Package 'fromo' reference manual

Title:	Fast Robust Moments
Description:	Fast, numerically robust computation of weighted moments via 'Rcpp'. Supports computation on vectors and matrices, and Monoidal append of moments. Moments and cumulants over running fixed length windows can be computed, as well as over time-based windows. Moment computations are via a generalization of Welford's method, as described by Bennett et. (2009) <doi:10.1109/CLUSTR.2009.5289161>.
Authors:	Steven E. Pav [aut, cre]
Maintainer:	Steven E. Pav <[email protected]>
License:	LGPL-3
Version:	0.2.4
Built:	2024-11-30 12:26:28 UTC
Source:	CRAN

Fast Robust Moments.

Description

Fast, numerically robust moments computations, along with computation of cumulants, running means, etc.

Robust Moments

Welford described a method for 'robust' one-pass computation of the standard deviation. By 'robust', we mean robust to round-off caused by a large shift in the mean. This method was generalized by Terriberry, and Bennett et. al. to the case of higher-order moments. This package provides those algorithms for computing moments.

Generally we should find that the stock implementations of sd, skewness and so on are already robust and likely using these algorithms under the hood. This package was written for a few reasons:

As an exercise to learn Rcpp.
Often I found I needed the first $k$ moments. For example, when computing the Z-score, the standard deviation and mean must be computed separately, which is inefficient. Similarly Merten's correction for the standard error of the Sharpe ratio uses the first four moments. These are all computed as a side effect of computation of the kurtosis, but discarded by the standard methods.

Legal Mumbo Jumbo

fromo is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

Note

The moment computations provided by fromo are numerically robust, but will often not provide the same results as the 'standard' implementations, due to differences in roundoff. We make every attempt to balance speed and robustness. User assumes all risk from using the fromo package.

This package was developed as an exercise in learning Rcpp.

Author(s)

Steven E. Pav [email protected]

Maintainer: Steven E. Pav [email protected] (ORCID)

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

unconcatenate centsums objects.

Description

Unconcatenate centsums objects.

Usage

x %-% y

## S4 method for signature 'centsums,centsums'
x %-% y
x %-% y

## S4 method for signature 'centsums,centsums'
x %-% y

Arguments

`x`	a `centsums` objects
`y`	a `centsums` objects

unconcatenate centcosums objects.

Description

Unconcatenate centcosums objects.

Usage

## S4 method for signature 'centcosums,centcosums'
x %-% y
## S4 method for signature 'centcosums,centcosums'
x %-% y

Arguments

`x`	a `centcosums` objects
`y`	a `centcosums` objects

Accessor methods.

Description

Access slot data from a centsums object.

Usage

sums(x)

## S4 method for signature 'centsums'
sums(x)

moments(x, type = c("central", "raw", "standardized"))

## S4 method for signature 'centsums'
moments(x, type = c("central", "raw", "standardized"))
sums(x)

## S4 method for signature 'centsums'
sums(x)

moments(x, type = c("central", "raw", "standardized"))

## S4 method for signature 'centsums'
moments(x, type = c("central", "raw", "standardized"))

Arguments

`x`	a `centsums` object.
`type`	the type of moment to compute.

Note

Author(s)

Steven E. Pav [email protected]

Coerce to a centcosums object.

Description

Convert data to a centcosums object.

Usage

as.centcosums(x, order=2, na.omit=TRUE)

## Default S3 method:
as.centcosums(x, order = 2, na.omit = TRUE)
as.centcosums(x, order=2, na.omit=TRUE)

## Default S3 method:
as.centcosums(x, order = 2, na.omit = TRUE)

Arguments

`x`	a matrix.
`order`	the order, defaulting to `2`.
`na.omit`	whether to remove rows with `NA`.

Details

Computes the raw cosums on data, and stuffs the results into a centcosums object.

Value

A centcosums object.

Note

Author(s)

Steven E. Pav [email protected]

Examples

set.seed(123)
x <- matrix(rnorm(100*3),ncol=3)
cs <- as.centcosums(x, order=2)
set.seed(123)
x <- matrix(rnorm(100*3),ncol=3)
cs <- as.centcosums(x, order=2)

Coerce to a centsums object.

Description

Convert data to a centsums object.

Usage

as.centsums(
  x,
  order = 3,
  na.rm = TRUE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = FALSE
)

## Default S3 method:
as.centsums(
  x,
  order = 3,
  na.rm = TRUE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = FALSE
)
as.centsums(
  x,
  order = 3,
  na.rm = TRUE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = FALSE
)

## Default S3 method:
as.centsums(
  x,
  order = 3,
  na.rm = TRUE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = FALSE
)

Arguments

`x`	a numeric, array, or matrix.
`order`	the order, defaulting to `length(sums)+1`.
`na.rm`	whether to remove `NA`.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.

Details

Computes the raw sums on data, and stuffs the results into a centsums object.

Value

A centsums object.

Note

Author(s)

Steven E. Pav [email protected]

Examples

set.seed(123)
x <- rnorm(1000)
cs <- as.centsums(x, order=5)
set.seed(123)
x <- rnorm(1000)
cs <- as.centsums(x, order=5)

concatenate centcosums objects.

Description

Concatenate centcosums objects.

Usage

\method{c}{centcosums}(...)
\method{c}{centcosums}(...)

Arguments

...

centcosums objects

concatenate centsums objects.

Description

Concatenate centsums objects.

Usage

\method{c}{centsums}(...)
\method{c}{centsums}(...)

Arguments

...

centsums objects

Multivariate centered sums; join and unjoined.

Description

Compute, join, or unjoin multivariate centered (co-) sums.

Usage

cent_cosums(v, max_order = 2L, na_omit = FALSE)

cent_comoments(v, max_order = 2L, used_df = 0L, na_omit = FALSE)

join_cent_cosums(ret1, ret2)

unjoin_cent_cosums(ret3, ret2)
cent_cosums(v, max_order = 2L, na_omit = FALSE)

cent_comoments(v, max_order = 2L, used_df = 0L, na_omit = FALSE)

join_cent_cosums(ret1, ret2)

unjoin_cent_cosums(ret3, ret2)

Arguments

`v`	an $m$ by $n$ matrix, each row an independent observation of some $n$ variate variable.
`max_order`	the maximum order of cosum to compute. For now this can only be 2; in the future higher order cosums should be possible.
`na_omit`	a boolean; if `TRUE`, then only rows of `v` with complete observations will be used.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the centered moments computation. These are subtracted from the number of observations.
`ret1`	a multdimensional array as output by `cent_cosums`.
`ret2`	a multdimensional array as output by `cent_cosums`.
`ret3`	a multdimensional array as output by `cent_cosums`.

Value

a multidimensional arry of dimension max_order, each side of length $1+n$ . For the case currently implemented where max_order must be 2, the output is a symmetric matrix, where the element in the 1,1 position is the count of complete) rows of v, the 2:(n+1),1 column is the mean, and the 2:(n+1),2:(n+1) is the co sums matrix, which is the covariance up to scaling by the count. cent_comoments performs this normalization for you.

Note

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples


 set.seed(1234)
 x1 <- matrix(rnorm(1e3*5,mean=1),ncol=5)
 x2 <- matrix(rnorm(1e3*5,mean=1),ncol=5)
 max_ord <- 2L
 rs1 <- cent_cosums(x1,max_ord)
 rs2 <- cent_cosums(x2,max_ord)
 rs3 <- cent_cosums(rbind(x1,x2),max_ord)
 rs3alt <- join_cent_cosums(rs1,rs2)
 stopifnot(max(abs(rs3 - rs3alt)) < 1e-7)
 rs1alt <- unjoin_cent_cosums(rs3,rs2)
 rs2alt <- unjoin_cent_cosums(rs3,rs1)
 stopifnot(max(abs(rs1 - rs1alt)) < 1e-7)
 stopifnot(max(abs(rs2 - rs2alt)) < 1e-7)

set.seed(1234)
 x1 <- matrix(rnorm(1e3*5,mean=1),ncol=5)
 x2 <- matrix(rnorm(1e3*5,mean=1),ncol=5)
 max_ord <- 2L
 rs1 <- cent_cosums(x1,max_ord)
 rs2 <- cent_cosums(x2,max_ord)
 rs3 <- cent_cosums(rbind(x1,x2),max_ord)
 rs3alt <- join_cent_cosums(rs1,rs2)
 stopifnot(max(abs(rs3 - rs3alt)) < 1e-7)
 rs1alt <- unjoin_cent_cosums(rs3,rs2)
 rs2alt <- unjoin_cent_cosums(rs3,rs1)
 stopifnot(max(abs(rs1 - rs1alt)) < 1e-7)
 stopifnot(max(abs(rs2 - rs2alt)) < 1e-7)

Centered sums; join and unjoined.

