yat
0.11.3pre
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Statistical methods, classes, and functions. More...
Classes | |
class | AUC |
Area Under ROC Curve. More... | |
class | averager_base |
Base class for averager classes. More... | |
class | averager_base2 |
Base class for averagers calculating mean and variance. More... | |
class | averager_base3 |
class | averager_base4 |
struct | Average |
Functor to take average of a range. More... | |
class | Averager |
Class to calculate simple (first and second moments) averages. More... | |
class | Averager1 |
class to calculate mean More... | |
class | Averager3 |
class to calculate 1st, 2nd, and 3rd central moments More... | |
class | Averager4 |
class to calculate 1st, 2nd, 3rd, and 4th central moments 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 | Kendall |
Kendall's tau rank coefficient. More... | |
class | KolmogorovSmirnov |
Kolmogorov Smirnov Test. More... | |
class | KolmogorovSmirnovOneSample |
Kolmogorov Smirnov Test for one class. 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 | Spearman |
Spearman rank correlation coefficient. 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:
double theplu::yat::statistics::percentile | ( | RandomAccessIterator | first, |
RandomAccessIterator | last, | ||
double | p, | ||
bool | sorted = false |
||
) |
The percentile is determined by the p, a number between 0 and
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).
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.