Running examples in ‘ModTools-Ex.R’ failed
The error most likely occurred in:

> ### Name: ModTools-package
> ### Title: Regression and Classification Tools
> ### Aliases: ModTools-package ModTools
> ### Keywords: package
> 
> ### ** Examples
> 
> 
> r.swiss <- FitMod(Fertility ~ ., swiss, fitfn="lm")
> r.swiss

Call:
lm(formula = Fertility ~ ., data = swiss)

Coefficients:
     (Intercept)       Agriculture       Examination         Education  
         66.9152           -0.1721           -0.2580           -0.8709  
        Catholic  Infant.Mortality  
          0.1041            1.0770  

> # PlotTA(r.swiss)
> # PlotQQNorm(r.swiss)
> 
> 
> ## Count models
> 
> data(housing, package="MASS")
> 
> # poisson count
> r.pois <- FitMod(Freq ~ Infl*Type*Cont + Sat, family=poisson, data=housing, fitfn="poisson")
> 
> # negative binomial count
> r.nb <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="negbin")
> summary(r.nb)

Call:
glm.nb(formula = Freq ~ Infl * Type * Cont + Sat, data = housing, 
    init.theta = 11.58904011, link = log)

Coefficients:
                                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)                        3.14037    0.20801  15.097  < 2e-16 ***
InflMedium                         0.26615    0.28836   0.923 0.356028    
InflHigh                          -0.25336    0.30111  -0.841 0.400113    
TypeApartment                      0.39532    0.28603   1.382 0.166945    
TypeAtrium                        -0.77634    0.32149  -2.415 0.015742 *  
TypeTerrace                       -0.80653    0.32296  -2.497 0.012515 *  
ContHigh                          -0.02395    0.29474  -0.081 0.935244    
Sat.L                              0.16860    0.07525   2.240 0.025070 *  
Sat.Q                              0.25749    0.07821   3.292 0.000994 ***
InflMedium:TypeApartment          -0.12807    0.39875  -0.321 0.748082    
InflHigh:TypeApartment             0.15140    0.41087   0.368 0.712507    
InflMedium:TypeAtrium             -0.41265    0.45633  -0.904 0.365849    
InflHigh:TypeAtrium               -0.13006    0.47471  -0.274 0.784108    
InflMedium:TypeTerrace             0.01666    0.44423   0.038 0.970080    
InflHigh:TypeTerrace              -0.06969    0.47429  -0.147 0.883177    
InflMedium:ContHigh               -0.12632    0.41042  -0.308 0.758256    
InflHigh:ContHigh                 -0.59072    0.44424  -1.330 0.183609    
TypeApartment:ContHigh             0.52179    0.40019   1.304 0.192281    
TypeAtrium:ContHigh                0.71556    0.43760   1.635 0.102013    
TypeTerrace:ContHigh               1.15028    0.43270   2.658 0.007852 ** 
InflMedium:TypeApartment:ContHigh  0.01178    0.56001   0.021 0.983224    
InflHigh:TypeApartment:ContHigh    0.13235    0.59111   0.224 0.822837    
InflMedium:TypeAtrium:ContHigh     0.14135    0.62032   0.228 0.819752    
InflHigh:TypeAtrium:ContHigh       0.42364    0.65770   0.644 0.519496    
InflMedium:TypeTerrace:ContHigh   -0.51739    0.60483  -0.855 0.392307    
InflHigh:TypeTerrace:ContHigh     -0.49705    0.66522  -0.747 0.454946    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(11.589) family taken to be 1)

    Null deviance: 275.558  on 71  degrees of freedom
Residual deviance:  67.887  on 46  degrees of freedom
AIC: 535.29

Number of Fisher Scoring iterations: 1


              Theta:  11.59 
          Std. Err.:  2.92 

 2 x log-likelihood:  -481.289 
> 
> r.log <- FitMod(log(Freq) ~ Infl*Type*Cont + Sat, data=housing, fitfn="lm")
> summary(r.log)