Description

Compute, join, or unjoin centered sums.

Usage

cent_sums(
  v,
  max_order = 5L,
  na_rm = FALSE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = TRUE
)

join_cent_sums(ret1, ret2)

unjoin_cent_sums(ret3, ret2)
cent_sums(
  v,
  max_order = 5L,
  na_rm = FALSE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = TRUE
)

join_cent_sums(ret1, ret2)

unjoin_cent_sums(ret3, ret2)

Arguments

`v`	a vector
`max_order`	the maximum order of the centered moment to be computed.
`na_rm`	whether to remove NA, false by default.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.
`ret1`	an $ord+1$ vector as output by `cent_sums` consisting of the count, the mean, then the k through ordth centered sum of some observations.
`ret2`	an $ord+1$ vector as output by `cent_sums` consisting of the count, the mean, then the k through ordth centered sum of some observations.
`ret3`	an $ord+1$ vector as output by `cent_sums` consisting of the count, the mean, then the k through ordth centered sum of some observations.

Value

a vector the same size as the input consisting of the adjusted version of the input. When there are not sufficient (non-nan) elements for the computation, NaN are returned.

Note

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples


 set.seed(1234)
 x1 <- rnorm(1e3,mean=1)
 x2 <- rnorm(1e3,mean=1)
 max_ord <- 6L
 rs1 <- cent_sums(x1,max_ord)
 rs2 <- cent_sums(x2,max_ord)
 rs3 <- cent_sums(c(x1,x2),max_ord)
 rs3alt <- join_cent_sums(rs1,rs2)
 stopifnot(max(abs(rs3 - rs3alt)) < 1e-7)
 rs1alt <- unjoin_cent_sums(rs3,rs2)
 rs2alt <- unjoin_cent_sums(rs3,rs1)
 stopifnot(max(abs(rs1 - rs1alt)) < 1e-7)
 stopifnot(max(abs(rs2 - rs2alt)) < 1e-7)

set.seed(1234)
 x1 <- rnorm(1e3,mean=1)
 x2 <- rnorm(1e3,mean=1)
 max_ord <- 6L
 rs1 <- cent_sums(x1,max_ord)
 rs2 <- cent_sums(x2,max_ord)
 rs3 <- cent_sums(c(x1,x2),max_ord)
 rs3alt <- join_cent_sums(rs1,rs2)
 stopifnot(max(abs(rs3 - rs3alt)) < 1e-7)
 rs1alt <- unjoin_cent_sums(rs3,rs2)
 rs2alt <- unjoin_cent_sums(rs3,rs1)
 stopifnot(max(abs(rs1 - rs1alt)) < 1e-7)
 stopifnot(max(abs(rs2 - rs2alt)) < 1e-7)

Convert between different types of moments, raw, central, standardized.

Description

Given raw or central or standardized moments, convert to another type.

Usage

cent2raw(input)
cent2raw(input)

Arguments

input

a vector of the count, then the mean, then the 2 through k raw or central moments.

Note

Author(s)

Steven E. Pav [email protected]

Accessor methods.

Description

Access slot data from a centcosums object.

Usage

cosums(x)

## S4 method for signature 'centcosums'
cosums(x)

comoments(x, type = c("central", "raw"))

## S4 method for signature 'centcosums'
comoments(x, type = c("central", "raw"))
cosums(x)

## S4 method for signature 'centcosums'
cosums(x)

comoments(x, type = c("central", "raw"))

## S4 method for signature 'centcosums'
comoments(x, type = c("central", "raw"))

Arguments

`x`	a `centcosums` object.
`type`	the type of moment to compute.

Note

Author(s)

Steven E. Pav [email protected]

centcosums Class.

Description

An S4 class to store (centered) cosums of data, and to support operations on the same.

Usage

## S4 method for signature 'centcosums'
initialize(.Object, cosums, order = NA_real_)

centcosums(cosums, order = NULL)
## S4 method for signature 'centcosums'
initialize(.Object, cosums, order = NA_real_)

centcosums(cosums, order = NULL)

Arguments

`.Object`	a `centcosums` object, or proto-object.
`cosums`	the output of `cent_cosums`, say.
`order`	the order, defaulting to `2`.

Details

A centcosums object contains a multidimensional array (now only 2-diemnsional), as output by cent_cosums.

Value

An object of class centcosums.

Slots

cosums: a multidimensional array of the cosums.
order: the maximum order. ignored for now.

Note

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples

obj <- new("centcosums",cosums=cent_cosums(matrix(rnorm(100*3),ncol=3),max_order=2),order=2)

obj <- new("centcosums",cosums=cent_cosums(matrix(rnorm(100*3),ncol=3),max_order=2),order=2)

centsums Class.

Description

An S4 class to store (centered) sums of data, and to support operations on the same.

Usage

## S4 method for signature 'centsums'
initialize(.Object, sums, order = NA_real_)

centsums(sums, order = NULL)
## S4 method for signature 'centsums'
initialize(.Object, sums, order = NA_real_)

centsums(sums, order = NULL)

Arguments

`.Object`	a `centsums` object, or proto-object.
`sums`	a numeric vector.
`order`	the order, defaulting to `length(sums)+1`.

Details

A centsums object contains a vector value of the data count, the mean, and the $k$ th centered sum, for $k$ up to some maximum order.

Value

An object of class centsums.

Slots

sums: a numeric vector of the sums.
order: the maximum order.

Note

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples

obj <- new("centsums",sums=c(1000,1.234,0.235),order=2)

obj <- new("centsums",sums=c(1000,1.234,0.235),order=2)

News for package 'fromo':

Description

News for package 'fromo'

Version 0.2.4 (2024-11-29)

adding running correlation, covariance, regression coefficients.
adding t-running correlation, covariance, regression coefficients.

Version 0.2.3 (2024-11-07)

adding flag and checking for negative even moments due to roundoff.
fixing divide by zero which would corrupt results when input was constant.

Version 0.2.2 (2024-11-03)

fix t_running_sum and others to act as documented when variable_win is flagged.

Version 0.2.1 (2019-01-29)

fix memory leak for case where the mean only need be computed via a Welford object.

Version 0.2.0 (2019-01-12)

add std_cumulants
add running_sum, running_mean.
Kahan compensated summation for these.
Welford object under the hood.
add weighted moments computation.
add time-based running window computations.
some speedups for obviously fast cases: no checking of NA, etc.
move github figures to location CRAN understands.

Version 0.1.3 (2016-04-04)

submit to CRAN

Initial Version 0.1.0 (2016-03-25)

start work

Compute approximate quantiles over a sliding window

Description

Computes cumulants up to some given order, then employs the Cornish-Fisher approximation to compute approximate quantiles using a Gaussian basis.

Usage

running_apx_quantiles(
  v,
  p,
  window = NULL,
  wts = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_apx_median(
  v,
  window = NULL,
  wts = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)
running_apx_quantiles(
  v,
  p,
  window = NULL,
  wts = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_apx_median(
  v,
  window = NULL,
  wts = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

Arguments

`v`	a vector
`p`	the probability points at which to compute the quantiles. Should be in the range (0,1).
`window`	the window size. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, equivalent to an infinite window size. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`max_order`	the maximum order of the centered moment to be computed.
`na_rm`	whether to remove NA, false by default.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned. This can be used to prevent moments from being computed on too few observations. Defaults to zero, meaning no restriction.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the centered moments computation. These are subtracted from the number of observations.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though less accurate results.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.
`check_negative_moments`	a boolean flag. Normal computation of running moments can result in negative estimates of even order moments due to loss of numerical precision. With this flag active, the computation checks for negative even order moments and restarts the computation when one is detected. This should eliminate the possibility of negative even order moments. The downside is the speed hit of checking on every output step. Note also the code checks for negative moments of every even order tracked, even if they are not output; that is if the kurtosis, say, is being computed, and a negative variance is detected, then the computation is restarted. Defaults to `TRUE` to avoid negative even moments. Set to `FALSE` only if you know what you are doing.

Details

Computes the cumulants, then approximates quantiles using AS269 of Lee & Lin.

Value

A matrix, with one row for each element of x, and one column for each element of q.

Note

The current implementation is not as space-efficient as it could be, as it first computes the cumulants for each row, then performs the Cornish-Fisher approximation on a row-by-row basis. In the future, this computation may be moved earlier into the pipeline to be more space efficient. File an issue if the memory footprint is an issue for you.

