XGBoost obtaining the iteration number at which the maximum AUC was achieved - r
I am going through the following code: Taken from here, which is just an xgb.cv parameter optimisation function.
system.time(
rmseErrorsHyperparameters <- apply(searchGridSubCol, 1, function(parameterList){
#Extract Parameters to test
currentSubsampleRate <- parameterList[["subsample"]]
currentColsampleRate <- parameterList[["colsample_bytree"]]
currentDepth <- parameterList[["max_depth"]]
currentEta <- parameterList[["eta"]]
currentMinChild <- parameterList[["min_child"]]
xgboostModelCV <- xgb.cv(data = dtrain, nrounds = ntrees, nfold = 2, showsd = TRUE,
metrics = "rmse", verbose = TRUE, "eval_metric" = "rmse",
"objective" = "reg:linear", "max.depth" = currentDepth, "eta" = currentEta,
"subsample" = currentSubsampleRate, "colsample_bytree" = currentColsampleRate
, print_every_n = 10, "min_child_weight" = currentMinChild, booster = "gbtree",
early_stopping_rounds = 10)
xvalidationScores <- as.data.frame(xgboostModelCV$evaluation_log)
****rmse <- tail(xvalidationScores$test_rmse_mean, 1)**
**trmse <- tail(xvalidationScores$train_rmse_mean,1)****
output <- return(c(rmse, trmse, currentSubsampleRate, currentColsampleRate, currentDepth, currentEta, currentMinChild))
}))
I am trying to modify the code in between the ****. What I want to do is replace tail with max like the following;
rmse <- max(xvalidationScores$test_rmse_mean)
trmse <- max(xvalidationScores$train_rmse_mean)
But also save the iteration at which the max occured. So if I set ntrees to 100 and the Max rmse was obtained at iteration 87 then I would like to save this value along with the iteration (87) at which it occured.
Later on the code goes as follows;
output <- as.data.frame(t(rmseErrorsHyperparameters))
head(output)
varnames <- c("TestRMSE", "TrainRMSE", "SubSampRate", "ColSampRate", "Depth", "eta", "currentMinChild")
names(output) <- varnames
Which looks like the following;
TestRMSE TrainRMSE SubSampRate ColSampRate Depth eta currentMinChild
96.07530 96.07417 0.5 0.5 3 0.01 1
96.07458 96.07509 0.6 0.5 3 0.01 1
96.07807 96.07794 0.5 0.6 3 0.01 1
96.07458 96.07557 0.6 0.6 3 0.01 1
96.07829 96.07875 0.5 0.5 4 0.01 1
96.07221 96.07182 0.6 0.5 4 0.01 1
What I am trying to do is to add an extra column at which point in the iteration the max value was obtained.
I hope I am making myself clear.
EDIT:
What I am trying to modify is once the model has been run, take the column testAUC find the max value and return the iter at which the max occured.
I paste a dput of the output I am dealing with (NOTE: I use the AUC for my particular problem but would be helpful to know how to obtain this within the function for the RMSE also)
list(structure(list(0.9196126, 0.9033623, 0.5270572, 0.5289016,
0.1631758, 0.1662138, iter = c(1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50), train_auc_mean = c(0.8951437,
0.8965371, 0.8978626, 0.8988707, 0.9001917, 0.9016094, 0.9024459,
0.9034401, 0.9038199, 0.9042312, 0.905216, 0.9054907, 0.9060906,
0.9065405, 0.9070835, 0.9078622, 0.9085047, 0.9092435, 0.9096677,
0.9103009, 0.9108435, 0.9114527, 0.9117993, 0.9123977, 0.9127984,
