rowQuantiles: Estimates quantiles for each row (column) in a matrix

Description Usage Arguments Value Author(s) See Also Examples

View source: R/rowQuantiles.R

Description

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

Usage

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rowQuantiles(x, rows = NULL, cols = NULL, probs = seq(from = 0, to = 1,
  by = 0.25), na.rm = FALSE, type = 7L, ..., drop = TRUE)

colQuantiles(x, rows = NULL, cols = NULL, probs = seq(from = 0, to = 1,
  by = 0.25), na.rm = FALSE, type = 7L, ..., drop = TRUE)

Arguments

x

An integer, numeric or logical NxK matrix with N >= 0.

rows

A vector indicating subset of rows to operate over. If NULL, no subsetting is done.

cols

A vector indicating subset of columns to operate over. If NULL, no subsetting is done.

probs

A numeric vector of J probabilities in [0, 1].

na.rm

If TRUE, missing values are excluded.

type

An integer specify the type of estimator. See quantile for more details.

...

Additional arguments passed to quantile.

drop

If TRUE, singleton dimensions in the result are dropped, otherwise not.

Value

Returns a NxJ (KxJ) matrix, where N (K) is the number of rows (columns) for which the J quantiles are calculated. The return type is either integer or numeric depending on type.

Author(s)

Henrik Bengtsson

See Also

quantile.

Examples

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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)
q_0 <- apply(x, MARGIN = 1, FUN = quantile, probs = probs)
stopifnot(all.equal(q_0, t(q)))

# Column IQRs
q <- colQuantiles(x, probs = probs)
print(q)
q_0 <- apply(x, MARGIN = 2, FUN = quantile, probs = probs)
stopifnot(all.equal(q_0, t(q)))

Example output

 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

matrixStats documentation built on June 1, 2021, 9:09 a.m.