# NAME

``  Regression.pm - weighted linear regression package (line+plane fitting)``

# SYNOPSIS

``````  use Statistics::Regression;

# Create regression object
my \$reg = Statistics::Regression->new( "sample regression", [ "const", "someX", "someY" ] );

\$reg->include( 2.0, [ 1.0, 3.0, -1.0 ] );
\$reg->include( 1.0, [ 1.0, 5.0, 2.0 ] );
\$reg->include( 20.0, [ 1.0, 31.0, 0.0 ] );
\$reg->include( 15.0, [ 1.0, 11.0, 2.0 ] );``````

or

``````  my %d;
\$d{const} = 1.0; \$d{someX}= 5.0; \$d{someY}= 2.0; \$d{ignored}="anything else";
\$reg->include( 3.0, \%d );  # names are picked off the Regression specification``````

Please note that *you* must provide the constant if you want one.

``````  # Finally, print the result
\$reg->print();``````

This prints the following:

``````  ****************************************************************
Regression 'sample regression'
****************************************************************
Name                         Theta          StdErr     T-stat
[0='const']                 0.2950          6.0512       0.05
[1='someX']                 0.6723          0.3278       2.05
[2='someY']                 1.0688          2.7954       0.38

R^2= 0.808, N= 4
****************************************************************``````

The hash input method has the advantage that you can now just fill the observation hashes with all your variables, and use the same code to run regression, changing the regression specification at one and only one spot (the new() invokation). You do not need to change the inputs in the include() statement. For example,

``````  my @obs;  ## a global variable.  observations are like: %oneobs= %{\$obs[1]};

sub run_regression {
my \$reg = Statistics::Regression->new( \$_[0], \$_[2] );
foreach my \$obshashptr (@obs) { \$reg->include( \$_[1], \$_[3] ); }
\$reg->print();
}

run_regression("bivariate regression",  \$obshashptr->{someY}, [ "const", "someX" ] );
run_regression("trivariate regression",  \$obshashptr->{someY}, [ "const", "someX", "someZ" ] );``````

Of course, you can use the subroutines to do the printing work yourself:

``````  my @theta  = \$reg->theta();
my @se     = \$reg->standarderrors();
my \$rsq    = \$reg->rsq();
my \$ybar   = \$reg->ybar();  ## the average of the y vector
my \$sst    = \$reg->sst();  ## the sum-squares-total
my \$sigmasq= \$reg->sigmasq();  ## the variance of the residual
my \$k      = \$reg->k();   ## the number of variables
my \$n      = \$reg->n();   ## the number of observations``````

In addition, there are some other helper routines, and a subroutine linearcombination_variance(). If you don't know what this is, don't use it.

# BACKGROUND WARNING

You should have an understanding of OLS regressions if you want to use this package. You can get this from an introductory college econometrics class and/or from most intermediate college statistics classes. If you do not have this background knowledge, then this package will remain a mystery to you. There is no support for this package--please don't expect any.

# DESCRIPTION

Regression.pm is a multivariate linear regression package. That is, it estimates the c coefficients for a line-fit of the type

``  y= c(0)*x(0) + c(1)*x1 + c(2)*x2 + ... + c(k)*xk``

given a data set of N observations, each with k independent x variables and one y variable. Naturally, N must be greater than k---and preferably considerably greater. Any reasonable undergraduate statistics book will explain what a regression is. Most of the time, the user will provide a constant ('1') as x(0) for each observation in order to allow the regression package to fit an intercept.

# ALGORITHM

## Original Algorithm (ALGOL-60):

``````        W.  M.  Gentleman, University of Waterloo, "Basic
Description For Large, Sparse Or Weighted Linear Least
Squares Problems (Algorithm AS 75)," Applied Statistics
(1974) Vol 23; No. 3``````

Gentleman's algorithm is the statistical standard. Insertion of a new observation can be done one observation at any time (WITH A WEIGHT!), and still only takes a low quadratic time. The storage space requirement is of quadratic order (in the indep variables). A practically infinite number of observations can easily be processed!

## Internal Data Structures

R=Rbar is an upperright triangular matrix, kept in normalized form with implicit 1's on the diagonal. D is a diagonal scaling matrix. These correspond to "standard Regression usage" as

``                X' X  = R' D R``

A backsubsitution routine (in thetacov) allows to invert the R matrix (the inverse is upper-right triangular, too!). Call this matrix H, that is H=R^(-1).

``````          (X' X)^(-1) = [(R' D^(1/2)') (D^(1/2) R)]^(-1)
= [ R^-1 D^(-1/2) ] [ R^-1 D^(-1/2) ]'``````

# BUGS/PROBLEMS

None known.

Perl Problem

Unfortunately, perl is unaware of IEEE number representations. This makes it a pain to test whether an observation contains any missing variables (coded as 'NaN' in Regression.pm).

# VERSION and RECENT CHANGES

2007/07/01: Added self-test use (if invoked as perl Regression.pm) at the end. cleaned up some print sprintf. changed syntax on new() to eliminate passing K.

2007/07/07: allowed passing hash with names to include().

# AUTHOR

Naturally, Gentleman invented this algorithm. It was adaptated by Ivo Welch. Alan Miller (alan\@dmsmelb.mel.dms.CSIRO.AU) pointed out nicer ways to compute the R^2. Ivan Tubert-Brohman helped wrap the module as as a standard CPAN distribution.