Fully runs a joint poisson regression model, returning the model and results

PoisRunJoint uses a list of formula, data.table, and list of controls to prepare and run a Colossus poisson regression function on a joint dataset

Usage

PoisRunJoint(
  model,
  df,
  a_n = list(c(0)),
  keep_constant = c(0),
  control = list(),
  gradient_control = list(),
  single = FALSE,
  observed_info = 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

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

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(), LikelihoodBound.poisres(), PoisRun(), PoissonCurveSolver(), Residual.poisres(), RunPoissonEventAssignment(), RunPoissonEventAssignment_bound(), RunPoissonRegression_Joint_Omnibus(), RunPoissonRegression_Omnibus(), RunPoissonRegression_Residual()

Examples

library(data.table)
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),
  "Flu_Status" = c(0, 1, 0, 0, 1, 0, 1),
  "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)
)
formula_list <- list(Pois(Ending_Age, Cancer_Status) ~ plinear(d, 0),
  Pois(Ending_Age, Flu_Status) ~ loglinear(d, 0),
  "shared" = Pois(Ending_Age) ~ loglinear(a, b, c, 0)
)
res <- PoisRunJoint(formula_list, df)