plsi.lr.v1: Partial linear single index linear regression for scalar...

In EPLSIM: Partial Linear Single Index Models for Environmental Mixture Analysis

Partial linear single index linear regression for scalar outcome

Description

Partial linear single index linear regression for scalar outcome

Usage

plsi.lr.v1(
  data,
  Y.name,
  X.name,
  Z.name,
  spline.num,
  spline.degree,
  initial.random.num
)

Arguments

data	A data set
Y.name	Variable name for scalar outcome
X.name	Variable name vector for exposures
Z.name	Variable name vector for confounders
spline.num	A number representing the degree of freedom of B-spline basis for link function
spline.degree	A number representing the degree of the piece-wise polynomial of B-spline basis for link function
initial.random.num	A number representing the number of random initials used in the function

Value

A list of model estimation and prediction results

Author(s)

Yuyan Wang

Examples

# example to run the function
data(nhanes.new)
dat <- nhanes.new

# specify variable names
Y.name <- "log.triglyceride"
X.name <- c("X1_trans.b.carotene", "X2_retinol", "X3_g.tocopherol", "X4_a.tocopherol",
            "X5_PCB99", "X6_PCB156", "X7_PCB206",
            "X8_3.3.4.4.5.pncb", "X9_1.2.3.4.7.8.hxcdf", "X10_2.3.4.6.7.8.hxcdf")
Z.name <- c("AGE.c", "SEX.Female", "RACE.NH.Black",
           "RACE.MexicanAmerican", "RACE.OtherRace", "RACE.Hispanic" )

# specify spline degree of freedom
spline.num = 5
# specify spline degree
spline.degree = 3
# specify number of random initials for estimation
initial.random.num = 1

# run the model
set.seed(2023)
model_1 <- plsi.lr.v1(data = dat, Y.name = Y.name, X.name = X.name, Z.name = Z.name,
                      spline.num, spline.degree, initial.random.num)

EPLSIM documentation built on March 31, 2023, 9:59 p.m.