Drops terms from a linear, generalized linear, generalized additive or nonlinear model.

### Options

`PRINT` = string tokens |
What to print (`model` , `deviance` , `summary` , `estimates` , `correlations` , `fittedvalues` , `accumulated` , `monitoring` , `confidence` ); default `mode, summ, esti` |
---|---|

`NONLINEAR` = string token |
How to treat nonlinear parameters between groups (`common` , `separate` , `unchanged` ); default `unch` |

`CONSTANT` = string token |
How to treat the constant (`estimate` , `omit` , `unchanged` , `ignore` ); default `unch` |

`FACTORIAL` = scalar |
Limit for expansion of model terms; default `*` i.e. that in previous `TERMS` statement |

`POOL` = string token |
Whether to pool ss in accumulated summary between all terms fitted in a linear model (`yes` , `no` ); default `no` |

`DENOMINATOR` = string token |
Whether to base ratios in accumulated summary on rms from model with smallest residual ss or smallest residual ms (`ss` , `ms` ); default `ss` |

`NOMESSAGE` = string tokens |
Which warning messages to suppress (`dispersion, leverage, residual, aliasing, marginality` , `df` , `inflation` ); default `*` |

`FPROBABILITY` = string token |
Printing of probabilities for variance and deviance ratios (`yes` , `no` ); default `no` |

`TPROBABILITY` = string token |
Printing of probabilities for t-statistics (`yes` , `no` ); default `no` |

`SELECTION` = string tokens |
Statistics to be displayed in the summary of analysis produced by `PRINT=summary` , `seobservations` is relevant only for a Normally distributed response, and `%cv` only for a gamma-distributed response (`%variance` , `%ss` , `adjustedr2` , `r2` , `seobservations` , `dispersion` , `%cv` , `%meandeviance` , `%deviance` , `aic` , `bic` , `sic` ); default `%var` , `seob` if `DIST=normal` , `%cv` if `DIST=gamma` , and `disp` for other distributions |

`PROBABILITY` = scalar |
Probability level for confidence intervals for parameter estimates; default 0.95 |

`AOVDESCRIPTION` = text |
Description for line in accumulated analysis of variance (or deviance) table when `POOL=yes` |

### Parameter

formula |
List of explanatory variates and factors, or model formula |
---|

### Description

`DROP`

deletes terms from the current regression model, which may be linear, generalized linear, generalized additive, standard curve or nonlinear. It is best to give a `TERMS`

statement before investigating sequences of models using `DROP`

, in order to define a common set of units for the models that are to be explored. If no model has been fitted since the `TERMS`

statement, the current model is taken to be the null model.

The model fitted by `DROP`

will include a constant term if the previous model included one, and will not include one if the previous model did not. You can, however, change this using the `CONSTANT`

option.

The options of `DROP`

are the same as those of the `FIT`

directive, but with the extra `NONLINEAR`

option which is relevant when fitting curves. For example, if we have a variate `Dilution`

and a factor `Solution`

, the program below will fit curves with separate linear and nonlinear parameters for the different solutions.

`MODEL Density`

`TERMS Dilution * Solution`

`FITCURVE [PRINT=model,estimates; CURVE=logistic;\`

` NONLINEAR=separate] Dilution * Solution`

If we then put

`DROP [NONLINEAR=common]`

the curves will be constrained to have common nonlinear parameters, but all linear parameters will still be estimated separately for each group.

Options: `PRINT`

, `NONLINEAR`

, `CONSTANT`

, `FACTORIAL`

, `POOL`

, `DENOMINATOR`

, `NOMESSAGE`

, `FPROBABILITY`

, `TPROBABILITY`

, `SELECTION`

, `PROBABILITY`

, `AOVDESCRIPTION`

.

Parameter: unnamed.

### See also

Directives: `MODEL`

, `TERMS`

, `FIT`

, `FITCURVE`

, `FITNONLINEAR`

, `ADD`

, `SWITCH`

, `TRY`

.

Functions: `COMPARISON`

, `POL`

, `REG`

, `LOESS`

, `SSPLINE`

.

Commands for: Regression analysis.

### Example

" Example FIT-3: Comparing linear regressions between groups Experiments on cauliflowers in 1957 and 1958 provided data on the mean number of florets in the plant and the temperature during the growing season (expressed as accumulated temperature above 0 deg C." " The counts and temperatures are in a file called 'FIT-3.DAT'" FILEREAD [NAME='%gendir%/examples/FIT-3.DAT'] MnCount,AccTemp " The first 7 values are from 1957 and the rest from 1958; set up a factor to distinguish the two years." FACTOR [LEVELS=!(1957,1958); VALUES=7(1957,1958)] Year " Fit a linear regression model of the mean count of florets on accumulated temperature - first ignoring the division into two years." MODEL MnCount TERMS AccTemp*Year FIT AccTemp " Fit parallel regressions for the two years." ADD Year " Fit separate regressions for the two years." ADD AccTemp.Year " Display the accumulated summary: an analysis of parallelism." RDISPLAY [PRINT=accumulated] " Show the parallel models." DROP [PRINT=*] AccTemp.Year RGRAPH [GRAPHICS=high] " Extract the parameter estimates and s.e.s and display the common slope and its s.e." RKEEP ESTIMATES=Esti; SE=Se CALC Slope,SlopeSE = (Esti,Se)$[2] PRINT Slope,SlopeSE