Package {Colossus}

Type:	Package
Title:	"Risk Model Regression and Analysis with Complex Non-Linear Models"
Version:	1.6.2
URL:	https://ericgiunta.github.io/Colossus/, https://github.com/ericgiunta/Colossus
BugReports:	https://github.com/ericgiunta/Colossus/issues
Description:	Performs risk analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards https://en.wikipedia.org/wiki/Proportional_hazards_model, Poisson https://en.wikipedia.org/wiki/Poisson_regression, and Fine-Gray competing risks https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.
License:	GPL (≥ 3)
Imports:	Rcpp, data.table, parallel, stats, utils, rlang, methods, callr, stringr, processx, dplyr, tibble, lubridate, pracma, withr
LinkingTo:	Rcpp, RcppEigen, testthat
SystemRequirements:	make, C++17
Encoding:	UTF-8
Suggests:	knitr, rmarkdown, testthat, pandoc, spelling, survival, splines, ggplot2
Depends:	R (≥ 4.1.0)
Config/testthat/edition:	3
VignetteBuilder:	knitr
Language:	en-US
Biarch:	TRUE
NeedsCompilation:	yes
Packaged:	2026-09-22 19:57:36 UTC; user
Author:	Eric King-Giunta [aut, cre], Amir Bahadori [ctb], Dan Andresen [ctb], Linda Walsh [ctb], Benjamin French [ctb], Lawrence Dauer [ctb], John Boice Jr [ctb], Kansas State University [cph], NASA [fnd], NCRP [fnd], NRC [fnd]
Maintainer:	Eric King-Giunta <egiunta@ksu.edu>
Config/roxygen2/version:	8.1.0
Repository:	CRAN
Date/Publication:	2026-09-22 21:50:02 UTC

Fully runs a case-control regression model, returning the model and results

Description

CaseControlRun uses a formula, data.table, and list of controls to prepare and run a Colossus matched case-control regression function

Usage

CaseControlRun(
  model,
  df,
  a_n = list(0),
  keep_constant = 0,
  control = list(),
  conditional_threshold = 50,
  gradient_control = list(),
  single = FALSE,
  observed_info = FALSE,
  cons_mat = as.matrix(0),
  cons_vec = 0,
  norm = "null",
  ...
)

Arguments

Value

returns a class fully describing the model and the regression results

Examples

library(data.table)
df <- 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 = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- CaseCon_Strata(Cancer_Status, e) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
res <- CaseControlRun(formula, df,
  a_n = list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4)),
  control = control
)

Fully runs a cox or fine-gray regression model, returning the model and results

Description

CoxRun uses a formula, data.table, and list of controls to prepare and run a Colossus cox or fine-gray regression function

Usage

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

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Cox Wrapper Functions: CoxRunMulti()

Examples

library(data.table)
df <- 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 = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
res <- CoxRun(formula, df,
  a_n = list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4)),
  control = control
)

Fully runs a cox or fine-gray regression model with multiple column realizations, returning the model and results

Description

CoxRunMulti uses a formula, data.table, and list of controls to prepare and run a Colossus cox or fine-gray regression function

Usage

CoxRunMulti(
  model,
  df,
  a_n = list(0),
  keep_constant = 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 = TRUE,
  mcml = FALSE,
  cons_mat = as.matrix(0),
  cons_vec = 0,
  ...
)

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Cox Wrapper Functions: CoxRun()

Examples

library(data.table)
df <- 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))
realization_columns <- matrix(c("rand0", "rand1", "rand2"), nrow = 1)
realization_index <- c("rand")
control <- list(
  ncores = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- Cox(t0, t1, lung) ~ loglinear(dose, rand, 0) + multiplicative()
res <- CoxRunMulti(formula, df,
  control = control,
  realization_columns = realization_columns,
  realization_index = realization_index
)

Automates creating a date difference column

Description

Date_Shift generates a new dataframe with a column containing time difference in a given unit

Usage

Date_Shift(df, dcol0, dcol1, col_name, units = "days")

Arguments

Value

returns the updated dataframe

See Also

Other Data Cleaning Functions: Joint_Multiple_Events(), Replace_Missing(), Time_Since(), factorize(), gen_time_dep()

