yat
0.8.3pre
|
Statistical methods, classes, and functions. More...
Classes | |
class | AUC |
Area Under ROC Curve. More... | |
struct | Average |
Functor to take average of a range. More... | |
class | Averager |
Class to calculate simple (first and second moments) averages. More... | |
class | AveragerPair |
Class for taking care of mean and covariance of two variables. More... | |
class | AveragerWeighted |
Class to calulate averages with weights. More... | |
class | AveragerPairWeighted |
Class for taking care of mean and covariance of two variables in a weighted manner. More... | |
struct | averager_traits |
struct | averager_traits< utility::unweighted_iterator_tag > |
struct | averager_traits< utility::weighted_iterator_tag > |
struct | averager |
struct | averager_pair |
struct | EuclideanDistance |
Calculates the Euclidean distance between two points given by elements of ranges. More... | |
class | Fisher |
Fisher's exact test. More... | |
class | FoldChange |
Score given by the difference by the group means. More... | |
class | Histogram |
Histograms provide a convenient way of presenting the distribution of a set of data. More... | |
class | KolmogorovSmirnov |
Kolmogow Smirnov Test. More... | |
class | Pearson |
Class for calculating Pearson correlation. More... | |
class | PearsonCorrelation |
Class for calculating Pearson correlation. More... | |
struct | PearsonDistance |
Calculates the Pearson correlation distance between two points given by elements of ranges. More... | |
class | Percentiler |
Functor to calculate percentile of a range. More... | |
class | ROC |
Reciever Operating Characteristic. More... | |
class | SAMScore |
Class for score used in Significance Analysis of Microarrays (SAM). More... | |
class | Score |
Interface Class for score classes. More... | |
class | Smoother |
Estimating a distribution in a smooth fashion. More... | |
class | SNRScore |
Class for score based on signal-to-noise ratio (SNRScore). More... | |
class | TukeyBiweightEstimator |
Tukey's Biweight Estimator. More... | |
class | tScore |
Class for Fisher's t-test. More... | |
class | tTest |
Class for Student's t-test. More... | |
struct | VectorFunction |
Interface Class for vector functors. More... | |
struct | Max |
Larget element. More... | |
struct | Median |
Median element. More... | |
struct | Mean |
Mean element. More... | |
struct | Min |
Smallest element. More... | |
class | Nth_Element |
class | WilcoxonFoldChange |
WilcoxonFoldChange. More... | |
Functions | |
template<typename T , typename ForwardIterator > | |
void | add (T &o, ForwardIterator first, ForwardIterator last, const classifier::Target &target) |
template<typename BidirectionalIterator1 , typename BidirectionalIterator2 > | |
void | benjamini_hochberg (BidirectionalIterator1 first, BidirectionalIterator1 last, BidirectionalIterator2 result) |
Benjamini Hochberg multiple test correction. | |
double | cdf_hypergeometric_P (unsigned int k, unsigned int n1, unsigned int n2, unsigned int t) |
double | pearson_p_value (double r, unsigned int n) |
one-sided p-value | |
double | kurtosis (const utility::VectorBase &) |
Computes the kurtosis of the data in a vector. | |
template<class RandomAccessIterator > | |
double | mad (RandomAccessIterator first, RandomAccessIterator last, bool sorted=false) |
Median absolute deviation from median. | |
template<class RandomAccessIterator > | |
double | median (RandomAccessIterator first, RandomAccessIterator last, bool sorted=false) |
template<class RandomAccessIterator > | |
double | percentile (RandomAccessIterator first, RandomAccessIterator last, double p, bool sorted=false) |
template<class RandomAccessIterator > | |
double | percentile2 (RandomAccessIterator first, RandomAccessIterator last, double p, bool sorted=false) |
double | skewness (const utility::VectorBase &) |
Computes the skewness of the data in a vector. |
Statistical methods, classes, and functions.
