monica.carro at libero.it
2009-Mar-05 13:35 UTC
[R] predict.fda - NAs are not allowed in subscripted assignments
Dear R users,
I'm trying to perform flexible discriminant analysis (fda) with
method bruto.
I applied the fda function on my training data:
bruto.fda <- fda
(fda.formula,data=train.data)
where fda.formula is: PRES ~ VA_D123 + VA_D124 +
VA_D127 + VA_DARU + VA_DCAN + VA_DFON +
VA_DLAP + VA_DRID + VA_DRIR +
VA_VVEG + VA_WDIN + VA_DIF3 +
VA_DIF4 + VA_DIF5 + VA_CAAC + VA_CABC +
VA_CABO + VA_CACA +
VA_CACC + VA_CAMP + VA_CAPA + VA_CAUB + VA_CMCA +
VA_CMIN +
VA_CMLA + VA_CMMU + VA_CMRO + VA_D109 + VA_D110 + VA_D111 +
VA_D112 + VA_D113 + VA_D114 + VA_D115 + VA_D116 + VA_D118 +
VA_D119 +
VA_D120 + VA_D121
and obtained this result:
Call:
fda(formula = fda.formula,
data = train.data)
Dimension: 1
Percent Between-Group Variance Explained:
v1
100
Degrees of Freedom (per dimension): 4
Training Misclassification
Error: 0 ( N = 4 )
My training data are:
'data.frame': 4 obs. of 41
variables:
$ VA_D123: num 120 240 610 340
$ VA_D124: num 2870 3000 1900
1170
$ VA_D127: num 430 1770 690 1470
$ VA_DARU: num 69 62 129 57
$
VA_DCAN: num 664 356 667 131
$ VA_DFON: num 235 650 361 489
$ VA_DLAP: num
30 2 242 219
$ VA_DRID: num 1 0 4 7
$ VA_DRIR: num 325 117 46 132
$
VA_VVEG: num 1.5 4.5 4.1 1.5
$ VA_WDIN: num 210 20 165 85
$ VA_DIF3: num
138 306 154 240
$ VA_DIF4: num 47 0 4 7
$ VA_DIF5: num 1 737 218 527
$
VA_CAAC: num 0.0 258.7 0.0 88.3
$ VA_CABC: num 117.9 137.6 79.8 38.1
$ VA_CABO: num 147.4 215.9 99.8 95.2
$ VA_CACA: num 117.9 163.0 79.8
19.0
$ VA_CACC: num 132.7 176.2 89.8 38.1
$ VA_CAMP: num 147.4 194.6
99.8 85.7
$ VA_CAPA: num 0.0 175.5 0.0 66.7
$ VA_CAUB: num 117.9
178.9 79.8 57.1
$ VA_CMCA: num 132.65 4.76 89.80 0.00
$ VA_CMIN: num
132.7 23.8 89.8 0.0
$ VA_CMLA: num 147.4 45.6 99.8 0.0
$ VA_CMMU:
num 132.65 4.76 89.80 0.00
$ VA_CMRO: num 0 0 0 0
$ VA_D109: num 3610
2740 4200 3420
$ VA_D110: num 310 3780 2960 4850
$ VA_D111: num 12930 7980
14630 9350
$ VA_D112: num 1580 6640 2460 4550
$ VA_D113: num 1030 10 200
370
$ VA_D114: num 450 1590 1480 670
$ VA_D115: num 10 20 0 0
$ VA_D116:
num 780 1120 570 410
$ VA_D118: num 1690 3260 1560 3930
$ VA_D119: num
13730 8660 15380 10070
$ VA_D120: num 1270 70 570 360
$ VA_D121: num 350
410 140 270
$ CAT : num 254935 294186 296143 306054
$ PRES : num 1 1 0
0
Now I want to predict fitted values for my new.data
'data.frame': 418507
obs. of 41 variables:
$ VA_D123: num 2560 2520 2480 2440 2400 2360 2320 2280
2230 2190 ...
