Classes | |
class | AUC |
Class calculating 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 |
Class for 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 | 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) |
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. |
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 | |||
) | [inline] |
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.
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 | |||
) | [inline] |
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 | |||
) | [inline] |
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 | |||
) | [inline] |
The percentile is determined by the 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).
double theplu::yat::statistics::percentile2 | ( | RandomAccessIterator | first, | |
RandomAccessIterator | last, | |||
double | p, | |||
bool | sorted = false | |||
) | [inline] |
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.