I am trying to built partial dependence plots for my rpartscore model, but I am encountering some difficulties, with variable names, and with the probability function..
after splitting data in training and testing, my tree model is:
tree <- rpartScore(Nutritional.Status.olr ~ VBT+`VBT/L`+`d/r`+SMI+`Residual M/L`+BMI+`M/L`+`G/L`+LMD, data = datatrain)
which works well in predicting the testing dataset, did a confusion matrix afterwards, etc.
Anyway, I am generating now partial dependence plots with this line of code of the dpd package:
partial(big.tree, pred.var = "VBT",prob=T, plot = T, type = "regression", smooth=TRUE)
and i get the following image:
Unfortunately, I would like the probabilities though, so not the actual predicted value, but how much the variable influences the model at that point, like described e.g. here: "Single variables shows how there value affect the model, on y-axis having a negative value means for that particular value of predictor variable it is less likely to predict the correct class on that observation and having a positive value means it has positive impact on predicting the correct class. Same applies to two variable plots, color represent the intensity of affect on model." https://rpubs.com/vishal1310/QuickIntroductiontoPartialDependencePlots
If i change the line to
partial(big.tree, pred.var = "VBT",prob=F, plot = T, type = "regression", smooth=TRUE)
nothing changes, I get the same plot. The use of autoplot doesnt work for me somehow..
Does someone has advise on how to get the probability plots here instead of the actual value plots? Correct me if im using wrong terms...
Additionally, I can not handle the other variable names again... I have tried "" and `` but the function doesnt accept them... any ideas? I would not like to go back to the beginning of the analysis and rename everything, as I wont have the correct tree variable names then...
result_VBT <- partial(big.tree, pred.var = "VBT", prob = TRUE, plot = TRUE, type = "regression", smooth = TRUE)
result_VBT_L <- partial(big.tree, pred.var = "VBT/L", prob = TRUE, plot = TRUE, type = "regression", smooth = TRUE)
result_d_r <- partial(big.tree, pred.var = "d/r", prob = TRUE, plot = TRUE, type = "regression", smooth = TRUE)
result_SMI <- partial(big.tree, pred.var = "SMI", prob = TRUE, plot = TRUE, type = "regression", smooth = TRUE)
result_Residual_M_L <- partial(big.tree, pred.var = "Residual M/L", prob = TRUE, plot = TRUE, type = "regression", smooth = TRUE)
result_BMI <- partial(big.tree, pred.var = "BMI", prob = TRUE, plot = TRUE, type = "regression", smooth = TRUE)
result_M_L <- partial(big.tree, pred.var = "M/L", prob = TRUE, plot = TRUE, type = "regression", smooth = TRUE)
result_G_L <- partial(big.tree, pred.var = "G/L", prob = TRUE, plot = TRUE, type = "regression", smooth = TRUE)
result_LMD <- partial(big.tree, pred.var = "LMD", prob = TRUE, plot = TRUE, type = "regression", smooth = TRUE)
result_VBT_L <- partial(big.tree, pred.var = `VBT/L`, prob = TRUE, plot = TRUE, type = "regression", smooth = TRUE)
here is an example of my data`, the data is now renamed to the variables used above in the model:
> selected_data
Nutritional.Status.olr VBT vbl dr SMI residuals BMI ML GL LMD
2 2 11 0.07482993 0.14666667 68.14410 -0.0412701853 0.001527141 0.2244898 0.5102041 2321.6374
3 2 12 0.07384615 0.15094340 64.96813 -0.0746683103 0.001609467 0.2615385 0.4892308 2346.4617
4 3 7 0.03333333 0.07821229 51.70707 -0.2663154538 0.001655329 0.3476190 0.4261905 1187.2612
5 2 11 0.04782609 0.08560311 71.16723 0.0661265660 0.002495274 0.5739130 0.5586957 1452.0101
6 2 10 0.04739336 0.08547009 55.86345 -0.1883204238 0.001796905 0.3791469 0.5545024 1624.0382
9 2 9 0.08653846 0.16363636 75.80390 0.0157902364 0.001201923 0.1250000 0.5288462 2545.5844
11 2 10 0.04950495 0.08849558 77.19702 0.1288989646 0.002377218 0.4801980 0.5594059 1443.0780