Examples

library(data.table)
m0 <- c(1, 1, 2, 2)
m1 <- c(2, 2, 3, 3)
d0 <- c(1, 2, 3, 4)
d1 <- c(6, 7, 8, 9)
y0 <- c(1990, 1991, 1997, 1998)
y1 <- c(2001, 2003, 2005, 2006)
df <- data.table(m0 = m0, m1 = m1, d0 = d0, d1 = d1, y0 = y0, y1 = y1)
df <- Date_Shift(df, c("m0", "d0", "y0"), c("m1", "d1", "y1"), "date_since")

Generic background/excess event calculation function

Description

EventAssignment Generic background/excess event calculation function for standard poisson regressions and likelihood based confidence interval results.

Usage

EventAssignment(x, df, ...)

Arguments

Value

returns matrices of events assigned to background and excess

See Also

Other Poisson Event Assignment: EventAssignment.poisres(), EventAssignment.poisresbound()

Predicts how many events are due to baseline vs excess for a completed poisson model

Description

EventAssignment.poisres uses user provided data, person-year/event columns, vectors specifying the model, and options to calculate background and excess events for a solved Poisson regression

Usage

## S3 method for class 'poisres'
EventAssignment(
  x,
  df,
  assign_control = list(),
  control = list(),
  a_n = NULL,
  ...
)

Arguments

Value

returns matrices of events assigned to background and excess

See Also

Other Poisson Event Assignment: EventAssignment(), EventAssignment.poisresbound()

Predicts how many events are due to baseline vs excess for a completed poisson likelihood boundary regression

Description

EventAssignment.poisresbound uses user provided data, person-year/event columns, vectors specifying the model, and options to calculate background and excess events for a solved Poisson regression

Usage

## S3 method for class 'poisresbound'
EventAssignment(
  x,
  df,
  assign_control = list(),
  control = list(),
  a_n = NULL,
  ...
)

Arguments

Value

returns matrices of events assigned to background and excess

See Also

Other Poisson Event Assignment: EventAssignment(), EventAssignment.poisres()

uses a table, list of categories, and list of event summaries to generate person-count tables

Description

Event_Count_Gen generates event-count tables

Usage

Event_Count_Gen(table, categ = list(), events = list())

Arguments

Value

returns a grouped table and a list of category boundaries used

See Also

Other Table Generation Functions: Event_Time_Gen()

Examples

library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 0, 0, 0, 1, 0)
table <- data.table(
  a = a,
  b = b,
  c = c
)
categ <- list(
  a = "0/3/5]7",
  b = list(
    lower = c(-1, 3, 6),
    upper = c(3, 6, 10),
    name = c("low", "medium", "high")
  )
)
event <- list(
  c = "count AS cases",
  a = "mean", b = "mean"
)
e <- Event_Count_Gen(table, categ, event)

uses a table, list of categories, list of summaries, list of events, and person-year information to generate person-time tables

Description

Event_Time_Gen generates event-time tables

Usage

Event_Time_Gen(
  table,
  pyr = list(),
  time_scale = list(),
  categ = list(),
  summaries = list(),
  events = NULL,
  fcount = FALSE,
  lcount = FALSE,
  studyid = "studyID",
  verbose = FALSE
)

Arguments

Value

returns a grouped table and a list of category boundaries used

See Also

Other Table Generation Functions: Event_Count_Gen()

Examples

library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 0, 0, 0, 1, 0)
d <- c(1, 2, 3, 4, 5, 6, 7)
e <- c(2, 3, 4, 5, 6, 7, 8)
f <- c(
  1900, 1900, 1900, 1900,
  1900, 1900, 1900
)
g <- c(1, 2, 3, 4, 5, 6, 7)
h <- c(2, 3, 4, 5, 6, 7, 8)
i <- c(
  1901, 1902, 1903, 1904,
  1905, 1906, 1907
)
table <- data.table(
  a = a, b = b, c = c,
  d = d, e = e, f = f,
  g = g, h = h, i = i
)
categ <- list(
  a = "-1/3/5]7"
)
calendar_categ <- list(
  type = "calendar",
  day = c(1, 1, 1),
  month = c(1, 1, 1),
  year = c(1901, 1904, 1908)
)
time_scale <- list(time = calendar_categ)
summary <- list(
  c = "count AS cases"
)
events <- list("c")
pyr <- list(
  entry = list(year = "f"),
  exit = list(year = "i"),
  unit = "years"
)
e <- Event_Time_Gen(table, pyr, time_scale, categ, summary, events)

