predict_command

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The Predict Command

Syntax

dismod_at database predict source
dismod_at database predict source zero_meas_value
dismod_at database predict source fit_var scale
dismod_at database predict source fit_var scale zero_meas_value

database

Is an sqlite database containing the dismod_at input tables which are not modified.

source

This argument specifies where model variable values to use for the predictions. The possible values are listed below:

sample

If source is sample , the values in the sample_table are used for the predictions. In this case there are number_simulate sets of model variables that predictions are computed for. If the samples were simulated using the asymptotic method, they may not be within the lower and upper limits for the corresponding variables. The variables are censored to be within their limits before the predictions are computed.

fit_var

If source is fit_var , the values in the fit_var_table are used for the predictions. In this case there is only one set of model variables that the predictions are computed for and sample_index is always zero.

truth_var

If source is truth_var , the values in the truth_var_table are used for the predictions. In this case there is only one set of model variables that the predictions are computed for and sample_index is always zero.

zero_meas_value

If this argument is present, the value zero is used for the meas_value covariate multipliers (instead of the value in the source table). This predicts what the mean of the corresponding data would be if there were no measurement value covariate effects.

fit_var scale

If fit_var scale follows source , source must be sample and the samples are scaled before the predictions are made. Let i be the sample_index in the sample table. Let j the var_id in the sample table and the fit_var_id in the fit_var table. The scaled samples are defined by

scaled_sample(i,j) = fit_var(j) + scale * ( sample(i,j) - fit_var(j) )

predict_table

A new predict_table is created each time this command is run. It contains the average integrand values for set of model variables and each avgint_id in the Avgint Subset .

Example

The files predict_command.py and user_predict_fit.py contain examples and tests using this command.