Description Usage Arguments Details Value Author(s) See Also Examples
Calculates quantiles for each row (column) of a matrix-like object.
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 | ## S4 method for signature 'DelayedMatrix'
colQuantiles(
x,
rows = NULL,
cols = NULL,
probs = seq(from = 0, to = 1, by = 0.25),
na.rm = FALSE,
type = 7L,
force_block_processing = FALSE,
...,
drop = TRUE
)
## S4 method for signature 'DelayedMatrix'
rowQuantiles(
x,
rows = NULL,
cols = NULL,
probs = seq(from = 0, to = 1, by = 0.25),
na.rm = FALSE,
type = 7L,
force_block_processing = FALSE,
...,
drop = TRUE
)
|
x |
A NxK DelayedMatrix. |
rows |
A |
cols |
A |
probs |
A numeric vector of J probabilities in [0, 1]. |
na.rm |
If |
type |
An integer specifying the type of estimator. See
|
force_block_processing |
|
... |
Additional arguments passed to specific methods. |
drop |
If |
The S4 methods for x
of type matrix
,
array
, or numeric
call
matrixStats::rowQuantiles
/ matrixStats::colQuantiles
.
a numeric
NxJ
(KxJ
)
matrix
, where N (K) is the number of rows (columns) for
which the J values are calculated.
Peter Hickey
matrixStats::rowQuantiles()
and
matrixStats::colQuantiles()
which
are used when the input is a matrix
or numeric
vector.
stats::quantile
1 2 3 4 5 6 7 8 9 10 | # A DelayedMatrix with a 'data.frame' seed
dm_df <- DelayedArray(data.frame(C1 = rep(1L, 5),
C2 = as.integer((0:4) ^ 2),
C3 = seq(-5L, -1L, 1L)))
# colnames, if present, are preserved as rownames on output
colQuantiles(dm_df)
# Input has no rownames so output has no rownames
rowQuantiles(dm_df)
|
Loading required package: MatrixGenerics
Loading required package: matrixStats
Attaching package: ‘MatrixGenerics’
The following objects are masked from ‘package:matrixStats’:
colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
colWeightedMeans, colWeightedMedians, colWeightedSds,
colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
rowWeightedSds, rowWeightedVars
Loading required package: DelayedArray
Loading required package: stats4
Loading required package: Matrix
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Loading required package: S4Vectors
Attaching package: ‘S4Vectors’
The following object is masked from ‘package:Matrix’:
expand
The following object is masked from ‘package:base’:
expand.grid
Loading required package: IRanges
Attaching package: ‘DelayedArray’
The following objects are masked from ‘package:base’:
aperm, apply, rowsum
Attaching package: ‘DelayedMatrixStats’
The following objects are masked from ‘package:matrixStats’:
colAnyMissings, rowAnyMissings
0% 25% 50% 75% 100%
C1 1 1 1 1 1
C2 0 1 4 9 16
C3 -5 -4 -3 -2 -1
0% 25% 50% 75% 100%
1 -5 -2.5 0 0.5 1
2 -4 -1.5 1 1.0 1
3 -3 -1.0 1 2.5 4
4 -2 -0.5 1 5.0 9
5 -1 0.0 1 8.5 16
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