Note that when weights are given, they are treated as replication weights. This can have subtle effects on computations which require minimum degrees of freedom, since the sum of weights will be compared to that minimum, not the number of data points. Weight values (much) less than 1 can cause computations to return NA somewhat unexpectedly due to this condition, while values greater than one might cause the computation to spuriously return a value with little precision.

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples

x <- rnorm(1e5)
xq <- running_apx_quantiles(x,c(0.1,0.25,0.5,0.75,0.9))
xm <- running_apx_median(x)

x <- rnorm(1e5)
xq <- running_apx_quantiles(x,c(0.1,0.25,0.5,0.75,0.9))
xm <- running_apx_median(x)

Compare data to moments computed over a sliding window.

Description

Computes moments over a sliding window, then adjusts the data accordingly, centering, or scaling, or z-scoring, and so on.

Usage

running_centered(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = FALSE,
  check_negative_moments = TRUE
)

running_scaled(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_zscored(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_sharpe(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  compute_se = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_tstat(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)
running_centered(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = FALSE,
  check_negative_moments = TRUE
)

running_scaled(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_zscored(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_sharpe(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  compute_se = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_tstat(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

Arguments

`v`	a vector
`window`	the window size. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, equivalent to an infinite window size. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`na_rm`	whether to remove NA, false by default.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned. This can be used to prevent e.g. Z-scores from being computed on only 3 observations. Defaults to zero, meaning no restriction, which can result in infinite Z-scores during the burn-in period.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the centered moments computation. These are subtracted from the number of observations.
`lookahead`	for some of the operations, the value is compared to mean and standard deviation possibly using 'future' or 'past' information by means of a non-zero lookahead. Positive values mean data are taken from the future.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though less accurate results.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.
`check_negative_moments`	a boolean flag. Normal computation of running moments can result in negative estimates of even order moments due to loss of numerical precision. With this flag active, the computation checks for negative even order moments and restarts the computation when one is detected. This should eliminate the possibility of negative even order moments. The downside is the speed hit of checking on every output step. Note also the code checks for negative moments of every even order tracked, even if they are not output; that is if the kurtosis, say, is being computed, and a negative variance is detected, then the computation is restarted. Defaults to `TRUE` to avoid negative even moments. Set to `FALSE` only if you know what you are doing.
`compute_se`	for `running_sharpe`, return an extra column of the standard error, as computed by Mertens' correction.

Details

Given the length $n$ vector $x$ , for a given index $i$ , define $x^{(i)}$ as the vector of $x_{i-window+1},x_{i-window+2},...,x_{i}$ , where we do not run over the 'edge' of the vector. In code, this is essentially x[(max(1,i-window+1)):i]. Then define $\mu_i$ , $\sigma_i$ and $n_i$ as, respectively, the sample mean, standard deviation and number of non-NA elements in $x^{(i)}$ .

We compute output vector $m$ the same size as $x$ . For the 'centered' version of $x$ , we have $m_i = x_i - \mu_i$ . For the 'scaled' version of $x$ , we have $m_i = x_i / \sigma_i$ . For the 'z-scored' version of $x$ , we have $m_i = (x_i - \mu_i) / \sigma_i$ . For the 't-scored' version of $x$ , we have $m_i = \sqrt{n_i} \mu_i / \sigma_i$ .

We also allow a 'lookahead' for some of these operations. If positive, the moments are computed using data from larger indices; if negative, from smaller indices. Letting $j = i + lookahead$ : For the 'centered' version of $x$ , we have $m_i = x_i - \mu_j$ . For the 'scaled' version of $x$ , we have $m_i = x_i / \sigma_j$ . For the 'z-scored' version of $x$ , we have $m_i = (x_i - \mu_j) / \sigma_j$ .

Value

a vector the same size as the input consisting of the adjusted version of the input. When there are not sufficient (non-nan) elements for the computation, NaN are returned.

Note

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples


if (require(moments)) {
    set.seed(123)
    x <- rnorm(5e1)
    window <- 10L
    rm1 <- t(sapply(seq_len(length(x)),function(iii) { 
                  xrang <- x[max(1,iii-window+1):iii]
                  c(sd(xrang),mean(xrang),length(xrang)) },
                  simplify=TRUE))
    rcent <- running_centered(x,window=window)
    rscal <- running_scaled(x,window=window)
    rzsco <- running_zscored(x,window=window)
    rshrp <- running_sharpe(x,window=window)
    rtsco <- running_tstat(x,window=window)
    rsrse <- running_sharpe(x,window=window,compute_se=TRUE)
    stopifnot(max(abs(rcent - (x - rm1[,2])),na.rm=TRUE) < 1e-12)
    stopifnot(max(abs(rscal - (x / rm1[,1])),na.rm=TRUE) < 1e-12)
    stopifnot(max(abs(rzsco - ((x - rm1[,2]) / rm1[,1])),na.rm=TRUE) < 1e-12)
    stopifnot(max(abs(rshrp - (rm1[,2] / rm1[,1])),na.rm=TRUE) < 1e-12)
    stopifnot(max(abs(rtsco - ((sqrt(rm1[,3]) * rm1[,2]) / rm1[,1])),na.rm=TRUE) < 1e-12)
    stopifnot(max(abs(rsrse[,1] - rshrp),na.rm=TRUE) < 1e-12)

    rm2 <- t(sapply(seq_len(length(x)),function(iii) { 
                  xrang <- x[max(1,iii-window+1):iii]
                  c(kurtosis(xrang)-3.0,skewness(xrang)) },
                  simplify=TRUE))
    mertens_se <- sqrt((1 + ((2 + rm2[,1])/4) * rshrp^2 - rm2[,2]*rshrp) / rm1[,3])
    stopifnot(max(abs(rsrse[,2] - mertens_se),na.rm=TRUE) < 1e-12)
}

if (require(moments)) {
    set.seed(123)
    x <- rnorm(5e1)
    window <- 10L
    rm1 <- t(sapply(seq_len(length(x)),function(iii) { 
                  xrang <- x[max(1,iii-window+1):iii]
                  c(sd(xrang),mean(xrang),length(xrang)) },
                  simplify=TRUE))
    rcent <- running_centered(x,window=window)
    rscal <- running_scaled(x,window=window)
    rzsco <- running_zscored(x,window=window)
    rshrp <- running_sharpe(x,window=window)
    rtsco <- running_tstat(x,window=window)
    rsrse <- running_sharpe(x,window=window,compute_se=TRUE)
    stopifnot(max(abs(rcent - (x - rm1[,2])),na.rm=TRUE) < 1e-12)
    stopifnot(max(abs(rscal - (x / rm1[,1])),na.rm=TRUE) < 1e-12)
    stopifnot(max(abs(rzsco - ((x - rm1[,2]) / rm1[,1])),na.rm=TRUE) < 1e-12)
    stopifnot(max(abs(rshrp - (rm1[,2] / rm1[,1])),na.rm=TRUE) < 1e-12)
    stopifnot(max(abs(rtsco - ((sqrt(rm1[,3]) * rm1[,2]) / rm1[,1])),na.rm=TRUE) < 1e-12)
    stopifnot(max(abs(rsrse[,1] - rshrp),na.rm=TRUE) < 1e-12)

    rm2 <- t(sapply(seq_len(length(x)),function(iii) { 
                  xrang <- x[max(1,iii-window+1):iii]
                  c(kurtosis(xrang)-3.0,skewness(xrang)) },
                  simplify=TRUE))
    mertens_se <- sqrt((1 + ((2 + rm2[,1])/4) * rshrp^2 - rm2[,2]*rshrp) / rm1[,3])
    stopifnot(max(abs(rsrse[,2] - mertens_se),na.rm=TRUE) < 1e-12)
}

Compute covariance, correlation, regression over a sliding window

Description

Computes 2nd moments and comoments, as well as the means, over an infinite or finite sliding window, returning a matrix with the correlation, covariance, regression coefficient, and so on.

