rowQuantiles: Estimates quantiles for each row (column) in a matrix
In matrixStats: Methods that apply to rows and columns of a matrix

Description Usage Arguments Value Author(s) See Also Examples

Estimates quantiles for each row (column) in a matrix.

1 2	rowQuantiles(x, probs=seq(from = 0, to = 1, by = 0.25), ..., drop=TRUE) colQuantiles(x, ...)

`x`	A `numeric` NxK `matrix` with N >= 0.
`probs`	A `numeric` `vector` of J probabilities in [0,1].
`...`	Additional arguments passed to `quantile`.
`drop`	If TRUE, singleton dimensions in the result are dropped, otherwise not.

Returns a numeric NxJ (KxJ) matrix, where N (K) is the number of rows (columns) for which the J quantiles are calculated.

Henrik Bengtsson

quantile.

set.seed(1)

x <- matrix(rnorm(50*40), nrow=50, ncol=40)
str(x)

probs <- c(0.25,0.5,0.75)

# Row quantiles
q <- rowQuantiles(x, probs=probs)
print(q)
q0 <- apply(x, MARGIN=1, FUN=quantile, probs=probs)
stopifnot(all.equal(q0, t(q)))

# Column IQRs
q <- colQuantiles(x, probs=probs)
print(q)
q0 <- apply(x, MARGIN=2, FUN=quantile, probs=probs)
stopifnot(all.equal(q0, t(q)))

 num [1:50, 1:40] -0.626 0.184 -0.836 1.595 0.33 ...
             25%          50%       75%
 [1,] -0.6218885  0.106762508 0.5512421
 [2,] -0.3049272  0.056473676 0.7057890
 [3,] -0.9231354 -0.118361292 0.7422711
 [4,] -0.9322740 -0.207686499 0.3216682
 [5,] -0.7084443 -0.035489532 0.6621024
 [6,] -0.9664491 -0.216591179 0.6740367
 [7,] -1.0384055  0.042690771 0.6611677
 [8,] -0.6330825 -0.126332443 0.6960917
 [9,] -1.0094086 -0.041113665 0.6791968
[10,] -0.6327419  0.054660577 0.6222087
[11,] -0.5645887 -0.041477859 0.3713240
[12,] -0.2766925  0.110955064 0.7428664
[13,] -0.8453155 -0.334769330 0.5554439
[14,] -0.8897809 -0.324080709 0.3301178
[15,] -0.7251620 -0.188349538 0.3535435
[16,] -0.4797823  0.091562203 0.9726725
[17,] -0.6200479 -0.262020284 0.6356199
[18,] -0.9034017 -0.140455912 0.7528919
[19,] -0.7707722 -0.152329236 0.1729031
[20,] -0.3200883  0.302574383 1.2132039
[21,] -0.5291880 -0.214782932 0.5513092
[22,] -0.5356002  0.284290389 0.7989148
[23,] -0.5700878  0.149494637 0.8077412
[24,] -0.7503936 -0.077770110 0.5309214
[25,] -0.4267110  0.263799916 0.7293052
[26,] -0.9591707 -0.137898692 0.3078452
[27,] -1.0171397 -0.150011032 0.6103426
[28,] -0.2687164  0.085033622 0.7435987
[29,] -0.7860127 -0.286445627 0.5151106
[30,] -0.3577578 -0.022684187 0.5654628
[31,] -0.9519151 -0.538929463 0.4145123
[32,] -0.6973686 -0.118983171 0.9168207
[33,] -0.8027962 -0.204718112 0.4979713
[34,] -0.9273236 -0.141973088 0.4361869
[35,] -0.4532102  0.365402869 0.7459632
[36,] -0.4184018  0.227594464 0.7615129
[37,] -0.6367977  0.088614257 0.9296158
[38,] -0.4039084 -0.010658183 0.7876346
[39,] -0.3561294  0.373172018 1.0347134
[40,] -0.7034831 -0.005856479 0.5258082
[41,] -0.6183522 -0.064312777 0.7153803
[42,] -0.4235112  0.125571403 0.6848875
[43,] -0.5556839  0.124678003 0.5593517
[44,] -0.5123758  0.439573098 0.8486753
[45,] -1.0042770 -0.060667550 0.9481150
[46,] -1.0340398 -0.310771412 0.1371124
[47,] -0.8865014  0.142643098 0.9203617
[48,] -0.7968222 -0.172298103 0.6482758
[49,] -1.2293839 -0.139233624 0.5217433
[50,] -0.6553804 -0.262142026 0.4742108
             25%          50%       75%
 [1,] -0.3720646  0.129104154 0.7279844
 [2,] -0.5721162  0.113797331 0.6065313
 [3,] -0.6469564 -0.246846356 0.2694256
 [4,] -0.7041278 -0.055939482 0.8196898
 [5,] -0.6089229 -0.029054330 0.6243079
 [6,] -0.4185202  0.146116784 0.7594495
 [7,] -0.4365107  0.091022110 0.8118479
 [8,] -0.6197929 -0.032415219 0.6618606
 [9,] -0.6727637 -0.273421020 0.6330157
[10,] -0.8825293 -0.011512761 0.5805665
[11,] -0.6381655  0.014950044 0.4831043
[12,] -0.7140804 -0.152388294 0.8106097
[13,] -0.7242296 -0.251628169 0.5387204
[14,] -1.0515879 -0.189977384 0.5571419
[15,] -0.9282411 -0.084192983 1.0093225
[16,] -0.8448598 -0.011103461 0.8216741
[17,] -0.5906504  0.161907795 0.7795111
[18,] -0.6499464 -0.059279451 0.5886333
[19,] -0.6094663  0.214748047 0.7317293
[20,] -0.7037132  0.022335956 0.5108679
[21,] -0.8504756  0.108484526 0.7602824
[22,] -0.7317770  0.007196516 0.7568682
[23,] -0.8515848 -0.319332762 0.5102586
[24,] -0.6366325 -0.249590203 0.6963329
[25,] -0.4791826  0.007958062 0.7657119
[26,] -0.4125492  0.291176656 1.0945407
[27,] -0.8272824 -0.267580711 0.3454911
[28,] -0.8544896 -0.197365516 0.4950755
[29,] -0.5364474  0.169648975 0.9752908
[30,] -0.6411633  0.057236706 0.5722226
[31,] -0.4458641 -0.018247105 0.7952345
[32,] -0.8358661  0.071609588 0.5724964
[33,] -0.5417449  0.124909830 0.5086025
[34,] -0.8415534 -0.215944585 0.5483784
[35,] -0.7080412 -0.017766239 1.0980467
[36,] -0.9491461 -0.256300105 0.4286593
[37,] -0.2463305  0.355645772 0.9952349
[38,] -0.5823101 -0.221579869 0.6592843
[39,] -0.8941790 -0.001721324 0.3406492
[40,] -0.6990172 -0.038793345 0.8817002