binYonX: Aggregate values in y for bins defined on x
In sneumann/xcms: LC-MS and GC-MS Data Analysis
View source: R/functions-binning.R
binYonX R Documentation
Aggregate values in y for bins defined on x
Description
This functions takes two same-sized numeric vectors x
and y, bins/cuts x into bins (either a pre-defined number
of equal-sized bins or bins of a pre-defined size) and aggregates values
in y corresponding to x values falling within each bin. By
default (i.e. method = "max") the maximal y value for the
corresponding x values is identified. x is expected to be
incrementally sorted and, if not, it will be internally sorted (in which
case also y will be ordered according to the order of x).
Usage
binYonX(
x,
y,
breaks,
nBins,
binSize,
binFromX,
binToX,
fromIdx = 1L,
toIdx = length(x),
method = "max",
baseValue,
sortedX = !is.unsorted(x),
shiftByHalfBinSize = FALSE,
returnIndex = FALSE,
returnX = TRUE
)
Arguments
x
Numeric vector to be used for binning.
y
Numeric vector (same length than x) from which the maximum
values for each bin should be defined. If not provided, x will
be used.
breaks
Numeric vector defining the breaks for the bins, i.e. the
lower and upper values for each bin. See examples below.
nBins
integer(1) defining the number of desired bins.
binSize
numeric(1) defining the desired bin size.
binFromX
Optional numeric(1) allowing to manually specify
the range of x-values to be used for binning.
This will affect only the calculation of the breaks for the bins
(i.e. if nBins or binSize is provided).
If not provided the minimal value in the sub-set
fromIdx-toIdx in input vector x will be used.
binToX
Same as binFromX, but defining the maximum x-value to be
used for binning.
fromIdx
Integer vector defining the start position of one or multiple
sub-sets of input vector x that should be used for binning.
toIdx
Same as toIdx, but defining the maximum index (or indices)
in x to be used for binning.
method
A character string specifying the method that should be used to
aggregate values in y. Allowed are "max", "min",
"sum" and "mean" to identify the maximal or minimal value
or to sum all values within a bin or calculate their mean value.
baseValue
The base value for empty bins (i.e. bins into which either
no values in x did fall, or to which only NA values in
y were assigned). By default (i.e. if not specified), NA
is assigned to such bins.
sortedX
Whether x is sorted.
shiftByHalfBinSize
Logical specifying whether the bins should be
shifted by half the bin size to the left. Thus, the first bin will have
its center at fromX and its lower and upper boundary are
fromX - binSize/2 and fromX + binSize/2. This argument is
ignored if breaks are provided.
returnIndex
Logical indicating whether the index of the max (if
method = "max") or min (if method = "min") value within
each bin in input vector x should also be reported. For methods
other than "max" or "min" this argument is ignored.
returnX
logical allowing to avoid returning $x, i.e. the
mid-points of the bins. returnX = FALSE might be useful in cases
where breaks are pre-defined as it considerably reduces the memory
demand.
Details
The breaks defining the boundary of each bin can be either passed
directly to the function with the argument breaks, or are
calculated on the data based on arguments nBins or binSize
along with fromIdx, toIdx and optionally binFromX
and binToX.
Arguments fromIdx and toIdx allow to specify subset(s) of
the input vector x on which bins should be calculated. The
default the full x vector is considered. Also, if not specified
otherwise with arguments binFromX and binToX , the range
of the bins within each of the sub-sets will be from x[fromIdx]
to x[toIdx]. Arguments binFromX and binToX allow to
overwrite this by manually defining the a range on which the breaks
should be calculated. See examples below for more details.
Calculation of breaks: for nBins the breaks correspond to
seq(min(x[fromIdx])), max(x[fromIdx], length.out = (nBins + 1)).
For binSize the breaks correspond to
seq(min(x[fromIdx]), max(x[toIdx]), by = binSize) with the
exception that the last break value is forced to be equal to
max(x[toIdx]). This ensures that all values from the specified
range are covered by the breaks defining the bins. The last bin could
however in some instances be slightly larger than binSize. See
breaks_on_binSize and breaks_on_nBins for
more details.
Value
Returns a list of length 2, the first element (named "x")
contains the bin mid-points, the second element (named "y") the
aggregated values from input vector y within each bin. For
returnIndex = TRUE the list contains an additional element
"index" with the index of the max or min (depending on whether
method = "max" or method = "min") value within each bin in
input vector x.
Note
The function ensures that all values within the range used to define
the breaks are considered in the binning (and assigned to a bin). This
means that for all bins except the last one values in x have to be
>= xlower and < xupper (with xlower
and xupper being the lower and upper boundary, respectively). For
the last bin the condition is x >= xlower & x <= xupper.
Note also that if shiftByHalfBinSize is TRUE the range of
values that is used for binning is expanded by binSize (i.e. the
lower boundary will be fromX - binSize/2, the upper
toX + binSize/2). Setting this argument to TRUE resembles
the binning that is/was used in profBin function from
xcms < 1.51.