Call:
lm(formula = log(Freq) ~ Infl * Type * Cont + Sat, data = housing)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.90539 -0.22284  0.01119  0.18857  0.96514 

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)                        3.140416   0.267518  11.739 1.95e-15 ***
InflMedium                         0.259891   0.378328   0.687   0.4956    
InflHigh                          -0.379083   0.378328  -1.002   0.3216    
TypeApartment                      0.219444   0.378328   0.580   0.5647    
TypeAtrium                        -0.785497   0.378328  -2.076   0.0435 *  
TypeTerrace                       -0.931069   0.378328  -2.461   0.0177 *  
ContHigh                          -0.075612   0.378328  -0.200   0.8425    
Sat.L                              0.199559   0.094582   2.110   0.0403 *  
Sat.Q                              0.199376   0.094582   2.108   0.0405 *  
InflMedium:TypeApartment           0.048725   0.535036   0.091   0.9278    
InflHigh:TypeApartment             0.398374   0.535036   0.745   0.4603    
InflMedium:TypeAtrium             -0.400214   0.535036  -0.748   0.4583    
InflHigh:TypeAtrium                0.002462   0.535036   0.005   0.9963    
InflMedium:TypeTerrace             0.143412   0.535036   0.268   0.7899    
InflHigh:TypeTerrace               0.154151   0.535036   0.288   0.7746    
InflMedium:ContHigh               -0.105500   0.535036  -0.197   0.8446    
InflHigh:ContHigh                 -0.737873   0.535036  -1.379   0.1745    
TypeApartment:ContHigh             0.697935   0.535036   1.304   0.1986    
TypeAtrium:ContHigh                0.763012   0.535036   1.426   0.1606    
TypeTerrace:ContHigh               1.114096   0.535036   2.082   0.0429 *  
InflMedium:TypeApartment:ContHigh -0.141229   0.756655  -0.187   0.8528    
InflHigh:TypeApartment:ContHigh    0.087753   0.756655   0.116   0.9082    
InflMedium:TypeAtrium:ContHigh     0.060731   0.756655   0.080   0.9364    
InflHigh:TypeAtrium:ContHigh       0.503181   0.756655   0.665   0.5094    
InflMedium:TypeTerrace:ContHigh   -0.531148   0.756655  -0.702   0.4862    
InflHigh:TypeTerrace:ContHigh     -0.296310   0.756655  -0.392   0.6972    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4634 on 46 degrees of freedom
Multiple R-squared:  0.751,	Adjusted R-squared:  0.6157 
F-statistic: 5.551 on 25 and 46 DF,  p-value: 2.796e-07

> 
> r.ols <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="lm")
> summary(r.ols)

Call:
lm(formula = Freq ~ Infl * Type * Cont + Sat, data = housing)

Residuals:
     Min       1Q   Median       3Q      Max 
-22.4861  -5.2361  -0.1944   3.5347  27.0556 

