# Predicts how many events are due to baseline vs excess at the confidence bounds of a single parameter `RunPoissonEventAssignment_bound` uses user provided data, the results of a poisson regression, and options to calculate background and excess events ```r RunPoissonEventAssignment_bound( df, pyr0 = "pyr", event0 = "event", alternative_model = list(), keep_constant = c(0), modelform = "M", fir = 0, der_iden = 0, check_num = 1, z = 2, control = list(), strat_col = "null", model_control = list() ) ``` ## Arguments - `df`: a data.table containing the columns of interest - `pyr0`: column used for person-years per row - `event0`: column used for event status - `alternative_model`: the new model of interest in list form, output from a poisson regression - `keep_constant`: binary values to denote which parameters to change - `modelform`: string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E - `fir`: term number for the initial term, used for models of the form T0*f(Ti) in which the order matters - `der_iden`: number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 - `check_num`: the parameter number to check at the bounds of, indexed from 1 using the order returned by Colossus - `z`: Z score to use for confidence interval - `control`: list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") - `strat_col`: column to stratify by if needed - `model_control`: controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details ## Returns returns a list of the final results ## Examples ```r library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case pyr <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e0 <- RunPoissonRegression_Omnibus( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col ) e <- RunPoissonEventAssignment_bound( df, pyr, event, e0, keep_constant, modelform, fir, der_iden, 4, 2, control ) ``` ## See Also Other Poisson Wrapper Functions: `RunPoissonEventAssignment()`, `RunPoissonRegression()`, `RunPoissonRegression_Guesses_CPP()`, `RunPoissonRegression_Joint_Omnibus()`, `RunPoissonRegression_Omnibus()`, `RunPoissonRegression_Residual()`, `RunPoissonRegression_STRATA()`, `RunPoissonRegression_Single()`, `RunPoissonRegression_Tier_Guesses()`

RunPoissonEventAssignment_bound function