I am trying to evaluate some linear discriminant models with the package caret. The problem is that when I use the predict function it predicts less values than the ones that are given in the data fame. For example, my testing sample has 664 observations, but only 550 values are predicted. I don't know why this is happening, and the weidest thing is that when I use a lda model from the package MASS this doesn't happen.
I leave a sample of my code:
hoja_excel <- function(nombre_libro, nombre_hoja, modelo, datos, submuestra) {
libro <- createWorkbook(type = "xlsx")
hoja <- createSheet(libro, sheetName = nombre_hoja)
datos_t <- datos
modelo_t <- modelo
prediccion <- predict(modelo_t, datos_t)
if (submuestra == "No") {
correctos <- ifelse(datos_t$d_evento == prediccion$class, 1, 0)
addDataFrame(modelo_t$scaling, hoja, startRow = 1, startColumn = 1)
addDataFrame(prediccion$class, hoja, startRow = 1, startColumn = 5,
row.names = FALSE)
addDataFrame(prediccion$posterior, hoja, startRow = 1, startColumn = 7,
row.names = FALSE)
} else if (submuestra == "Si") {
correctos <- ifelse(datos_t$d_evento == prediccion, 1, 0)
addDataFrame(modelo_t$finalModel$scaling, hoja, startRow = 1, startColumn = 1)
addDataFrame(prediccion, hoja, startRow = 1, startColumn = 5, row.names = FALSE)
} else {
print("Valor no válido para submuestra")
break
}
addDataFrame(correctos, hoja, startRow = 1, startColumn = 6, row.names = FALSE)
addDataFrame(datos_t$d_evento, hoja, startRow = 1, startColumn = 4,
row.names = FALSE)
direccion <- paste("C:\\Users\\Documents\\Indicador riesgo quiebra\\Bases R\\",
gsub(" ", "", paste(nombre_libro, ".xlsx")))
saveWorkbook(libro, direccion)
}
where modelo_t is a lda model using:
train_index <- createDataPartition(datos_temporal$d_evento, p = 0.8, list = FALSE)
training <- datos_temporal[train_index,]
testing <- datos_temporal[-train_index,]
control <- trainControl(method = "boot", number = 100)
indice_fila <- as.data.frame(as.numeric(row.names(training)))
colnames(indice_fila)[1] <- "x"
indice_fila <- filter(indice_fila, x > 103)
por_quitar <- sample(indice_fila$x, 457)
temp_cambio <- training[por_quitar,]
training <- training[!(as.numeric(row.names(training)) %in% por_quitar),]
sum(training$Concurso)
testing <- rbind(testing, temp_cambio)
modelo_temp <- train(as.factor(d_evento) ~ WCTA + RETA_div + EBITTA + MKTTL, data = training, method = "lda", trControl = control)
The data looks like this:
d_evento WCTA RETA_div EBITTA MKTL
[1,] 1 0.102328417 0.172985238 0.023823373 0.28150867
[2,] 1 0.104033108 0.179734113 0.016205173 0.29846677
[3,] 1 0.062691788 0.176511714 0.016526565 0.27814601
[4,] 1 -0.006734752 0.176793439 -0.002690483 0.13984261
[5,] 1 -0.531363807 -0.126163036 -0.370620144 0.09432537
[6,] 1 -0.290474435 -0.211740091 -0.011615337 0.36301834
[7,] 1 -0.466771743 -0.512553803 -0.224013967 0.25744777
[8,] 1 -0.479582320 -0.511735167 0.002644372 0.09629447
[9,] 1 -0.505509630 -0.515334951 0.000907695 0.08729133
[10,] 1 -0.505772731 -0.519775895 0.003711561 0.07597238
[11,] 1 -0.081045926 0.021407478 0.010135867 0.12172863
[12,] 1 -0.089806538 -0.004288951 0.006143543 0.07635110
[13,] 1 -0.089806538 -0.004288951 0.006143543 0.07635110
[14,] 1 -0.089658569 -0.003487434 0.007065595 0.05881547
[15,] 1 -0.071556860 0.000327249 0.009000073 0.04642630
[16,] 1 -0.189795369 -0.082028980 -0.011347470 NA
[17,] 1 -0.211664986 0.089646083 -0.013821279 NA