Automates creating data for a joint competing risks analysis

Description

Joint_Multiple_Events generates input for a regression with multiple non-independent events and models

Usage

Joint_Multiple_Events(
  df,
  events,
  name_list,
  term_n_list = list(),
  tform_list = list(),
  keep_constant_list = list(),
  a_n_list = list()
)

Arguments

Value

returns the updated dataframe and model inputs

See Also

Other Data Cleaning Functions: Date_Shift(), Replace_Missing(), Time_Since(), factorize(), gen_time_dep()

Examples

library(data.table)
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)
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 <- 0
term_n_e1 <- 0
term_n_shared <- c(0, 0)
tform_e0 <- "loglin"
tform_e1 <- "loglin"
tform_shared <- c("quad_slope", "loglin_top")
keep_constant_e0 <- 0
keep_constant_e1 <- 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)
val <- Joint_Multiple_Events(
  df, events, name_list, term_n_list,
  tform_list, keep_constant_list, a_n_list
)

Generic likelihood boundary calculation function

Description

LikelihoodBound Generic likelihood boundary calculation function. Used by the coxres, poisres, and logitres objects to calculate likelihood based confidence intervals.

Usage

LikelihoodBound(x, df, curve_control = list(), control = list(), ...)

Arguments

Value

returns a summary of the likelihood boundary

See Also

Other Likelihood Boundaries: LikelihoodBound.coxres(), LikelihoodBound.logitres(), LikelihoodBound.poisres()

Calculates the likelihood boundary for a completed cox model

Description

LikelihoodBound.coxres solves the confidence interval for a cox model, starting at the optimum point and iteratively optimizing end-points of intervals.

Usage

## S3 method for class 'coxres'
LikelihoodBound(x, df, curve_control = list(), control = list(), ...)

Arguments

Value

returns a summary of the likelihood boundary

See Also

Other Likelihood Boundaries: LikelihoodBound(), LikelihoodBound.logitres(), LikelihoodBound.poisres()

Calculates the likelihood boundary for a completed Logistic model

Description

LikelihoodBound.logitres solves the confidence interval for a Poisson model, starting at the optimum point and iteratively optimizing end-points of intervals.

Usage

## S3 method for class 'logitres'
LikelihoodBound(x, df, curve_control = list(), control = list(), ...)

Arguments

Value

returns a summary of the likelihood boundary

See Also

Other Likelihood Boundaries: LikelihoodBound(), LikelihoodBound.coxres(), LikelihoodBound.poisres()

Calculates the likelihood boundary for a completed Poisson model

Description

LikelihoodBound.poisres solves the confidence interval for a Poisson model, starting at the optimum point and iteratively optimizing end-points of intervals.

Usage

## S3 method for class 'poisres'
LikelihoodBound(x, df, curve_control = list(), control = list(), ...)

Arguments

Value

returns a summary of the likelihood boundary

See Also

Other Likelihood Boundaries: LikelihoodBound(), LikelihoodBound.coxres(), LikelihoodBound.logitres()

Defines the likelihood ratio test

Description

Likelihood_Ratio_Test uses two models and calculates the ratio

Usage

Likelihood_Ratio_Test(alternative_model, null_model)

Arguments

Value

returns the score statistic

Examples

library(data.table)
# In an actual example, one would run two seperate RunCoxRegression regressions,
#    assigning the results to e0 and e1
a <- c(0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7)
c <- c(1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0)
d <- c(3, 4, 5, 6, 7, 8, 9, 1, 2, 1, 1, 2, 1, 2)
e <- c(1, 2, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 1, 2)
df <- data.table(a = a, b = b, c = c, d = d, e = e)
keep_constant <- 0
a_n <- c(-0.1, 0.1, 0.1, 0.2)
control <- list(ncores = 1, maxiter = 10, verbose = 0)
model <- Cox(a, b, c) ~ plinear(d * d, 0) + loglinear(factor(e))
alternative_model <- CoxRun(model, df,
  control = control,
  a_n = a_n, keep_constant = c(0, 1, 0)
)
null_model <- CoxRun(Cox(a, b, c) ~ null(), df, control = control)
score <- Likelihood_Ratio_Test(alternative_model, null_model)

