g02ga fits a generalized linear model with normal errors.
Syntax
C# 

public static void g02ga(
string link,
string mean,
string offset,
string weight,
int n,
double[,] x,
int m,
int[] isx,
int ip,
double[] y,
double[] wt,
ref double s,
double a,
out double rss,
out int idf,
double[] b,
out int irank,
double[] se,
double[] cov,
double[,] v,
double tol,
int maxit,
int iprint,
double eps,
out int ifail
) 
Visual Basic 

Public Shared Sub g02ga ( _
link As String, _
mean As String, _
offset As String, _
weight As String, _
n As Integer, _
x As Double(,), _
m As Integer, _
isx As Integer(), _
ip As Integer, _
y As Double(), _
wt As Double(), _
ByRef s As Double, _
a As Double, _
<OutAttribute> ByRef rss As Double, _
<OutAttribute> ByRef idf As Integer, _
b As Double(), _
<OutAttribute> ByRef irank As Integer, _
se As Double(), _
cov As Double(), _
v As Double(,), _
tol As Double, _
maxit As Integer, _
iprint As Integer, _
eps As Double, _
<OutAttribute> ByRef ifail As Integer _
) 
Visual C++ 

public:
static void g02ga(
String^ link,
String^ mean,
String^ offset,
String^ weight,
int n,
array<double,2>^ x,
int m,
array<int>^ isx,
int ip,
array<double>^ y,
array<double>^ wt,
double% s,
double a,
[OutAttribute] double% rss,
[OutAttribute] int% idf,
array<double>^ b,
[OutAttribute] int% irank,
array<double>^ se,
array<double>^ cov,
array<double,2>^ v,
double tol,
int maxit,
int iprint,
double eps,
[OutAttribute] int% ifail
) 
Parameters
 link
 Type: System..::..String
On entry: indicates which link function is to be used.
 ${\mathbf{link}}=\text{"E"}$
 An exponent link is used.
 ${\mathbf{link}}=\text{"I"}$
 An identity link is used. You are advised not to use g02ga with an identity link as g02da provides a more efficient way of fitting such a model.
 ${\mathbf{link}}=\text{"L"}$
 A log link is used.
 ${\mathbf{link}}=\text{"S"}$
 A square root link is used.
 ${\mathbf{link}}=\text{"R"}$
 A reciprocal link is used.
Constraint:
${\mathbf{link}}=\text{"E"}$, $\text{"I"}$, $\text{"L"}$, $\text{"S"}$ or $\text{"R"}$.
 mean
 Type: System..::..String
On entry: indicates if a mean term is to be included.
 ${\mathbf{mean}}=\text{"M"}$
 A mean term, intercept, will be included in the model.
 ${\mathbf{mean}}=\text{"Z"}$
 The model will pass through the origin, zeropoint.
Constraint:
${\mathbf{mean}}=\text{"M"}$ or $\text{"Z"}$.
 offset
 Type: System..::..String
On entry: indicates if an offset is required.
 ${\mathbf{offset}}=\text{"Y"}$
 An offset is required and the offsets must be supplied in the seventh column of v.
 ${\mathbf{offset}}=\text{"N"}$
 No offset is required.
Constraint:
${\mathbf{offset}}=\text{"N"}$ or $\text{"Y"}$.
 weight
 Type: System..::..String
On entry: indicates if prior weights are to be used.
 ${\mathbf{weight}}=\text{"U"}$
 No prior weights are used.
 ${\mathbf{weight}}=\text{"W"}$
 Prior weights are used and weights must be supplied in wt.
Constraint:
${\mathbf{weight}}=\text{"U"}$ or $\text{"W"}$.
 n
 Type: System..::..Int32
On entry: $n$, the number of observations.
Constraint:
${\mathbf{n}}\ge 2$.
 x
 Type: array<System..::..Double,2>[,](,)[,][,]
An array of size [
dim1,
m]
Note: dim1 must satisfy the constraint:
$\mathrm{dim1}\ge {\mathbf{n}}$
On entry: ${\mathbf{x}}[\mathit{i}1,\mathit{j}1]$ must contain the $\mathit{i}$th observation for the $\mathit{j}$th independent variable, for $\mathit{i}=1,2,\dots ,{\mathbf{n}}$ and $\mathit{j}=1,2,\dots ,{\mathbf{m}}$.
 m
 Type: System..::..Int32
On entry: $m$, the total number of independent variables.
Constraint:
${\mathbf{m}}\ge 1$.
 isx
 Type: array<System..::..Int32>[]()[][]
On entry: indicates which independent variables are to be included in the model.
If
${\mathbf{isx}}\left[j1\right]>0$, the variable contained in the
$j$th column of
x is included in the regression model.