[18,] 1 -0.240479274 0.076507841 -0.009437117 NA
[19,] 1 -0.304366126 -0.154540326 -0.009604424 NA
[20,] 1 -0.375355273 -0.199723398 -0.024588066 NA
[21,] 1 -0.029508414 -0.403511498 -0.019443447 NA
[22,] 1 -0.091914540 -0.488177906 -0.048564395 NA
[23,] 1 -0.298168224 -0.732771269 -0.172331020 NA
[24,] 1 -0.359041383 -0.790119097 -0.039597788 NA
[25,] 1 -0.363742236 -0.803537586 -0.006065854 NA
[26,] 1 0.589143817 0.028219641 0.026082170 0.32713918
[27,] 1 0.593191389 0.036273767 0.020408613 0.26683046
[28,] 1 0.173117821 0.235442662 -0.001729736 0.24438442
[29,] 1 0.585950807 0.003848530 0.003906928 0.16745067
[30,] 1 0.096615605 -0.240512670 -0.267238705 0.07577302
[31,] 1 -0.236399326 -0.198074835 -0.082469329 0.02060548
[32,] 1 -0.232475389 -0.162193604 0.008983155 0.02490547
[33,] 1 -0.264922798 -0.195772340 0.004671963 0.01937249
[34,] 1 -0.252437477 -0.177531447 0.008358117 0.01538876
[35,] 1 -0.309372439 -0.157687566 -0.014696488 0.01566678
[36,] 1 0.066139324 0.065790059 0.012793123 NA
[37,] 1 0.019243390 -0.021989191 0.005530848 NA
[38,] 1 -0.322167102 -0.076949045 0.009095588 NA
[39,] 1 -0.339073771 -0.088969769 0.004273673 NA
[40,] 1 -0.341548755 -0.095563596 0.004611532 NA
[41,] 1 0.119794925 -0.362801650 0.017772949 0.12511261
[42,] 1 0.117203237 -0.358876513 0.013895464 0.17573193
[43,] 1 0.118311588 -0.349316342 0.011311633 0.16578745
[44,] 1 0.108041129 -0.354728885 0.007139983 0.18585194
[45,] 1 0.116226413 0.176991083 0.008437309 0.14523250
[46,] 1 -0.417426682 -0.351217302 -0.001490772 NA
[47,] 1 -0.521614557 -0.372067598 -0.004148459 NA
[48,] 1 -0.556880835 -0.410263995 -0.010783372 NA
[49,] 1 -0.538665877 -0.516306250 -0.015715087 NA
[50,] 1 -0.552574629 -0.539557001 0.000512035 NA
[51,] 1 -0.107124224 -0.198470866 0.009393807 0.37339624
[52,] 1 -0.138723041 -0.180931099 0.009621652 0.37314090
[53,] 1 -0.185465637 -0.209724465 0.007502654 0.15136401
[54,] 1 -0.215398142 -0.194322294 0.005454704 0.10933219
[55,] 1 -0.349924680 -0.181051506 0.007898349 0.10242043
[56,] 1 -0.083508805 -0.234198161 0.014025553 0.21949797
[57,] 1 -0.111655586 -0.153118296 0.000395625 0.20500320
[58,] 1 -0.525398938 -0.204706959 0.004258002 0.22263430
[59,] 1 -0.515984436 0.006542066 0.010284407 0.06157929
[60,] 1 -0.472184418 -0.077255180 0.025169087 0.06360715
[61,] 1 -0.915619436 -0.571898297 -0.964069641 0.06954642
[62,] 1 -0.976531176 -0.604232871 0.002430217 0.07139988
[63,] 1 -1.065185735 -0.769319144 -0.057748756 0.06288110
[64,] 1 -1.105191220 -0.809208711 -0.008095136 0.06166852
[65,] 1 -1.178063409 -0.878734357 -0.039329161 0.06082261
[66,] 1 -0.535907882 -0.222222213 0.005872205 0.08846701
[67,] 1 -0.589313029 -0.258109442 0.008729062 0.05007868
[68,] 1 -0.551131177 -0.238096813 0.009023884 0.07187476
[69,] 1 -0.608438635 -0.283387832 0.013695457 0.08705984
[70,] 1 -0.535464916 -0.262771019 0.008364580 0.09314005
[71,] 1 -0.452234218 -0.215778265 -0.000239063 0.02542849
[72,] 1 -0.464330590 -0.218106230 0.002584991 0.03582109
[73,] 1 -0.563726013 -0.224167223 0.000208095 0.04189381
[74,] 1 -0.528027421 -0.198080661 -0.003679190 0.04983422
[75,] 1 -0.575153818 -0.285992633 -0.005243519 0.03923600
[76,] 1 -0.066170557 -0.239054398 -0.013494570 0.26130971
[77,] 1 -0.066170557 -0.233443586 0.014990892 0.26130971
[78,] 1 -0.121724697 -0.303849542 -0.040604471 0.28052208
[79,] 1 -0.154056271 -0.343746431 -0.012184790 0.20584284
