createGlmModel

Create a generalized linear model that can be used in the PatientLevelPrediction package.

Usage

createGlmModel(
  coefficients,
  intercept = 0,
  mapping = "logistic",
  targetId = NULL,
  outcomeId = NULL,
  populationSettings = createStudyPopulationSettings(),
  restrictPlpDataSettings = createRestrictPlpDataSettings(),
  covariateSettings = FeatureExtraction::createDefaultCovariateSettings(),
  featureEngineering = NULL,
  tidyCovariates = NULL,
  requireDenseMatrix = FALSE
)

Arguments

coefficients

A dataframe containing two columns, coefficients and covariateId, both of type numeric. The covariateId column must contain valid covariateIds that match those used in the FeatureExtraction package.

intercept

A numeric value representing the intercept of the model.

mapping

A string representing the mapping from the linear predictors to outcome probabilities. For generalized linear models this is the inverse of the link function. Supported values is only "logistic" for logistic regression model at the moment.

targetId

Add the development targetId here

outcomeId

Add the development outcomeId here

populationSettings

Add development population settings (this includes the time-at-risk settings).

restrictPlpDataSettings

Add development restriction settings

covariateSettings

Add the covariate settings here to specify how the model covariates are created from the OMOP CDM

featureEngineering

Add any feature engineering here (e.g., if you need to modify the covariates before applying the model) This is a list of lists containing a string named funct specifying the engineering function to call and settings that are inputs to that function. funct must take as input trainData (a plpData object) and settings (a list).

tidyCovariates

Add any tidyCovariates mappings here (e.g., if you need to normalize the covariates)

requireDenseMatrix

Specify whether the model needs a dense matrix (TRUE or FALSE)

Value

A model object containing the model (Coefficients and intercept) and the prediction function.

Examples

coefficients <- data.frame(
  covariateId = c(1002),
  coefficient = c(0.05))
model <- createGlmModel(coefficients, intercept = -2.5)
data("simulationProfile")
plpData <- simulatePlpData(simulationProfile, n = 50, seed = 42)
#> Generating covariates
#> Generating cohorts
#> Generating outcomes
prediction <- predictPlp(model, plpData, plpData$cohorts)
#> did FE
#> did tidy
#> predict risk probabilities using predictGlm
#> Prediction took 0.134 secs
#> Prediction done in: 0.142 secs
# see the predicted risk values
prediction$value
#>  [1] 0.3208213 0.3543437 0.3318122 0.2994329 0.3658644 0.3543437 0.3318122
#>  [8] 0.3429895 0.2994329 0.3318122 0.4013123 0.4133824 0.3208213 0.3318122
#> [15] 0.3893608 0.3429895 0.3429895 0.3429895 0.3658644 0.4255575 0.3208213
#> [22] 0.3775407 0.4133824 0.3429895 0.3318122 0.3775407 0.3100255 0.3658644
#> [29] 0.3318122 0.4378235 0.3318122 0.3893608 0.3318122 0.3429895 0.3318122
#> [36] 0.3208213 0.3100255 0.3429895 0.3893608 0.3318122 0.2994329 0.3429895
#> [43] 0.3318122 0.3893608 0.3318122 0.2788848 0.3775407 0.2890505 0.3658644
#> [50] 0.4133824