Usage

running_correlation(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

running_covariance(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_covariance_3(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_regression_slope(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

running_regression_intercept(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

running_regression_fit(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

running_regression_diagnostics(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 2,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)
running_correlation(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

running_covariance(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_covariance_3(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_regression_slope(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

running_regression_intercept(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

running_regression_fit(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

running_regression_diagnostics(
  x,
  y,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 2,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

Arguments

`x`	a vector
`y`	a vector
`window`	the window size. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, equivalent to an infinite window size. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`na_rm`	whether to remove NA, false by default.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned. This can be used to prevent moments from being computed on too few observations. Defaults to zero, meaning no restriction.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though less accurate results.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`check_negative_moments`	a boolean flag. Normal computation of running moments can result in negative estimates of even order moments due to loss of numerical precision. With this flag active, the computation checks for negative even order moments and restarts the computation when one is detected. This should eliminate the possibility of negative even order moments. The downside is the speed hit of checking on every output step. Note also the code checks for negative moments of every even order tracked, even if they are not output; that is if the kurtosis, say, is being computed, and a negative variance is detected, then the computation is restarted. Defaults to `TRUE` to avoid negative even moments. Set to `FALSE` only if you know what you are doing.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the standard errors computation. These are subtracted from the number of observations.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.

Details

Computes the correlation or covariance, or OLS regression coefficients and standard errors. These are computed via the numerically robust one-pass method of Bennett et. al.

Value

Typically a matrix, usually only one row of the output value. More specifically:

running_covariance: Returns a single column of the covariance of x and y.
running_correlation: Returns a single column of the correlation of x and y.
running_covariance_3: Returns three columns: the variance of x, the covariance of x and y, and the variance of y, in that order.
running_regression_slope: Returns a single column of the slope of the OLS regression.
running_regression_intercept: Returns a single column of the intercept of the OLS regression.
running_regression_fit: Returns two columns: the regression intercept and the regression slope of the OLS regression.
running_regression_diagnostics: Returns five columns: the regression intercept, the regression slope, the regression standard error, the standard error of the intercept, the standard error of the slope of the OLS regression.

Note

As this code may add and remove observations, numerical imprecision may result in negative estimates of squared quantities, like the second or fourth moments. By default we check for this condition in running computations. It may also be mitigated somewhat by setting a smaller restart_period. Post an issue if you experience this bug.

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples

x <- rnorm(1e5)
y <- rnorm(1e5) + x
rho <- running_correlation(x, y, window=100L)

x <- rnorm(1e5)
y <- rnorm(1e5) + x
rho <- running_correlation(x, y, window=100L)

Compute first K moments over a sliding window

Description

Compute the (standardized) 2nd through kth moments, the mean, and the number of elements over an infinite or finite sliding window, returning a matrix.

Usage

running_sd3(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_skew4(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_kurt5(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_sd(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_skew(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_kurt(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_cent_moments(
  v,
  window = NULL,
  wts = NULL,
  max_order = 5L,
  na_rm = FALSE,
  max_order_only = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_std_moments(
  v,
  window = NULL,
  wts = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_cumulants(
  v,
  window = NULL,
  wts = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)
running_sd3(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_skew4(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_kurt5(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_sd(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_skew(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_kurt(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_cent_moments(
  v,
  window = NULL,
  wts = NULL,
  max_order = 5L,
  na_rm = FALSE,
  max_order_only = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_std_moments(
  v,
  window = NULL,
  wts = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

running_cumulants(
  v,
  window = NULL,
  wts = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

Arguments

`v`	a vector
`window`	the window size. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, equivalent to an infinite window size. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`na_rm`	whether to remove NA, false by default.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned. This can be used to prevent moments from being computed on too few observations. Defaults to zero, meaning no restriction.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the centered moments computation. These are subtracted from the number of observations.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though less accurate results.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.
`check_negative_moments`	a boolean flag. Normal computation of running moments can result in negative estimates of even order moments due to loss of numerical precision. With this flag active, the computation checks for negative even order moments and restarts the computation when one is detected. This should eliminate the possibility of negative even order moments. The downside is the speed hit of checking on every output step. Note also the code checks for negative moments of every even order tracked, even if they are not output; that is if the kurtosis, say, is being computed, and a negative variance is detected, then the computation is restarted. Defaults to `TRUE` to avoid negative even moments. Set to `FALSE` only if you know what you are doing.
`max_order`	the maximum order of the centered moment to be computed.
`max_order_only`	for `running_cent_moments`, if this flag is set, only compute the maximum order centered moment, and return in a vector.

Details

Computes the number of elements, the mean, and the 2nd through kth centered (and typically standardized) moments, for $k=2,3,4$ . These are computed via the numerically robust one-pass method of Bennett et. al.

Given the length $n$ vector $x$ , we output matrix $M$ where $M_{i,j}$ is the $order - j + 1$ moment (i.e. excess kurtosis, skewness, standard deviation, mean or number of elements) of $x_{i-window+1},x_{i-window+2},...,x_{i}$ . Barring NA or NaN, this is over a window of size window. During the 'burn-in' phase, we take fewer elements.

Value

Typically a matrix, where the first columns are the kth, k-1th through 2nd standardized, centered moments, then a column of the mean, then a column of the number of (non-nan) elements in the input, with the following exceptions:

running_cent_moments: Computes arbitrary order centered moments. When max_order_only is set, only a column of the maximum order centered moment is returned.
running_std_moments: Computes arbitrary order standardized moments, then the standard deviation, the mean, and the count. There is not yet an option for max_order_only, but probably should be.
running_cumulants: Computes arbitrary order cumulants, and returns the kth, k-1th, through the second (which is the variance) cumulant, then the mean, and the count.

Note

the kurtosis is excess kurtosis, with a 3 subtracted, and should be nearly zero for Gaussian input.

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples

x <- rnorm(1e5)
xs3 <- running_sd3(x,10)
xs4 <- running_skew4(x,10)

if (require(moments)) {
    set.seed(123)
    x <- rnorm(5e1)
    window <- 10L
    kt5 <- running_kurt5(x,window=window)
    rm1 <- t(sapply(seq_len(length(x)),function(iii) { 
                xrang <- x[max(1,iii-window+1):iii]
                c(moments::kurtosis(xrang)-3.0,moments::skewness(xrang),
                sd(xrang),mean(xrang),length(xrang)) },
             simplify=TRUE))
    stopifnot(max(abs(kt5 - rm1),na.rm=TRUE) < 1e-12)
}

xc6 <- running_cent_moments(x,window=100L,max_order=6L)

x <- rnorm(1e5)
xs3 <- running_sd3(x,10)
xs4 <- running_skew4(x,10)

if (require(moments)) {
    set.seed(123)
    x <- rnorm(5e1)
    window <- 10L
    kt5 <- running_kurt5(x,window=window)
    rm1 <- t(sapply(seq_len(length(x)),function(iii) { 
                xrang <- x[max(1,iii-window+1):iii]
                c(moments::kurtosis(xrang)-3.0,moments::skewness(xrang),
                sd(xrang),mean(xrang),length(xrang)) },
             simplify=TRUE))
    stopifnot(max(abs(kt5 - rm1),na.rm=TRUE) < 1e-12)
}

xc6 <- running_cent_moments(x,window=100L,max_order=6L)

Compute sums or means over a sliding window.

Description

Compute the mean or sum over an infinite or finite sliding window, returning a vector the same size as the input.

Usage

running_sum(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  restart_period = 10000L,
  check_wts = FALSE
)

running_mean(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 10000L,
  check_wts = FALSE
)
running_sum(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  restart_period = 10000L,
  check_wts = FALSE
)

running_mean(
  v,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 10000L,
  check_wts = FALSE
)

Arguments

`v`	a vector.
`window`	the window size. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, equivalent to an infinite window size. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`na_rm`	whether to remove NA, false by default.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though potentially less accurate results. Unlike in the computation of even order moments, loss of precision is unlikely to be disastrous, so the default value is rather large.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned, only for the means computation. This can be used to prevent moments from being computed on too few observations. Defaults to zero, meaning no restriction.

Details

Computes the mean or sum of the elements, using a Kahan's Compensated Summation Algorithm, a numerically robust one-pass method.

Given the length $n$ vector $x$ , we output matrix $M$ where $M_{i,1}$ is the sum or mean of $x_{i-window+1},x_{i-window+2},...,x_{i}$ . Barring NA or NaN, this is over a window of size window. During the 'burn-in' phase, we take fewer elements. If fewer than min_df for running_mean, returns NA.

Value

A vector the same size as the input.

Note

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Kahan, W. "Further remarks on reducing truncation errors," Communications of the ACM, 8 (1), 1965. doi:10.1145/363707.363723

Wikipedia contributors "Kahan summation algorithm," Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=Kahan_summation_algorithm&oldid=777164752 (accessed May 31, 2017).

Examples

x <- rnorm(1e5)
xs <- running_sum(x,10)
xm <- running_mean(x,100)

x <- rnorm(1e5)
xs <- running_sum(x,10)
xm <- running_mean(x,100)

Compute first K moments

Description

Compute the (standardized) 2nd through kth moments, the mean, and the number of elements.

