interactionRCS (version 0.1.1)

intEST: Returns the estimates of for an unspecified interaction model

Description

This function is a dispatcher that generate OR, HR or linear estimates values for a simple or restricted cubic spline interaction model from a logistic, Cox or linear regression

Usage

intEST(
  var2values,
  model,
  data,
  var1,
  var2,
  ci = TRUE,
  conf = 0.95,
  ci.method = "delta",
  ci.boot.method = "perc",
  R = 100,
  parallel = "multicore",
  ...
)

Value

if ci = FALSE, a dataframe with initial values and OR/HR/linear estimates , if ci = TRUE a dataframe with 5 columns, initial values, OR/HR/linear estimates, lower CI, upper CI and SE

Arguments

var2values

numeric vector of var2 points to estimate

model

model of class cph, coxph, lrm, glm or Glm. If data is NULL, the function expects to find the data in model$x.

data

data used in the model. If absent, we will attempt to recover the data from the model. Only used for bootstrap and glm class models

var1

variable that increases by 1 unit from 0

var2

variable to spline. var2values belong to var2

ci

calculate 95% CI?

conf

confidence level. Default 0.95

ci.method

confidence interval method. "delta" performs delta method. "bootstrap" performs bootstrapped CI (slower)

ci.boot.method

one of the available bootstrap CI methods from boot.ci. Default percentile

R

number of bootstrap samples if ci.method = "bootstrap". Default 100

parallel

can take values "no", "multicore", "snow" if ci.method = "bootstrap". Default multicore

...

other parameters for boot

Examples

library(rms)
library(mlbench)
data(PimaIndiansDiabetes)
# Set age on a 5-year scale
PimaIndiansDiabetes$age <- PimaIndiansDiabetes$age/5
# Recode diabetes as 0/1
PimaIndiansDiabetes$diabetes <- ifelse(PimaIndiansDiabetes$diabetes=="pos" , 1 , 0)
# Logistic model predicting diabetes over BMI, age and glucose
myformula <- diabetes ~ mass + age * rcs( glucose , 3 )
model <- lrm(myformula , data = PimaIndiansDiabetes )
intEST( var2values = 20:80
       , model = model , data = PimaIndiansDiabetes , var1 ="age", var2="glucose"
       , ci=TRUE , conf = 0.95 , ci.method = "delta")
# Linear model predicting BMI over diabetes, age and glucose
myformula2 <- mass ~ diabetes + age * rcs( glucose , 3 )
model2 <- glm(myformula2 , data = PimaIndiansDiabetes , family = "gaussian")
intEST( var2values = 20:80
       , model = model2 , data = PimaIndiansDiabetes , var1 ="age", var2="glucose"
       , ci=TRUE , conf = 0.95 , ci.method = "delta")