tatami_stats::variances Namespace Reference

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

Classes
struct	Options
	Variance calculation options. More...
class	RunningDense
	Running variances from dense data. More...
class	RunningSparse
	Running variances from sparse data. More...

Functions
template<typename Output_ = double, typename Value_ , typename Index_ >
std::pair< Output_, Output_ >	direct (const Value_ *value, Index_ num_nonzero, Index_ num_all, bool skip_nan)
template<typename Output_ = double, typename Value_ , typename Index_ >
std::pair< Output_, Output_ >	direct (const Value_ *ptr, Index_ num, bool skip_nan)
template<typename Value_ , typename Index_ , typename Output_ >
void	apply (bool row, const tatami::Matrix< Value_, Index_ > &mat, Output_ *output, const Options &vopt)
template<typename Output_ = double, typename Value_ , typename Index_ >
std::vector< Output_ >	by_column (const tatami::Matrix< Value_, Index_ > &mat, const Options &vopt)
template<typename Output_ = double, typename Value_ , typename Index_ >
std::vector< Output_ >	by_row (const tatami::Matrix< Value_, Index_ > &mat, const Options &vopt)

Detailed Description

Functions for computing dimension-wise variances.

Function Documentation

direct() [1/2]

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

std::pair< Output_, Output_ > tatami_stats::variances::direct	(	const Value_ *	value,
		Index_	num_nonzero,
		Index_	num_all,
		bool	skip_nan )

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

Output_	Type of the output data.
Value_	Type of the input data.
Index_	Type of the row/column indices.

Parameters

[in]	value	Pointer to an array of length num, containing the values of the structural non-zeros.
	num_nonzero	Length of the array pointed to by value.
	num_all	Length of the objective vector, including the structural zeros not in value. This should be greater than or equal to num_nonzero.
	skip_nan	See Options::skip_nan.

Returns

The sample mean and variance of values in the sparse array. This may be NaN if there are not enough (non-NaN) values in value.

direct() [2/2]

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

std::pair< Output_, Output_ > tatami_stats::variances::direct	(	const Value_ *	ptr,
		Index_	num,
		bool	skip_nan )

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

Output_	Type of the output data.
Value_	Type of the input data.
Index_	Type of the row/column indices.

Parameters

[in]	ptr	Pointer to an array of length num, containing the values of the objective vector.
	num	Length of the objective vector, i.e., length of the array at ptr.
	skip_nan	See Options::skip_nan.

Returns

The sample mean and variance of values in [ptr, ptr + num). This may be NaN if there are not enough (non-NaN) values in ptr.

apply()

template<typename Value_ , typename Index_ , typename Output_ >

void tatami_stats::variances::apply	(	bool	row,
		const tatami::Matrix< Value_, Index_ > &	mat,
		Output_ *	output,
		const Options &	vopt )

Compute variances for each element of a chosen dimension of a tatami::Matrix. 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_	Type of the matrix value, should be numeric.
Index_	Type of the row/column indices.
Output_	Type of the output value.

Parameters

	row	Whether to compute the variance for each row. If false, the variance is computed for each column instead.
	mat	Instance of a tatami::Matrix.
[out]	output	Pointer to 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.
	vopt	Variance calculation options.

by_column()

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

std::vector< Output_ > tatami_stats::variances::by_column	(	const tatami::Matrix< Value_, Index_ > &	mat,
		const Options &	vopt )

Wrapper around apply() for column variances.

Template Parameters

Output_	Type of the output value.
Value_	Type of the matrix value, should be numeric.
Index_	Type of the row/column indices.

Parameters

mat	Instance of a tatami::Matrix.
vopt	Variance calculation options.

Returns

A vector of length equal to the number of columns, containing the column variances.

by_row()

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

std::vector< Output_ > tatami_stats::variances::by_row	(	const tatami::Matrix< Value_, Index_ > &	mat,
		const Options &	vopt )

Wrapper around apply() for column variances.

Template Parameters

Output_	Type of the output value.
Value_	Type of the matrix value, should be numeric.
Index_	Type of the row/column indices.

Parameters

mat	Instance of a tatami::Matrix.
vopt	Variance calculation options.

Returns

A vector of length equal to the number of rows, containing the row variances.