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)                        2.333e+01  7.076e+00   3.298  0.00189 **
InflMedium                         7.333e+00  1.001e+01   0.733  0.46737   
InflHigh                          -4.333e+00  1.001e+01  -0.433  0.66700   
TypeApartment                      1.033e+01  1.001e+01   1.033  0.30717   
TypeAtrium                        -1.267e+01  1.001e+01  -1.266  0.21195   
TypeTerrace                       -1.300e+01  1.001e+01  -1.299  0.20037   
ContHigh                          -1.816e-14  1.001e+01   0.000  1.00000   
Sat.L                              2.976e+00  2.502e+00   1.190  0.24034   
Sat.Q                              5.835e+00  2.502e+00   2.332  0.02412 * 
InflMedium:TypeApartment          -1.667e+00  1.415e+01  -0.118  0.90676   
InflHigh:TypeApartment             3.333e+00  1.415e+01   0.236  0.81483   
InflMedium:TypeAtrium             -8.667e+00  1.415e+01  -0.612  0.54328   
InflHigh:TypeAtrium                1.000e+00  1.415e+01   0.071  0.94397   
InflMedium:TypeTerrace            -4.000e+00  1.415e+01  -0.283  0.77871   
InflHigh:TypeTerrace               1.667e+00  1.415e+01   0.118  0.90676   
InflMedium:ContHigh               -4.000e+00  1.415e+01  -0.283  0.77871   
InflHigh:ContHigh                 -8.667e+00  1.415e+01  -0.612  0.54328   
TypeApartment:ContHigh             2.200e+01  1.415e+01   1.555  0.12689   
TypeAtrium:ContHigh                1.033e+01  1.415e+01   0.730  0.46897   
TypeTerrace:ContHigh               2.067e+01  1.415e+01   1.460  0.15098   
InflMedium:TypeApartment:ContHigh  2.333e+00  2.001e+01   0.117  0.90769   
InflHigh:TypeApartment:ContHigh   -1.200e+01  2.001e+01  -0.600  0.55171   
InflMedium:TypeAtrium:ContHigh     3.000e+00  2.001e+01   0.150  0.88150   
InflHigh:TypeAtrium:ContHigh       3.667e+00  2.001e+01   0.183  0.85544   
InflMedium:TypeTerrace:ContHigh   -8.667e+00  2.001e+01  -0.433  0.66700   
InflHigh:TypeTerrace:ContHigh     -1.167e+01  2.001e+01  -0.583  0.56278   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 12.26 on 46 degrees of freedom
Multiple R-squared:  0.6882,	Adjusted R-squared:  0.5187 
F-statistic: 4.061 on 25 and 46 DF,  p-value: 1.939e-05

> 
> r.gam <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="gamma")
> summary(r.gam)

Call:
glm(formula = Freq ~ Infl * Type * Cont + Sat, family = "Gamma", 
    data = housing)

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                        4.355e-02  1.143e-02   3.809 0.000412 ***
InflMedium                        -1.005e-02  1.433e-02  -0.702 0.486503    
InflHigh                           9.653e-03  1.811e-02   0.533 0.596679    
TypeApartment                     -1.288e-02  1.386e-02  -0.929 0.357851    
TypeAtrium                         5.053e-02  2.761e-02   1.830 0.073681 .  
TypeTerrace                        5.354e-02  2.835e-02   1.889 0.065241 .  
ContHigh                           7.502e-17  1.615e-02   0.000 1.000000    
Sat.L                             -3.211e-03  3.155e-03  -1.018 0.314213    
Sat.Q                             -9.144e-03  4.197e-03  -2.179 0.034492 *  
InflMedium:TypeApartment           5.915e-03  1.766e-02   0.335 0.739147    
InflHigh:TypeApartment            -8.770e-03  2.134e-02  -0.411 0.683073    
InflMedium:TypeAtrium              2.340e-02  4.077e-02   0.574 0.568768    
InflHigh:TypeAtrium                3.286e-02  4.794e-02   0.685 0.496500    
InflMedium:TypeTerrace            -1.346e-02  3.553e-02  -0.379 0.706642    
InflHigh:TypeTerrace               2.393e-02  4.718e-02   0.507 0.614453    
InflMedium:ContHigh                4.784e-03  2.086e-02   0.229 0.819606    
InflHigh:ContHigh                  4.389e-02  3.364e-02   1.305 0.198497    
TypeApartment:ContHigh            -1.121e-02  1.856e-02  -0.604 0.548836    
TypeAtrium:ContHigh               -4.583e-02  3.247e-02  -1.412 0.164758    
TypeTerrace:ContHigh              -6.393e-02  3.174e-02  -2.014 0.049831 *  
InflMedium:TypeApartment:ContHigh -1.765e-03  2.412e-02  -0.073 0.941979    
InflHigh:TypeApartment:ContHigh   -3.385e-02  3.662e-02  -0.924 0.360209    
InflMedium:TypeAtrium:ContHigh    -1.225e-02  4.752e-02  -0.258 0.797727    
InflHigh:TypeAtrium:ContHigh      -5.532e-02  6.091e-02  -0.908 0.368570    
InflMedium:TypeTerrace:ContHigh    3.236e-02  4.143e-02   0.781 0.438774    
InflHigh:TypeTerrace:ContHigh      1.462e-02  6.501e-02   0.225 0.823077    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Gamma family taken to be 0.2163103)

