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Type 'q()' to quit R. > library("party") Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich > > 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 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 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 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 3.92 0.76 4.65