g02ga fits a generalized linear model with normal errors.

# Syntax

C#
```public static void g02ga(
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 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 ( _
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^ 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] 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
)```
F#
```static member g02ga :
mean : string *
offset : string *
weight : string *
n : int *
x : float[,] *
m : int *
isx : int[] *
ip : int *
y : float[] *
wt : float[] *
s : float byref *
a : float *
idf : int byref *
b : float[] *
irank : int byref *
se : float[] *
cov : float[] *
v : float[,] *
tol : float *
maxit : int *
iprint : int *
eps : float *
ifail : int byref -> unit
```

#### Parameters

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, zero-point.
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: ${\mathbf{x}}\left[\mathit{i}-1,\mathit{j}-1\right]$ 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>[]()[][]
An array of size [m]
On entry: indicates which independent variables are to be included in the model.
If ${\mathbf{isx}}\left[j-1\right]>0$, the variable contained in the $j$th column of x is included in the regression model.
Constraints:
• ${\mathbf{isx}}\left[j-1\right]\ge 0$, 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>[]()[][]
An array of size [n]
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 ${\mathbf{wt}}\left[i-1\right]=0.0$, 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 $\mathit{i}=0,1,\dots ,n-1$.
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, ${\stackrel{^}{\sigma }}^{2}$.
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 ${\mathbf{link}}=\text{"E"}$, ${\mathbf{a}}\ne 0.0$.
Type: System..::..Double%
On exit: the residual sum of squares for the fitted model.
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>[]()[][]
An array of size [ip]
On exit: the estimates of the parameters of the generalized linear model, $\stackrel{^}{\beta }$.
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 ${\mathbf{isx}}\left[j-1\right]$ is the $i$th positive value in the array isx.
If ${\mathbf{mean}}=\text{"Z"}$, ${\mathbf{b}}\left[i-1\right]$ will contain the coefficient of the variable contained in column $j$ of ${\mathbf{x}}$, where ${\mathbf{isx}}\left[j-1\right]$ 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 ${\mathbf{eps}}×\text{}$ (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>[]()[][]
An array of size [ip]
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 [${\mathbf{ip}}×\left({\mathbf{ip}}+1\right)/2$]
On exit: the upper triangular part of the variance-covariance 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 ${\mathbf{b}}\left[i-1\right]$ and the parameter estimate given in ${\mathbf{b}}\left[j-1\right]$, $j\ge i$, is stored in ${\mathbf{cov}}\left[\left(j×\left(j-1\right)/2+i\right)-1\right]$.
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 ${\mathbf{offset}}=\text{"Y"}$, ${\mathbf{v}}\left[\mathit{i}-1,6\right]$, 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.
 ${\mathbf{v}}\left[i-1,0\right]$ contains the linear predictor value, ${\eta }_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$. ${\mathbf{v}}\left[i-1,1\right]$ contains the fitted value, ${\stackrel{^}{\mu }}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$. ${\mathbf{v}}\left[i-1,2\right]$ is only included for consistency with other methods. ${\mathbf{v}}\left[\mathit{i}-1,2\right]=1.0$, for $\mathit{i}=1,2,\dots ,n$. ${\mathbf{v}}\left[i-1,3\right]$ contains the square root of the working weight, ${w}_{\mathit{i}}^{\frac{1}{2}}$, for $\mathit{i}=1,2,\dots ,n$. ${\mathbf{v}}\left[i-1,4\right]$ contains the residual, ${r}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$. ${\mathbf{v}}\left[i-1,5\right]$ contains the leverage, ${h}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$. ${\mathbf{v}}\left[i-1,6\right]$ contains the offset, for $i=1,2,\dots ,n$. If ${\mathbf{offset}}=\text{"N"}$, all values will be zero. ${\mathbf{v}}\left[i-1,j-1\right]$, 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 ${\mathbf{tol}}×\left(1.0+\text{current residual sum of squares}\right)$. 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 , g02ga will use .
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 , 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:
 $12πσexp-y-μ22σ2,$
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:
 $η=∑βjxj.$
(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: $\eta =\mathrm{log} \mu$, (iv) square root link: $\eta =\sqrt{\mu }$, (v) reciprocal link: $\eta =\frac{1}{\mu }$.
(e) a measure of fit, the residual sum of squares $\text{}=\sum {\left({y}_{i}-{\stackrel{^}{\mu }}_{i}\right)}^{2}$.
