--factor_adaptation

Switch

--factor_adaptation

Description

Applies factor adaptation: rather than using the outcome controls directly, selects a smaller number of derived "factors" from them (or from --adaptation_factors) and adapts language features against those. Aliased as --fa.

Argument and Default Value

No argument. Boolean switch, default: False. Only takes effect with --nfold_test_regression.

Details

Verified end-to-end with --factor_selection_type pca (the default rfe is currently broken — see the Known issue on that page). If --adaptation_factors isn't given, falls back to using all of --outcome_controls as the factor pool. Sets an internal integrationMethod of "fa" (or "fa_plus" if combined with --factor_addition).

Other Switches

Required Switches:

• --nfold_test_regression

• --outcome_controls

Optional Switches:

• --adaptation_factors, --num_of_factors, --factor_selection_type

• --paired_factors, --factor_addition, --report

Example Commands

Using the packaged dla_tutorial dataset (see Differential Language Analysis (DLA) Tutorial):

dlatkInterface.py -d dla_tutorial -t msgs -c user_id -f 'feat$1to3gram$msgs$user_id$0_05' \
--outcome_table blog_outcomes --outcomes age --outcome_controls gender is_student is_education \
--combo_test_regression --factor_adaptation --adaptation_factors gender is_student is_education \
--num_of_factors 2 --factor_selection_type pca