0.9131131, 0.9134794, 0.9137633, 0.9141034, 0.9144531, 0.9147366,
0.9150188, 0.9153703, 0.915693, 0.9159701, 0.9162106, 0.9164605,
0.91667, 0.916952, 0.9172335, 0.9174861, 0.9176767, 0.9179821,
0.9181687, 0.9184403, 0.9186785, 0.9189445, 0.9191476, 0.9193911,
0.9196126), train_auc_std = c(0.000873146499706188, 0.0013455819150254,
0.00166724126631134, 0.00229178960858422, 0.00211358610182748,
0.0021685266057748, 0.00178882483490829, 0.0014330867001235,
0.00133037614601899, 0.00142908416826212, 0.00157062987366438,
0.00149423853857791, 0.00158350732239001, 0.00175414020251288,
0.00154147242919222, 0.00130750822564675, 0.0011531176913531,
0.0012810284345129, 0.00107513022934053, 0.00118107725829082,
0.00108282835659572, 0.00095217813982074, 0.000916731045586615,
0.000914402652021944, 0.000925037534358363, 0.000933982596136908,
0.000968171596397885, 0.000996830080752117, 0.000999102317069,
0.000957599858991975, 0.000912228830929314, 0.000911015894471406,
0.000830530318576475, 0.00086741697008366, 0.00080877184050021,
0.000851959529597412, 0.000820639293489715, 0.000882399229361504,
0.000820522028932439, 0.000808661146575655, 0.00077692206173079,
0.000769767503869777, 0.000840192412513504, 0.000893997097236334,
0.00085496456651761, 0.000847552977747195, 0.000842637674261389,
0.000835508252579566, 0.000825488879407905, 0.000849932844426632
), train_logloss_mean = c(0.688288, 0.6835267, 0.6788556,
0.6742688, 0.6697813, 0.665378, 0.6610398, 0.6567732, 0.652598,
0.6484957, 0.644454, 0.6404987, 0.6366067, 0.6327767, 0.6290132,
0.6252961, 0.6216444, 0.6180431, 0.6145105, 0.6110207, 0.6075911,
0.6042145, 0.6008939, 0.5975965, 0.5943897, 0.5912184, 0.5880742,
0.5849882, 0.5819479, 0.5789466, 0.5759881, 0.5730966, 0.5702313,
0.5674024, 0.5646118, 0.5618702, 0.5591583, 0.5564953, 0.5538569,
0.551244, 0.5486734, 0.5461692, 0.5436563, 0.5412049, 0.5387673,
0.5363635, 0.5339838, 0.5316552, 0.5293452, 0.5270572), train_logloss_std = c(2.99566351190152e-05,
5.34697108016365e-05, 7.323141406123e-05, 0.000100988910298054,
0.000116763050504875, 0.000136931369482075, 0.000156058835328824,
0.00015649587866062, 0.000172431435629269, 0.000191563070610692,
0.000193559293379142, 0.000203347510654373, 0.000215757757692594,
0.000218031672188399, 0.000243628323581981, 0.000235267698553247,
0.000255690907102828, 0.000264689044000474, 0.000285014473441321,
0.000286014352719424, 0.000297951489435599, 0.000305110226016343,
0.000305255122802726, 0.000323379111926704, 0.000337747257559746,
0.00035705691416933, 0.000357268750415862, 0.000375639667841919,
0.000381227346929675, 0.000411041652483442, 0.000407012395390132,
0.000423362303532172, 0.000422746271412242, 0.000423491251426115,
0.000445850602814743, 0.000456149931454845, 0.000469240460804438,
0.000483815057580709, 0.000489986214114286, 0.000486560582135563,
0.000498071721766642, 0.000503954124841367, 0.000513733208988433,
0.000517833844033052, 0.000526785354830916, 0.000504555299266918,
0.000531380240476864, 0.000504611494165455, 0.000509877200954216,
0.000497739650806203), train_error_mean = c(0.2136593, 0.2115807,
0.2094728, 0.2058215, 0.2010147, 0.2011363, 0.1976663, 0.1963016,