    Null deviance: 38.140  on 71  degrees of freedom
Residual deviance: 10.671  on 46  degrees of freedom
AIC: 538.18

Number of Fisher Scoring iterations: 6

> 
> r.gami <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="gamma", link="identity")
> summary(r.gami)

Call:
glm(formula = Freq ~ Infl * Type * Cont + Sat, family = function () 
Gamma(link = identity), data = housing)

Coefficients:
                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                        23.3635     5.7553   4.060 0.000189 ***
InflMedium                          7.1563     9.5281   0.751 0.456438    
InflHigh                           -7.2034     6.9307  -1.039 0.304079    
TypeApartment                      12.6892    10.6939   1.187 0.241486    
TypeAtrium                        -11.1942     6.3987  -1.749 0.086883 .  
TypeTerrace                       -10.9532     6.4273  -1.704 0.095097 .  
ContHigh                           -1.6216     7.8406  -0.207 0.837058    
Sat.L                               4.9368     1.4858   3.323 0.001755 ** 
Sat.Q                               3.2415     1.1421   2.838 0.006731 ** 
InflMedium:TypeApartment           -3.4540    16.4721  -0.210 0.834835    
InflHigh:TypeApartment              1.4504    13.6446   0.106 0.915810    
InflMedium:TypeAtrium              -9.2801    10.1778  -0.912 0.366629    
InflHigh:TypeAtrium                 3.5471     7.6884   0.461 0.646714    
InflMedium:TypeTerrace             -4.5638    10.5680  -0.432 0.667870    
InflHigh:TypeTerrace                2.9450     7.6893   0.383 0.703488    
InflMedium:ContHigh                -3.5733    12.5840  -0.284 0.777715    
InflHigh:ContHigh                  -7.1152     8.8705  -0.802 0.426610    
TypeApartment:ContHigh             22.5209    18.6701   1.206 0.233888    
TypeAtrium:ContHigh                11.5520     9.9375   1.162 0.251043    
TypeTerrace:ContHigh               22.9328    11.8581   1.934 0.059288 .  
InflMedium:TypeApartment:ContHigh   0.9018    27.5339   0.033 0.974015    
InflHigh:TypeApartment:ContHigh   -13.2180    21.9476  -0.602 0.549965    
InflMedium:TypeAtrium:ContHigh      2.3032    14.8708   0.155 0.877591    
InflHigh:TypeAtrium:ContHigh        0.3769    11.2392   0.034 0.973392    
InflMedium:TypeTerrace:ContHigh    -8.8401    16.8767  -0.524 0.602931    
InflHigh:TypeTerrace:ContHigh     -14.3587    12.7806  -1.123 0.267063    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Gamma family taken to be 0.1923107)

    Null deviance: 38.1400  on 71  degrees of freedom
Residual deviance:  9.0822  on 46  degrees of freedom
AIC: 526.3

