PoisRunMulti: Fully runs a poisson regression model with multiple column...

In Colossus: "Risk Model Regression and Analysis with Complex Non-Linear Models"

Fully runs a poisson regression model with multiple column realizations, returning the model and results

Description

PoisRunMulti uses a formula, data.table, and list of controls to prepare and run a Colossus poisson regression function

Usage

PoisRunMulti(
  model,
  df,
  a_n = list(c(0)),
  keep_constant = c(0),
  realization_columns = matrix(c("temp00", "temp01", "temp10", "temp11"), nrow = 2),
  realization_index = c("temp0", "temp1"),
  control = list(),
  gradient_control = list(),
  single = FALSE,
  observed_info = FALSE,
  fma = FALSE,
  mcml = FALSE,
  cons_mat = as.matrix(c(0)),
  cons_vec = c(0),
  ...
)

Arguments

model	either a formula written for the get_form function, or the model result from the get_form function.
df	a data.table containing the columns of interest
a_n	list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector.
keep_constant	binary values to denote which parameters to change
realization_columns	used for multi-realization regressions. Matrix of column names with rows for each column with realizations, columns for each realization
realization_index	used for multi-realization regressions. Vector of column names, one for each column with realizations. Each name should be used in the "names" variable in the equation definition
control	list of parameters controlling the convergence, see the Control_Options vignette for details
gradient_control	a list of control options for the gradient descent algorithm. If any value is given, a gradient descent algorithm is used instead of Newton-Raphson. See the Control_Options vignette for details
single	a boolean to denote that only the log-likelihood should be calculated and returned, no derivatives or iterations
observed_info	a boolean to denote that the observed information matrix should be used to calculate the standard error for parameters, not the expected information matrix
fma	a boolean to denote that the Frequentist Model Averaging method should be used
mcml	a boolean to denote that the Monte Carlo Maximum Likelihood method should be used
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
...	can include the named entries for the control list parameter

Value

returns a class fully describing the model and the regression results

See Also

Other Poisson Wrapper Functions: EventAssignment.poisres(), EventAssignment.poisresbound(), LikelihoodBound.poisres(), PoisRun(), PoisRunJoint(), Residual.poisres()

Examples

library(data.table)
df <- data.table::data.table(
  "UserID" = c(112, 114, 213, 214, 115, 116, 117),
  "t0" = c(18, 20, 18, 19, 21, 20, 18),
  "t1" = c(30, 45, 57, 47, 36, 60, 55),
  "lung" = c(0, 0, 1, 0, 1, 0, 0),
  "dose" = c(0, 1, 1, 0, 1, 0, 1)
)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
df$rand0 <- floor(runif(nrow(df), min = 0, max = 5))
df$rand1 <- floor(runif(nrow(df), min = 0, max = 5))
df$rand2 <- floor(runif(nrow(df), min = 0, max = 5))
names <- c("dose", "rand")
realization_columns <- matrix(c("rand0", "rand1", "rand2"), nrow = 1)
realization_index <- c("rand")
control <- list(
  "ncores" = 1, "lr" = 0.75, "maxiter" = 1,
  "halfmax" = 2, "epsilon" = 1e-6,
  "deriv_epsilon" = 1e-6, "step_max" = 1.0,
  "thres_step_max" = 100.0,
  "verbose" = 0, "ties" = "breslow", "double_step" = 1
)
formula <- Pois(t1, lung) ~ loglinear(CONST, dose, rand, 0) + multiplicative()
res <- PoisRun(formula, df, control = control)

Colossus documentation built on Feb. 20, 2026, 5:10 p.m.