Usage

sd3(
  v,
  na_rm = FALSE,
  wts = NULL,
  sg_df = 1,
  check_wts = FALSE,
  normalize_wts = TRUE
)

skew4(
  v,
  na_rm = FALSE,
  wts = NULL,
  sg_df = 1,
  check_wts = FALSE,
  normalize_wts = TRUE
)

kurt5(
  v,
  na_rm = FALSE,
  wts = NULL,
  sg_df = 1,
  check_wts = FALSE,
  normalize_wts = TRUE
)

cent_moments(
  v,
  max_order = 5L,
  used_df = 0L,
  na_rm = FALSE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = TRUE
)

std_moments(
  v,
  max_order = 5L,
  used_df = 0L,
  na_rm = FALSE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = TRUE
)

cent_cumulants(
  v,
  max_order = 5L,
  used_df = 0L,
  na_rm = FALSE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = TRUE
)

std_cumulants(
  v,
  max_order = 5L,
  used_df = 0L,
  na_rm = FALSE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = TRUE
)
sd3(
  v,
  na_rm = FALSE,
  wts = NULL,
  sg_df = 1,
  check_wts = FALSE,
  normalize_wts = TRUE
)

skew4(
  v,
  na_rm = FALSE,
  wts = NULL,
  sg_df = 1,
  check_wts = FALSE,
  normalize_wts = TRUE
)

kurt5(
  v,
  na_rm = FALSE,
  wts = NULL,
  sg_df = 1,
  check_wts = FALSE,
  normalize_wts = TRUE
)

cent_moments(
  v,
  max_order = 5L,
  used_df = 0L,
  na_rm = FALSE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = TRUE
)

std_moments(
  v,
  max_order = 5L,
  used_df = 0L,
  na_rm = FALSE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = TRUE
)

cent_cumulants(
  v,
  max_order = 5L,
  used_df = 0L,
  na_rm = FALSE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = TRUE
)

std_cumulants(
  v,
  max_order = 5L,
  used_df = 0L,
  na_rm = FALSE,
  wts = NULL,
  check_wts = FALSE,
  normalize_wts = TRUE
)

Arguments

`v`	a vector
`na_rm`	whether to remove NA, false by default.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`sg_df`	the number of degrees of freedom consumed in the computation of the variance or standard deviation. This defaults to 1 to match the ‘Bessel correction’.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.
`max_order`	the maximum order of the centered moment to be computed.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the centered moments computation. These are subtracted from the number of observations.

Details

Computes the number of elements, the mean, and the 2nd through kth centered standardized moment, for $k=2,3,4$ . These are computed via the numerically robust one-pass method of Bennett et. al. In general they will not match exactly with the 'standard' implementations, due to differences in roundoff.

These methods are reasonably fast, on par with the 'standard' implementations. However, they will usually be faster than calling the various standard implementations more than once.

Moments are computed as follows, given some values $x_i$ and optional weights $w_i$ , defaulting to 1, the weighted mean is computed as

$\mu = \frac{\sum_i x_i w_i}{\sum w_i}.$

The weighted kth central sum is computed as

$S_k = \sum_i \left(x_i - \mu\right)^k w_i.$

Let $n = \sum_i w_i$ be the sum of weights (or number of observations in the unweighted case). Then the weighted kth central moment is computed as that weighted sum divided by the adjusted sum weights:

$\mu_k = \frac{\sum_i \left(x_i - \mu\right)^k w_i}{n - \nu},$

where $\nu$ is the ‘used df’, provided by the user to adjust the denominator. (Typical values are 0 or 1.) The weighted kth standardized moment is the central moment divided by the second central moment to the $k/2$ power:

$\tilde{\mu}_k = \frac{\mu_k}{\mu_2^{k/2}}.$

The (centered) rth cumulant, for $r \ge 2$ is then computed using the formula of Willink, namely

$\kappa_r = \mu_r - \sum_{j=1}^{r - 2} {r - 1 \choose j} \kappa_{r-j} \mu_{j},$

That is

$\kappa_2 = \mu_2,$

$\kappa_3 = \mu_3,$

$\kappa_4 = \mu_4 - 3 \mu_2^2,$

$\kappa_5 = \mu_5 - 10 \mu_3\mu_2,$

and so on.

The standardized rth cumulant is the rth centered cumulant divided by $\mu_2^{r/2}$ .

Value

a vector, filled out as follows:

sd3: A vector of the (sample) standard devation, mean, and number of elements (or the total weight when wts are given).
skew4: A vector of the (sample) skewness, standard devation, mean, and number of elements (or the total weight when wts are given).
kurt5: A vector of the (sample) excess kurtosis, skewness, standard devation, mean, and number of elements (or the total weight when wts are given).
cent_moments: A vector of the (sample) $k$ th centered moment, then $k-1$ th centered moment, ..., then the variance, the mean, and number of elements (total weight when wts are given).
std_moments: A vector of the (sample) $k$ th standardized (and centered) moment, then $k-1$ th, ..., then standard devation, mean, and number of elements (total weight).
cent_cumulants: A vector of the (sample) $k$ th (centered, but this is redundant) cumulant, then the $k-1$ th, ..., then the variance (which is the second cumulant), then the mean, then the number of elements (total weight).
std_cumulants: A vector of the (sample) $k$ th standardized (and centered, but this is redundant) cumulant, then the $k-1$ th, ..., down to the third, then the variance, the mean, then the number of elements (total weight).

Note

The first centered (and standardized) moment is often defined to be identically 0. Instead cent_moments and std_moments returns the mean. Similarly, the second standardized moments defined to be identically 1; std_moments instead returns the standard deviation. The reason is that a user can always decide to ignore the results and fill in a 0 or 1 as they need, but could not efficiently compute the mean and standard deviation from scratch if we discard it. The antepenultimate element of the output of std_cumulants is not a one, even though that ‘should’ be the standardized second cumulant.

The antepenultimate element of the output of cent_moments, cent_cumulants and std_cumulants is the variance, not the standard deviation. All other code return the standard deviation in that place.

The kurtosis is excess kurtosis, with a 3 subtracted, and should be nearly zero for Gaussian input.

The term 'centered cumulants' is redundant. The intent was to avoid possible collision with existing code named 'cumulants'.

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Willink, R. "Relationships Between Central Moments and Cumulants, with Formulae for the Central Moments of Gamma Distributions." Communications in Statistics - Theory and Methods, 32 no 4 (2003): 701-704. doi:10.1081/STA-120018823

Examples

x <- rnorm(1e5)
sd3(x)[1] - sd(x)
skew4(x)[4] - length(x)
skew4(x)[3] - mean(x)
skew4(x)[2] - sd(x)
if (require(moments)) {
  skew4(x)[1] - skewness(x)
}


# check 'robustness'; only the mean should change:
kurt5(x + 1e12) - kurt5(x)
# check speed
if (require(microbenchmark) && require(moments)) {
  dumbk <- function(x) { c(kurtosis(x) - 3.0,skewness(x),sd(x),mean(x),length(x)) }
  set.seed(1234)
  x <- rnorm(1e6)
  print(kurt5(x) - dumbk(x))
  microbenchmark(dumbk(x),kurt5(x),times=10L)
}
y <- std_moments(x,6)
cml <- cent_cumulants(x,6)
std <- std_cumulants(x,6)

# check that skew matches moments::skewness
if (require(moments)) {
    set.seed(1234)
    x <- rnorm(1000)
    resu <- fromo::skew4(x)

    msku <- moments::skewness(x)
    stopifnot(abs(msku - resu[1]) < 1e-14)
}

# check skew vs e1071 skewness, which has a different denominator
if (require(e1071)) {
    set.seed(1234)
    x <- rnorm(1000)
    resu <- fromo::skew4(x)

    esku <- e1071::skewness(x,type=3)
    nobs <- resu[4]
    stopifnot(abs(esku - resu[1] * ((nobs-1)/nobs)^(3/2)) < 1e-14)

    # similarly:
    resu <- fromo::std_moments(x,max_order=3,used_df=0)
    stopifnot(abs(esku - resu[1] * ((nobs-1)/nobs)^(3/2)) < 1e-14)
}

x <- rnorm(1e5)
sd3(x)[1] - sd(x)
skew4(x)[4] - length(x)
skew4(x)[3] - mean(x)
skew4(x)[2] - sd(x)
if (require(moments)) {
  skew4(x)[1] - skewness(x)
}