Calculates Full Parameter list for Special Dose Formula

Description

Linked_Dose_Formula Calculates all parameters for linear-quadratic and linear-exponential linked formulas

Usage

Linked_Dose_Formula(tforms, paras)

Arguments

Value

returns list of full parameters

See Also

Other Piece-wise Assistance Functions: Linked_Lin_Exp_Para()

Examples

library(data.table)
tforms <- list(cov_0 = "quad", cov_1 = "exp")
paras <- list(cov_0 = c(1, 3.45), cov_1 = c(1.2, 4.5, 0.1))
full_paras <- Linked_Dose_Formula(tforms, paras)

Calculates The Additional Parameter For a linear-exponential formula with known maximum

Description

Linked_Lin_Exp_Para Calculates what the additional parameter would be for a desired maximum

Usage

Linked_Lin_Exp_Para(y, a0, a1_goal)

Arguments

Value

returns parameter used by Colossus

See Also

Other Piece-wise Assistance Functions: Linked_Dose_Formula()

Examples

library(data.table)
y <- 7.6
a0 <- 1.2
a1_goal <- 15
full_paras <- Linked_Lin_Exp_Para(y, a0, a1_goal)

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

Description

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

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Logistic Wrapper Functions: LogisticRunMulti(), LogisticRunMultiOut()

Examples

library(data.table)
df <- 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 = 1, 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)

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

Description

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

Usage

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

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Logistic Wrapper Functions: LogisticRun(), LogisticRunMultiOut()

Examples

library(data.table)
df <- 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))
realization_columns <- matrix(c("rand0", "rand1", "rand2"), nrow = 1)
realization_index <- c("rand")
control <- list(
  ncores = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- logit(lung) ~ loglinear(CONST, dose, rand, 0) + multiplicative()
res <- LogisticRunMulti(formula, df,
  control = control,
  realization_columns = realization_columns,
  realization_index = realization_index
)

Fully runs a logistic regression model with multiple event realizations, returning the model and results

Description

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

Usage

LogisticRunMultiOut(
  model,
  df,
  a_n = list(0),
  keep_constant = 0,
  realization_columns = c("event0", "event1"),
  control = list(),
  gradient_control = list(),
  link = "odds",
  single = FALSE,
  observed_info = FALSE,
  cons_mat = as.matrix(0),
  cons_vec = 0,
  ...
)

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Logistic Wrapper Functions: LogisticRun(), LogisticRunMulti()

Examples

library(data.table)
df <- 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),
  event = c(0, 0, 1, 0, 1, 0, 0),
  dose = c(0, 1, 1, 0, 1, 0, 1)
)
set.seed(3742)
df$event0 <- rbinom(nrow(df), size = 1, prob = 0.3)
df$event1 <- rbinom(nrow(df), size = 1, prob = 0.3)
df$event2 <- rbinom(nrow(df), size = 1, prob = 0.3)
realization_columns <- c("event0", "event1", "event2")
control <- list(
  ncores = 1, lr = 0.75, maxiter = 10,
  verbose = 0
)
formula <- logit(event) ~ loglinear(dose, 0) + multiplicative()
res <- LogisticRunMultiOut(formula, df,
  realization_columns = realization_columns,
  control = control
)

Checks the OMP flag

Description

OMP_Check Called directly from R, checks the omp flag and returns true if omp is enabled

Usage

OMP_Check()

Value

boolean: True for OMP allowed

See Also

Other Output and Information Functions: System_Version()

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

Description

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

Usage

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

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Poisson Wrapper Functions: PoisRunJoint(), PoisRunMulti(), PoisRunMultiOut()

Examples

library(data.table)
df <- 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 = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- Pois(Ending_Age, Cancer_Status) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
res <- PoisRun(formula, df, a_n = c(1.1, -0.1, 0.2, 0.5), control = control)