Constraints:
 ${\mathbf{isx}}\left[j1\right]\ge 0$, for $\mathit{i}=0,1,\dots ,{\mathbf{m}}1$;
 if ${\mathbf{mean}}=\text{"M"}$, exactly ${\mathbf{ip}}1$ values of isx must be $\text{}>0$;
 if ${\mathbf{mean}}=\text{"Z"}$, exactly ip values of isx must be $\text{}>0$.
 ip
 Type: System..::..Int32
On entry: the number of independent variables in the model, including the mean or intercept if present.
Constraint:
${\mathbf{ip}}>0$.
 y
 Type: array<System..::..Double>[]()[][]
On entry: the observations on the dependent variable,
${y}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$.
 wt
 Type: array<System..::..Double>[]()[][]
An array of size [dim1]
Note: the dimension of the array
wt
must be at least
${\mathbf{n}}$ if
${\mathbf{weight}}=\text{"W"}$, and at least
$1$ otherwise.
On entry: if
${\mathbf{weight}}=\text{"W"}$,
wt must contain the weights to be used with the model,
${\omega}_{i}$. If
${\mathbf{wt}}\left[i1\right]=0.0$, the
$i$th observation is not included in the model, in which case the effective number of observations is the number of observations with nonzero weights.
If
${\mathbf{weight}}=\text{"U"}$,
wt is not referenced and the effective number of observations is
$n$.
Constraint:
if ${\mathbf{weight}}=\text{"W"}$, ${\mathbf{wt}}\left[\mathit{i}\right]\ge 0.0$, for $\mathit{i}=0,1,\dots ,n1$.
 s
 Type: System..::..Double%
On entry: the scale parameter for the model,
${\sigma}^{2}$.
If ${\mathbf{s}}=0.0$, the scale parameter is estimated with the method using the residual mean square.
On exit: if on input
${\mathbf{s}}=0.0$,
s contains the estimated value of the scale parameter,
${\hat{\sigma}}^{2}$.
If on input
${\mathbf{s}}\ne 0.0$,
s is unchanged on exit.
Constraint:
${\mathbf{s}}\ge 0.0$.
 a
 Type: System..::..Double
On entry: if
${\mathbf{link}}=\text{"E"}$,
a must contain the power of the exponential.
If
${\mathbf{link}}\ne \text{"E"}$,
a is not referenced.
Constraint:
if ${\mathbf{link}}=\text{"E"}$, ${\mathbf{a}}\ne 0.0$.
 idf
 Type: System..::..Int32%
On exit: the degrees of freedom associated with the residual sum of squares for the fitted model.
 b
 Type: array<System..::..Double>[]()[][]
On exit: the estimates of the parameters of the generalized linear model,
$\hat{\beta}$.
If
${\mathbf{mean}}=\text{"M"}$,
${\mathbf{b}}\left[0\right]$ will contain the estimate of the mean parameter and
${\mathbf{b}}\left[i\right]$ will contain the coefficient of the variable contained in column
$j$ of
${\mathbf{x}}$, where
${\mathbf{isx}}\left[j1\right]$ is the
$i$th positive value in the array
isx.
If
${\mathbf{mean}}=\text{"Z"}$,
${\mathbf{b}}\left[i1\right]$ will contain the coefficient of the variable contained in column
$j$ of
${\mathbf{x}}$, where
${\mathbf{isx}}\left[j1\right]$ is the
$i$th positive value in the array
isx.
 irank
 Type: System..::..Int32%
On exit: the rank of the independent variables.
If the model is of full rank, ${\mathbf{irank}}={\mathbf{ip}}$.
If the model is not of full rank,
irank is an estimate of the rank of the independent variables.
irank is calculated as the number of singular values greater than
${\mathbf{eps}}\times \text{}$ (largest singular value). It is possible for the SVD to be carried out but for
irank to be returned as
ip.
 se
 Type: array<System..::..Double>[]()[][]
On exit: the standard errors of the linear parameters.
${\mathbf{se}}\left[\mathit{i}1\right]$ contains the standard error of the parameter estimate in ${\mathbf{b}}\left[\mathit{i}1\right]$, for $\mathit{i}=1,2,\dots ,{\mathbf{ip}}$.
 cov
 Type: array<System..::..Double>[]()[][]
An array of size [${\mathbf{ip}}\times \left({\mathbf{ip}}+1\right)/2$]
On exit: the upper triangular part of the variancecovariance matrix of the
ip parameter estimates given in
b. They are stored packed by column, i.e., the covariance between the parameter estimate given in
${\mathbf{b}}\left[i1\right]$ and the parameter estimate given in
${\mathbf{b}}\left[j1\right]$,
$j\ge i$, is stored in
${\mathbf{cov}}\left[\left(j\times \left(j1\right)/2+i\right)1\right]$.
 v
 Type: array<System..::..Double,2>[,](,)[,][,]
An array of size [dim1, ${\mathbf{ip}}+7$]
Note: dim1 must satisfy the constraint:
$\mathrm{dim1}\ge {\mathbf{n}}$
On entry: if
${\mathbf{offset}}=\text{"N"}$,
v need not be set.