# check 'robustness'; only the mean should change:
kurt5(x + 1e12) - kurt5(x)
# check speed
if (require(microbenchmark) && require(moments)) {
  dumbk <- function(x) { c(kurtosis(x) - 3.0,skewness(x),sd(x),mean(x),length(x)) }
  set.seed(1234)
  x <- rnorm(1e6)
  print(kurt5(x) - dumbk(x))
  microbenchmark(dumbk(x),kurt5(x),times=10L)
}
y <- std_moments(x,6)
cml <- cent_cumulants(x,6)
std <- std_cumulants(x,6)

# check that skew matches moments::skewness
if (require(moments)) {
    set.seed(1234)
    x <- rnorm(1000)
    resu <- fromo::skew4(x)

    msku <- moments::skewness(x)
    stopifnot(abs(msku - resu[1]) < 1e-14)
}

# check skew vs e1071 skewness, which has a different denominator
if (require(e1071)) {
    set.seed(1234)
    x <- rnorm(1000)
    resu <- fromo::skew4(x)

    esku <- e1071::skewness(x,type=3)
    nobs <- resu[4]
    stopifnot(abs(esku - resu[1] * ((nobs-1)/nobs)^(3/2)) < 1e-14)

    # similarly:
    resu <- fromo::std_moments(x,max_order=3,used_df=0)
    stopifnot(abs(esku - resu[1] * ((nobs-1)/nobs)^(3/2)) < 1e-14)
}

Show a centsums object.

Description

Displays the centsums object.

Usage

show(object)

## S4 method for signature 'centsums'
show(object)
show(object)

## S4 method for signature 'centsums'
show(object)

Arguments

object

a centsums object.

Note

Author(s)

Steven E. Pav [email protected]

Examples

set.seed(123)
x <- rnorm(1000)
obj <- as.centsums(x, order=5)
obj
set.seed(123)
x <- rnorm(1000)
obj <- as.centsums(x, order=5)
obj

Compute approximate quantiles over a sliding time window

Description

Computes cumulants up to some given order, then employs the Cornish-Fisher approximation to compute approximate quantiles using a Gaussian basis.

Usage

t_running_apx_quantiles(
  v,
  p,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_apx_median(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)
t_running_apx_quantiles(
  v,
  p,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_apx_median(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

Arguments

`v`	a vector of data.
`p`	the probability points at which to compute the quantiles. Should be in the range (0,1).
`time`	an optional vector of the timestamps of `v`. If given, must be the same length as `v`. If not given, we try to infer it by summing the `time_deltas`.
`time_deltas`	an optional vector of the deltas of timestamps. If given, must be the same length as `v`. If not given, and `wts` are given and `wts_as_delta` is true, we take the `wts` as the time deltas. The deltas must be positive. We sum them to arrive at the times.
`window`	the window size, in time units. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, and `variable_win` is true, then we infer the window from the lookback times: the first window is infinite, but the remaining is the deltas between lookback times. If `variable_win` is false, then these undefined values are equivalent to an infinite window. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`lb_time`	a vector of the times from which lookback will be performed. The output should be the same size as this vector. If not given, defaults to `time`.
`max_order`	the maximum order of the centered moment to be computed.
`na_rm`	whether to remove NA, false by default.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned. This can be used to prevent moments from being computed on too few observations. Defaults to zero, meaning no restriction.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the centered moments computation. These are subtracted from the number of observations.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though less accurate results.
`variable_win`	if true, and the `window` is not a concrete number, the computation window becomes the time between lookback times.
`wts_as_delta`	if true and the `time` and `time_deltas` are not given, but `wts` are given, we take `wts` as the `time_deltas`.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.
`check_negative_moments`	a boolean flag. Normal computation of running moments can result in negative estimates of even order moments due to loss of numerical precision. With this flag active, the computation checks for negative even order moments and restarts the computation when one is detected. This should eliminate the possibility of negative even order moments. The downside is the speed hit of checking on every output step. Note also the code checks for negative moments of every even order tracked, even if they are not output; that is if the kurtosis, say, is being computed, and a negative variance is detected, then the computation is restarted. Defaults to `TRUE` to avoid negative even moments. Set to `FALSE` only if you know what you are doing.

Details

Computes the cumulants, then approximates quantiles using AS269 of Lee & Lin.

Value

A matrix, with one row for each element of x, and one column for each element of q.

Time Windowing

This function supports time (or other counter) based running computation. Here the input are the data $x_i$ , and optional weights vectors, $w_i$ , defaulting to 1, and a vector of time indices, $t_i$ of the same length as $x$ . The times must be non-decreasing:

$t_1 \le t_2 \le \ldots$

It is assumed that $t_0 = -\infty$ . The window, $W$ is now a time-based window. An optional set of lookback times are also given, $b_j$ , which may have different length than the $x$ and $w$ . The output will correspond to the lookback times, and should be the same length. The $j$ th output is computed over indices $i$ such that

$b_j - W < t_i \le b_j.$

For comparison functions (like Z-score, rescaling, centering), which compare values of $x_i$ to local moments, the lookbacks may not be given, but a lookahead $L$ is admitted. In this case, the $j$ th output is computed over indices $i$ such that

$t_j - W + L < t_i \le t_j + L.$

If the times are not given, ‘deltas’ may be given instead. If $\delta_i$ are the deltas, then we compute the times as

$t_i = \sum_{1 \le j \le i} \delta_j.$

The deltas must be the same length as $x$ . If times and deltas are not given, but weights are given and the ‘weights as deltas’ flag is set true, then the weights are used as the deltas.

Some times it makes sense to have the computational window be the space between lookback times. That is, the $j$ th output is to be computed over indices $i$ such that

$b_{j-1} - W < t_i \le b_j.$

This can be achieved by setting the ‘variable window’ flag true and setting the window to null. This will not make much sense if the lookback times are equal to the times, since each moment computation is over a set of a single index, and most moments are underdefined.

Note

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples

x <- rnorm(1e5)
xq <- t_running_apx_quantiles(x,c(0.1,0.25,0.5,0.75,0.9),
       time=seq_along(x),window=200,lb_time=c(100,200,400))

xq <- t_running_apx_median(x,time=seq_along(x),window=200,lb_time=c(100,200,400))
xq <- t_running_apx_median(x,time=cumsum(runif(length(x),min=0.5,max=1.5)),
      window=200,lb_time=c(100,200,400))

# weighted median?
wts <- runif(length(x),min=1,max=5)
xq <- t_running_apx_median(x,wts=wts,wts_as_delta=TRUE,window=1000,lb_time=seq(1000,10000,by=1000))

# these should give the same answer:
xr <- running_apx_median(x,window=200);
xt <- t_running_apx_median(x,time=seq_along(x),window=199.99)

x <- rnorm(1e5)
xq <- t_running_apx_quantiles(x,c(0.1,0.25,0.5,0.75,0.9),
       time=seq_along(x),window=200,lb_time=c(100,200,400))

xq <- t_running_apx_median(x,time=seq_along(x),window=200,lb_time=c(100,200,400))
xq <- t_running_apx_median(x,time=cumsum(runif(length(x),min=0.5,max=1.5)),
      window=200,lb_time=c(100,200,400))

# weighted median?
wts <- runif(length(x),min=1,max=5)
xq <- t_running_apx_median(x,wts=wts,wts_as_delta=TRUE,window=1000,lb_time=seq(1000,10000,by=1000))

# these should give the same answer:
xr <- running_apx_median(x,window=200);
xt <- t_running_apx_median(x,time=seq_along(x),window=199.99)

Compare data to moments computed over a time sliding window.

Description

Computes moments over a sliding window, then adjusts the data accordingly, centering, or scaling, or z-scoring, and so on.