[80,] 1 -0.147762785 -0.565698665 -0.020414877 0.17744193
[81,] 0 0.150063189 0.474291335 0.062842129 4.06784731
[82,] 0 0.216434477 0.400717535 0.153657901 5.36813447
[83,] 0 0.227828887 0.414654219 0.060695982 6.78856190
[84,] 0 0.074576430 0.452425070 0.048256387 3.48195705
[85,] 0 0.075965420 0.053845661 0.032662793 4.23785100
[86,] 0 0.068738217 0.179754934 0.026999530 5.81196756
[87,] 0 0.002872408 0.176252411 0.036614004 5.22779285
[88,] 0 -0.004109057 0.200224409 0.039233767 3.28458093
[89,] 0 0.059667688 0.213209500 0.026436922 2.81650866
[90,] 0 0.097700665 0.250799825 0.022532687 2.15307406
[91,] 0 0.069989183 0.279071982 0.022807557 1.90599648
[92,] 0 0.068567070 0.597104453 0.007370365 1.45980346
[93,] 0 -0.055451852 0.005400669 0.006371529 0.80260752
[94,] 0 0.063809942 0.002149151 0.003025512 1.62787332
[95,] 0 0.156181914 0.148482202 0.018242339 1.54990111
[96,] 0 0.191253103 0.152796761 0.028705654 1.27834785
[97,] 0 0.179348459 0.026865682 0.021577617 1.10293585
[98,] 0 0.220742857 0.257825369 0.025130265 1.38404540
[99,] 0 0.197537878 0.247273292 0.032118478 1.17929573
[100,] 0 0.201030587 0.289748972 0.023089870 1.45426949
[101,] 0 0.198196754 0.251331400 0.018783216 0.78407849
[102,] 0 0.396957178 0.156756494 0.011094761 6.50241941
[103,] 0 0.417743322 0.192454416 0.021462730 4.17671640
[104,] 0 0.317611590 0.220966177 0.019126507 3.14608329
[105,] 0 0.292430463 0.236581805 0.027051067 1.23086306
[106,] 0 0.121646123 0.264528001 0.015549968 0.43782518
[107,] 0 0.254774431 0.313459475 0.020259342 0.90682847
[108,] 0 0.057719295 0.376094461 0.018757697 0.70161209
[109,] 0 0.111145476 0.074000580 0.021489573 0.50180140
[110,] 0 0.129263860 0.113104859 0.025172665 0.77670065
[111,] 0 0.161287802 0.364706919 0.027780009 1.71408772
[112,] 0 0.102491082 0.336435093 0.019495483 1.87903084
[113,] 0 0.102749342 0.179662445 0.019143735 0.66004885
[114,] 0 0.070178639 0.181968540 0.015834552 0.56760009
[115,] 0 0.060546180 0.144755108 0.023018262 0.41864727
[116,] 0 0.055087904 0.166166504 0.012629684 0.33324991
[117,] 0 0.303542935 0.156757706 0.031636761 1.64602627
[118,] 0 0.289001786 0.153547671 -0.016374501 1.76725379
[119,] 0 0.279778721 0.153493561 0.020405527 1.58651203
[120,] 0 0.236821990 0.133645930 0.022444817 1.26843879
[121,] 0 0.146744855 0.052687622 0.029447256 0.89323894
[122,] 0 0.077842968 0.127891750 0.014711354 0.71416451
[123,] 0 0.223389189 0.181411663 0.048342490 0.59089094
[124,] 0 0.011758736 0.293931328 0.036917562 2.87365445
[125,] 0 0.057638756 0.262690499 0.054119272 4.66983166
[126,] 0 -0.019033595 0.302711214 0.057767564 6.10251167
[127,] 0 -0.031502878 0.221783981 0.050314677 2.05150776
[128,] 0 0.053267372 0.068925682 0.042385349 1.91082180
[129,] 0 -0.057991241 0.028315757 0.040734790 1.38620499
[130,] 0 -0.036327184 0.103362962 0.036318668 0.84866029
[131,] 0 -0.068812042 0.109087403 0.020497603 0.58923088
[132,] 0 -0.075085174 0.138765553 0.023057622 0.86040295
[133,] 0 -0.053108777 0.129443014 0.023743674 0.89238510
[134,] 0 -0.109530408 0.173890428 0.025422135 0.76808692
[135,] 0 0.820570693 0.489072341 0.038025378 3.80283909
[136,] 0 0.755131402 0.480378231 0.023438901 1.83815578
[137,] 0 0.784152123 0.466333064 0.009787795 0.73929691
[138,] 0 0.770736924 0.535517543 0.010150374 0.99090286
[139,] 0 0.765962039 0.582485921 0.003677606 1.21952273
[140,] 0 0.799087764 0.590003093 0.012371284 1.41523522