If ${\mathbf{offset}}=\text{"Y"}$,
${\mathbf{v}}[\mathit{i}1,6]$, for $\mathit{i}=1,2,\dots ,n$, must contain the offset values ${o}_{\mathit{i}}$. All other values need not be set.
On exit: auxiliary information on the fitted model.
${\mathbf{v}}[i1,0]$ 
contains the linear predictor value,
${\eta}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$. 
${\mathbf{v}}[i1,1]$ 
contains the fitted value,
${\hat{\mu}}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$. 
${\mathbf{v}}[i1,2]$ 
is only included for consistency with other methods.
${\mathbf{v}}[\mathit{i}1,2]=1.0$, for $\mathit{i}=1,2,\dots ,n$. 
${\mathbf{v}}[i1,3]$ 
contains the square root of the working weight,
${w}_{\mathit{i}}^{\frac{1}{2}}$, for $\mathit{i}=1,2,\dots ,n$. 
${\mathbf{v}}[i1,4]$ 
contains the residual,
${r}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$. 
${\mathbf{v}}[i1,5]$ 
contains the leverage,
${h}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$. 
${\mathbf{v}}[i1,6]$ 
contains the offset, for $i=1,2,\dots ,n$. If ${\mathbf{offset}}=\text{"N"}$, all values will be zero. 
${\mathbf{v}}[i1,j1]$, 
for $j=8,\dots ,{\mathbf{ip}}+7$, contains the results of the $QR$ decomposition or the singular value decomposition. 
If the model is not of full rank, i.e.,
${\mathbf{irank}}<{\mathbf{ip}}$, the first
ip rows of columns
$8$ to
${\mathbf{ip}}+7$ contain the
${P}^{*}$ matrix.
 tol
 Type: System..::..Double
On entry: indicates the accuracy required for the fit of the model.
The iterative weighted least squares procedure is deemed to have converged if the absolute change in deviance between interactions is less than ${\mathbf{tol}}\times \left(1.0+\text{current residual sum of squares}\right)$. This is approximately an absolute precision if the residual sum of squares is small and a relative precision if the residual sum of squares is large.
If
$0.0\le {\mathbf{tol}}<\mathit{machineprecision}$,
g02ga will use
$10\times \mathit{machineprecision}$.
Constraint:
${\mathbf{tol}}\ge 0.0$.
 maxit
 Type: System..::..Int32
On entry: the maximum number of iterations for the iterative weighted least squares.
If ${\mathbf{maxit}}=0$, a default value of $10$ is used.
Constraint:
${\mathbf{maxit}}\ge 0$.
 iprint
 Type: System..::..Int32
On entry: indicates if the printing of information on the iterations is required.
 ${\mathbf{iprint}}\le 0$
 There is no printing.
 ${\mathbf{iprint}}>0$
 Every iprint iteration, the following is printed:
 the deviance, 
 the current estimates, 
 and if the weighted least squares equations are singular then this is indicated. 
When printing occurs the output is directed to the current advisory message unit (see (X04ABF not in this release)).
 eps
 Type: System..::..Double
On entry: the value of
eps is used to decide if the independent variables are of full rank and, if not, what is the rank of the independent variables. The smaller the value of
eps the stricter the criterion for selecting the singular value decomposition.
If $0.0\le {\mathbf{eps}}<\mathit{machineprecision}$, the method will use machine precision instead.
Constraint:
${\mathbf{eps}}\ge 0.0$.
 ifail
 Type: System..::..Int32%
On exit:
${\mathbf{ifail}}={0}$ unless the method detects an error or a warning has been flagged (see
[Error Indicators and Warnings]).
Description
A generalized linear model with Normal errors consists of the following elements:
(a) 
a set of $n$ observations, ${y}_{i}$, from a Normal distribution with probability density function:
where $\mu $ is the mean and ${\sigma}^{2}$ is the variance. 
(b) 
$X$, a set of $p$ independent variables for each observation, ${x}_{1},{x}_{2},\dots ,{x}_{p}$. 