Number of Fisher Scoring iterations: 14

> 
> old <-options(digits=3)
> TMod(r.pois, r.nb, r.log, r.ols, r.gam, r.gami)
Waiting for profiling to be done...
Waiting for profiling to be done...
Waiting for profiling to be done...
Waiting for profiling to be done...
                                coef   r.pois         r.nb       r.log    
1                        (Intercept)    3.136 ***    3.140 ***   3.140 ***
2                         InflMedium    0.273 .      0.266       0.260    
3                           InflHigh   -0.205       -0.253      -0.379    
4                      TypeApartment    0.367 *      0.395       0.219    
5                         TypeAtrium   -0.783 ***   -0.776 *    -0.785 *  
6                        TypeTerrace   -0.815 ***   -0.807 *    -0.931 *  
7                           ContHigh   -2.151e-15   -0.024      -0.076    
8                              Sat.L    0.116 **     0.169 *     0.200 *  
9                              Sat.Q    0.263 ***    0.257 ***   0.199 *  
10          InflMedium:TypeApartment   -0.118       -0.128       0.049    
11            InflHigh:TypeApartment    0.175        0.151       0.398    
12             InflMedium:TypeAtrium   -0.407       -0.413      -0.400    
13               InflHigh:TypeAtrium   -0.169       -0.130       0.002    
14            InflMedium:TypeTerrace    0.006        0.017       0.143    
15              InflHigh:TypeTerrace   -0.093       -0.070       0.154    
16               InflMedium:ContHigh   -0.140       -0.126      -0.105    
17                 InflHigh:ContHigh   -0.609 *     -0.591      -0.738    
18            TypeApartment:ContHigh    0.503 *      0.522       0.698    
19               TypeAtrium:ContHigh    0.677 *      0.716       0.763    
20              TypeTerrace:ContHigh    1.099 ***    1.150 **    1.114 *  
21 InflMedium:TypeApartment:ContHigh    0.054        0.012      -0.141    
22   InflHigh:TypeApartment:ContHigh    0.146        0.132       0.088    
23    InflMedium:TypeAtrium:ContHigh    0.156        0.141       0.061    
24      InflHigh:TypeAtrium:ContHigh    0.478        0.424       0.503    
25   InflMedium:TypeTerrace:ContHigh   -0.498       -0.517      -0.531    
26     InflHigh:TypeTerrace:ContHigh   -0.447       -0.497      -0.296    
27                               ---                                      
28                         r.squared    -            -           0.751    
29                     adj.r.squared    -            -           0.616    
30                             sigma    -            -           0.463    
31                            logLik -279.213     -240.645     -30.648    
32                           logLik0 -587.313     -290.899       -        
33                                G2  616.201      100.508       -        
34                          deviance    -            -           9.876    
35                               AIC  610.426      535.289     115.296    
36                               BIC  669.619      596.759     176.766    
37                             numdf   26           27          25        
38                             dendf    -            -          46        
39                                 N   72           72          72        
40                            n vars    8            8           8        
41                            n coef   26           26          26        
42                                 F    -            -           5.551    
43                                 p    -            -           0.000    
44                               MAE    6.641        6.608       0.276    
45                              MAPE    0.354        0.345       0.104    
46                               MSE   92.754       97.134       0.137    
47                              RMSE    9.631        9.856       0.370    
48                          McFadden    0.525        0.173       -        
49                       McFaddenAdj    0.480        0.083       -        
50                        Nagelkerke    1.000        0.753       -        
51                          CoxSnell    1.000        0.752       -        
      r.ols        r.gam       r.gami    
1    23.333 **     0.044 ***   23.363 ***
2     7.333       -0.010        7.156    
3    -4.333        0.010       -7.203    
4    10.333       -0.013       12.689    
5   -12.667        0.051 .    -11.194 .  
6   -13.000        0.054 .    -10.953 .  
7    -1.816e-14    0.000       -1.622    
8     2.976       -0.003        4.937 ** 
9     5.835 *     -0.009 *      3.241 ** 
10   -1.667        0.006       -3.454    
11    3.333       -0.009        1.450    
12   -8.667        0.023       -9.280    
13    1.000        0.033        3.547    
14   -4.000       -0.013       -4.564    
15    1.667        0.024        2.945    
16   -4.000        0.005       -3.573    
17   -8.667        0.044       -7.115    
18   22.000       -0.011       22.521    
19   10.333       -0.046       11.552    
20   20.667       -0.064 *     22.933 .  
21    2.333       -0.002        0.902    
22  -12.000       -0.034      -13.218    
23    3.000       -0.012        2.303    
24    3.667       -0.055        0.377    
25   -8.667        0.032       -8.840    
26  -11.667        0.015      -14.359    
27                                       
28    0.688        -            -        
29    0.519        -            -        
30   12.256        -            -        
31 -266.465     -242.088     -236.152    
32    -         -290.204     -290.204    
33    -           96.232      108.104    
34 6909.139        -            -        
35  586.930      538.176      526.304    
36  648.400      599.646      587.774    
37   25           27           27        
38   46            -            -        
39   72           72           72        
40    8            8            8        
41   26           26           26        
42    4.061        -            -        
43    0.000        -            -        
44    7.050        6.883        6.823    
45    0.385        0.371        0.321    
46   95.960       98.993      102.471    
47    9.796        9.950       10.123    
48    -            0.166        0.186    
49    -            0.076        0.097    
50    -            0.737        0.777    
51    -            0.737        0.777    
> options(old)
> 
> 
> ## Ordered Regression
> 
> r.polr <- FitMod(Sat ~ Infl + Type + Cont, data=housing, fitfn="polr", weights = Freq)
> 
> # multinomial Regression
> # r.mult <- FitMod(factor(Sat, ordered=FALSE) ~ Infl + Type + Cont, data=housing,
> #                  weights = housing$Freq, fitfn="multinom")
> 
> 
> # Regression tree
> r.rp <- FitMod(factor(Sat, ordered=FALSE) ~ Infl + Type + Cont, data=housing,
+                  weights = housing$Freq, fitfn="rpart")
> 
> # compare predictions
> d.p <- expand.grid(Infl=levels(housing$Infl), Type=levels(housing$Type), Cont=levels(housing$Cont))
> d.p$polr <- predict(r.polr, newdata=d.p)
> # ??
> # d.p$ols <- factor(round(predict(r.ols, newdata=d.p)^2), labels=levels(housing$Sat))
> # d.p$mult <- predict(r.mult, newdata=d.p)
> d.p$rp <- predict(r.rp, newdata=d.p, type="class")
> 
> d.p
     Infl      Type Cont polr   rp
1     Low     Tower  Low  Low High
2  Medium     Tower  Low High High
3    High     Tower  Low High High
4     Low Apartment  Low  Low  Low
5  Medium Apartment  Low  Low High
6    High Apartment  Low High High
7     Low    Atrium  Low  Low High
8  Medium    Atrium  Low High High
9    High    Atrium  Low High High
10    Low   Terrace  Low  Low  Low
11 Medium   Terrace  Low  Low High
12   High   Terrace  Low High High
13    Low     Tower High High High
14 Medium     Tower High High High
15   High     Tower High High High
16    Low Apartment High  Low  Low
17 Medium Apartment High High High
18   High Apartment High High High
19    Low    Atrium High  Low High
20 Medium    Atrium High High High
21   High    Atrium High High High
22    Low   Terrace High  Low  Low
23 Medium   Terrace High  Low High
24   High   Terrace High High High
> 
> 
> # Classification with 2 classes  ***************
> 
> r.pima <- FitMod(diabetes ~ ., d.pima, fitfn="logit")
> r.pima