0.1947679, 0.1944829, 0.1950778, 0.1926892, 0.1922478, 0.1925048,
0.1926181, 0.1916276, 0.1913063, 0.1898618, 0.1895588, 0.1876229,
0.1854967, 0.1847466, 0.1834166, 0.1827868, 0.1812264, 0.1811634,
0.1802512, 0.1795696, 0.1783446, 0.1770231, 0.1767397, 0.1762297,
0.1752729, 0.1746709, 0.1739863, 0.1735267, 0.1723827, 0.1719105,
0.1717445, 0.1709158, 0.1700597, 0.1698614, 0.1684056, 0.1680232,
0.1671723, 0.1666052, 0.1652125, 0.1646313, 0.1641831, 0.1631758
), train_error_std = c(0.0162186588659482, 0.0166668028250771,
0.016164905120662, 0.0154685546593727, 0.0140211525849342,
0.0153654141372761, 0.0114450236526621, 0.0104903397771476,
0.0101908302061216, 0.0105073437409266, 0.00951226085428692,
0.0104100300460661, 0.00956246733641493, 0.0105556220072529,
0.0104740322841777, 0.00975581748701749, 0.00972889988693526,
0.00905565947681336, 0.00811476540387954, 0.00769535982849394,
0.00694964026479031, 0.00679897888215549, 0.00586689270738753,
0.0057987687969083, 0.00558790694625423, 0.00548896125327865,
0.0058105301272773, 0.00520588054415421, 0.00556398600285821,
0.00461605928146561, 0.00440342818835443, 0.00422396041766551,
0.00443108845431895, 0.00436686478952625, 0.00382032069465461,
0.00399028547976202, 0.00390278308518453, 0.00443162686267732,
0.00428972738644375, 0.0045192104133351, 0.00449508178012347,
0.00420268264802401, 0.00448433832800408, 0.00430475734043122,
0.00393493812022508, 0.00385852199164364, 0.00405450323097614,
0.00428838281989813, 0.00386824460059093, 0.00348298627042897
), test_auc_mean = c(0.8811229, 0.8821012, 0.8830099, 0.8842433,
0.8855852, 0.8866878, 0.8877955, 0.8888536, 0.8891683, 0.8894846,
0.8903621, 0.8906483, 0.8912014, 0.8914397, 0.8919142, 0.8926466,
0.8933535, 0.8940554, 0.8943622, 0.8950556, 0.8956304, 0.8962909,
0.896744, 0.8972643, 0.8975589, 0.8979465, 0.8982284, 0.8985075,
0.898786, 0.8991277, 0.8993767, 0.8995949, 0.8997907, 0.9001205,
0.9003218, 0.9004286, 0.9007523, 0.9009127, 0.9011547, 0.9013411,
0.9015791, 0.9017718, 0.9020105, 0.9021692, 0.9024216, 0.9026098,
0.9028157, 0.902953, 0.9031686, 0.9033623), test_auc_std = c(0.00754330327441656,
0.00754328296963419, 0.00806933700436532, 0.00790173398754956,
0.00667076577014675, 0.00650584392373234, 0.00638239292508034,
0.00654034346498086, 0.00636851100414996, 0.00645713921486273,
0.00588706627192151, 0.00644870064510048, 0.00604052243104865,
0.00604576752862067, 0.00580967446248283, 0.0059154939472606,
0.00591113505597135, 0.00602747522931076, 0.00617529822437917,
0.00612948071536362, 0.00587437571491952, 0.00618200494095612,
0.00603584683370449, 0.00602057940815899, 0.00616550108993821,
0.00590366130888615, 0.00599993273628292, 0.0058843139829564,
0.00601529726614352, 0.00593407575028349, 0.00598719293241462,
0.00597193897239703, 0.00597705621606727, 0.00603783070730093,
0.00595028283026266, 0.00600288719200781, 0.00608745137229766,
0.00596840690720039, 0.00591811708316459, 0.00598834670756388,
0.00600767183608287, 0.00599411786337455, 0.00608440926055143,
0.00610958063699259, 0.00609401368885199, 0.00613583449580456,
0.00613656653268947, 0.00626081916364727, 0.00621396780165399,
