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PoissonCurveSolver solves the confidence interval for a poisson model, starting at the optimum point and iteratively optimizing each point to using the bisection method

Usage

PoissonCurveSolver(
  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(),
  strat_col = "null",
  model_control = list(),
  cons_mat = as.matrix(c(0)),
  cons_vec = c(0)
)

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")

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

cons_mat

Matrix containing coefficients for system of linear constraints, formatted as matrix

cons_vec

Vector containing constants for system of linear constraints, formatted as vector

Value

returns a list of the final results