Call:  glm(formula = diabetes ~ ., family = "binomial", data = d.pima)

Coefficients:
(Intercept)     pregnant      glucose     pressure      triceps      insulin  
  -8.404696     0.123182     0.035164    -0.013296     0.000619    -0.001192  
       mass     pedigree          age  
   0.089701     0.945180     0.014869  

Degrees of Freedom: 767 Total (i.e. Null);  759 Residual
Null Deviance:	    993.5 
Residual Deviance: 723.4 	AIC: 741.4
> Conf(r.pima)

Confusion Matrix and Statistics

          Reference
Prediction pos neg
       pos 156  55
       neg 112 445

                Total n : 768
               Accuracy : 0.7826
                 95% CI : (0.7520, 0.8103)
    No Information Rate : 0.6510
    P-Value [Acc > NIR] : 1.37e-15

                  Kappa : 0.4966
 Mcnemar's Test P-Value : 1.47e-05

            Sensitivity : 0.5821
            Specificity : 0.8900
         Pos Pred Value : 0.7393
         Neg Pred Value : 0.7989
             Prevalence : 0.3490
         Detection Rate : 0.2747
   Detection Prevalence : 0.2031
      Balanced Accuracy : 0.7360
         F-val Accuracy : 0.6514
     Matthews Cor.-Coef : 0.5041

