This function computes the Pearson's correlation for each row
vector in x.
See the Details section below for further information.
Usage
rowPearsonCor(
x,
y,
alternative = c("two.sided", "greater", "less"),
conf.level = 0.95
)Arguments
- x
matrixordata.frame.- y
numeric vector of data values. Must have the same length as
ncol(x).- alternative
character string or vector of length
nrow(x). The alternative hypothesis for each row ofx. Values must be one of"two.sided"(default),"greater"or"less".- conf.level
numerical value or numeric vector of length
nrow(x). The confidence levels of the intervals. All values must be in the range of \([0:1]\) orNA.
Value
A list containing two elements:
- statistic
A numeric vector, the values of the test statistic
- significance
A numeric vector, the p-values of the selected test
Details
It is a wrapper to row_cor_pearson
function.
Examples
#Seed
set.seed(1010)
#Data
x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(20)
#Compute
rowPearsonCor(x = x, y = y)
#> $statistic
#> [1] -1.562687801 0.858321163 0.637163042 0.128135983 -0.862080975
#> [6] 0.598241121 1.212132923 0.069343265 0.456858601 -1.898899711
#> [11] 0.184459567 -0.339190941 -3.007207334 0.336614733 0.617155315
#> [16] -2.481769120 -0.861755878 0.455952916 -0.949161902 -1.068207821
#> [21] -1.907514254 0.108342487 0.853011464 0.394641243 -0.975175694
#> [26] 0.864453082 -0.276135713 0.397042009 0.135430056 -0.462988385
#> [31] -0.361123747 1.461778755 -0.511219918 -1.082653209 0.004997978
#> [36] 0.479980846 -0.828477145 1.318518418 -1.782204536 -0.998935926
#> [41] 0.811267590 0.778846860 0.063646496 0.807429722 -0.287617366
#> [46] 0.229086538 -0.614220396 0.407021288 0.548241677 1.311804083
#> [51] 1.083779739 -1.889587922 0.546321065 0.100801842 0.101202574
#> [56] -2.561201443 1.583823429 -0.730766741 -1.048014217 -0.319427625
#> [61] 0.579442956 0.862395912 1.496058535 -0.397273649 -0.474920610
#> [66] -0.235018504 0.728067837 -0.876637740 0.034058214 0.550229079
#> [71] 2.323603386 -1.613238444 -1.424067416 -0.119842224 0.400028195
#> [76] -0.809365837 0.194148780 0.353125443 -0.662352249 -0.767740894
#> [81] -0.007011127 -2.543693293 0.726090012 0.420845627 -0.732734821
#> [86] 0.724719987 -1.240322324 -2.395388623 0.245192102 0.731896295
#> [91] 1.163954688 -0.976294907 1.556730531 1.753549523 -1.007125364
#> [96] -0.408247435 -0.468959552 -0.592881300 -1.119771117 -1.314586148
#>
#> $significance
#> [1] 0.135536058 0.402000439 0.532039815 0.899462121 0.399982690 0.557132482
#> [7] 0.241132739 0.945480943 0.653237919 0.073727039 0.855715230 0.738391072
#> [13] 0.007565871 0.740300062 0.544860776 0.023168646 0.400156896 0.653876795
#> [19] 0.355108974 0.299544083 0.072539393 0.914922608 0.404861217 0.697744610
#> [25] 0.342397511 0.398713075 0.785589293 0.696004803 0.893774879 0.648921201
#> [31] 0.722210668 0.161041167 0.615410709 0.293253139 0.996067183 0.637021871
#> [37] 0.418250745 0.203861755 0.091592575 0.331066169 0.427809764 0.446188963
#> [43] 0.949953191 0.429960192 0.776924829 0.821384399 0.546755396 0.688791618
#> [49] 0.590261338 0.206071641 0.292766609 0.075030171 0.591553341 0.920822078
#> [55] 0.920508440 0.019634212 0.130645338 0.474327179 0.308501179 0.753079743
#> [61] 0.569472138 0.399813976 0.151966290 0.695837027 0.640554996 0.816847228
#> [67] 0.475937508 0.392233089 0.973205569 0.588925893 0.032056824 0.124086665
#> [73] 0.171532459 0.905935620 0.693843169 0.428874501 0.848233670 0.728096097
#> [79] 0.516137234 0.452595599 0.994483097 0.020366462 0.477119661 0.678849812
#> [85] 0.473154954 0.477939550 0.230781390 0.027687610 0.809081343 0.473654184
#> [91] 0.259639152 0.341857780 0.136941960 0.096519592 0.327222224 0.687907437
#> [97] 0.644728453 0.560636387 0.277531110 0.205153701
#>