All classes and functions related to statistical methods or functions are defined within this namespace. See Weighted Statistics
.
void theplu::yat::statistics::add | ( | T & | o, |
ForwardIterator | first, | ||
ForwardIterator | last, | ||
const classifier::Target & | target | ||
) |
Adding a range [first, last) into an object of type T. The requirements for the type T is to have an add(double, bool, double) function.
void theplu::yat::statistics::benjamini_hochberg | ( | BidirectionalIterator1 | first, |
BidirectionalIterator1 | last, | ||
BidirectionalIterator2 | result | ||
) |
Benjamini Hochberg multiple test correction.
Given a sorted range of p-values such that
a Benjamnini-Hochberg corrected p-value, q
, is calculated recursively as min with the anchor constraint that .
Requirements: BidirectionalIterator1
should be a Bidirectional Iterator and BidirectionalIterator2
should be a mutable Bidirectional Iterator
double theplu::yat::statistics::cdf_hypergeometric_P | ( | unsigned int | k, |
unsigned int | n1, | ||
unsigned int | n2, | ||
unsigned int | t | ||
) |
Calculates the probability to get k or smaller from a hypergeometric distribution with parameters n1 n2 t. Hypergeomtric situation you get in the following situation: Let there be n1 ways for a "good" selection and n2 ways for a "bad" selection out of a total of possibilities. Take t samples without replacement and k of those are "good" samples. k will follow a hypergeomtric distribution.
double theplu::yat::statistics::kurtosis | ( | const utility::VectorBase & | ) |
Computes the kurtosis of the data in a vector.
The kurtosis measures how sharply peaked a distribution is, relative to its width. The kurtosis is normalized to zero for a gaussian distribution.
double theplu::yat::statistics::mad | ( | RandomAccessIterator | first, |
RandomAccessIterator | last, | ||
bool | sorted = false |
||
) |
Median absolute deviation from median.
Function is non-mutable function
Requirements: RandomAccessIterator
should be a Data Iterator and Random Access Iterator
Since 0.6 function also work with a Weighted Iterator
double theplu::yat::statistics::median | ( | RandomAccessIterator | first, |
RandomAccessIterator | last, | ||
bool | sorted = false |
||
) |
Median is defined to be value in the middle. If number of values is even median is the average of the two middle values. the median value is given by p equal to 50. If sorted is false (default), the range is copied, the copy is sorted, and then used to calculate the median.
Function is a non-mutable function, i.e., first and last can be const_iterators.
Requirements: RandomAccessIterator
should be a Data Iterator and Random Access Iterator
double theplu::yat::statistics::pearson_p_value | ( | double | r, |
unsigned int | n | ||
) |
one-sided p-value
This function uses the t-distribution to calculate the one-sided p-value. Given that the true correlation is zero (Null hypothesis) the estimated correlation, r, after a transformation is t-distributed:
\return Probability that correlation is larger than \a r by chance when having \a n samples. For \a r larger or equal to 1.0, 0.0 is returned. For \a r smaller or equal to -1.0, 1.0 is returned.
double theplu::yat::statistics::percentile | ( | RandomAccessIterator | first, |
RandomAccessIterator | last, | ||
double | p, | ||
bool | sorted = false |
||
) |
The percentile is determined by the \a p, a number between 0 and 100. The percentile is found by interpolation, using the formula
where p is floor and is .Thus the minimum value of the vector is given by p equal to zero, the maximum is given by p equal to 100 and the median value is given by p equal to 50. If sorted is false (default), the vector is copied, the copy is sorted, and then used to calculate the median.
Function is a non-mutable function, i.e., first and last can be const_iterators.
Requirements: RandomAccessIterator is an iterator over a range of doubles (or any type being convertable to double).
\note the definition of percentile used here is not identical to that one used in percentile2 and Percentile. The difference is smaller for large ranges.
double theplu::yat::statistics::percentile2 | ( | RandomAccessIterator | first, |
RandomAccessIterator | last, | ||
double | p, | ||
bool | sorted = false |
||
) |
double theplu::yat::statistics::skewness | ( | const utility::VectorBase & | ) |
Computes the skewness of the data in a vector.
The skewness measures the asymmetry of the tails of a distribution.