       'Positive' Class : pos

> plot(ROC(r.pima))
Setting levels: control = neg, case = pos
Setting direction: controls < cases
> OddsRatio(r.pima)

Call:
glm(formula = diabetes ~ ., family = "binomial", data = d.pima)

Odds Ratios:
               or or.lci or.uci  Pr(>|z|)    
(Intercept) 0.000  0.000  0.001 < 2.2e-16 ***
pregnant    1.131  1.062  1.204  1.23e-04 ***
glucose     1.036  1.028  1.043 < 2.2e-16 ***
pressure    0.987  0.977  0.997    0.0111 *  
triceps     1.001  0.987  1.014    0.9285    
insulin     0.999  0.997  1.001    0.1861    
mass        1.094  1.062  1.127  2.76e-09 ***
pedigree    2.573  1.432  4.625    0.0016 ** 
age         1.015  0.997  1.034    0.1112    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Brier Score: 0.153     Nagelkerke R2: 0.408

> 
> 
> # rpart tree
> rp.pima <- FitMod(diabetes ~ ., d.pima, fitfn="rpart")
> rp.pima
n= 768 

node), split, n, loss, yval, (yprob)
      * denotes terminal node

  1) root 768 268 neg (0.65104167 0.34895833)  
    2) glucose< 127.5 485  94 neg (0.80618557 0.19381443)  
      4) age< 28.5 271  23 neg (0.91512915 0.08487085) *
      5) age>=28.5 214  71 neg (0.66822430 0.33177570)  
       10) mass< 26.35 41   2 neg (0.95121951 0.04878049) *
       11) mass>=26.35 173  69 neg (0.60115607 0.39884393)  
         22) glucose< 99.5 55  10 neg (0.81818182 0.18181818) *
         23) glucose>=99.5 118  59 neg (0.50000000 0.50000000)  
           46) pedigree< 0.561 84  34 neg (0.59523810 0.40476190)  
             92) pedigree< 0.2 21   4 neg (0.80952381 0.19047619) *
             93) pedigree>=0.2 63  30 neg (0.52380952 0.47619048)  
              186) pregnant>=1.5 52  21 neg (0.59615385 0.40384615)  
                372) pressure>=67 40  12 neg (0.70000000 0.30000000) *
                373) pressure< 67 12   3 pos (0.25000000 0.75000000) *
              187) pregnant< 1.5 11   2 pos (0.18181818 0.81818182) *
           47) pedigree>=0.561 34   9 pos (0.26470588 0.73529412) *
    3) glucose>=127.5 283 109 pos (0.38515901 0.61484099)  
      6) mass< 29.95 76  24 neg (0.68421053 0.31578947)  
       12) glucose< 145.5 41   6 neg (0.85365854 0.14634146) *
       13) glucose>=145.5 35  17 pos (0.48571429 0.51428571)  
         26) insulin< 14.5 21   8 neg (0.61904762 0.38095238) *
         27) insulin>=14.5 14   4 pos (0.28571429 0.71428571) *
      7) mass>=29.95 207  57 pos (0.27536232 0.72463768)  
       14) glucose< 157.5 115  45 pos (0.39130435 0.60869565)  
         28) age< 30.5 50  23 neg (0.54000000 0.46000000)  
           56) pressure>=61 40  13 neg (0.67500000 0.32500000)  
            112) mass< 41.8 31   7 neg (0.77419355 0.22580645) *
            113) mass>=41.8 9   3 pos (0.33333333 0.66666667) *
           57) pressure< 61 10   0 pos (0.00000000 1.00000000) *
         29) age>=30.5 65  18 pos (0.27692308 0.72307692) *
       15) glucose>=157.5 92  12 pos (0.13043478 0.86956522) *
> Conf(rp.pima)

Confusion Matrix and Statistics

          Reference
Prediction neg pos
       neg 449  72
       pos  51 196

                Total n : 768
               Accuracy : 0.8398
                 95% CI : (0.8122, 0.8641)
    No Information Rate : 0.6510
    P-Value [Acc > NIR] : < 2.2e-16

