R Under development (unstable) (2014-06-29 r66051) -- "Unsuffered Consequences"

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> library("party")

Loading required package: grid

Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich

Loading required package: strucchange

Loading required package: modeltools

Loading required package: stats4

> 

> data("BostonHousing", package = "mlbench")

> BostonHousing$lstat <- log(BostonHousing$lstat)

> BostonHousing$rm <- BostonHousing$rm^2

> BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, labels = c("no", "yes"))

> BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE)

> fmBH <- mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis + rad + tax + crim + b + ptratio,

+   control = mob_control(minsplit = 40, verbose = TRUE),

+   data = BostonHousing, model = linearModel)

-------------------------------------------

Fluctuation tests of splitting variables:

                    zn        indus         chas          nox          age

statistic 3.363356e+01 6.532322e+01 2.275635e+01 8.136281e+01 3.675850e+01

p.value   1.023987e-04 1.363602e-11 4.993053e-04 3.489797e-15 2.263798e-05

                   dis          rad          tax         crim            b

statistic 6.848533e+01 1.153641e+02 9.068440e+01 8.655065e+01 3.627629e+01

p.value   2.693904e-12 7.087680e-13 2.735524e-17 2.356348e-16 2.860686e-05

               ptratio

statistic 7.221524e+01

p.value   3.953623e-13

Best splitting variable: tax

Perform split? yes

-------------------------------------------

Node properties:

tax <= 432; criterion = 1, statistic = 115.364

-------------------------------------------

Fluctuation tests of splitting variables:

                    zn      indus      chas          nox        age

statistic 27.785009791 21.3329346 8.0272421 23.774323202 11.9204284

p.value    0.001494064  0.0285193 0.4005192  0.009518732  0.7666366

                   dis          rad          tax         crim         b

statistic 24.268011081 50.481593270 3.523250e+01 3.276813e+01 9.0363245

p.value    0.007601532  0.003437763 4.275527e-05 1.404487e-04 0.9871502

               ptratio

statistic 4.510680e+01

p.value   3.309747e-07

Best splitting variable: ptratio

Perform split? yes

-------------------------------------------

Node properties:

ptratio <= 15.2; criterion = 1, statistic = 50.482

-------------------------------------------

Fluctuation tests of splitting variables:

                    zn       indus        chas         nox         age

statistic 3.233350e+01 22.26864036 12.93407112 22.10510234 20.41295354

p.value   1.229678e-04  0.01504788  0.05259509  0.01622098  0.03499731

                 dis          rad          tax        crim         b

statistic 17.7204735 5.526565e+01 2.879128e+01 20.28503194 6.5549665

p.value    0.1091769 7.112214e-04 6.916307e-04  0.03706934 0.9999522

               ptratio

statistic 4.789850e+01

p.value   4.738855e-08

Best splitting variable: ptratio

Perform split? yes

-------------------------------------------

Node properties:

ptratio <= 19.6; criterion = 1, statistic = 55.266

-------------------------------------------

Fluctuation tests of splitting variables:

                 zn      indus      chas        nox       age        dis

statistic 14.971474 14.6477733 7.1172962 14.3455158 8.2176363 16.1112185

p.value    0.280361  0.3134649 0.5405005  0.3467974 0.9906672  0.1847818

                  rad          tax     crim         b    ptratio

statistic 43.17824350 3.447271e+01 9.340075 8.7773142 10.8469969

p.value    0.03281124 4.281939e-05 0.952996 0.9772696  0.8202694

Best splitting variable: tax

Perform split? yes

-------------------------------------------

Node properties:

tax <= 265; criterion = 1, statistic = 43.178

-------------------------------------------

Fluctuation tests of splitting variables:

                 zn     indus     chas       nox        age       dis

statistic 11.998039 7.3971233 7.227770 9.2936189 14.3023962 8.9239826

p.value    0.574642 0.9931875 0.522447 0.9119621  0.2886603 0.9389895

                 rad        tax       crim         b    ptratio

statistic 33.1746444 16.6666129 11.7143758 9.9050903 11.5927528

p.value    0.3926249  0.1206412  0.6153455 0.8539893  0.6328381

Best splitting variable: tax

Perform split? no

-------------------------------------------

-------------------------------------------

Fluctuation tests of splitting variables:

                  zn     indus         chas         nox       age        dis

statistic 10.9187926 9.0917078 2.754081e+01 17.39203006 4.6282349 11.9581600

p.value    0.7091039 0.9172303 4.987667e-05  0.08922543 0.9999992  0.5607267

                rad        tax     crim        b   ptratio

statistic 0.2557803 10.9076165 3.711175 3.158329 9.8865054

p.value   1.0000000  0.7106612 1.000000 1.000000 0.8410064

Best splitting variable: chas

Perform split? yes

-------------------------------------------

Splitting factor variable, objective function: 

 no 

Inf 

No admissable split found in 'chas'

