.6669589 1.4402546 2.3670266 3.1850012 2.4612464 2.9269333 2.0766590 2.4129670 4.819604 1.8793244 4.464398 4.332448 2.5206459
23 1.9447922 1.7115713 2.5050753 1.5217241 1.8785743 1.0064341 1.4592874 2.1852450 3.361642 3.0464670 3.732269 3.182426 0.5102366
24 2.4264129 2.1746755 2.9162964 1.5043709 2.2133985 0.9553319 1.8630534 2.5808839 3.348124 3.3473598 3.915171 3.199630 0.5593332
25 2.6473345 2.8382680 3.8142171 2.8430414 3.2952020 2.4144407 2.9204753 3.6207625 4.768853 2.9621935 5.141940 4.317412 1.9766887
 39 40
1 2.7928897 4.0034071
2 3.4145581 1.9159496
3 1.0059746 3.4118161
4 2.9911153 3.4038894
5 2.3723018 2.3794919
6 2.6970456 2.1746043
7 1.4498092 1.4402546
8 4.4362662 3.7829545
9 2.5222158 0.9702130
10 4.7579080 1.9877387
11 4.3793637 3.3744707
12 2.5548676 1.0419021
13 3.2005047 3.9750583
14 1.8893813 0.9502232
15 3.7498719 2.9802423
16 0.4861878 2.3615829
17 5.2698053 2.7688728
18 2.7283544 1.4809060
19 1.9874095 1.1033747
20 2.2961570 1.6511358
21 2.9975688 0.9381200
22 1.4424108 3.1998833
23 1.0565712 1.9358746
24 1.3519041 2.1002795
25 1.2142840 3.4753009
 [ reached getOption("max.print") -- omitted 15 rows ]
Rodar a análise hierárquica com method=ward
> hclust1=hclust(gdist,method="ward.D")
> plot(hclust1)
> plot(hclust1,xlab="",sub="")
Determinar visualmente o número de grupos
> abline(h=10)
> rect.hclust(hclust1,k=3,border="red")
Criar uma variável com os nomes dos grupos
> gclust=cutree(hclust1,k=3)
> gfinal=as.data.frame(cbind(GRAMPERS,gclust))
Calcular as distâncias entre os cortes para determinar onde ocorre o maior salto
Alturas dos agrupamentos
> alt1=hclust1$height
> length(alt1)
[1] 39
> alt1[-length(alt1)]
 [1] 0.0193499 0.2031740 0.2612237 0.3579732 0.4861878 0.4861878 0.4934225 0.5725396 0.5804970 0.6362524 0.6585035
[12] 0.6669589 0.6677304 0.6968946 0.7639477 0.8019294 1.0707859 1.0970810 1.1033747 1.2136399 1.2184315 1.2703767
[23] 1.4208837 1.5069458 1.5344150 1.7010156 2.0456098 2.1618541 2.3562702 2.54952153.3579000 3.5815019 3.8620021
[34] 4.3477895 7.5456519 8.8562412 9.6626468 13.0525720
Deslocar os saltos de uma casa começando pelo 0
> alt1.lag=c(0,alt1[-length(alt1)])
Calcular os saltos entre fusões consecutivas
> salto1=alt1-alt1.lag
> max(salto1)
[1] 3.389925
> which.max(salto1)
[1] 38
Dar a ordem das fusões
> hclust1$merge
 [,1] [,2]
 [1,] -9 -29
 [2,] -19 -31
 [3,] -3 -25
 [4,] -7 -38
 [5,] -16 -39
 [6,] -20 -33
 [7,] -23 -24
 [8,] -18 -30
 [9,] -14 -32
[10,] -21 1
[11,] -12 2
[12,] -22 -26
[13,] -1 -13
[14,] -28 6
[15,] 4 7
[16,] -11 -15
[17,] -6 -27
[18,] -35 13
[19,] -17 -36
[20,] 8 17
[21,] -40 9
[22,] -4 -5
[23,] 10 11
[24,] -10 -34
[25,] 14 20
[26,] -2 -37
[27,] 3 5
[28,] -8 22
[29,] 19 24
[30,] 21 25
[31,] 26 28
[32,] 15 27
[33,] 12 18
[34,] 16 31
[35,] 23 32
[36,] 33 34
[37,] 30 36
[38,] 29 37
[39,] 35 38
Descrição dos clusters
Comparar os clusters quanto às drivers numéricas
> aggregate(gnum[-1],by=list(gclust),median)
 Group.1 DESPESAS MAX_INT ITENS FREQUÊNCIA IDADE DEPENDENTES
1 1 280.5 12 3.0 3.5 44 1
2 2 84.0 10 2.5 1.0 30 1
3 3 187.0 29 5.0 1.0 49 0
> boxplot(gnum$DESPESAS~gclust,main="DESPESAS")
> boxplot(gnum$MAX_INT~gclust,main="MAX_INT")
> boxplot(gnum$FREQUÊNCIA~gclust,main="FREQUÊNCIA")
> boxplot(gnum$ITENS~gclust,main="ITENS")
Discriminação – Análise das “não drivers”
> prop.table(table(GRAMPERS$REGIAO,gclust),1)
 gclust
 1 2 3
 AA 0.84615385 0.15384615 0.00000000
 BB 0.57142857 0.35714286 0.07142857
 CC 0.23076923 0.53846154 0.23076923
> prop.table(table(GRAMPERS$REGIAO,gclust),2)
 gclust
 1 2 3
 AA 0.5000000 0.1428571 0.0000000
 BB 0.3636364 0.3571429 0.2500000
 CC 0.1363636 0.5000000 0.7500000
> boxplot(GRAMPERS$IDADE~gclust,main="IDADE",xlab="cluster")