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

Description

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(0),
  keep_constant = 0,
  control = list(),
  gradient_control = list(),
  single = FALSE,
  observed_info = FALSE,
  cons_mat = as.matrix(0),
  cons_vec = 0,
  norm = "null",
  ...
)

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Poisson Wrapper Functions: PoisRun(), PoisRunMulti(), PoisRunMultiOut()

Examples

library(data.table)
df <- 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)
)
control <- list(
  ncores = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 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, control = control)

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(0),
  keep_constant = 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 = TRUE,
  mcml = FALSE,
  cons_mat = as.matrix(0),
  cons_vec = 0,
  ...
)

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Poisson Wrapper Functions: PoisRun(), PoisRunJoint(), PoisRunMultiOut()

Examples

library(data.table)
df <- 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))
realization_columns <- matrix(c("rand0", "rand1", "rand2"), nrow = 1)
realization_index <- c("rand")
control <- list(
  ncores = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- Pois(t1, lung) ~ loglinear(CONST, dose, rand, 0) + multiplicative()
res <- PoisRunMulti(formula, df,
  control = control,
  realization_columns = realization_columns,
  realization_index = realization_index
)

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

Description

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

Usage

PoisRunMultiOut(
  model,
  df,
  a_n = list(0),
  keep_constant = 0,
  realization_columns = c("event0", "event1"),
  control = list(),
  gradient_control = list(),
  single = FALSE,
  observed_info = FALSE,
  cons_mat = as.matrix(0),
  cons_vec = 0,
  ...
)

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Poisson Wrapper Functions: PoisRun(), PoisRunJoint(), PoisRunMulti()

Examples

library(data.table)
df <- 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$rand <- c(1, floor(runif(nrow(df) - 1, min = 0, max = 1)))
df$lung0 <- c(1, floor(runif(nrow(df) - 1, min = 0, max = 1)))
df$lung1 <- c(1, floor(runif(nrow(df) - 1, min = 0, max = 1)))
df$lung2 <- c(1, floor(runif(nrow(df) - 1, min = 0, max = 1)))
realization_columns <- c("lung", "lung1", "lung2")
control <- list(
  ncores = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- Pois(t1, lung) ~ loglinear(CONST, dose, rand, 0) + multiplicative()
res <- PoisRunMultiOut(formula, df,
  control = control,
  realization_columns = realization_columns
)

Generic relative risk calculation function

Description

RelativeRisk Generic relative risk calculation function, currently only used by the coxres object, to calculate relative risks for either the last parameter or a custom point.

Usage

RelativeRisk(x, df, ...)

Arguments

Value

returns matrices of the relatives risks

See Also

Other Predicted Risk and Rate: RelativeRisk.coxmodel(), RelativeRisk.coxres(), RelativeRisk.formula()

Calculates hazard ratios for a reference vector and model

Description

RelativeRisk.coxmodel uses a cox model object and data, to evaluate relative risk in the data using the risk model from the result

Usage

## S3 method for class 'coxmodel'
RelativeRisk(x, df, a_n = NULL, ...)

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Predicted Risk and Rate: RelativeRisk(), RelativeRisk.coxres(), RelativeRisk.formula()

Examples

library(data.table)
df <- 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 = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
model <- get_form(formula, df)$model
res_risk <- RelativeRisk(model, df, a_n = c(1.1, -0.1, 0.2, 0.5))

Calculates hazard ratios for a reference vector

Description

coxres.RelativeRisk uses a cox result object and data, to evaluate relative risk in the data using the risk model from the result

Usage

## S3 method for class 'coxres'
RelativeRisk(x, df, a_n = NULL, ...)

Arguments

Value

returns a class fully describing the model and the regression results

See Also

Other Predicted Risk and Rate: RelativeRisk(), RelativeRisk.coxmodel(), RelativeRisk.formula()

Examples

library(data.table)
df <- 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 = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
res <- CoxRun(formula, df,
  a_n = list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4)),
  control = control
)
res_risk <- RelativeRisk(res, df)