Usage

t_running_centered(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_scaled(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_zscored(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_sharpe(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  compute_se = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_tstat(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  compute_se = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)
t_running_centered(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_scaled(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_zscored(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  lookahead = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_sharpe(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  compute_se = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_tstat(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  compute_se = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

Arguments

`v`	a vector of data.
`time`	an optional vector of the timestamps of `v`. If given, must be the same length as `v`. If not given, we try to infer it by summing the `time_deltas`.
`time_deltas`	an optional vector of the deltas of timestamps. If given, must be the same length as `v`. If not given, and `wts` are given and `wts_as_delta` is true, we take the `wts` as the time deltas. The deltas must be positive. We sum them to arrive at the times.
`window`	the window size, in time units. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, and `variable_win` is true, then we infer the window from the lookback times: the first window is infinite, but the remaining is the deltas between lookback times. If `variable_win` is false, then these undefined values are equivalent to an infinite window. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`na_rm`	whether to remove NA, false by default.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned. This can be used to prevent e.g. Z-scores from being computed on only 3 observations. Defaults to zero, meaning no restriction, which can result in infinite Z-scores during the burn-in period.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the centered moments computation. These are subtracted from the number of observations.
`lookahead`	for some of the operations, the value is compared to mean and standard deviation possibly using 'future' or 'past' information by means of a non-zero lookahead. Positive values mean data are taken from the future. This is in time units, and so should be a real.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though less accurate results.
`variable_win`	if true, and the `window` is not a concrete number, the computation window becomes the time between lookback times.
`wts_as_delta`	if true and the `time` and `time_deltas` are not given, but `wts` are given, we take `wts` as the `time_deltas`.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.
`check_negative_moments`	a boolean flag. Normal computation of running moments can result in negative estimates of even order moments due to loss of numerical precision. With this flag active, the computation checks for negative even order moments and restarts the computation when one is detected. This should eliminate the possibility of negative even order moments. The downside is the speed hit of checking on every output step. Note also the code checks for negative moments of every even order tracked, even if they are not output; that is if the kurtosis, say, is being computed, and a negative variance is detected, then the computation is restarted. Defaults to `TRUE` to avoid negative even moments. Set to `FALSE` only if you know what you are doing.
`lb_time`	a vector of the times from which lookback will be performed. The output should be the same size as this vector. If not given, defaults to `time`.
`compute_se`	for `running_sharpe`, return an extra column of the standard error, as computed by Mertens' correction.

Details

Given the length $n$ vector $x$ , for a given index $i$ , define $x^{(i)}$ as the elements of $x$ defined by the sliding time window (see the section on time windowing). Then define $\mu_i$ , $\sigma_i$ and $n_i$ as, respectively, the sample mean, standard deviation and number of non-NA elements in $x^{(i)}$ .

We also allow a 'lookahead' for some of these operations. If positive, the moments are computed using data from larger indices; if negative, from smaller indices.

Value

a vector the same size as the input consisting of the adjusted version of the input. When there are not sufficient (non-nan) elements for the computation, NaN are returned.

Time Windowing

$t_1 \le t_2 \le \ldots$

$b_j - W < t_i \le b_j.$

$t_j - W + L < t_i \le t_j + L.$

If the times are not given, ‘deltas’ may be given instead. If $\delta_i$ are the deltas, then we compute the times as

$t_i = \sum_{1 \le j \le i} \delta_j.$

The deltas must be the same length as $x$ . If times and deltas are not given, but weights are given and the ‘weights as deltas’ flag is set true, then the weights are used as the deltas.

Some times it makes sense to have the computational window be the space between lookback times. That is, the $j$ th output is to be computed over indices $i$ such that

$b_{j-1} - W < t_i \le b_j.$

Note

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Compute covariance, correlation, regression over a sliding time-based window

Description

Computes 2nd moments and comoments, as well as the means, over an infinite or finite sliding time based window, returning a matrix with the correlation, covariance, regression coefficient, and so on.

Usage

t_running_correlation(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

t_running_covariance(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_covariance_3(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_regression_slope(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

t_running_regression_intercept(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

t_running_regression_fit(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

t_running_regression_diagnostics(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 2,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)
t_running_correlation(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

t_running_covariance(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_covariance_3(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_regression_slope(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

t_running_regression_intercept(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

t_running_regression_fit(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  check_negative_moments = TRUE
)

t_running_regression_diagnostics(
  x,
  y,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 2,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

Arguments

`x`	a vector
`y`	a vector
`time`	an optional vector of the timestamps of `x` and `y`. If given, must be the same length as `x` and `y`. If not given, we try to infer it by summing the `time_deltas`.
`time_deltas`	an optional vector of the deltas of timestamps. If given, must be the same length as `x` and `y`. If not given, and `wts` are given and `wts_as_delta` is true, we take the `wts` as the time deltas. The deltas must be positive. We sum them to arrive at the times.
`window`	the window size, in time units. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, and `variable_win` is true, then we infer the window from the lookback times: the first window is infinite, but the remaining is the deltas between lookback times. If `variable_win` is false, then these undefined values are equivalent to an infinite window. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`lb_time`	a vector of the times from which lookback will be performed. The output should be the same size as this vector. If not given, defaults to `time`.
`na_rm`	whether to remove NA, false by default.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned. This can be used to prevent moments from being computed on too few observations. Defaults to zero, meaning no restriction.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though less accurate results.
`variable_win`	if true, and the `window` is not a concrete number, the computation window becomes the time between lookback times.
`wts_as_delta`	if true and the `time` and `time_deltas` are not given, but `wts` are given, we take `wts` as the `time_deltas`.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`check_negative_moments`	a boolean flag. Normal computation of running moments can result in negative estimates of even order moments due to loss of numerical precision. With this flag active, the computation checks for negative even order moments and restarts the computation when one is detected. This should eliminate the possibility of negative even order moments. The downside is the speed hit of checking on every output step. Note also the code checks for negative moments of every even order tracked, even if they are not output; that is if the kurtosis, say, is being computed, and a negative variance is detected, then the computation is restarted. Defaults to `TRUE` to avoid negative even moments. Set to `FALSE` only if you know what you are doing.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the standard errors computation. These are subtracted from the number of observations.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.

Details

Computes the correlation or covariance, or OLS regression coefficients and standard errors. These are computed via the numerically robust one-pass method of Bennett et. al.

Value

Typically a matrix, usually only one row of the output value. More specifically:

running_covariance: Returns a single column of the covariance of x and y.
running_correlation: Returns a single column of the correlation of x and y.
running_covariance_3: Returns three columns: the variance of x, the covariance of x and y, and the variance of y, in that order.
running_regression_slope: Returns a single column of the slope of the OLS regression.
running_regression_intercept: Returns a single column of the intercept of the OLS regression.
running_regression_fit: Returns two columns: the regression intercept and the regression slope of the OLS regression.
running_regression_diagnostics: Returns five columns: the regression intercept, the regression slope, the regression standard error, the standard error of the intercept, the standard error of the slope of the OLS regression.

Time Windowing

$t_1 \le t_2 \le \ldots$

$b_j - W < t_i \le b_j.$

$t_j - W + L < t_i \le t_j + L.$

If the times are not given, ‘deltas’ may be given instead. If $\delta_i$ are the deltas, then we compute the times as

$t_i = \sum_{1 \le j \le i} \delta_j.$

The deltas must be the same length as $x$ . If times and deltas are not given, but weights are given and the ‘weights as deltas’ flag is set true, then the weights are used as the deltas.

Some times it makes sense to have the computational window be the space between lookback times. That is, the $j$ th output is to be computed over indices $i$ such that

$b_{j-1} - W < t_i \le b_j.$

Note

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples

x <- rnorm(1e5)
y <- rnorm(1e5) + x
rho <- t_running_correlation(x, y, time=seq_along(x), window=100L)

x <- rnorm(1e5)
y <- rnorm(1e5) + x
rho <- t_running_correlation(x, y, time=seq_along(x), window=100L)

Compute first K moments over a sliding time-based window

Description

Compute the (standardized) 2nd through kth moments, the mean, and the number of elements over an infinite or finite sliding time based window, returning a matrix.

Usage

t_running_sd3(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_skew4(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_kurt5(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_sd(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_skew(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_kurt(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_cent_moments(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  max_order = 5L,
  na_rm = FALSE,
  max_order_only = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_std_moments(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_cumulants(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)
t_running_sd3(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_skew4(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_kurt5(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_sd(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_skew(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_kurt(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 1,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_cent_moments(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  max_order = 5L,
  na_rm = FALSE,
  max_order_only = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_std_moments(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

t_running_cumulants(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  max_order = 5L,
  na_rm = FALSE,
  min_df = 0L,
  used_df = 0,
  restart_period = 100L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE,
  normalize_wts = TRUE,
  check_negative_moments = TRUE
)

Arguments

`v`	a vector of data.
`time`	an optional vector of the timestamps of `v`. If given, must be the same length as `v`. If not given, we try to infer it by summing the `time_deltas`.
`time_deltas`	an optional vector of the deltas of timestamps. If given, must be the same length as `v`. If not given, and `wts` are given and `wts_as_delta` is true, we take the `wts` as the time deltas. The deltas must be positive. We sum them to arrive at the times.
`window`	the window size, in time units. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, and `variable_win` is true, then we infer the window from the lookback times: the first window is infinite, but the remaining is the deltas between lookback times. If `variable_win` is false, then these undefined values are equivalent to an infinite window. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`lb_time`	a vector of the times from which lookback will be performed. The output should be the same size as this vector. If not given, defaults to `time`.
`na_rm`	whether to remove NA, false by default.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned. This can be used to prevent moments from being computed on too few observations. Defaults to zero, meaning no restriction.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the centered moments computation. These are subtracted from the number of observations.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though less accurate results.
`variable_win`	if true, and the `window` is not a concrete number, the computation window becomes the time between lookback times.
`wts_as_delta`	if true and the `time` and `time_deltas` are not given, but `wts` are given, we take `wts` as the `time_deltas`.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.
`check_negative_moments`	a boolean flag. Normal computation of running moments can result in negative estimates of even order moments due to loss of numerical precision. With this flag active, the computation checks for negative even order moments and restarts the computation when one is detected. This should eliminate the possibility of negative even order moments. The downside is the speed hit of checking on every output step. Note also the code checks for negative moments of every even order tracked, even if they are not output; that is if the kurtosis, say, is being computed, and a negative variance is detected, then the computation is restarted. Defaults to `TRUE` to avoid negative even moments. Set to `FALSE` only if you know what you are doing.
`max_order`	the maximum order of the centered moment to be computed.
`max_order_only`	for `running_cent_moments`, if this flag is set, only compute the maximum order centered moment, and return in a vector.