$ VA_D124: num 3410 3420 3430 3440 3460 3470 3480 3490 3500
3510 ...
$ VA_D127: num 1710 1700 1690 1680 1670 1650 1640 1630 1610 1580 ...
$ VA_DARU: num 29 24 19 14 9 4 1 6 11 16 ...
$ VA_DCAN: num 882 881 879 878
877 876 875 873 872 871 ...
$ VA_DFON: num 1742 1741 1740 1739 1738 ...
$
VA_DLAP: num 346 341 336 331 326 321 316 311 306 301 ...
$ VA_DRID: num 16
18 19 21 22 21 19 18 16 15 ...
$ VA_DRIR: num 1419 1420 1421 1422 1423 ...
$
VA_VVEG: num 1 2 2 2 1 1 4 4 4 4 ...
$ VA_WDIN: num 327 340 353 367 380 393
406 420 434 447 ...
$ VA_DIF3: num 36 32 29 26 23 21 19 18 16 15 ...
$
VA_DIF4: num 119 114 109 104 99 94 89 84 79 74 ...
$ VA_DIF5: num 2136 2133
2130 2127 2124 ...
$ VA_CAAC: num 12.6 25.2 22.1 25.2 129.3 ...
$
VA_CABC: num 0.0 0.0 0.0 23.8 54.0 ...
$ VA_CABO: num 17.5 17.5 17.5 46.5
88.0 ...
$ VA_CACA: num 17.5 17.5 17.5 39.7 98.4 ...
$ VA_CACC: num 20.0
20.0 20.0 38.5 73.2 ...
$ VA_CAMP: num 17.5 17.5 17.5 43.1 102.7 ...
$
VA_CAPA: num 20.0 20.0 20.0 45.4 105.7 ...
$ VA_CAUB: num 15.0 15.0 15.0
20.4 45.4 ...
$ VA_CMCA: num 0 0 0 0 0 0 0 0 0 0 ...
$ VA_CMIN: num 0 0 0 0
0 0 0 0 0 0 ...
$ VA_CMLA: num 0.0 0.0 0.0 13.6 36.3 ...
$ VA_CMMU: num
0 0 0 0 0 0 0 0 0 0 ...
$ VA_CMRO: num 0 0 0 0 0 ...
$ VA_D109: num 5050
5010 4960 4920 4880 4840 4800 4760 4720 4680 ...
$ VA_D110: num 5000 4970
4940 4910 4880 4850 4820 4790 4760 4730 ...
$ VA_D111: num 2600 2550 2500
2450 2400 2350 2300 2250 2200 2150 ...
$ VA_D112: num 19400 19350 19300 19250
19200 ...
$ VA_D113: num 770 740 710 680 650 620 590 570 540 520 ...
$
VA_D114: num 480 450 420 400 360 310 260 210 160 110 ...
$ VA_D115: num 0 0
0 0 0 0 50 100 100 120 ...
$ VA_D116: num 1050 1010 970 940 910 880 850 820
790 760 ...
$ VA_D118: num 620 630 640 650 670 690 720 750 780 810 ...
$
VA_D119: num 16320 16270 16220 16170 16120 ...
$ VA_D120: num 1980 1940 1900
1860 1820 1780 1730 1690 1660 1630 ...
$ VA_D121: num 230 240 250 240 230 210
210 220 230 250 ...
$ CAT : num 1 2 3 4 5 6 7 8 9 10 ...
$ PRES : num
NA NA NA NA NA NA NA NA NA NA ...
I'm using:
bruto.fitted <- predict(bruto.fda,
new.data)
but obtained the following message:
Error in mindist[l] <- ndist[l]
:
NAs are not allowed in subscripted assignments
What does it means? I can
I solve the problem?
P.S The same error is returned when I do:
bruto.fitted <-
predict(bruto.fda,train.data)
Any help is appreciated...thanks in advance!
Monica