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RazorBack Namespace Reference


Compounds

class  RazorBack::Anova1_
class  RazorBack::Anova1_::Group_
 This class implements an observation group. More...

class  RazorBack::Distrapprox_
class  RazorBack::Distrapprox_::Bin_
 A Bin_ object represents the i:th histogram bin. More...

class  RazorBack::Distrbase_
 Distrbase_: an abstract base class for univariate continuous distributions. More...

class  RazorBack::Distrfunc_
 Distrfunc_: function object abstract base class that implements a univariate PDF with an arbitrary number of parameters. More...

class  RazorBack::Uniform_
 Uniform_: implements the uniform distribution in a range. More...

class  RazorBack::Gaussian_
 Gaussian_: implements the Normal distribution. More...

class  RazorBack::Linls_
 Linls_: Class for carrying out linear least-squares using the SVD. More...

class  RazorBack::Linreg_
 Class Linreg_: class for linear regression. More...

class  RazorBack::Mvn_
 Mvn_: a class representing multivariate Normal distributions. More...

class  RazorBack::Nonlinfunc_
 Class Nonlinfunc_: this is an abstract base class for function objects that can be used by the Nonlinreg_ class. More...

class  RazorBack::Nonlinreg_
 Class Nonlinreg_: this class implements a general-purpose multivariate weighted nonlinear regression with Marquardt's method. More...

class  RazorBack::Orthpoly_
 This class implements parameter estimation using orthogonal polynomials. More...

class  RazorBack::Paramest_
 Class Paramest_: base class for parameter estimation. More...

class  RazorBack::Randombase_
 Randombase_: random number generator abstract base class. More...

class  RazorBack::Randomuni_
 Randomuni_: generates uniform random numbers. More...

class  RazorBack::Randomnorm_
 Randomnorm_: random number generator class that produces normally distributed random numbers. More...

class  RazorBack::Splstorage_
 Splstorage_: this is a helper class that stores the data needed by the Spl_ class. More...

class  RazorBack::Spl_
 Class Spl_ : implements third-order splines. More...

struct  RazorBack::Spl_::Datapoint_
class  RazorBack::Stat_
 Class Stat_: one-variable statistics. More...

class  RazorBack::Stat2_
 Class Stat2_: Two-variable simple statistics class. More...

class  RazorBack::Toofewexc_
 Class Toofewexc_: an exception object that stores an unsigned integer and can be thrown if there was not enough data available. More...

class  RazorBack::Sdevexc_
 Class Sdevexc_: an exception object that stores an invalid standard deviation value (usually <=0.0). More...

class  RazorBack::Forgottenexc_
class  RazorBack::Statprob_

Functions

unsigned int std_data (vector< double > &X, double &Mean, double &Sd)
 std_data(X, Mean, Sd): standardizes the data vector X by calculating the mean and SD, and then subtracting the mean from each entry and divide the centralised value by the SD. More...

double correl_data (const vector< double > &X, const vector< double > &Y)
 correl_data(X, Y): calculates the correlation coefficient between the data vectors X and Y. More...

int calc_loadings (const Symmat_< double > &Correl, Rectmat_< double > &Load, double Tol, double *Qual=NULL)
 calc_loadings(Correl, Load, Tol, Qual): decomposes the correlation matrix Correl so that the loadings will be returned in Load. More...


Function Documentation

unsigned int RazorBack::std_data ( vector< double > & X,
double & Mean,
double & Sd )
 

std_data(X, Mean, Sd): standardizes the data vector X by calculating the mean and SD, and then subtracting the mean from each entry and divide the centralised value by the SD.

Returns the original mean in Mean, the SD in Sd. Return value: the no. of entries converted or 0 on error.

double RazorBack::correl_data ( const vector< double > & X,
const vector< double > & Y )
 

correl_data(X, Y): calculates the correlation coefficient between the data vectors X and Y.

X and Y are assumed to have been standardised by std_data() before the call. Return value: the correlation coefficient (-1..+1).

int RazorBack::calc_loadings ( const Symmat_< double > & Correl,
Rectmat_< double > & Load,
double Tol,
double * Qual = NULL )
 

calc_loadings(Correl, Load, Tol, Qual): decomposes the correlation matrix Correl so that the loadings will be returned in Load.

Return value: the number of factors which belong to eigenvalues higher than Tol times the largest eigenvalue. If Qual!=NULL, then a quality coefficient is returned in *Qual.


Generated at Wed Aug 21 09:33:20 2002 for The Razorback C++ Library: Statistics by doxygen1.2.6 written by Dimitri van Heesch, © 1997-2001