Used to calculate observation details based on
cohorts created with
vector of parameters to be estimated
ubiquity system object
If estimation is TRUE then the output is a matrix of observation details of the format:
od$pred = [TIME, OBS, PRED, VAR, OUTPUT, COHORT]
The values are the observed (
OBS) data, predicted
PRED) and variance (
VAR) at the given
TIME. The columns
COHORT can be used for sorting. These should be unique numbers.
When estimation is
FALSE we output
od$pred is a data frame with the
od$pred = [TIME, OBS, PRED, VAR, SMOOTH, OUTPUT, COHORT]
VAR are the same as those listed above. The
FALSE for rows that correspond to records in the dataset and
TRUE when the
PRED represents the smooth predictions. The
columns here are text values used when defining the cohorts.
od$all list item is created with all of the simulation information
stored for each cohort:
tstime - timescale of the system
ts.ts1, ... ts.tsn - timescales defined in the system
pred - smooth prediction
name - state or output name corresponding to the prediction
cohort - name of the cohort for these predictions
Lastly the field
isgood will be set to
FALSE if any problems are encountered, and
TRUE if everything worked.
od$isgood = TRUE
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