rtreecartdecision-treerpart

rpart plot rename nodes, mathematic sign in name

I am using rpart score and then rpart plot to built the following tree:

tree computed

I use the following code:

library(rpartScore)
library(rpart.plot)
library(caret)

tree <- rpartScore(Nutritional.Status.olr ~ Blubber..V.+vbl+dr+SMI+residuals+BMI+ML+GL+LMD, data = datatrain)

rpart.plot(tree, type=5, extra=101)

I will need to rename the nodes in the tree, as well as the final ones and I could not find a solution so far..

I would like to get the following image:

treewanted

I did try the following code which didn't work.

tree <- rpartScore(Nutritional.Status.olr ~ Blubber..V.+vbl+dr+SMI+residuals+BMI+ML+GL+LMD, data = datatrain)


tree$names[tree$variable == "vbl"] <- "VB/L"

rpart.plot(tree, type=5, extra=101)

as a second line in between the others but it wont work. If I rename the column before and then try to feed in the model 'VB/L' instead vbl it gives me the error:

Error in terms.formula(formula, data = data) : 
  invalid model formula in ExtractVars

I am also having the same problem with the variable importance plot. The plot I have: VIPcomputed The plot I would like: VIPwanted

and the code I use:

library(vip)

vi(tree)  # why is SMI in the tree if vbl is more important??

tree$names[tree$variable == "vbl"] <- "VB/L" # doesn't work.. :(

p1 <- vip(tree) + theme_classic(base_size = 18)

# Rename the variable labels

p1

Any advise will be much appreciated! I cant find a rellabelling function in the part plot pdf..

here a part of my dataset:

> 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
Solution

  • I have fixed it renaming factors before the model and then including the signs in the tree function:

    tree <- rpartScore(Nutritional.Status.olr ~ VBT+`VBT/L`+`d/r`+SMI+`Residual M/L`+BMI+`M/L`+`G/L`+LMD, data = datatrain)