NA handling: by default the function ignores NA values in
y (thus inherently assumes na.rm = TRUE). No NA
values are allowed in x.
Author(s)
Johannes Rainer
See Also
imputeLinInterpol
Examples
########
## Simple example illustrating the breaks and the binning.
##
## Define breaks for 5 bins:
brks <- seq(2, 12, length.out = 6)
## The first bin is then [2,4), the second [4,6) and so on.
brks
## Get the max value falling within each bin.
binYonX(x = 1:16, y = 1:16, breaks = brks)
## Thus, the largest value in x = 1:16 falling into the bin [2,4) (i.e. being
## >= 2 and < 4) is 3, the largest one falling into [4,6) is 5 and so on.
## Note however the function ensures that the minimal and maximal x-value
## (in this example 1 and 12) fall within a bin, i.e. 12 is considered for
## the last bin.
#######
## Performing the binning ons sub-set of x
##
X <- 1:16
## Bin X from element 4 to 10 into 5 bins.
X[4:10]
binYonX(X, X, nBins = 5L, fromIdx = 4, toIdx = 10)
## This defines breaks for 5 bins on the values from 4 to 10 and bins
## the values into these 5 bins. Alternatively, we could manually specify
## the range for the binning, i.e. the minimal and maximal value for the
## breaks:
binYonX(X, X, nBins = 5L, fromIdx = 4, toIdx = 10, binFromX = 1, binToX = 16)
## In this case the breaks for 5 bins were defined from a value 1 to 16 and
## the values 4 to 10 were binned based on these breaks.
#######
## Bin values within a sub-set of x, second example
##
## This example illustrates how the fromIdx and toIdx parameters can be used.
## x defines 3 times the sequence form 1 to 10, while y is the sequence from
## 1 to 30. In this very simple example x is supposed to represent M/Z values
## from 3 consecutive scans and y the intensities measured for each M/Z in
## each scan. We want to get the maximum intensities for M/Z value bins only
## for the second scan, and thus we use fromIdx = 11 and toIdx = 20. The breaks
## for the bins are defined with the nBins, binFromX and binToX.
X <- rep(1:10, 3)
Y <- 1:30
## Bin the M/Z values in the second scan into 5 bins and get the maximum
## intensity for each bin. Note that we have to specify sortedX = TRUE as
## the x and y vectors would be sorted otherwise.
binYonX(X, Y, nBins = 5L, sortedX = TRUE, fromIdx = 11, toIdx = 20)
#######
## Bin in overlapping sub-sets of X
##
## In this example we define overlapping sub-sets of X and perform the binning
## within these.
X <- 1:30
## Define the start and end indices of the sub-sets.
fIdx <- c(2, 8, 21)
tIdx <- c(10, 25, 30)
binYonX(X, nBins = 5L, fromIdx = fIdx, toIdx = tIdx)
## The same, but pre-defining also the desired range of the bins.
binYonX(X, nBins = 5L, fromIdx = fIdx, toIdx = tIdx, binFromX = 4, binToX = 28)
## The same bins are thus used for each sub-set.
sneumann/xcms documentation built on Nov. 3, 2024, 10:33 p.m.
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View source: R/functions-binning.R
| binYonX | R Documentation |
Aggregate values in y for bins defined on x
Description
This functions takes two same-sized numeric vectors x
and y, bins/cuts x into bins (either a pre-defined number
of equal-sized bins or bins of a pre-defined size) and aggregates values
in y corresponding to x values falling within each bin. By
default (i.e. method = "max") the maximal y value for the
corresponding x values is identified. x is expected to be
incrementally sorted and, if not, it will be internally sorted (in which
case also y will be ordered according to the order of x).
Usage
binYonX(
x,
y,
breaks,
nBins,
binSize,
binFromX,
binToX,
fromIdx = 1L,
toIdx = length(x),
method = "max",
baseValue,
sortedX = !is.unsorted(x),
shiftByHalfBinSize = FALSE,
returnIndex = FALSE,
returnX = TRUE
)
Arguments
x |
Numeric vector to be used for binning. |
y |
Numeric vector (same length than |
breaks |
Numeric vector defining the breaks for the bins, i.e. the lower and upper values for each bin. See examples below. |
nBins |
integer(1) defining the number of desired bins. |
binSize |
numeric(1) defining the desired bin size. |
binFromX |
Optional numeric(1) allowing to manually specify
the range of x-values to be used for binning.
This will affect only the calculation of the breaks for the bins
(i.e. if |
binToX |
Same as |
fromIdx |
Integer vector defining the start position of one or multiple
sub-sets of input vector |
toIdx |
Same as |
method |
A character string specifying the method that should be used to
aggregate values in |
baseValue |
The base value for empty bins (i.e. bins into which either
no values in |
sortedX |
Whether |
shiftByHalfBinSize |
Logical specifying whether the bins should be
shifted by half the bin size to the left. Thus, the first bin will have
its center at |
returnIndex |
Logical indicating whether the index of the max (if
|
returnX |
|
Details
The breaks defining the boundary of each bin can be either passed
directly to the function with the argument breaks, or are
calculated on the data based on arguments nBins or binSize
along with fromIdx, toIdx and optionally binFromX
and binToX.