12 3 6 0.05106383 0.11320755 56.60686 -0.2587833845 0.001014033 0.1191489 0.4510638 1738.2257
13 2 9 0.07377049 0.12857143 88.49998 0.1934610387 0.001646063 0.2008197 0.5737705 2008.3499
14 3 9 0.03982301 0.09000000 49.44048 -0.3006442848 0.001703344 0.3849558 0.4424779 1450.5647
16 1 13 0.07142857 0.13000000 73.98986 0.0715626259 0.002052892 0.3736264 0.5494505 2126.7899
18 1 16 0.07547170 0.14035088 66.78048 -0.0091440723 0.002158241 0.4575472 0.5377358 2365.3861
19 2 10 0.05714286 0.11764706 58.74927 -0.1646930384 0.001567347 0.2742857 0.4857143 1909.4065
20 3 5 0.03105590 0.06329114 54.22662 -0.2567188843 0.001330967 0.2142857 0.4906832 1080.1234
21 3 9 0.03947368 0.09000000 64.47786 -0.0338325271 0.002241074 0.5109649 0.4385965 1259.0616
22 3 9 0.05921053 0.11920530 67.24187 -0.0498173834 0.001558172 0.2368421 0.4967105 1849.3242
23 3 8 0.05442177 0.12121212 55.75426 -0.2419408807 0.001249479 0.1836735 0.4489796 1866.6667
24 2 13 0.05842697 0.12500000 55.38356 -0.1893621604 0.001878551 0.4179775 0.4674157 2010.7908
27 3 9 0.05263158 0.12676056 NA NA NA NA 0.4152047 NA
28 1 10 0.04975124 0.09661836 65.43038 -0.0371844924 0.002004901 0.4029851 0.5149254 1575.2719
29 2 9 0.04627249 0.08866995 65.52536 -0.0404329435 0.001942890 0.3778920 0.5218509 1464.0592
32 3 8 0.03478261 0.08247423 45.55781 -0.3799238222 0.001597353 0.3673913 0.4217391 1319.8530
34 2 17 0.08292683 0.15315315 74.99949 0.1021267929 0.002343843 0.4804878 0.5414634 2452.4928
35 2 16 0.07804878 0.14545455 67.76604 0.0007066144 0.002117787 0.4341463 0.5365854 2428.2976
36 1 19 0.09004739 0.16521739 65.63956 -0.0270522762 0.002111363 0.4454976 0.5450237 2846.6292
37 3 8 0.04733728 0.09523810 44.84638 -0.4397158548 0.001155422 0.1952663 0.4970414 1810.4076
40 2 17 0.08056872 0.14655172 NA NA NA NA 0.5497630 NA
41 1 20 0.11428571 0.20512821 56.30139 -0.2072526528 0.001502041 0.2628571 0.5571429 3900.9475
42 3 7 0.04487179 0.08045977 56.15378 -0.2263065100 0.001335470 0.2083333 0.5576923 1533.6232
43 1 18 0.08780488 0.15254237 76.14161 0.1172404307 0.002379536 0.4878049 0.5756098 2577.2078
44 1 17 0.08947368 0.14782609 78.89924 0.1419553389 0.002285319 0.4342105 0.6052632 2579.8755
45 3 7 0.04402516 0.09722222 52.21905 -0.2962301524 0.001265773 0.2012579 0.4528302 1560.3485
46 3 5 0.02336449 0.04716981 52.20826 -0.2539721024 0.001703206 0.3644860 0.4953271 828.1893
47 3 3 0.01408451 0.02912621 51.92880 -0.2600087350 0.001686173 0.3591549 0.4835681 500.5879
48 3 8 0.03921569 0.06956522 65.29044 -0.0372078506 0.002030469 0.4142157 0.5637255 1243.0160
49 3 5 0.02202643 0.04347826 61.12768 -0.0878179414 0.002115314 0.4801762 0.5066079 721.5554
50 3 9 0.04205607 0.07964602 64.25631 -0.0463327376 0.002096253 0.4485981 0.5280374 1343.7355
53 2 16 0.07339450 0.13675214 NA NA NA NA 0.5366972 NA
54 3 7 0.04458599 0.09210526 61.02015 -0.1422828991 0.001460505 0.2292994 0.4840764 1461.8291
55 3 9 0.04639175 0.09729730 60.19361 -0.1256719681 0.001780210 0.3453608 0.4768041 1531.4611
56 3 8 0.05177994 0.10810811 58.69450 -0.1834355860 0.001382474 0.2135922 0.4789644 1731.0008
57 2 12 0.05797101 0.12500000 52.50873 -0.2529873296 0.001656981 0.3429952 0.4637681 2048.9778
58 2 14 0.07821229 0.15730337 59.47313 -0.1492165826 0.001622921 0.2905028 0.4972067 2597.4840
60 1 16 0.08163265 0.14814815 74.48678 0.0888496336 0.002225635 0.4362245 0.5510204 2422.5066
61 3 7 0.03977273 0.08139535 59.55838 -0.1502002559 0.001598011 0.2812500 0.4886364 1319.9327
62 2 12 0.08695652 0.15189873 81.11648 0.1239603610 0.001706574 0.2355072 0.5724638 2472.7437
63 3 9 0.04545455 0.09625668 56.19633 -0.1914694605 0.001696255 0.3358586 0.4722222 1552.9743
64 2 9 0.06498195 0.13432836 56.78606 -0.2321180194 0.001199025 0.1660650 0.4837545 2208.5309
65 2 14 0.07142857 0.14000000 64.03250 -0.0623813361 0.001913265 0.3750000 0.5102041 2286.1904
66 2 12 0.05839416 0.11428571 57.44628 -0.1641560653 0.001799658 0.3698297 0.5109489 1973.2421