(c) 
a linear model:

(d) 
a link between the linear predictor, $\eta $, and the mean of the distribution, $\mu $, i.e., $\eta =g\left(\mu \right)$. The possible link functions are:
(i) 
exponent link: $\eta ={\mu}^{a}$, for a constant $a$, 
(ii) 
identity link: $\eta =\mu $, 
(iii) 
log link: $\eta =\mathrm{log}\u200a\mu $, 
(iv) 
square root link: $\eta =\sqrt{\mu}$, 
(v) 
reciprocal link: $\eta =\frac{1}{\mu}$. 

(e) 
a measure of fit, the residual sum of squares $\text{}=\sum {\left({y}_{i}{\hat{\mu}}_{i}\right)}^{2}$. 
The linear parameters are estimated by iterative weighted least squares. An adjusted dependent variable,
$z$, is formed:
and a working weight,
$w$,
At each iteration an approximation to the estimate of
$\beta $,
$\hat{\beta}$, is found by the weighted least squares regression of
$z$ on
$X$ with weights
$w$.
g02ga finds a
$QR$ decomposition of
${w}^{\frac{1}{2}}X$, i.e.,
${w}^{\frac{1}{2}}X=QR$ where
$R$ is a
$p$ by
$p$ triangular matrix and
$Q$ is an
$n$ by
$p$ column orthogonal matrix.
If
$R$ is of full rank, then
$\hat{\beta}$ is the solution to
If
$R$ is not of full rank a solution is obtained by means of a singular value decomposition (SVD) of
$R$.
where
$D$ is a
$k$ by
$k$ diagonal matrix with nonzero diagonal elements,
$k$ being the rank of
$R$ and
${w}^{\frac{1}{2}}X$.
This gives the solution
${P}_{1}$ being the first
$k$ columns of
$P$, i.e.,
$P=\left({P}_{1}{P}_{0}\right)$.
The iterations are continued until there is only a small change in the residual sum of squares.
The initial values for the algorithm are obtained by taking
The fit of the model can be assessed by examining and testing the residual sum of squares, in particular comparing the difference in residual sums of squares between nested models, i.e., when one model is a submodel of the other.
Let
${\mathrm{RSS}}_{f}$ be the residual sum of squares for the full model with degrees of freedom
${\nu}_{f}$ and let
${\mathrm{RSS}}_{s}$ be the residual sum of squares for the submodel with degrees of freedom
${\nu}_{s}$ then:
has, approximately, an
$F$distribution with (
${\nu}_{s}{\nu}_{f}$),
${\nu}_{f}$ degrees of freedom.
The parameter estimates,
$\hat{\beta}$, are asymptotically Normally distributed with variancecovariance matrix:
 $C={R}^{1}{{R}^{1}}^{\mathrm{T}}{\sigma}^{2}$ in the full rank case,
 otherwise $C={P}_{1}{D}^{2}{P}_{1}^{\mathrm{T}}{\sigma}^{2}$
The residuals and influence statistics can also be examined.
The estimated linear predictor $\hat{\eta}=X\hat{\beta}$, can be written as $H{w}^{\frac{1}{2}}z$ for an $n$ by $n$ matrix $H$. The $i$th diagonal elements of $H$, ${h}_{i}$, give a measure of the influence of the $i$th values of the independent variables on the fitted regression model. These are sometimes known as leverages.
The fitted values are given by $\hat{\mu}={g}^{1}\left(\hat{\eta}\right)$.
g02ga also computes the residuals,
$r$:
An option allows prior weights
${\omega}_{i}$ to be used; this gives a model with:
In many linear regression models the first term is taken as a mean term or an intercept, i.e.,
${x}_{\mathit{i},1}=1$, for
$\mathit{i}=1,2,\dots ,n$; this is provided as an option.
Often only some of the possible independent variables are included in a model, the facility to select variables to be included in the model is provided.
If part of the linear predictor can be represented by a variable with a known coefficient, then this can be included in the model by using an offset,
$o$:
If the model is not of full rank the solution given will be only one of the possible solutions. Other estimates may be obtained by applying constraints to the parameters. These solutions can be obtained by using
g02gk after using
g02ga. Only certain linear combinations of the parameters will have unique estimates; these are known as estimable functions and can be estimated and tested using
g02gn.
Details of the SVD are made available, in the form of the matrix
${P}^{*}$:
References
Cook R D and Weisberg S (1982) Residuals and Influence in Regression Chapman and Hall
McCullagh P and Nelder J A (1983) Generalized Linear Models Chapman and Hall
Error Indicators and Warnings
Accuracy
The accuracy is determined by
tol as described in
[Parameters]. As the residual sum of squares is a function of
${\mu}^{2}$ the accuracy of the
$\hat{\beta}$ will depend on the link used and may be of the order
$\sqrt{{\mathbf{tol}}}$.
Parallelism and Performance
Further Comments
Example
See Also