Arguments fromIdx and toIdx allow to specify subset(s) of
the input vector x on which bins should be calculated. The
default the full x vector is considered. Also, if not specified
otherwise with arguments binFromX and binToX , the range
of the bins within each of the sub-sets will be from x[fromIdx]
to x[toIdx]. Arguments binFromX and binToX allow to
overwrite this by manually defining the a range on which the breaks
should be calculated. See examples below for more details.
Calculation of breaks: for nBins the breaks correspond to
seq(min(x[fromIdx])), max(x[fromIdx], length.out = (nBins + 1)).
For binSize the breaks correspond to
seq(min(x[fromIdx]), max(x[toIdx]), by = binSize) with the
exception that the last break value is forced to be equal to
max(x[toIdx]). This ensures that all values from the specified
range are covered by the breaks defining the bins. The last bin could
however in some instances be slightly larger than binSize. See
breaks_on_binSize and breaks_on_nBins for
more details.
Value
Returns a list of length 2, the first element (named "x")
contains the bin mid-points, the second element (named "y") the
aggregated values from input vector y within each bin. For
returnIndex = TRUE the list contains an additional element
"index" with the index of the max or min (depending on whether
method = "max" or method = "min") value within each bin in
input vector x.
Note
The function ensures that all values within the range used to define
the breaks are considered in the binning (and assigned to a bin). This
means that for all bins except the last one values in x have to be
>= xlower and < xupper (with xlower
and xupper being the lower and upper boundary, respectively). For
the last bin the condition is x >= xlower & x <= xupper.
Note also that if shiftByHalfBinSize is TRUE the range of
values that is used for binning is expanded by binSize (i.e. the
lower boundary will be fromX - binSize/2, the upper
toX + binSize/2). Setting this argument to TRUE resembles
the binning that is/was used in profBin function from
xcms < 1.51.
NA handling: by default the function ignores NA values in
y (thus inherently assumes na.rm = TRUE). No NA
values are allowed in x.
Author(s)
Johannes Rainer
See Also
imputeLinInterpol
Examples
########
## Simple example illustrating the breaks and the binning.
##
## Define breaks for 5 bins:
brks <- seq(2, 12, length.out = 6)
## The first bin is then [2,4), the second [4,6) and so on.
brks
## Get the max value falling within each bin.
binYonX(x = 1:16, y = 1:16, breaks = brks)
## Thus, the largest value in x = 1:16 falling into the bin [2,4) (i.e. being
## >= 2 and < 4) is 3, the largest one falling into [4,6) is 5 and so on.
## Note however the function ensures that the minimal and maximal x-value
## (in this example 1 and 12) fall within a bin, i.e. 12 is considered for
## the last bin.
#######
## Performing the binning ons sub-set of x
##
X <- 1:16
## Bin X from element 4 to 10 into 5 bins.
X[4:10]
binYonX(X, X, nBins = 5L, fromIdx = 4, toIdx = 10)
## This defines breaks for 5 bins on the values from 4 to 10 and bins
## the values into these 5 bins. Alternatively, we could manually specify
## the range for the binning, i.e. the minimal and maximal value for the
## breaks:
binYonX(X, X, nBins = 5L, fromIdx = 4, toIdx = 10, binFromX = 1, binToX = 16)
## In this case the breaks for 5 bins were defined from a value 1 to 16 and
## the values 4 to 10 were binned based on these breaks.
#######
## Bin values within a sub-set of x, second example
##
## This example illustrates how the fromIdx and toIdx parameters can be used.
## x defines 3 times the sequence form 1 to 10, while y is the sequence from
## 1 to 30. In this very simple example x is supposed to represent M/Z values
## from 3 consecutive scans and y the intensities measured for each M/Z in
## each scan. We want to get the maximum intensities for M/Z value bins only
## for the second scan, and thus we use fromIdx = 11 and toIdx = 20. The breaks
## for the bins are defined with the nBins, binFromX and binToX.
X <- rep(1:10, 3)
Y <- 1:30
## Bin the M/Z values in the second scan into 5 bins and get the maximum
## intensity for each bin. Note that we have to specify sortedX = TRUE as
## the x and y vectors would be sorted otherwise.
binYonX(X, Y, nBins = 5L, sortedX = TRUE, fromIdx = 11, toIdx = 20)
#######
## Bin in overlapping sub-sets of X
##
## In this example we define overlapping sub-sets of X and perform the binning
## within these.
X <- 1:30
## Define the start and end indices of the sub-sets.
fIdx <- c(2, 8, 21)
tIdx <- c(10, 25, 30)
binYonX(X, nBins = 5L, fromIdx = fIdx, toIdx = tIdx)
## The same, but pre-defining also the desired range of the bins.
binYonX(X, nBins = 5L, fromIdx = fIdx, toIdx = tIdx, binFromX = 4, binToX = 28)
## The same bins are thus used for each sub-set.
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