The linear parameters are estimated by iterative weighted least squares. An adjusted dependent variable, $z$, is formed:
 $z=η+y-μdηdμ$
and a working weight, $w$,
 $w=dηdμ2.$
At each iteration an approximation to the estimate of $\beta$, $\stackrel{^}{\beta }$, 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 $\stackrel{^}{\beta }$ is the solution to
 $Rβ^=QTw12z.$
If $R$ is not of full rank a solution is obtained by means of a singular value decomposition (SVD) of $R$.
 $R=Q*D000PT,$
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
 $β^=P1D-1Q*00IQTw12z$
${P}_{1}$ 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
 $η^=gy.$
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 sub-model 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 sub-model with degrees of freedom ${\nu }_{s}$ then:
 $F=RSSs-RSSf/νs-νfRSSf/νf,$
has, approximately, an $F$-distribution with (${\nu }_{s}-{\nu }_{f}$), ${\nu }_{f}$ degrees of freedom.
The parameter estimates, $\stackrel{^}{\beta }$, are asymptotically Normally distributed with variance-covariance 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 $\stackrel{^}{\eta }=X\stackrel{^}{\beta }$, 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 $\stackrel{^}{\mu }={g}^{-1}\left(\stackrel{^}{\eta }\right)$.
g02ga also computes the residuals, $r$:
 $ri=yi-μ^i.$
An option allows prior weights ${\omega }_{i}$ to be used; this gives a model with:
 $σi2=σ2ωi.$
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, $o$:
 $η=o+∑βjxj.$
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 ${P}^{*}$:
 $P*=D-1P1TP0T.$

# 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

Note: g02ga may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the method:
Some error messages may refer to parameters that are dropped from this interface (LDX, LDV) In these cases, an error in another parameter has usually caused an incorrect value to be inferred.
${\mathbf{ifail}}=1$
 On entry, ${\mathbf{n}}<2$, or ${\mathbf{m}}<1$, or ${\mathbf{ip}}<1$, or ${\mathbf{link}}\ne \text{"E"},\text{"I"},\text{"L"},\text{"S"}$ or "R", or ${\mathbf{s}}<0.0$, or ${\mathbf{link}}=\text{"E"}$ and ${\mathbf{a}}=0.0$, or ${\mathbf{mean}}\ne \text{"M"}$ or $\text{"Z"}$, or ${\mathbf{weight}}\ne \text{"U"}$ or $\text{"W"}$, or ${\mathbf{offset}}\ne \text{"N"}$ or "Y", or ${\mathbf{maxit}}<0$, or ${\mathbf{tol}}<0.0$, or ${\mathbf{eps}}<0.0$.
${\mathbf{ifail}}=3$
 On entry, a value of ${\mathbf{isx}}<0$, or the value of ip is incompatible with the values of mean and isx, or ip is greater than the effective number of observations.
${\mathbf{ifail}}=4$
A fitted value is at a boundary. This will only occur with ${\mathbf{link}}=\text{"L"}$, $\text{"R"}$ or $\text{"E"}$. This may occur if there are small values of $y$ and the model is not suitable for the data. The model should be reformulated with, perhaps, some observations dropped.
${\mathbf{ifail}}=5$
The singular value decomposition has failed to converge. This is an unlikely error exit, see (F02WUF not in this release).
${\mathbf{ifail}}=6$
The iterative weighted least squares has failed to converge in maxit (or default $10$) iterations. The value of maxit could be increased but it may be advantageous to examine the convergence using the iprint option. This may indicate that the convergence is slow because the solution is at a boundary in which case it may be better to reformulate the model.
${\mathbf{ifail}}=7$
The rank of the model has changed during the weighted least squares iterations. The estimate for $\beta$ returned may be reasonable, but you should check how the deviance has changed during iterations.
${\mathbf{ifail}}=8$
The degrees of freedom for error are $0$. A saturated model has been fitted.
${\mathbf{ifail}}=-9000$
An error occured, see message report.
${\mathbf{ifail}}=-6000$
Invalid Parameters $〈\mathit{\text{value}}〉$
${\mathbf{ifail}}=-4000$
Invalid dimension for array $〈\mathit{\text{value}}〉$
${\mathbf{ifail}}=-8000$
Negative dimension for array $〈\mathit{\text{value}}〉$
${\mathbf{ifail}}=-6000$
Invalid Parameters $〈\mathit{\text{value}}〉$

# 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 $\stackrel{^}{\beta }$ will depend on the link used and may be of the order $\sqrt{{\mathbf{tol}}}$.

None.

None.

# Example

The model:
 $y=1β1+β2x+ε$
for a sample of five observations.

Example program (C#): g02gae.cs

Example program data: g02gae.d

Example program results: g02gae.r