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Performs joint Poisson regression using the omnibus function

Source: R/Poisson_Regression.R
RunPoissonRegression_Joint_Omnibus.Rd

RunPoissonRegression_Joint_Omnibus 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, uses joint competing risks equation

Usage

RunPoissonRegression_Joint_Omnibus(
  df,
  pyr0,
  events,
  name_list,
  term_n_list = list(),
  tform_list = list(),
  keep_constant_list = list(),
  a_n_list = list(),
  modelform = "M",
  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

events

vector of event column names

name_list

list of vectors for columns for event specific or shared model elements, required

term_n_list

list of vectors for term numbers for event specific or shared model elements, defaults to term 0

tform_list

list of vectors for subterm types for event specific or shared model elements, defaults to loglinear

keep_constant_list

list of vectors for constant elements for event specific or shared model elements, defaults to free (0)

a_n_list

list of vectors for parameter values for event specific or shared model elements, defaults to term 0

modelform

string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E

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 a system of linear constraints, formatted as matrix

cons_vec

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

Value

returns a list of the final results

See also

Other Poisson Wrapper Functions: PoissonCurveSolver(), RunPoissonEventAssignment(), RunPoissonEventAssignment_bound(), RunPoissonRegression(), RunPoissonRegression_Guesses_CPP(), RunPoissonRegression_Omnibus(), RunPoissonRegression_Residual(), RunPoissonRegression_Single(), RunPoissonRegression_Strata(), RunPoissonRegression_Tier_Guesses()

Examples

library(data.table)
## basic example code reproduced from the starting-description vignette
a <- c(0, 0, 0, 1, 1, 1)
b <- c(1, 1, 1, 2, 2, 2)
c <- c(0, 1, 2, 2, 1, 0)
d <- c(1, 1, 0, 0, 1, 1)
e <- c(0, 1, 1, 1, 0, 0)
f <- c(0, 1, 0, 0, 1, 1)
df <- data.table("t0" = a, "t1" = b, "e0" = c, "e1" = d, "fac" = e)
time1 <- "t0"
time2 <- "t1"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
events <- c("e0", "e1")
names_e0 <- c("fac")
names_e1 <- c("fac")
names_shared <- c("t0", "t0")
term_n_e0 <- c(0)
term_n_e1 <- c(0)
term_n_shared <- c(0, 0)
tform_e0 <- c("loglin")
tform_e1 <- c("loglin")
tform_shared <- c("quad_slope", "loglin_top")
keep_constant_e0 <- c(0)
keep_constant_e1 <- c(0)
keep_constant_shared <- c(0, 0)
a_n_e0 <- c(-0.1)
a_n_e1 <- c(0.1)
a_n_shared <- c(0.001, -0.02)
name_list <- list("shared" = names_shared, "e0" = names_e0, "e1" = names_e1)
term_n_list <- list("shared" = term_n_shared, "e0" = term_n_e0, "e1" = term_n_e1)
tform_list <- list("shared" = tform_shared, "e0" = tform_e0, "e1" = tform_e1)
keep_constant_list <- list(
  "shared" = keep_constant_shared,
  "e0" = keep_constant_e0, "e1" = keep_constant_e1
)
a_n_list <- list("shared" = a_n_shared, "e0" = a_n_e0, "e1" = a_n_e1)
modelform <- "M"
control <- list(
  "ncores" = 2, "lr" = 0.75, "maxiter" = 5,
  "halfmax" = 5, "epsilon" = 1e-3,
  "deriv_epsilon" = 1e-3, "abs_max" = 1.0,
  "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 <- "f"
e <- RunPoissonRegression_Joint_Omnibus(
  df, pyr, events, name_list,
  term_n_list,
  tform_list, keep_constant_list,
  a_n_list,
  modelform,
  control, strat_col
)