[141,] 0 0.801033182 0.603842470 0.014718538 1.26282958
[142,] 0 0.655760851 0.603277797 0.013366921 1.29486993
[143,] 0 0.655626473 0.590216820 0.012908812 0.67140092
[144,] 0 -0.000931802 0.123956770 -0.009416716 17.37868387
[145,] 0 0.062881194 0.007211106 -0.009665915 41.78087232
[146,] 0 0.060060119 0.050727308 0.007171143 38.72104468
[147,] 0 0.044467119 0.036380079 0.003481140 60.80616220
[148,] 0 0.046114372 0.053310689 0.001938528 31.03505882
[149,] 0 0.160540154 0.069396180 0.012203609 8.54585817
[150,] 0 0.031013787 0.031720938 0.003055935 2.95383008
[151,] 0 0.132943141 0.012581698 0.005493957 0.36059554
[152,] 0 0.471412105 0.059070746 0.010334952 1.42581109
[153,] 0 0.479138052 0.060875122 0.013471127 1.45272493
[154,] 0 0.422477019 0.052316363 0.012458129 1.54526316
[155,] 0 0.388659477 0.052890384 0.005573127 1.95571714
[156,] 0 0.094920272 0.089369140 0.012778447 11.38579900
[157,] 0 0.118271048 0.118019690 0.018606786 11.99411451
[158,] 0 0.112967304 0.040162629 0.016856771 6.17653857
[159,] 0 0.118361014 0.101731873 0.026049746 5.51451976
[160,] 0 0.133620815 0.166899626 0.036407153 9.23376411
[161,] 0 0.088069784 0.282453608 0.030611453 9.54196168
[162,] 0 0.116470349 0.439463793 0.044199097 13.61400937
[163,] 0 0.090907488 0.382101833 0.041835183 8.40030914
[164,] 0 0.047117508 0.333423687 0.032047521 4.36771819
[165,] 0 0.096818844 0.392443561 0.035572024 4.76322219
[166,] 0 -0.209518025 -0.067392822 0.001041811 0.16838672
[167,] 0 -0.023669209 0.254000499 0.020112737 0.33192460
[168,] 0 0.083063378 0.240007756 0.004917441 0.77924814
[169,] 0 0.177073698 0.477966128 0.057321814 2.36844916
[170,] 0 0.159060297 0.210387595 -0.003819296 6.75228878
[171,] 0 0.169452935 0.278012721 0.031039004 2.34712579
[172,] 0 0.154945204 0.333207323 0.002886411 1.09408749
[173,] 0 -0.048780622 0.215153508 0.018259530 0.72343952
[174,] 0 0.055472408 0.285980563 0.006076962 0.31754091
[175,] 0 0.142383229 0.319639681 0.038277115 0.89486568
[176,] 0 0.077797895 0.345464464 0.031875647 0.72488824
[177,] 0 0.022357482 0.323707987 0.012566844 0.35923083
[178,] 0 0.346321819 0.941763870 0.015834019 3.93125929
[179,] 0 0.371809560 0.625682702 0.026887381 4.11670803
[180,] 0 0.298619068 0.598086800 0.001809750 2.89054586
[181,] 0 0.374593821 0.622298863 -0.010043981 3.12315505
[182,] 0 0.369303660 0.610048598 0.027247316 2.38288866
[183,] 0 0.379150968 0.643351880 0.014669807 3.03208857
[184,] 0 0.410197501 0.647344411 0.023170184 3.92832805
[185,] 0 0.403354158 0.650000682 0.050002996 3.75353977
[186,] 0 0.396623201 0.660019793 0.031378145 3.82157439
[187,] 0 0.404596355 0.676637410 0.021312227 3.44883813
[188,] 0 0.096942055 0.585831667 0.021951244 1.94042889
[189,] 0 0.102554188 0.670393761 0.021269146 3.90273154
[190,] 0 0.076417187 0.564216020 0.023645144 2.84388566
[191,] 0 0.080883097 0.524531395 0.016433972 2.60734449
[192,] 0 0.081513318 0.603303084 0.022487537 3.65309376
[193,] 0 0.024881995 0.610932352 0.023067898 5.39627899
[194,] 0 0.098384456 0.437738944 0.019957790 2.17333603
[195,] 0 0.019979034 0.461563686 0.020225360 2.24382732
[196,] 0 0.105557738 0.359034869 0.020744405 1.74355384
[197,] 0 -0.015178663 0.326976028 0.020274742 1.43860776
[198,] 0 0.206363998 0.079733555 0.001920620 1.39776775
[199,] 0 0.094099801 0.070677090 0.013279499 1.18434471
[200,] 0 0.066358250 -0.019835931 0.020976708 2.46798269
