PLearn 0.1
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#include "stats_utils.h"
#include "TMat_maths.h"
#include "pl_erf.h"
#include "random.h"
#include <plearn/base/RemoteDeclareMethod.h>
Go to the source code of this file.
Namespaces | |
namespace | PLearn |
< for swap | |
Functions | |
TMat< int > | PLearn::SpearmanRankCorrelation (const VMat &x, const VMat &y, Mat &r, bool ignore_missing=false) |
Compute the Spearman Rank correlation statistic. | |
real | PLearn::testNoCorrelationAsymptotically (real r, int n) |
Return P(|R|>|r|) two-sided p-value for the null-hypothesis that there is no monotonic dependency, with r the observed Spearman Rank correlation between two paired samples of length n. | |
void | PLearn::testSpearmanRankCorrelationPValues (const VMat &x, const VMat &y, Mat &pvalues, bool ignore_missing=false) |
Compute P(|R|>|r|) two-sided p-value for the null-hypothesis that there is no monotonic dependency, with r the observed Spearman Rank correlation between two paired samples x and y of length n (column matrices). | |
void | PLearn::testSpearmanRankCorrelation (const VMat &x, const VMat &y, Mat &r, Mat &pvalues, bool ignore_missing=false) |
same as above but return also in r the rank correlations | |
real | PLearn::max_cdf_diff (Vec &v1, Vec &v2) |
Returns the max of the difference between the empirical cdf of 2 series of values. | |
real | PLearn::KS_test (real D, real N, int conv) |
void | PLearn::KS_test (Vec &v1, Vec &v2, int conv, real &D, real &p_value) |
Kolmogorov-Smirnov test. | |
void | PLearn::KS_test (const VMat &m1, const VMat &m2, const int conv, Vec &Ds, Vec &p_values, const bool report_progress) |
This version work with nan value. | |
real | PLearn::KS_test (Vec &v1, Vec &v2, int conv=10) |
Returns result of Kolmogorov-Smirnov test between 2 samples The call sorts v1 and v2. | |
tuple< real, real > | PLearn::remote_KS_test (Vec &v1, Vec &v2, int conv) |
Returns result of Kolmogorov-Smirnov test between 2 samples (D and p-value) The call sorts v1 and v2. | |
tuple< Vec, Vec > | PLearn::remote_KS_tests (VMat &m1, VMat &m2, int conv) |
Returns result of Kolmogorov-Smirnov test for each pair of variable between the two VMat (Ds and p-values) The call sorts v1 and v2. | |
real | PLearn::paired_t_test (Vec u, Vec v) |
Given two paired sets u and v of n measured values, the paired t-test determines whether they differ from each other in a significant way under the assumptions that the paired differences are independent and identically normally distributed. | |
void | PLearn::DirichletEstimatorMMoments (const Mat &p, Vec &alpha) |
Estimate the parameters of a Dirichlet by maximum-likelihood. | |
void | PLearn::DirichletEstimatorMaxLik (const Mat &p, Vec alpha) |
Estimate the parameters of a Dirichlet by maximum-likelihood. | |
BEGIN_DECLARE_REMOTE_FUNCTIONS | PLearn::declareFunction ("KS_test",&remote_KS_test,(BodyDoc("Returns result of Kolmogorov-Smirnov test between 2 samples.\n"), ArgDoc("v1","Vec1: first distr."), ArgDoc("v2","Vec2: second distr."), ArgDoc("conv","precision"), RetDoc("tuple of (D, p-value)"))) |
PLearn::declareFunction ("KS_tests",&remote_KS_tests,(BodyDoc("Returns result of Kolmogorov-Smirnov test between 2 VMats, for each column.\n"), ArgDoc("m1","VMat1: first distr."), ArgDoc("m2","VMat2: second distr."), ArgDoc("conv","precision"), RetDoc("tuple of (Ds, p-values)"))) |
Definition in file stats_utils.cc.