# test_phevis ```r test_phevis( train_param, df_test, surparam, model, START_DATE, PATIENT_NUM, ENCOUNTER_NUM ) ``` ## Arguments - `train_param`: Parameters for the model training (variables used, main ICD and CUIS, half_life, gold standard, omega). Usually obtained from train_phevis() function. - `df_test`: The dataframe on which to make the prediction. - `surparam`: The parameters used to compute the surrogate. Usually obtained by train_phevis() function. - `model`: The random intercept logistic regression. Usually obtained by train_phevis() function. - `START_DATE`: Column name of the time column. The time column should be numeric - `PATIENT_NUM`: Column name of the patient id column. - `ENCOUNTER_NUM`: Column name of the encounter id column. ## Returns A dataframe with the predictions. ## Examples ```r library(dplyr) library(PRROC) PheVis::data_phevis PheVis::data_perf var_vec <- c(paste0("var",1:10), "mainCUI", "mainICD") main_icd <- "mainICD" main_cui <- "mainCUI" GS <- "PR_state" half_life <- Inf df <- data_phevis %>% mutate(ENCOUNTER_NUM = row_number(), time = round(as.numeric(time))) trainsize <- 0.8*length(unique(df$subject)) trainid <- sample(x = unique(df$subject), size = trainsize) testid <- unique(df$subject)[!unique(df$subject) %in% trainid] df_train <- as.data.frame(df[df$subject %in% trainid,]) df_test <- as.data.frame(df[df$subject %in% testid,]) ##### train and test model ##### train_model <- PheVis::train_phevis(half_life = half_life, df = df_train, START_DATE = "time", PATIENT_NUM = "subject", ENCOUNTER_NUM = "ENCOUNTER_NUM", var_vec = var_vec, main_icd = main_icd, main_cui = main_cui) test_perf <- PheVis::test_phevis(train_param = train_model$train_param, df_test = df_test, START_DATE = "time", PATIENT_NUM = "subject", ENCOUNTER_NUM = "ENCOUNTER_NUM", surparam = train_model$surparam, model = train_model$model) pr_curve <-PRROC::pr.curve(scores.class0 = test_perf$df_result$PREDICTION, weights.class0 = df_test$PR_state) roc_curve <- PRROC::roc.curve(scores.class0 = test_perf$df_result$PREDICTION, weights.class0 = df_test$PR_state) ```

test_phevis function