Defines the fixed model for a hierarchical or double hierarchical generalized linear model (R.W. Payne, Y. Lee, J.A. Nelder & M. Noh).

### Options

`DISTRIBUTION` = string token |
Distribution of the data (`binomial` , `poisson` , `normal` , `gamma` ); default `norm` |
---|---|

`LINK` = string token |
Link for the fixed model (`identity` , `logarithm` , `logit` , `reciprocal` , `probit` , `complementaryloglog` ); default `iden` |

`DISPERSION` = scalar |
Value of dispersion parameter in calculation of s.e.s etc; default `*` for `DIST=norm` or `gamm` , and 1 for `DIST=pois` or `bino` |

`DLINK` = string token |
Link for the dispersion model (`logarithm` , `reciprocal` ); default `loga` |

`DTERMS` = formula |
Dispersion model; default `*` i.e. none |

`CONSTANT` = string token |
How to treat the constant (`estimate` , `omit` ) default `esti` |

`FACTORIAL` = scalar |
Limit on number of variates and/or factors in a fixed model term; default 3 |

`WEIGHTS` = variate |
Prior weights; default `*` i.e. 1 |

`OFFSET` = variate |
Offset variate; default `*` i.e. none |

`DOFFSET` = variate |
Offset variate for dispersion model; default `*` i.e. none |

`DDISPERSION` = scalar |
Dispersion parameter to use in a dispersion model for the residual dispersion parameter phi; default 1 |

`IDISPERSION` = scalar |
Initial value for the residual dispersion parameter phi; default `*` i.e. formed automatically |

### Parameter

`TERMS` = formula |
Fixed model |
---|

### Description

`HGFIXEDMODEL`

is one of several procedures with the prefix HG, which provide tools for fitting the hierarchical generalized linear models defined by Lee & Nelder (1996, 2001, 2006) and described by Lee, Nelder & Pawitan (2006). These models extend generalized linear models (GLMs) to include additional random terms in the linear predictor. They include generalized linear mixed models (GLMMs) as a special case, but do not constrain the additional terms to follow a Normal distribution and to have an identity link (as in the GLMM). For example, if the basic generalized linear model is a log-linear model (Poisson distribution and log link), a more appropriate assumption or the additional random terms might be a gamma distribution and a log link.

The role of `HGFIXEDMODEL`

is to specify the fixed model terms in the HGLM, and to define the distribution of the data (this corresponds to error distribution of a GLM). The fixed model is given by the `TERMS`

parameter. Most of the options operate similarly to those occurring in the directives `FIT`

and `MODEL`

. The link function for the fixed model is defined by the `LINK`

option, and the `FACTORIAL`

option sets a limit on the number of variates and/or factors for a term to be included in the fixed model (default 3). The `CONSTANT`

option indicates whether or not to include a constant term or intercept (by default this is included), and the `OFFSET`

option allows an offset variate to be included. The `DISTRIBUTION`

option defines the distribution of the data, the `WEIGHTS`

option lets you specify a variate of prior weights, and the `DISPERSION`

option governs how the dispersion parameter is obtained.

The HGLM methodology also caters for structured dispersion models, in which fixed terms are included in the generalized linear models that are used to estimate the dispersion parameters. Currently these GLMs must have a gamma distribution. The `DTERMS`

option lets you specify fixed terms for the GLM that estimates the residual dispersion parameter phi. The `DLINK`

parameter specifies the link to use with the dispersion model, the `DOFFSET`

option lets you specify an offset variate, and the `DDISPERSION`

option defines the dispersion parameter for the dispersion GLM (default 1). You can also extend the GLM to become an HGLM (thus making the full model a *double hierarchical generalized linear model* or *DHGLM*), by using the `HGDRANDOMMODEL`

procedure to add some random terms.

The `IDISPERSION`

option lets you define an initial value for the residual dispersion parameter phi. Initial values for the dispersion parameters of the additional random terms of the HGLM can be defined using the `IDISPERSION`

parameter of the `HGRANDOMMODEL`

procedure. If you set both of these, the `HGANALYSE`

procedure will then use them to initialize the weights that are involved in the fitting of the augmented mean model; for details see Chapter 6 of Lee, Nelder & Pawitan (2006). The default weights that are formed automatically if either of these is unset are satisfactory in most circumstances, but you may want to try your own initial values if you encounter convergemce problems.

Options: `DISTRIBUTION`

, `LINK`

, `DISPERSION`

, `DLINK`

, `DTERMS`

, `CONSTANT`

, `FACTORIAL`

, `WEIGHTS`

, `OFFSET`

, `DOFFSET`

, `DDISPERSION`

, `IDISPERSION`

.

Parameter: `TERMS`

.

### Method

The information is stored in a workspace `G5PL_HG`

(accessed using the `WORKSPACE`

directive) for later use by `HGANALYSE`

.

### References

Lee, Y., & Nelder, J.A. (1996). Hierarchical generalized linear models (with discussion). *Journal of the Royal Statistical Society, Series B*, 58, 619-678.

Lee, Y., & Nelder, J.A. (2001). Hierarchical generalized linear models: a synthesis of generalised linear models, random-effect models and structured dispersions. *Biometrika*, 88, 987-1006.

Lee, Y. & Nelder, J.A. (2006). Double hierarchical generalized linear models (with discussion). *Appl. Statist.*, 55, 139-185.

Lee, Y., Nelder, J.A. & Pawitan, Y. (2006). *Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood*. Chapman & Hall, London.

Lee, Y., Nelder, J.A. & Pawitan, Y. (2006). *Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood*. Chapman and Hall, Boca Raton.

### See also

Procedures: `HGANALYSE`

, `HGDISPLAY`

, `HGDRANDOMMODEL`

, `HGFTEST`

, `HGGRAPH`

, `HGKEEP`

, `HGNONLINEAR`

, `HGPLOT`

, `HGPREDICT`

, `HGRANDOMMODEL`

, `HGRTEST`

, `HGSTATUS`

, `HGWALD`

.

Commands for: Regression analysis.

### Example

CAPTION 'HGFIXEDMODEL example',!t(\ 'Number of faults in rolls of fabric of various lengths',\ '(data from Bissell (1972) Biometrika, 59, 435-441).'),\ 'Fit negative binomial: var(y) = mu + alpha * mu * mu',\ '(equivalent to Poisson gamma HGLM with saturated random effect).';\ STYLE=meta,3(plain) VARIATE [NVALUES=32] length,faults READ length,faults 551 6 651 4 832 17 375 9 715 14 868 8 271 5 630 7 491 7 372 7 645 6 441 8 895 28 458 4 642 10 492 4 543 8 842 9 905 23 542 9 522 6 122 1 657 9 170 4 738 9 371 14 735 17 749 10 495 7 716 3 952 9 417 2 : CALCULATE loglength = log(length) & loglength = loglength - mean(loglength) FACTOR [LEVELS=32; VALUES=1...32] saturated HGFIXEDMODEL [DISTRIBUTION=poisson; LINK=log] loglength HGRANDOMMODEL [DISTRIBUTION=normal; LINK=identity] saturated HGANALYSE faults