Calculates hazard ratios for a reference vector and model

Description

RelativeRisk.formula uses a formula and data, to evaluate relative risk in the data using the risk model from the result

Usage

## S3 method for class 'formula'
RelativeRisk(x, df, a_n = NULL, ...)

Arguments

Value

returns matrices of the relatives risks

See Also

Other Predicted Risk and Rate: RelativeRisk(), RelativeRisk.coxmodel(), RelativeRisk.coxres()

Examples

library(data.table)
df <- 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 = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
res_risk <- RelativeRisk(formula, df, a_n = c(1.1, -0.1, 0.2, 0.5))

Automatically assigns missing values in listed columns

Description

Replace_Missing checks each column and fills in NA values

Usage

Replace_Missing(df, name_list, msv, verbose = FALSE)

Arguments

Value

returns a filled datatable

See Also

Other Data Cleaning Functions: Date_Shift(), Joint_Multiple_Events(), Time_Since(), factorize(), gen_time_dep()

Examples

library(data.table)
## basic example code reproduced from the starting-description vignette
df <- 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, NA, 47, 36, NA, 55),
  Cancer_Status = c(0, 0, 1, 0, 1, 0, 0)
)
df <- Replace_Missing(df, c("Starting_Age", "Ending_Age"), 70)

Generic Residual calculation function

Description

Residual Generic residual calculation function for regression results. Note that the Cox residuals are calculated using the various plotting based functions.

Usage

Residual(x, df, ...)

Arguments

Value

returns matrices of residuals

See Also

Other Residuals: Residual.logitmodel(), Residual.logitres(), Residual.poismodel(), Residual.poisres()

Calculates the Residuals for a logistic model

Description

Residual.logitmodel uses user provided data, person-year/event columns, vectors specifying the model, and options to calculate residuals for a solved Poisson regression

Usage

## S3 method for class 'logitmodel'
Residual(
  x,
  df,
  control = list(),
  a_n = NULL,
  link = "odds",
  pearson = FALSE,
  deviance = FALSE,
  ...
)

Arguments

Value

returns matrices of residuals

See Also

Other Residuals: Residual(), Residual.logitres(), Residual.poismodel(), Residual.poisres()

Calculates the Residuals for a completed logistic model

Description

Residual.logitres uses user provided data, person-year/event columns, vectors specifying the model, and options to calculate residuals for a solved Poisson regression

Usage

## S3 method for class 'logitres'
Residual(
  x,
  df,
  control = list(),
  a_n = NULL,
  pearson = FALSE,
  deviance = FALSE,
  ...
)

Arguments

Value

returns matrices of residuals

See Also

Other Residuals: Residual(), Residual.logitmodel(), Residual.poismodel(), Residual.poisres()

Calculates the Residuals for a poisson formula

Description

Residual.poismodel uses user provided data, person-year/event columns, vectors specifying the model, and options to calculate residuals for a solved Poisson regression

Usage

## S3 method for class 'poismodel'
Residual(
  x,
  df,
  control = list(),
  a_n = NULL,
  pearson = FALSE,
  deviance = FALSE,
  ...
)

Arguments

Value

returns matrices of residuals

See Also

Other Residuals: Residual(), Residual.logitmodel(), Residual.logitres(), Residual.poisres()

Calculates the Residuals for a completed poisson model

Description

Residual.poisres uses user provided data, person-year/event columns, vectors specifying the model, and options to calculate residuals for a solved Poisson regression

Usage

## S3 method for class 'poisres'
Residual(
  x,
  df,
  control = list(),
  a_n = NULL,
  pearson = FALSE,
  deviance = FALSE,
  ...
)

Arguments

Value

returns matrices of residuals

See Also

Other Residuals: Residual(), Residual.logitmodel(), Residual.logitres(), Residual.poismodel()

Checks OS, compilers, and OMP

Description

System_Version checks OS, default R c++ compiler, and if OMP is enabled

Usage

System_Version()

Value

returns a list of results

See Also

Other Output and Information Functions: OMP_Check()

Examples

System_Version()

Automates creating a date since a reference column

Description

Time_Since generates a new dataframe with a column containing time since a reference in a given unit

Usage

Time_Since(df, dcol0, tref, col_name, units = "days")