0.00625697591573605), test_logloss_mean = c(0.6883233, 0.6836026,
0.6789709, 0.6744245, 0.6699811, 0.6656353, 0.6613301, 0.657108,
0.652976, 0.6489123, 0.6449008, 0.6409902, 0.6371309, 0.6333362,
0.6296182, 0.625937, 0.6223281, 0.6187571, 0.6152755, 0.6118127,
0.6084108, 0.6050751, 0.6017758, 0.598529, 0.5953517, 0.5922165,
0.5891215, 0.5860721, 0.583079, 0.5801211, 0.5772012, 0.5743363,
0.5715108, 0.56872, 0.5659637, 0.5632635, 0.5605788, 0.5579482,
0.5553503, 0.5527782, 0.55024, 0.5477804, 0.5453004, 0.5428688,
0.5404586, 0.5380859, 0.5357354, 0.5334368, 0.5311633, 0.5289016
), test_logloss_std = c(5.68613228217379e-05, 0.000109613138236144,
0.000154851186578381, 0.000202748242988687, 0.000252505623714928,
0.000297037388133673, 0.000343537028504947, 0.00039417280473763,
0.000445152108809884, 0.000490523200283454, 0.000536685159023661,
0.0005805109473879, 0.000627699840607671, 0.000673758829247374,
0.000710880693211346, 0.000738838006625038, 0.00079860033178686,
0.000843823494603547, 0.000901632990750814, 0.000930852625294849,
0.000972119622271643, 0.00101327710425968, 0.00104329600787638,
0.00108068654106726, 0.00113131675935848, 0.00117079445247857,
0.00119185336764085, 0.0012290305488644, 0.00125289776117929,
0.00128907559512411, 0.00132504330496146, 0.00132748589822506,
0.00135826829455953, 0.00137503134509442, 0.00141204476205794,
0.00145400538168406, 0.00147068200505347, 0.00149451054194676,
0.00150767503462061, 0.00153264808746057, 0.00155560971970793,
0.00159670831401678, 0.00163016405309845, 0.00163873816091979,
0.00170482827286317, 0.00170039315748469, 0.00176976649308563,
0.0018018847798633, 0.00180684720160447, 0.00181675981903026
), test_error_mean = c(0.2154208, 0.2136102, 0.2114981, 0.207626,
0.203138, 0.2036158, 0.1998065, 0.1983859, 0.1969524, 0.1964246,
0.1972165, 0.1945513, 0.194086, 0.194501, 0.1944506, 0.1935582,
0.193269, 0.1920118, 0.191534, 0.1895979, 0.1878002, 0.1868699,
0.1862034, 0.1854242, 0.1836765, 0.1835887, 0.1826585, 0.1820548,
0.1805965, 0.1794273, 0.1789999, 0.1783083, 0.177818, 0.1771392,
0.1765609, 0.1759952, 0.1751652, 0.1749138, 0.1747126, 0.1739585,
0.1729149, 0.1729148, 0.1714439, 0.1714063, 0.1704382, 0.1699102,
0.1682128, 0.1676346, 0.1671192, 0.1662138), test_error_std = c(0.0159501571014207,
0.0163406308311522, 0.0153050457068903, 0.014898956648034,
0.0123790320219314, 0.0142987541681082, 0.00940067057448582,
0.0088897350292345, 0.00897252415098445, 0.00948333106244869,
0.00889585276687978, 0.0091076255418198, 0.00887665868443747,
0.00955196525328665, 0.00961350794663399, 0.00919105141754684,
0.00905392616492976, 0.00836850337635058, 0.00725348700970739,
0.00717273843730541, 0.00623918106485095, 0.00592587428908145,
0.00519975147867688, 0.00519286860607813, 0.00448201265616255,
0.00465992927092304, 0.00478653503591049, 0.0044566891029114,
0.00439628043350302, 0.00417364188808735, 0.00360300248265351,
0.00380256084895415, 0.00359530413178045, 0.0035239984335982,
0.0033875729497689, 0.00366222912445446, 0.00349675529026618,
0.00399751942584358, 0.00390744927542223, 0.00395275595629218,
0.00375911754139128, 0.00325926435871614, 0.00359755951861786,