Details

Given the length $n$ vector $x$ , we output matrix $M$ where $M_{i,j}$ is the $order - j + 1$ moment (i.e. excess kurtosis, skewness, standard deviation, mean or number of elements) of some elements $x_i$ defined by the sliding time window. Barring NA or NaN, this is over a window of time width window.

Value

t_running_cent_moments: Computes arbitrary order centered moments. When max_order_only is set, only a column of the maximum order centered moment is returned.
t_running_std_moments: Computes arbitrary order standardized moments, then the standard deviation, the mean, and the count. There is not yet an option for max_order_only, but probably should be.
t_running_cumulants: Computes arbitrary order cumulants, and returns the kth, k-1th, through the second (which is the variance) cumulant, then the mean, and the count.

Time Windowing

$t_1 \le t_2 \le \ldots$

$b_j - W < t_i \le b_j.$

$t_j - W + L < t_i \le t_j + L.$

If the times are not given, ‘deltas’ may be given instead. If $\delta_i$ are the deltas, then we compute the times as

$t_i = \sum_{1 \le j \le i} \delta_j.$

The deltas must be the same length as $x$ . If times and deltas are not given, but weights are given and the ‘weights as deltas’ flag is set true, then the weights are used as the deltas.

Some times it makes sense to have the computational window be the space between lookback times. That is, the $j$ th output is to be computed over indices $i$ such that

$b_{j-1} - W < t_i \le b_j.$

Note

the kurtosis is excess kurtosis, with a 3 subtracted, and should be nearly zero for Gaussian input.

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Examples

x <- rnorm(1e5)
xs3 <- t_running_sd3(x,time=seq_along(x),window=10)
xs4 <- t_running_skew4(x,time=seq_along(x),window=10)
# but what if you only cared about some middle values?
xs4 <- t_running_skew4(x,time=seq_along(x),lb_time=(length(x) / 2) + 0:10,window=20)

x <- rnorm(1e5)
xs3 <- t_running_sd3(x,time=seq_along(x),window=10)
xs4 <- t_running_skew4(x,time=seq_along(x),window=10)
# but what if you only cared about some middle values?
xs4 <- t_running_skew4(x,time=seq_along(x),lb_time=(length(x) / 2) + 0:10,window=20)

Compute sums or means over a sliding time window.

Description

Compute the mean or sum over an infinite or finite sliding time window, returning a vector the same size as the lookback times.

Usage

t_running_sum(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 10000L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE
)

t_running_mean(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 10000L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE
)
t_running_sum(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 10000L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE
)

t_running_mean(
  v,
  time = NULL,
  time_deltas = NULL,
  window = NULL,
  wts = NULL,
  lb_time = NULL,
  na_rm = FALSE,
  min_df = 0L,
  restart_period = 10000L,
  variable_win = FALSE,
  wts_as_delta = TRUE,
  check_wts = FALSE
)

Arguments

`v`	a vector.
`time`	an optional vector of the timestamps of `v`. If given, must be the same length as `v`. If not given, we try to infer it by summing the `time_deltas`.
`time_deltas`	an optional vector of the deltas of timestamps. If given, must be the same length as `v`. If not given, and `wts` are given and `wts_as_delta` is true, we take the `wts` as the time deltas. The deltas must be positive. We sum them to arrive at the times.
`window`	the window size, in time units. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, and `variable_win` is true, then we infer the window from the lookback times: the first window is infinite, but the remaining is the deltas between lookback times. If `variable_win` is false, then these undefined values are equivalent to an infinite window. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`lb_time`	a vector of the times from which lookback will be performed. The output should be the same size as this vector. If not given, defaults to `time`.
`na_rm`	whether to remove NA, false by default.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned, only for the means computation. This can be used to prevent moments from being computed on too few observations. Defaults to zero, meaning no restriction.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though potentially less accurate results. Unlike in the computation of even order moments, loss of precision is unlikely to be disastrous, so the default value is rather large.
`variable_win`	if true, and the `window` is not a concrete number, the computation window becomes the time between lookback times.
`wts_as_delta`	if true and the `time` and `time_deltas` are not given, but `wts` are given, we take `wts` as the `time_deltas`.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.

Details

Computes the mean or sum of the elements, using a Kahan's Compensated Summation Algorithm, a numerically robust one-pass method.

Given the length $n$ vector $x$ , we output matrix $M$ where $M_{i,1}$ is the sum or mean of some elements $x_i$ defined by the sliding time window. Barring NA or NaN, this is over a window of time width window.

Value

A vector the same size as the lookback times.

Time Windowing

$t_1 \le t_2 \le \ldots$

$b_j - W < t_i \le b_j.$

$t_j - W + L < t_i \le t_j + L.$

If the times are not given, ‘deltas’ may be given instead. If $\delta_i$ are the deltas, then we compute the times as

$t_i = \sum_{1 \le j \le i} \delta_j.$

The deltas must be the same length as $x$ . If times and deltas are not given, but weights are given and the ‘weights as deltas’ flag is set true, then the weights are used as the deltas.

Some times it makes sense to have the computational window be the space between lookback times. That is, the $j$ th output is to be computed over indices $i$ such that

$b_{j-1} - W < t_i \le b_j.$

Note

Author(s)

Steven E. Pav [email protected]

References

Terriberry, T. "Computing Higher-Order Moments Online." https://web.archive.org/web/20140423031833/http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. doi:10.1109/CLUSTR.2009.5289161

Cook, J. D. "Accurately computing running variance." https://www.johndcook.com/standard_deviation/

Cook, J. D. "Comparing three methods of computing standard deviation." https://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/

Kahan, W. "Further remarks on reducing truncation errors," Communications of the ACM, 8 (1), 1965. doi:10.1145/363707.363723

Wikipedia contributors "Kahan summation algorithm," Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=Kahan_summation_algorithm&oldid=777164752 (accessed May 31, 2017).

Examples

x <- rnorm(1e5)
xs <- t_running_sum(x,time=seq_along(x),window=10)
xm <- t_running_mean(x,time=cumsum(runif(length(x))),window=7.3)

x <- rnorm(1e5)
xs <- t_running_sum(x,time=seq_along(x),window=10)
xm <- t_running_mean(x,time=cumsum(runif(length(x))),window=7.3)

Package 'fromo'

Help Index

Fast Robust Moments.

Description

Robust Moments

Legal Mumbo Jumbo

Note

Author(s)

References

See Also

unconcatenate centsums objects.

Description

Usage

Arguments

See Also

unconcatenate centcosums objects.

Description

Usage

Arguments

See Also

Accessor methods.

Description

Usage

Arguments

Note

Author(s)

Coerce to a centcosums object.

Description

Usage

Arguments

Details

Value

Note

Author(s)

Examples

Coerce to a centsums object.

Description

Usage

Arguments

Details

Value

Note

Author(s)

Examples

concatenate centcosums objects.

Description

Usage

Arguments

See Also

concatenate centsums objects.

Description

Usage

Arguments

See Also

Multivariate centered sums; join and unjoined.

Description

Usage

Arguments

Value

Note

Author(s)

References

See Also

Examples

Centered sums; join and unjoined.

Description

Usage

Arguments

Value

Note

Author(s)

References

Examples

Convert between different types of moments, raw, central, standardized.

Description

Usage

Arguments

Note

Author(s)

Accessor methods.