tatami_stats::skip_nan Namespace Reference

Compute statistics from a tatami::Matrix while skipping NaNs. More...

Classes
struct	GroupRssBuffers
	Result buffers for skip_nan::group_rss(). More...
struct	GroupRssOptions
	Options for skip_nan::group_rss(). More...
struct	GroupRssResult
	Results of skip_nan::group_rss(). More...
struct	RssBuffers
	Result buffers for skip_nan::rss(). More...
struct	RssOptions
	Options for skip_nan::rss(). More...
struct	RssResult
	Results of skip_nan::rss(). More...

Functions
template<typename Value_ , typename Index_ , typename Group_ , typename Output_ , typename Count_ >
void	group_rss (bool row, const tatami::Matrix< Value_, Index_ > &mat, const Group_ *const group, const std::size_t num_groups, GroupRssBuffers< Output_, Count_ > &output, const GroupRssOptions &opt)
template<typename Output_ , typename Count_ , typename Value_ , typename Index_ , typename Group_ >
GroupRssResult< Output_, Count_ >	group_rss (bool row, const tatami::Matrix< Value_, Index_ > &mat, const Group_ *const group, const std::size_t num_groups, const GroupRssOptions &opt)
template<typename Value_ , typename Index_ , typename Output_ , typename Count_ >
void	rss (bool row, const tatami::Matrix< Value_, Index_ > &mat, RssBuffers< Output_, Count_ > &output, const RssOptions &opt)
template<typename Output_ = double, typename Count_ , typename Value_ , typename Index_ >
RssResult< Output_, Count_ >	rss (bool row, const tatami::Matrix< Value_, Index_ > &mat, const RssOptions &opt)

Detailed Description

Compute statistics from a tatami::Matrix while skipping NaNs.

Function Documentation

group_rss() [1/2]

template<typename Value_ , typename Index_ , typename Group_ , typename Output_ , typename Count_ >

void tatami_stats::skip_nan::group_rss	(	bool	row,
		const tatami::Matrix< Value_, Index_ > &	mat,
		const Group_ *const	group,
		const std::size_t	num_groups,
		GroupRssBuffers< Output_, Count_ > &	output,
		const GroupRssOptions &	opt )

Compute per-group variances for each element of a chosen dimension of a tatami::Matrix.

Template Parameters

Value_	Numeric type of the matrix value.
Index_	Integer type of the row/column indices.
Group_	Integer type of the group assignments for each row/column.
Output_	Floating-point type of the output value. This should be capable of storing NaNs.
Count_	Numeric type of the non-NaN counts. This is typically an integer type.

Parameters

	row	Whether to compute variances for the rows.
	mat	Instance of a tatami::Matrix.
[in]	group	Pointer to an array of length equal to the number of columns (if row = true) or rows (otherwise). Each value should be an integer that specifies the group assignment. Values should lie in \([0, N)\) where \(N\) is the number of unique groups.
	num_groups	Number of groups, i.e., \(N\).
[out]	output	Buffers in which to store the results. On output, each array stores the means and variances of the corresponding group.
	opt	Further options.

group_rss() [2/2]

template<typename Output_ , typename Count_ , typename Value_ , typename Index_ , typename Group_ >

GroupRssResult< Output_, Count_ > tatami_stats::skip_nan::group_rss	(	bool	row,
		const tatami::Matrix< Value_, Index_ > &	mat,
		const Group_ *const	group,
		const std::size_t	num_groups,
		const GroupRssOptions &	opt )

Compute per-group variances for each element of a chosen dimension of a tatami::Matrix.

Template Parameters

Output_	Floating-point type of the output value. This should be capable of storing NaNs.
Count_	Numeric type of the non-NaN counts. This is typically an integer type.
Value_	Numeric type of the matrix value.
Index_	Integer type of the row/column indices.
Group_	Integer type of the group assignments for each row/column.

Parameters

	row	Whether to compute variances for the rows.
	mat	Instance of a tatami::Matrix.
[in]	group	Pointer to an array of length equal to the number of columns (if row = true) or rows (otherwise). Each value should be an integer that specifies the group assignment. Values should lie in \([0, N)\) where \(N\) is the number of unique groups.
	num_groups	Number of groups, i.e., \(N\).
	opt	Further options.

Returns

RSS and mean of each group for each row/column.

rss() [1/2]

template<typename Value_ , typename Index_ , typename Output_ , typename Count_ >

void tatami_stats::skip_nan::rss	(	bool	row,
		const tatami::Matrix< Value_, Index_ > &	mat,
		RssBuffers< Output_, Count_ > &	output,
		const RssOptions &	opt )

Compute residual sums of squares (RSS) for each element of a chosen dimension of a tatami::Matrix, after skipping any NaNs. This may use either Welford's method or the standard two-pass method, depending on the dimension in row and the preferred access dimension of p.

Template Parameters

Value_	Numeric type of the input data.
Index_	Integer type of the row/column indices.
Output_	Floating-point type of the output data. This should be capable of storing NaNs.
Count_	Numeric type of the non-NaN counts. This is typically an integer type.

Parameters

	row	Whether to compute the RSS for each row. If false, the RSS is computed for each column instead.
	mat	Instance of a tatami::Matrix.
[out]	output	Buffers to output arrays. On output, this will contain the row/column RSSs.
	opt	Further options.

rss() [2/2]

template<typename Output_ = double, typename Count_ , typename Value_ , typename Index_ >

RssResult< Output_, Count_ > tatami_stats::skip_nan::rss	(	bool	row,
		const tatami::Matrix< Value_, Index_ > &	mat,
		const RssOptions &	opt )

Overload of skip_nan::rss() that allocates memory for the output arrays.

Template Parameters

Output_	Floating-point type of the output data. This should be capable of storing NaNs.
Count_	Numeric type of the non-NaN counts. This is typically an integer type.
Value_	Numeric type of the input data.
Index_	Integer type of the row/column indices.

Parameters

row	Whether to compute the RSS for each row. If false, the RSS is computed for each column instead.
mat	Instance of a tatami::Matrix.
opt	Further options.

Returns

The mean and RSS of each row/column.