> fmBH

1) tax <= 432; criterion = 1, statistic = 115.364

  2) ptratio <= 15.2; criterion = 1, statistic = 50.482

    3)*  weights = 72 

Terminal node model

Linear model with coefficients:

(Intercept)        lstat           rm  

     9.2349      -4.9391       0.6859  

  2) ptratio > 15.2

    4) ptratio <= 19.6; criterion = 1, statistic = 55.266

      5) tax <= 265; criterion = 1, statistic = 43.178

        6)*  weights = 63 

Terminal node model

Linear model with coefficients:

(Intercept)        lstat           rm  

     3.9637      -2.7663       0.6881  

      5) tax > 265

        7)*  weights = 162 

Terminal node model

Linear model with coefficients:

(Intercept)        lstat           rm  

    -1.7984      -0.2677       0.6539  

    4) ptratio > 19.6

      8)*  weights = 56 

Terminal node model

Linear model with coefficients:

(Intercept)        lstat           rm  

    17.5865      -4.6190       0.3387  

1) tax > 432

  9)*  weights = 153 

Terminal node model

Linear model with coefficients:

(Intercept)        lstat           rm  

    68.2971     -16.3540      -0.1478  

> summary(fmBH)

$`3`

Call:

NULL

Weighted Residuals:

   Min     1Q Median     3Q    Max 

-7.910  0.000  0.000  0.000  6.632 

Coefficients:

            Estimate Std. Error t value Pr(>|t|)    

(Intercept)  9.23488    3.95128   2.337   0.0223 *  

lstat       -4.93910    0.88285  -5.595 4.14e-07 ***

rm           0.68591    0.05136  13.354  < 2e-16 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.413 on 69 degrees of freedom

Multiple R-squared:  0.922, Adjusted R-squared:  0.9197 

F-statistic: 407.8 on 2 and 69 DF,  p-value: < 2.2e-16

$`6`

Call:

NULL

Weighted Residuals:

   Min     1Q Median     3Q    Max 

-4.614  0.000  0.000  0.000 12.473 

Coefficients:

            Estimate Std. Error t value Pr(>|t|)    

(Intercept)  3.96372    5.00781   0.792  0.43177    

lstat       -2.76629    1.00406  -2.755  0.00776 ** 

rm           0.68813    0.07716   8.918 1.36e-12 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.2 on 60 degrees of freedom

Multiple R-squared:  0.8176,    Adjusted R-squared:  0.8115 

F-statistic: 134.5 on 2 and 60 DF,  p-value: < 2.2e-16

$`7`

Call:

NULL

Weighted Residuals:

   Min     1Q Median     3Q    Max 

-9.092  0.000  0.000  0.000 10.236 

Coefficients:

            Estimate Std. Error t value Pr(>|t|)    

(Intercept) -1.79839    2.84702  -0.632    0.529    

lstat       -0.26771    0.69581  -0.385    0.701    

rm           0.65389    0.03757  17.404   <2e-16 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.652 on 159 degrees of freedom

Multiple R-squared:  0.8173,    Adjusted R-squared:  0.815 

F-statistic: 355.6 on 2 and 159 DF,  p-value: < 2.2e-16

$`8`

Call:

NULL

Weighted Residuals:

   Min     1Q Median     3Q    Max 

-8.466  0.000  0.000  0.000  4.947 

Coefficients:

            Estimate Std. Error t value Pr(>|t|)    

(Intercept) 17.58649    4.21666   4.171 0.000113 ***

lstat       -4.61897    0.84025  -5.497 1.13e-06 ***

rm           0.33867    0.07574   4.472 4.13e-05 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.197 on 53 degrees of freedom

Multiple R-squared:  0.6446,    Adjusted R-squared:  0.6312 

F-statistic: 48.07 on 2 and 53 DF,  p-value: 1.238e-12

$`9`

Call:

NULL

Weighted Residuals:

   Min     1Q Median     3Q    Max 

-10.56   0.00   0.00   0.00  24.28 

Coefficients:

             Estimate Std. Error t value Pr(>|t|)    

(Intercept)  68.29709    3.83284  17.819  < 2e-16 ***

lstat       -16.35401    0.96577 -16.934  < 2e-16 ***

rm           -0.14779    0.05047  -2.928  0.00394 ** 

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.689 on 150 degrees of freedom

Multiple R-squared:  0.6649,    Adjusted R-squared:  0.6604 

F-statistic: 148.8 on 2 and 150 DF,  p-value: < 2.2e-16

> 

> ### check for one-node tree

> fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE),

+   data = BostonHousing, model = linearModel))

> stopifnot(class(fmBH) != "try-error")

> 

> 

> data("PimaIndiansDiabetes", package = "mlbench")

> fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age,

+   control = mob_control(verbose = TRUE),

+   data = PimaIndiansDiabetes, model = glinearModel, family = binomial())