0.00353682479209724, 0.0031308523695628, 0.00366587181445227,
0.00373223744689414, 0.004797147823447, 0.00457111983653927,
0.00401040810392153), 1, 1, 5, 0.01, 0, 1, 0, 0, 1, 1), .Names = c("",
"", "", "", "", "", "iter", "train_auc_mean", "train_auc_std",
"train_logloss_mean", "train_logloss_std", "train_error_mean",
"train_error_std", "test_auc_mean", "test_auc_std", "test_logloss_mean",
"test_logloss_std", "test_error_mean", "test_error_std", "",
"", "", "", "", "", "", "", "", "")))
If your output is saved to an object called res, then using str(), you can see that res is a list. Furthermore, res also contains only one element which is also a list. Check for yourself with
str(res)
str(res[[1]])
The values you want seem to be stored at res[[1]]$test_auc_mean. So if you are only after the index of the iteration that resulted in the max test_auc_mean, you can type
which.max(res[[1]]$test_auc_mean)
# [1] 50
I don't think adding a column for the max iteration is necessary in this case. You can look for the max value of the test_auc_mean by typing
max(res[[1]]$test_auc_mean)
# [1] 0.9034
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matrixList<-list()
#sequentially fill the list with the matrices
#using dim method to get the length of the estimates
for (i in 1:dim(mcListFit$estimate)) {
myMatr<- mcListFit$estimate[[i]]#transitionMatrix
matrixList[[i]]<-myMatr
}
matrixList
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df <- data.frame(
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theta=c(1, 1, 1, 1, 0.65, 1, 0.65, 1, 1),
detectl=c(3, 5, 1, 1, 5, 3, 5, 5, 1),
NSMOOTH=c(10, 5, 20, 20, 5, 20, 10, 10, 40),
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sma=c(15, 15, 15, 15, 15, 15, 15, 15, 15),
lma=c(33, 33, 33, 33, 33, 33, 33, 33, 33),
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)
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Table for gama
Value Average Median Standard Deviation
10 (Avg of 10) (median of 10) (Stdev of 10)
1 (Avg of 1) (median of 1) (Stdev of 1)
0.1 (Avg of 0.1) (median of 0.1) (Stdev of 0.1)
Table for theta
Value Average Median Standard Deviation
1 (Avg of 10) (median of 10) (Stdev of 10)
0.65 (Avg of 0.65) (median of 0.65) (Stdev of 0.65)
Table for detectionsLimit
Value Average Median Standard Deviation
3 (Avg of 3) (median of 3) (Stdev of 3)
5 (Avg of 5) (median of 5) (Stdev of 5)
...
The columns to be used as ID's are:
ids <- c("gama", "theta","detectl", "NSMOOTH", "NREF", "NOBS", "sma", "lma")
Summary statistics should be computed over the following columns:
vals <- c("PosTrades", "NegTrades", "Acc", "AvgWin", "AvgLoss", "Return")
I have tried using data.table package/function, but I cannot figuring out how to develop an approach using data.table without renaming values one by one; also, when pursuing this approach, my code gets very complicated.
Clever use of melt() and tapply() can help you. I made the following assumptions:
You have to get the mean, median and average of the last three columns
You need to group the data for each of the first ten columns (gama, theta, ..., negTrades)
For reproducibility, here's the input:
# Your example data
df <- data.frame(
gama=c(10, 1, 1, 1, 1, 1, 10, 0.1, 10),
theta=c(1, 1, 1, 1, 0.65, 1, 0.65, 1, 1),
detectl=c(3, 5, 1, 1, 5, 3, 5, 5, 1),
NSMOOTH=c(10, 5, 20, 20, 5, 20, 10, 10, 40),
NREF=c(50, 80, 80, 50, 80, 50, 10, 100, 30),
NOBS=c(10, 40, 40, 20, 20, 20, 10, 40, 10),
sma=c(15, 15, 15, 15, 15, 15, 15, 15, 15),
lma=c(33, 33, 33, 33, 33, 33, 33, 33, 33),
PosTrades=c(11, 7, 6, 3, 9, 3, 6, 6, 5),
NegTrades=c(2, 2, 1, 0, 1, 0, 1, 5, 1),
Acc=c(0.846154, 0.777778, 0.857143, 1, 0.9, 1, 0.857143, 0.545455, 0.833333),
AvgWin=c(0.0451529, 0.0676022, 0.0673241, 0.13204, 0.0412913, 0.126522, 0.0630061, 0.0689745, 0.0748437),
AvgLoss=c(-0.0194498, -0.0083954, -0.0174653, NaN, -0.00264179, NaN, -0.0161558, -0.013903, -0.0278908), Return=c(1.54942, 1.54916, 1.44823, 1.44716, 1.42789, 1.42581, 1.40993, 1.38605, 1.38401)
)
And here's my proposed solution:
library(reshape)
md <- melt(df, id=colnames(df)[1:10]) # This will create one row for each
# 'id' combination, and will store
# the rest of the column headers
# in the `variable` column, and
# each value corresponding to the
# variable. Like this:
head(md)
## gama theta detectl NSMOOTH NREF NOBS sma lma PosTrades NegTrades variable value
## 1 10 1.00 3 10 50 10 15 33 11 2 Acc 0.846154
## 2 1 1.00 5 5 80 40 15 33 7 2 ## Acc 0.777778
## 3 1 1.00 1 20 80 40 15 33 6 1 ## Acc 0.857143
## 4 1 1.00 1 20 50 20 15 33 3 0 ## Acc 1.000000
## 5 1 0.65 5 5 80 20 15 33 9 1 ## Acc 0.900000
## 6 1 1.00 3 20 50 20 15 33 3 0 ## Acc 1.000000
results <- list() # Prepare the results list
for(i in unique(md$variable)) { # For each variable you have...
results[[i]] <- list() # ... create a new list to hold the 'summary'
tmp_data <- subset(md, variable==i) # Filter the data you'll use
for(j in colnames(tmp_data)[1:10]) { # For each variable, use tapply()
# to get what you need, and
# store it into a data frame
# inside the results
results[[i]][[j]] <- as.data.frame(
t(
rbind(
tapply(tmp_data$value, tmp_data[,j], mean),
tapply(tmp_data$value, tmp_data[,j], median),
tapply(tmp_data$value, tmp_data[,j], sd))
)
)
colnames(results[[i]][[j]]) <- c('average', 'median', 'sd')
}
rm(tmp_data) # You'll no longer need this
}
Now what? Check out the summary for results:
summary(results)
## Length Class Mode
## Acc 10 -none- list
## AvgWin 10 -none- list
## AvgLoss 10 -none- list
## Return 10 -none- list
You have a list for each variable. Now, if you check out the summary for any results "sublist", you'll see this:
summary(results$Acc)
## Length Class Mode
## gama 3 data.frame list
## theta 3 data.frame list
## detectl 3 data.frame list
## NSMOOTH 3 data.frame list
## NREF 3 data.frame list
## NOBS 3 data.frame list
## sma 3 data.frame list
## lma 3 data.frame list
## PosTrades 3 data.frame list
## NegTrades 3 data.frame list
See what happens when you peek into the results$Acc$gama list:
results$Acc$gama
## average median sd
## 0.1 0.5454550 0.545455 NA
## 1 0.9069842 0.900000 0.09556548
## 10 0.8455433 0.846154 0.01191674
So, for each variable and each "id" column, you have the data summary you want.
Hope this helps.
I have an approach involving data.table.
