Functions for computing dimension-wise grouped variances. More...

Classes
struct	Options
	Grouped summation options. More...

Functions
template<typename Value_ , typename Index_ , typename Group_ , typename Output_ >
void	direct (const Value_ ptr, Index_ num, const Group_ group, std::size_t num_groups, const Index_ group_size, Output_ output_means, Output_ output_variances, bool skip_nan, Index_ valid_group_size)

template<typename Value_ , typename Index_ , typename Group_ , typename Output_ >
void	direct (const Value_ value, const Index_ index, Index_ num_nonzero, const Group_ group, std::size_t num_groups, const Index_ group_size, Output_ output_means, Output_ output_variances, Index_ output_nonzero, bool skip_nan, Index_ valid_group_size)

template<typename Value_ , typename Index_ , typename Group_ , typename Output_ >
void	apply (bool row, const tatami::Matrix< Value_, Index_ > &mat, const Group_ group, std::size_t num_groups, const Index_ group_size, Output_ **output, const Options &sopt)

template<typename Output_ = double, typename Value_ , typename Index_ , typename Group_ >
std::vector< std::vector< Output_ > >	by_row (const tatami::Matrix< Value_, Index_ > &mat, const Group_ *group, const Options &sopt)

template<typename Output_ = double, typename Value_ , typename Index_ , typename Group_ >
std::vector< std::vector< Output_ > >	by_column (const tatami::Matrix< Value_, Index_ > &mat, const Group_ *group, const Options &sopt)

Detailed Description

Functions for computing dimension-wise grouped variances.

Function Documentation

◆ direct() [1/2]

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

void tatami_stats::grouped_variances::direct	(	const Value_ *	ptr,
		Index_	num,
		const Group_ *	group,
		std::size_t	num_groups,
		const Index_ *	group_size,
		Output_ *	output_means,
		Output_ *	output_variances,
		bool	skip_nan,
		Index_ *	valid_group_size )

Compute the mean and variance from a dense objective vector. This uses the standard two-pass algorithm with naive accumulation of the sum of squared differences; thus, it is best used with a sufficiently high-precision Output_ like double.

Template Parameters

Value_	Type of the input data.
Index_	Integer type of the matrix indices.
Group_	Integer type of the group assignments.
Output_	Type of the output data.

Parameters

[in]	ptr	Pointer to an array of values of length `num`.
	num	Length of the objective vector, i.e., length of the array at `ptr`.
[in]	group	Pointer to an array of length `num`, containing the group assignment for each entry of `ptr`. Entries of `group` should lie in \([0, N)\) where \(N\) is the number of unique groups.
	num_groups	Number of groups, i.e., \(N\).
[in]	group_size	Pointer to an array of length `num_groups`, containing the size of each group. This can be obtained by calling `tabulate_groups()` on `group`.
[out]	output_means	Pointer to an array of length `num_groups`. This is filled with the per-group mean on output. Values may be NaN if there are not enough (non-NaN) values in a group.
[out]	output_variances	Pointer to an array of length `num_groups`. This is filled with the per-group variances on output. Values may be NaN if there are not enough (non-NaN) values in a group.
	skip_nan	See `Options::skip_nan`.
[out]	valid_group_size	Pointer to an array of length `num_groups`. This is used to store the number of non-NaN entries. Only used if `skip_nan = true`.

◆ direct() [2/2]

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

void tatami_stats::grouped_variances::direct	(	const Value_ *	value,
		const Index_ *	index,
		Index_	num_nonzero,
		const Group_ *	group,
		std::size_t	num_groups,
		const Index_ *	group_size,
		Output_ *	output_means,
		Output_ *	output_variances,
		Index_ *	output_nonzero,
		bool	skip_nan,
		Index_ *	valid_group_size )

Compute the mean and variance from a sparse objective vector. This uses the standard two-pass algorithm with naive accumulation of the sum of squared differences; thus, it is best used with a sufficiently high-precision Output_ like double.

Template Parameters

Value_	Type of the input data.
Index_	Integer type of the matrix indices.
Group_	Integer type of the group assignments.
Output_	Type of the output data.