                  Kappa : 0.6410
 Mcnemar's Test P-Value : 0.0713

            Sensitivity : 0.8980
            Specificity : 0.7313
         Pos Pred Value : 0.8618
         Neg Pred Value : 0.7935
             Prevalence : 0.6510
         Detection Rate : 0.6784
   Detection Prevalence : 0.5846
      Balanced Accuracy : 0.8147
         F-val Accuracy : 0.8795
     Matthews Cor.-Coef : 0.6422

       'Positive' Class : neg

> lines(ROC(rp.pima), col=hblue)
Setting levels: control = neg, case = pos
Setting direction: controls < cases
> # to be improved
> plot(rp.pima, col=SetAlpha(c("blue","red"), 0.4), cex=0.7)
> 
> 
> # Random Forest
> rf.pima <- FitMod(diabetes ~ ., d.pima, method="class", fitfn="randomForest")
> rf.pima

Call:
 randomForest(formula = diabetes ~ ., data = d.pima, method = "class",      na.action = function (object, ...)      UseMethod("na.omit")) 
               Type of random forest: classification
                     Number of trees: 500
No. of variables tried at each split: 2

        OOB estimate of  error rate: 23.18%
Confusion matrix:
    neg pos class.error
neg 430  70   0.1400000
pos 108 160   0.4029851
> Conf(rf.pima)

Confusion Matrix and Statistics

          Reference
Prediction neg pos
       neg 430 108
       pos  70 160

                Total n : 768
               Accuracy : 0.7682
                 95% CI : (0.7371, 0.7967)
    No Information Rate : 0.6510
    P-Value [Acc > NIR] : 1.29e-12

                  Kappa : 0.4726
 Mcnemar's Test P-Value : 0.0055

            Sensitivity : 0.8600
            Specificity : 0.5970
         Pos Pred Value : 0.7993
         Neg Pred Value : 0.6957
             Prevalence : 0.6510
         Detection Rate : 0.7005
   Detection Prevalence : 0.5599
      Balanced Accuracy : 0.7285
         F-val Accuracy : 0.8285
     Matthews Cor.-Coef : 0.4756

       'Positive' Class : neg

> lines(ROC(r.pima), col=hred)
Setting levels: control = neg, case = pos
Setting direction: controls < cases
> 
> 
> 
> # more models to compare
> 
> d.pim <- SplitTrainTest(d.pima, p = 0.2)
> mdiab <- formula(diabetes ~ pregnant + glucose + pressure + triceps
+                             + insulin + mass + pedigree + age)
> 
> r.glm <- FitMod(mdiab, data=d.pim$train, fitfn="logit")
> r.rp <- FitMod(mdiab, data=d.pim$train, fitfn="rpart")
> r.rf <- FitMod(mdiab, data=d.pim$train, fitfn="randomForest")
> r.svm <- FitMod(mdiab, data=d.pim$train, fitfn="svm")
> r.c5 <- FitMod(mdiab, data=d.pim$train, fitfn="C5.0")
> r.nn <- FitMod(mdiab, data=d.pim$train, fitfn="nnet")
> r.nb <- FitMod(mdiab, data=d.pim$train, fitfn="naive_bayes")
> r.lda <- FitMod(mdiab, data=d.pim$train, fitfn="lda")
> r.qda <- FitMod(mdiab, data=d.pim$train, fitfn="qda")
> r.lb <- FitMod(mdiab, data=d.pim$train, fitfn="lb")
> 
> mods <- list(glm=r.glm, rp=r.rp, rf=r.rf, svm=r.svm, c5=r.c5
+              , nn=r.nn, nb=r.nb, lda=r.lda, qda=r.qda, lb=r.lb)
> 
> # insight in the Regression tree
> plot(r.rp, box.palette = as.list(Pal("Helsana", alpha = 0.5)))
> 
> # Insample accuracy ...
> TModC(mods, ord="auc")
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Error in if (attr(attr(data, "terms"), "response")) { : 
  argument is of length zero
Calls: TModC ... sapply -> lapply -> FUN -> model.extract -> model.response
Execution halted