EDIT: I tried to submit an edit to the question, but I took some liberties so it'll probably get rejected. I made assumptions about which columns were to be used as "id" columns (columns whose values subset data), and which should be "measure" columns (columns whose values are used to calculate the summary statistics). See here for these designations:
ids <- c("gama", "theta","detectl", "NSMOOTH", "NREF", "NOBS", "sma", "lma")
vals <- c("PosTrades", "NegTrades", "Acc", "AvgWin", "AvgLoss", "Return")
Setup
# Convert to data.table
df <- data.table(df)
# Helper function to convert a string to a call
# useful in a data.table j
s2c <- function (x, type = "list"){
as.call(lapply(c(type, x), as.symbol))
}
# Function to computer the desired summary stats
smry <- function(x) list(Average=mean(x, na.rm=T), Median=median(x, na.rm=T), StandardDeviation=sd(x, na.rm=T))
# Define some names to use later
ids <- c("gama", "theta","detectl", "NSMOOTH", "NREF", "NOBS", "sma", "lma")
vals <- c("PosTrades", "NegTrades", "Acc", "AvgWin", "AvgLoss", "Return")
usenames <- paste(rep(c("Average","Median","StdDev"),each=length(vals)), vals,sep="_")
Calculations in data.table
# Compute the summary statistics
df2 <- df[,j={
for(i in 1:length(ids)){ # loop through each id
t.id <- ids[i]
t.out <- .SD[,j={
t.vals <- .SD[,eval(s2c(vals))] # this line returns a data.table with each vals as a column
sapply(t.vals, smry) # apply summary statistics
},by=t.id] # this by= loops through each value of the current id (t.id)
setnames(t.out, c("id.val", usenames)) # fix the names of the data.table to be returned for this i
t.out <- cbind(id=t.id, t.out) # add a column indicating the variable name (t.id)
if(i==1){big.out <- t.out}else{big.out <- rbind(big.out, t.out)} # accumulate the output data.table
}
big.out
}]
Formatting
df2 <- data.table:::melt.data.table(df2, id.vars=c("id","id.val")) # melt into "long" format
df2[,c("val","metric"):=list(gsub(".*_","",variable),gsub("_.*","",variable))] # splice names to create id's
df2[,variable:=NULL] # delete old column that had the names we just split up
df2 <- data.table:::dcast.data.table(df2, id+id.val+val~metric) # go a bit wider, so stats in diff columns
# reshape2:::acast(df2, id+id.val~metric~val) # maybe replace the above line with this
Result
id id.val val Average Median StdDev
1: NOBS 10 Acc 3.214550 0.01191674 0.006052701
2: NOBS 10 AvgLoss 1.000000 0.06300610 1.409930000
3: NOBS 10 AvgWin 1.333333 0.06100090 1.447786667
4: NOBS 10 NegTrades 6.000000 0.84615400 -0.019449800
5: NOBS 10 PosTrades 7.333333 0.84554333 -0.021165467
---
128: theta 1 AvgLoss 1.000000 0.06897450 1.447160000
129: theta 1 AvgWin 1.571429 0.08320849 1.455691429
130: theta 1 NegTrades 6.000000 0.84615400 -0.017465300
131: theta 1 PosTrades 5.857143 0.83712329 -0.017420860
132: theta 1 Return 1.718249 0.03285638 0.068957635
How to calculate a mean value from multiple maximal values
I have a variable e.g. c(0, 8, 7, 15, 85, 12, 46, 12, 10, 15, 15) how can I calculate a mean value out of random maximal values in R? for example, I would like to calculate a mean value with three maximal values?
First step: You draw a sample of 3 from your data and store it in x Second step: You calculate the mean of the sample try dat <- c(0,8,7,15, 85, 12, 46, 12, 10, 15,15) x <- sample(dat,3) x mean(x) possible output: > x <- sample(dat,3) > x [1] 85 15 0 > mean(x) [1] 33.33333
If you mean the three highest values, just sort your vector and subset: > mean(sort(c(0,8,7,15, 85, 12, 46, 12, 10, 15,15), decreasing=T)[1:3]) [1] 48.66667
Disk space Prediction using R
I have 2 vectors days = c(1, 2, 3, 4, 5, 6, 7) pct_used = c(22.3, 22.1, 22.1, 22.1, 55.660198413, 56.001746032, 55.988769841) fit <- lm(days ~ poly(pct_used,2,raw=TRUE)) prediction <- predict(fit, data.frame(pct_used=85)) days_remain <- (prediction - tail(days,1)) pct_used is basically disk space . So this code predicts when disk space will reach 85. The prediction value returned is 325.something which is wierd I feel.Does that mean it will take 325 days to reach pct_used = 85 ? Where am i going wrong ?
Try this to see what is happening: plot(pct_used, days) lines(pct_used, predict(fit plot(pct_used, days, xlim=c(min(pct_used), 85) ,ylim= c(-50,350)) lines(seq(min(pct_used), 85, length=50), predict(fit, newdata=data.frame( pct_used=seq( min(pct_used), 85, length=50))))