# This demo will run a logistic regression model on simulated data and will show the Shiny App
library(PatientLevelPrediction)
devAskNewPage(ask = FALSE)
### Simulated data from a database profile
set.seed(1234)
data(plpDataSimulationProfile)
sampleSize <- 2000
plpData <- simulatePlpData(plpDataSimulationProfile, n = sampleSize)
### Define the study population
populationSettings <- createStudyPopulationSettings(
binary = TRUE,
firstExposureOnly = FALSE,
washoutPeriod = 0,
removeSubjectsWithPriorOutcome = FALSE,
priorOutcomeLookback = 99999,
requireTimeAtRisk = TRUE,
minTimeAtRisk = 0,
riskWindowStart = 0,
startAnchor = 'cohort start',
riskWindowEnd = 365,
endAnchor = 'cohort start'
)
### Regularised logistic regression
lr_model <- setLassoLogisticRegression()
lr_results <- runPlp(
plpData = plpData,
outcomeId = 2,
analysisId = 'demo',
analysisName = 'run plp demo',
populationSettings = populationSettings,
splitSettings = createDefaultSplitSetting(
type = "time",
testFraction = 0.25,
nfold = 2
),
sampleSettings = createSampleSettings(),
preprocessSettings = createPreprocessSettings(
minFraction = 0,
normalize = T
),
modelSettings = lr_model,
executeSettings = createDefaultExecuteSettings(),
saveDirectory = "./plpdemo"
)
### Have a look at the results object.
### You can start the Shiny App by using this command now:
### viewPlp(lr_results)
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