Parameters

[in]	value	Pointer to an array of length `num_nonzero`, containing the values of the structural non-zeros.
[in]	index	Pointer to an array of length `num_nonzero`, containing the indices of the structural non-zeros. All indices should be non-negative and less than the length of the objective vector.
	num_nonzero	Number of structural non-zeros.
[in]	group	Pointer to an array of length equal to the length of the objective vector, containing the group assignment for each vector element. Entries of `group` should lie in \([0, N)\) where \(N\) is the number of unique groups.
	num_groups	Number of groups, i.e., \(N\).
[in]	group_size	Pointer to an array of length `num_groups`, containing the size of each group. This can be obtained by calling `tabulate_groups()` on `group`.
[out]	output_means	Pointer to an array of length `num_groups`. This is filled with the per-group mean on output. Values may be NaN if there are not enough (non-NaN) values in a group.
[out]	output_variances	Pointer to an array of length `num_groups`. This is filled with the per-group variances on output. Values may be NaN if there are not enough (non-NaN) values in a group.
[out]	output_nonzero	Pointer to an array of length `num_groups`. On output, this is filled with the number of structural non-zeros in each group.
	skip_nan	See `Options::skip_nan`.
[out]	valid_group_size	Pointer to an array of length `num_groups`. This is used to store the number of non-NaN entries. Only used if `skip_nan = true`.

◆ apply()

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

void tatami_stats::grouped_variances::apply	(	bool	row,
		const tatami::Matrix< Value_, Index_ > &	mat,
		const Group_ *	group,
		std::size_t	num_groups,
		const Index_ *	group_size,
		Output_ **	output,
		const Options &	sopt )

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

Template Parameters

Value_	Type of the matrix value, should be numeric.
Index_	Type of the row/column indices.
Group_	Type of the group assignments for each column.
Output_	Type of the output value. This should be floating-point to store potential averages.

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., \(N\). This can be determined by calling `tatami_stats::total_groups()` on `group`.
[in]	group_size	Pointer to an array of length `num_groups`, containing the size of each group.
[out]	output	Pointer to an array of pointers of length equal to the number of groups. Each inner pointer should reference an array of length equal to the number of rows (if `row = true`) or columns (otherwise). On output, this will contain the row/column variances for each group (indexed according to the assignment in `group`).
	sopt	Summation options.

◆ by_row()

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

std::vector< std::vector< Output_ > > tatami_stats::grouped_variances::by_row	(	const tatami::Matrix< Value_, Index_ > &	mat,
		const Group_ *	group,
		const Options &	sopt )

Wrapper around apply() for row-wise grouped variances.

Template Parameters

Output_	Type of the output.
Value_	Type of the matrix value.
Index_	Type of the row/column indices.
Group_	Type of the group assignments for each row.

Parameters

	mat	Instance of a `tatami::Matrix`.
[in]	group	Pointer to an array of length equal to the number of columns. 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.
	sopt	Summation options.

Returns: Vector of length equal to the number of groups. Each entry is a vector of length equal to the number of rows, containing the row-wise variances for the corresponding group.

◆ by_column()

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

std::vector< std::vector< Output_ > > tatami_stats::grouped_variances::by_column	(	const tatami::Matrix< Value_, Index_ > &	mat,
		const Group_ *	group,
		const Options &	sopt )

Wrapper around apply() for column-wise grouped variances.

Template Parameters

Output_	Type of the output.
Value_	Type of the matrix value.
Index_	Type of the column/column indices.
Group_	Type of the group assignments for each column.

Parameters

	mat	Instance of a `tatami::Matrix`.
[in]	group	Pointer to an array of length equal to the number of rows. 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.
	sopt	Summation options.

Returns: Vector of length equal to the number of groups. Each entry is a vector of length equal to the number of columns, containing the column-wise variances for the corresponding group.

Classes

Functions

Detailed Description

Function Documentation

◆ direct() [1/2]

◆ direct() [2/2]

◆ apply()

◆ by_row()

◆ by_column()