Arguments

Value

returns the updated dataframe

See Also

Other Data Cleaning Functions: Date_Shift(), Joint_Multiple_Events(), Replace_Missing(), factorize(), gen_time_dep()

Examples

library(data.table)
m0 <- c(1, 1, 2, 2)
m1 <- c(2, 2, 3, 3)
d0 <- c(1, 2, 3, 4)
d1 <- c(6, 7, 8, 9)
y0 <- c(1990, 1991, 1997, 1998)
y1 <- c(2001, 2003, 2005, 2006)
df <- data.table(
  m0 = m0, m1 = m1,
  d0 = d0, d1 = d1,
  y0 = y0, y1 = y1
)
tref <- strptime("3-22-1997", format = "%m-%d-%Y", tz = "UTC")
df <- Time_Since(df, c("m1", "d1", "y1"), tref, "date_since")

Splits a parameter into factors

Description

factorize uses user provided list of columns to define new parameter for each unique value and update the data.table. Not for interaction terms

Usage

factorize(df, col_list, verbose = 0)

Arguments

Value

returns a list with two named fields. df for the updated dataframe, and cols for the new column names

See Also

Other Data Cleaning Functions: Date_Shift(), Joint_Multiple_Events(), Replace_Missing(), Time_Since(), gen_time_dep()

Examples

library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 2, 1, 0, 1, 0)
df <- data.table(a = a, b = b, c = c)
col_list <- c("c")
val <- factorize(df, col_list)
df <- val$df
new_col <- val$cols

Applies time dependence to parameters

Description

gen_time_dep generates a new dataframe with time dependent covariates by applying a grid in time

Usage

gen_time_dep(
  df,
  time1,
  time2,
  event0,
  iscox,
  dt,
  new_names,
  dep_cols,
  func_form,
  fname,
  tform,
  nthreads = as.numeric(detectCores())
)

Arguments

Value

returns the updated dataframe

See Also

Other Data Cleaning Functions: Date_Shift(), Joint_Multiple_Events(), Replace_Missing(), Time_Since(), factorize()

Examples

library(data.table)
# Adapted from the tests
a <- c(20, 20, 5, 10, 15)
b <- c(1, 2, 1, 1, 2)
c <- c(0, 0, 1, 1, 1)
df <- data.table(a = a, b = b, c = c)
time1 <- "%trunc%"
time2 <- "a"
event <- "c"
grt_f <- function(df, time_col) {
  return((df[, "b"] * df[, get(time_col)])[[1]])
}
func_form <- "lin"
df_new <- gen_time_dep(
  df, time1, time2, event, TRUE, 0.01, c("grt"), NULL,
  c(grt_f), paste0(tempfile(), "test_new.csv"), func_form, 1
)

Interprets a Colossus formula and makes necessary changes to data

Description

get_form uses a formula and data.table, to fully describe the model for a Colossus regression function.

Usage

get_form(formula, df, nthreads = as.numeric(detectCores())/2)

Arguments

Value

returns a class fully describing the model and the updated data

See Also

Other Formula Interpretation: get_form_joint()

Examples

library(data.table)
## basic example code reproduced from the starting-description vignette
df <- 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)
)
formula <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
model <- get_form(formula, df, 1)

Interprets a Poisson joint formula and makes necessary changes to data

Description

get_form_joint uses two event formula, a shared formula, and data.table, to fully describe the model for a joint Poisson model.

Usage

get_form_joint(formula_list, df, nthreads = as.numeric(detectCores())/2)

Arguments

Value

returns a class fully describing the model and the updated data

See Also

Other Formula Interpretation: get_form()

Performs Cox Proportional Hazard model plots

Description

plot.coxres uses user provided data, time/event columns, vectors specifying the model, and options to choose and save plots. The 'plot_options' variable can be used to select the types of plots run.

