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	RangeBuffers
	Result buffers for `skip_nan::range()`. More...

struct	RangeOptions
	Options for `range()`. More...

struct	RangeResult
	Results of `skip_nan::range()`. 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 Group_ num_groups, const GroupRssBuffers< Output_, Count_ > &output, const GroupRssOptions< Output_ > &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 Group_ num_groups, const GroupRssOptions< Output_ > &opt)

template<typename Output_ >
constexpr Output_	default_minimum_placeholder ()

template<typename Output_ >
constexpr Output_	default_maximum_placeholder ()

template<typename Value_ , typename Index_ , typename Output_ , typename Count_ >
void	range (bool row, const tatami::Matrix< Value_, Index_ > &mat, RangeBuffers< Output_, Count_ > &output, const RangeOptions< Output_ > &opt)

template<typename Value_ , typename Index_ , typename Output_ = Value_, typename Count_ = Index_>
RangeResult< Output_, Count_ >	range (bool row, const tatami::Matrix< Value_, Index_ > &mat, const RangeOptions< Output_ > &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< Output_ > &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< Output_ > &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 Group_	num_groups,
		const GroupRssBuffers< Output_, Count_ > &	output,
		const GroupRssOptions< Output_ > &	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.
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 Group_	num_groups,
		const GroupRssOptions< Output_ > &	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.
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.

◆ default_minimum_placeholder()

template<typename Output_ >

Output_ tatami_stats::skip_nan::default_minimum_placeholder ( )

constexpr

Template Parameters

Output_ Numeric type of the output of skip_nan::range().

Returns: Default placeholder value for the minimum in the output of skip_nan::range(). This is positive infinity if supported by Output_, otherwise it is the largest finite value.

◆ default_maximum_placeholder()

template<typename Output_ >

Output_ tatami_stats::skip_nan::default_maximum_placeholder ( )

constexpr

Template Parameters

Output_ Numeric type of the output of skip_nan::range().

Returns: Default placeholder value for the maximum in the output of skip_nan::range(). This is negative infinity if supported by Output_, otherwise it is the smallest finite value.

◆ range() [1/2]

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

void tatami_stats::skip_nan::range	(	bool	row,
		const tatami::Matrix< Value_, Index_ > &	mat,
		RangeBuffers< Output_, Count_ > &	output,
		const RangeOptions< Output_ > &	opt )

Compute ranges for each element of a chosen dimension of a tatami::Matrix, after skipping any NaNs.

Template Parameters

Value_	Numeric type of the input data.
Index_	Integer type of the row/column indices.
Output_	Numeric type of the output data. It is assumed that this is large enough to store the maxima/minima.
Count_	Numeric type of the non-NaN counts. This is typically an integer type.

Parameters

	row	Whether to compute the range for each row. If false, the range 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 variances.
	opt	Further options.

◆ range() [2/2]

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

RangeResult< Output_, Count_ > tatami_stats::skip_nan::range	(	bool	row,
		const tatami::Matrix< Value_, Index_ > &	mat,
		const RangeOptions< Output_ > &	opt )

Overload of skip_nan::range() that allocates memory for the minimum/maximum.

Template Parameters

Value_	Numeric type of the input data.
Index_	Integer type of the row/column indices.
Output_	Numeric type of the output data. It is assumed that this is large enough to store the maxima/minima.

Parameters

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

Returns: Minimum and maximum 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< Output_ > &	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.
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< Output_ > &	opt )

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

Template Parameters

Output_	Floating-point type of the output data.
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.

Classes

Functions

Detailed Description

Function Documentation

◆ group_rss() [1/2]

◆ group_rss() [2/2]

◆ default_minimum_placeholder()

◆ default_maximum_placeholder()

◆ range() [1/2]

◆ range() [2/2]

◆ rss() [1/2]

◆ rss() [2/2]