Skip to contents

Fully runs a logistic regression model, returning the model and results

Source: R/BasicRun.R
LogisticRun.Rd

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

Usage

LogisticRun(
  model,
  df,
  a_n = list(c(0)),
  keep_constant = c(0),
  control = list(),
  gradient_control = list(),
  link = "odds",
  single = FALSE,
  observed_info = FALSE,
  cons_mat = as.matrix(c(0)),
  cons_vec = c(0),
  norm = "null",
  ...
)

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

Used in logistic regression, the linking function relating the input model and event probability. Current options are "odds", "ident", and "loglink" for the odds ratio, identity, and complimentary loglink options.

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

norm

methods used to normalize the covariates. Default is 'null' for no normalization. Other options include 'max' to normalize by the absolute maximum and 'mean' to normalize by the mean

...

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 Logistic Wrapper Functions: RunLogisticRegression_Omnibus()

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),
  "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)
)
control <- list(
  "ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1),
  "halfmax" = 1
)
formula <- logit(Cancer_Status) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
res <- LogisticRun(formula, df, a_n = c(1.1, -0.1, 0.2, 0.5), control = control)