Usage

## S3 method for class 'coxres'
plot(x, df, plot_options, a_n = NULL, ...)

Arguments

Value

returns the data used for plots

See Also

Other Plotting Wrapper Functions: plotMartingale.coxres(), plotRisk.coxres(), plotSchoenfeld.coxres(), plotSurvival.coxres()

Examples

library(data.table)
## basic example code reproduced from the starting-description vignette
df <- 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)
)
control <- list(
  ncores = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
res <- CoxRun(formula, df,
  control = control,
  a_n = list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4))
)
plot_options <- list(
  type = c("surv", paste0(
    tempfile(),
    "run"
  )), studyid = "UserID",
  verbose = FALSE
)
res_plot <- plot(res, df, plot_options)

Generic Martingale Residual Plotting function

Description

plotMartingale Generic martingale residual plotting, used by the coxres object to plot martingale residuals. Currently only returns data that could be plotted.

Usage

plotMartingale(x, df, plot_options, a_n = NULL, ...)

Arguments

Value

returns the data used for plots

Performs Cox Proportional Hazard model martingale residual plots

Description

plotMartingale.coxres uses user provided data, time/event columns, vectors specifying the model, and options to choose and save plots

Usage

## S3 method for class 'coxres'
plotMartingale(x, df, plot_options, a_n = NULL, ...)

Arguments

Value

returns the data used for plots

See Also

Other Plotting Wrapper Functions: plot.coxres(), plotRisk.coxres(), plotSchoenfeld.coxres(), plotSurvival.coxres()

Generic Risk Plotting function

Description

plotRisk Generic risk plotting function. Used by the coxres object to plot the hazard ratios at different levels of individual covariates. Currently only returns data that could be plotted.

Usage

plotRisk(x, df, plot_options, a_n = NULL, ...)

Arguments

Value

returns the data used for plots

Performs Cox Proportional Hazard model hazard ratio plots

Description

plotRisk.coxres uses user provided data, time/event columns, vectors specifying the model, and options to choose and save plots

Usage

## S3 method for class 'coxres'
plotRisk(x, df, plot_options, a_n = NULL, ...)

Arguments

Value

returns the data used for plots

See Also

Other Plotting Wrapper Functions: plot.coxres(), plotMartingale.coxres(), plotSchoenfeld.coxres(), plotSurvival.coxres()

Generic Schoenfeld Residual Plotting function

Description

plotSchoenfeld Generic Schoenfeld residual plotting function, used by the coxres object to calculate Schoenfeld residuals. Currently only returns data that could be plotted.

Usage

plotSchoenfeld(x, df, plot_options, a_n = NULL, ...)

Arguments

Value

returns the data used for plots

Performs Cox Proportional Hazard model schoenfeld residual plots

Description

plotSchoenfeld.coxres uses user provided data, time/event columns, vectors specifying the model, and options to choose and save plots

Usage

## S3 method for class 'coxres'
plotSchoenfeld(x, df, plot_options, a_n = NULL, ...)

Arguments

Value

returns the data used for plots

See Also

Other Plotting Wrapper Functions: plot.coxres(), plotMartingale.coxres(), plotRisk.coxres(), plotSurvival.coxres()

Generic Survival Plotting function

Description

plotSurvival Generic survival plotting, used by the coxres object. Calculates the baseline hazard, cumulative hazard, and survival curve for the list of event times. Currently only returns data that could be plotted.

Usage

plotSurvival(x, df, plot_options, a_n = NULL, ...)

Arguments

Value

returns the data used for plots

Performs Cox Proportional Hazard model survival plots

Description

plotSurvival.coxres uses user provided data, time/event columns, vectors specifying the model, and options to choose and save plots

Usage

## S3 method for class 'coxres'
plotSurvival(x, df, plot_options, a_n = NULL, ...)

Arguments

Value

returns the data used for plots

See Also

Other Plotting Wrapper Functions: plot.coxres(), plotMartingale.coxres(), plotRisk.coxres(), plotSchoenfeld.coxres()

Examples

library(data.table)
## basic example code reproduced from the starting-description vignette
df <- 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)
)
control <- list(
  ncores = 1, lr = 0.75, maxiters = c(1, 1),
  halfmax = 1
)
formula <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
res <- CoxRun(formula, df,
  control = control,
  a_n = list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4))
)
plot_options <- list(
  fname = paste0(
    tempfile(),
    "run"
  ), studyid = "UserID",
  verbose = FALSE
)
res_plot <- plotSurvival(res, df, plot_options)