Drops terms from a linear, generalized linear, generalized additive or nonlinear model.
|What to print (
||How to treat nonlinear parameters between groups (
||How to treat the constant (
||Limit for expansion of model terms; default
||Whether to pool ss in accumulated summary between all terms fitted in a linear model (
||Whether to base ratios in accumulated summary on rms from model with smallest residual ss or smallest residual ms (
||Which warning messages to suppress (
||Printing of probabilities for variance and deviance ratios (
||Printing of probabilities for t-statistics (
||Statistics to be displayed in the summary of analysis produced by
||Probability level for confidence intervals for parameter estimates; default 0.95|
||Description for line in accumulated analysis of variance (or deviance) table when
|formula||List of explanatory variates and factors, or model formula|
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
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.
TERMS Dilution * Solution
FITCURVE [PRINT=model,estimates; CURVE=logistic;\
NONLINEAR=separate] Dilution * Solution
If we then put
the curves will be constrained to have common nonlinear parameters, but all linear parameters will still be estimated separately for each group.
Commands for: Regression analysis.
" 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)$ PRINT Slope,SlopeSE