On exit: auxiliary information on the fitted model.
${\mathbf{v}}\left[\left(\mathit{i}-1\right)\times {\mathbf{tdv}}+0\right]$, contains the linear predictor value, ${\eta}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$.
${\mathbf{v}}\left[\left(\mathit{i}-1\right)\times {\mathbf{tdv}}+1\right]$, contains the fitted value, ${\hat{\mu}}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$.
${\mathbf{v}}\left[\left(\mathit{i}-1\right)\times {\mathbf{tdv}}+2\right]$, is only included for consistency with other functions. ${\mathbf{v}}\left[\left(\mathit{i}-1\right)\times {\mathbf{tdv}}+2\right]=1.0$, for $\mathit{i}=1,2,\dots ,n$.
${\mathbf{v}}\left[\left(\mathit{i}-1\right)\times {\mathbf{tdv}}+3\right]$, contains the working weight, ${w}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$.
${\mathbf{v}}\left[\left(\mathit{i}-1\right)\times {\mathbf{tdv}}+4\right]$, contains the standardized residual, ${r}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$.
${\mathbf{v}}\left[\left(\mathit{i}-1\right)\times {\mathbf{tdv}}+5\right]$, contains the leverage, ${h}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$.
${\mathbf{v}}\left[\left(i-1\right)\times {\mathbf{tdv}}+\mathit{j}-1\right]$, for $\mathit{j}=7,8,\dots ,{\mathbf{ip}}+6$, contains the results of the $QR$ decomposition or the singular value decomposition.
If the model is not of full rank, i.e.,
${\mathbf{rank}}<{\mathbf{ip}}$, then the first
ip rows of columns
$7$ to
${\mathbf{ip}}+6$ contain the
${P}^{*}$ matrix.