# Performs basic Poisson regression, generates multiple starting guesses on c++ side `RunPoissonRegression_Guesses_CPP` uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions. Has additional options to starting with several initial guesses ```r RunPoissonRegression_Guesses_CPP( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), guesses_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 - `names`: columns for elements of the model, used to identify data columns - `term_n`: term numbers for each element of the model - `tform`: list of string function identifiers, used for linear/step - `keep_constant`: binary values to denote which parameters to change - `a_n`: list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. - `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 - `control`: list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") - `guesses_control`: list of parameters to control how the guessing works, see Def_Control_Guess() 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" e <- RunPoissonRegression_Guesses_CPP( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, guesses_control, strat_col ) ``` ## See Also Other Poisson Wrapper Functions: `RunPoissonEventAssignment()`, `RunPoissonEventAssignment_bound()`, `RunPoissonRegression()`, `RunPoissonRegression_Joint_Omnibus()`, `RunPoissonRegression_Omnibus()`, `RunPoissonRegression_Residual()`, `RunPoissonRegression_STRATA()`, `RunPoissonRegression_Single()`, `RunPoissonRegression_Tier_Guesses()`

RunPoissonRegression_Guesses_CPP function