This function loads all the models in a multiple plp analysis folder and validates the models on new data
evaluateMultiplePlp(
analysesLocation,
outputLocation,
connectionDetails,
validationSchemaTarget,
validationSchemaOutcome,
validationSchemaCdm,
databaseNames,
validationTableTarget,
validationTableOutcome,
validationIdTarget = NULL,
validationIdOutcome = NULL,
oracleTempSchema = NULL,
verbosity = "INFO",
keepPrediction = F,
recalibrate = NULL,
sampleSize = NULL
)
The location where the multiple plp analyses are
The location to save to validation results
The connection details for extracting the new data
A string or list of strings specifying the database containing the target cohorts
A string or list of strings specifying the database containing the outcome cohorts
A string or list of strings specifying the database containing the cdm
A string of lift of strings specifying sharing friendly database names corresponding to validationSchemaCdm
A string or list of strings specifying the table containing the target cohorts
A string or list of strings specifying the table containing the outcome cohorts
An iteger or list of integers specifying the cohort id for the target cohorts
An iteger or list of integers specifying the cohort id for the outcome cohorts
The temp oracle schema requires read/write
Sets the level of the verbosity. If the log level is at or higher in priority than the logger threshold, a message will print. The levels are:
DEBUGHighest verbosity showing all debug statements
TRACEShowing information about start and end of steps
INFOShow informative information (Default)
WARNShow warning messages
ERRORShow error messages
FATALBe silent except for fatal errors
Whether to keep the predicitons for the new data
A vector of recalibration methods (currently supports 'RecalibrationintheLarge' and/or 'weakRecalibration')
If not NULL, the number of people to sample from the target cohort
Users need to input a location where the results of the multiple plp analyses are found and the connection and database settings for the new data