-------------------------------------------

Fluctuation tests of splitting variables:

              pregnant  pressure     triceps   insulin         mass    pedigree

statistic 2.988542e+01 7.5024235 15.94095417 6.5969297 4.880982e+01 18.33476114

p.value   9.778517e-05 0.9104325  0.06660773 0.9701412 8.316815e-09  0.02275017

                   age

statistic 4.351412e+01

p.value   1.182811e-07

Best splitting variable: mass

Perform split? yes

-------------------------------------------

Node properties:

mass <= 26.3; criterion = 1, statistic = 48.81

-------------------------------------------

Fluctuation tests of splitting variables:

            pregnant pressure  triceps   insulin       mass  pedigree       age

statistic 10.3924070 4.353740 5.911229 3.7855726 10.4748907 3.6263026 6.0978662

p.value    0.4903221 0.999824 0.986895 0.9999888  0.4785454 0.9999958 0.9817742

Best splitting variable: mass

Perform split? no

-------------------------------------------

-------------------------------------------

Fluctuation tests of splitting variables:

              pregnant  pressure  triceps   insulin      mass    pedigree

statistic 2.673912e+01 6.1757583 7.346804 7.8963977 9.1545915 17.96438828

p.value   4.434356e-04 0.9845137 0.922646 0.8700398 0.7033477  0.02677105

                   age

statistic 3.498466e+01

p.value   8.098640e-06

Best splitting variable: age

Perform split? yes

-------------------------------------------

Node properties:

age <= 30; criterion = 1, statistic = 34.985

-------------------------------------------

Fluctuation tests of splitting variables:

           pregnant  pressure  triceps   insulin      mass pedigree       age

statistic 4.3749991 9.4006532 7.661457 9.0583568 5.4287861 5.640420 6.3088818

p.value   0.9998989 0.6656073 0.893893 0.7168659 0.9967316 0.994611 0.9804133

Best splitting variable: pressure

Perform split? no

-------------------------------------------

-------------------------------------------

Fluctuation tests of splitting variables:

           pregnant pressure   triceps   insulin      mass   pedigree

statistic 7.7282903 1.935271 3.6078314 4.9703223 10.136944 11.9004129

p.value   0.8882324 1.000000 0.9999987 0.9991162  0.555382  0.3205095

                 age

statistic 10.1330698

p.value    0.5559631

Best splitting variable: pedigree

Perform split? no

-------------------------------------------

> fmPID

1) mass <= 26.3; criterion = 1, statistic = 48.81

  2)*  weights = 167 

Terminal node model

Binomial GLM with coefficients:

(Intercept)      glucose  

   -9.95151      0.05871  

1) mass > 26.3

  3) age <= 30; criterion = 1, statistic = 34.985

    4)*  weights = 304 

Terminal node model

Binomial GLM with coefficients:

(Intercept)      glucose  

   -6.70559      0.04684  

  3) age > 30

    5)*  weights = 297 

Terminal node model

Binomial GLM with coefficients:

(Intercept)      glucose  

   -2.77095      0.02354  

> summary(fmPID)

$`2`

Call:

NULL

Deviance Residuals: 

   Min      1Q  Median      3Q     Max  

-1.817   0.000   0.000   0.000   2.718  

Coefficients:

            Estimate Std. Error z value Pr(>|z|)    

(Intercept) -9.95151    1.74013  -5.719 1.07e-08 ***

glucose      0.05871    0.01211   4.846 1.26e-06 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 96.202  on 166  degrees of freedom

Residual deviance: 60.502  on 165  degrees of freedom

AIC: 64.502

Number of Fisher Scoring iterations: 6

$`4`

Call:

NULL

Deviance Residuals: 

    Min       1Q   Median       3Q      Max  

-1.9438  -0.3484   0.0000   0.0000   2.4893  

Coefficients:

             Estimate Std. Error z value Pr(>|z|)    

(Intercept) -6.705586   0.800193  -8.380  < 2e-16 ***

glucose      0.046837   0.006208   7.544 4.54e-14 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 364.01  on 303  degrees of freedom

Residual deviance: 280.98  on 302  degrees of freedom

AIC: 284.98

Number of Fisher Scoring iterations: 5

$`5`

Call:

NULL

Deviance Residuals: 

   Min      1Q  Median      3Q     Max  

-2.005   0.000   0.000   0.000   2.380  

Coefficients:

             Estimate Std. Error z value Pr(>|z|)    

(Intercept) -2.770954   0.548241  -5.054 4.32e-07 ***

glucose      0.023536   0.004202   5.601 2.13e-08 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 407.11  on 296  degrees of freedom

Residual deviance: 369.43  on 295  degrees of freedom

AIC: 373.43

Number of Fisher Scoring iterations: 4

> 

> 

> proc.time()

   user  system elapsed 

  3.252   0.076   3.332