Package 'EPLSIM' reference manual

Title:	Partial Linear Single Index Models for Environmental Mixture Analysis
Description:	Collection of ancillary functions and utilities for Partial Linear Single Index Models for Environmental mixture analyses, which currently provides functions for scalar outcomes. The outputs of these functions include the single index function, single index coefficients, partial linear coefficients, mixture overall effect, exposure main and interaction effects, and differences of quartile effects. In the future, we will add functions for binary, ordinal, Poisson, survival, and longitudinal outcomes, as well as models for time-dependent exposures. See Wang et al (2020) <doi:10.1186/s12940-020-00644-4> for an overview.
Authors:	Yuyan Wang [aut, cre] , Mengling Liu [aut, ctb]
Maintainer:	Yuyan Wang <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0
Built:	2025-03-26 03:47:36 UTC
Source:	https://github.com/yuyanwangsixtwo/eplsim

Transformation for confounder vector Z

Description

Transformation for confounder vector Z

Usage

confounder.trans(Z_continuous, Z_discrete, data)
confounder.trans(Z_continuous, Z_discrete, data)

Arguments

`Z_continuous`	A character name vector for continuous confounders
`Z_discrete`	A character name vector for discrete confounders
`data`	Orginial data set

Value

Transformed confounder vector and data set ready for further analysis.

Author(s)

Yuyan Wang

Examples

# example to normalize the continuous confounders and
# make dummy variables for categorical confoduners
dat.cov <- data.frame(
  age = c(1.5, 2.3, 3.1, 4.8, 5.2),
  sex = c(1, 2, 1, 2, 2),
  race = c(1, 2, 3, 4, 5)
)

# specify the confounder vector
Z.name <- c("age", "sex", "race")

# set levels and make the reference level first for categorical confounders
dat.cov$sex <- factor(dat.cov$sex, 1:2, c('Male', 'Female'))
dat.cov$race <- factor(dat.cov$race,1:5,c("NH-White", "NH-Black",
                                        "MexicanAmerican", "OtherRace", "Hispanic"))

# transform the confounder vector and check
cov_m <- confounder.trans(Z_continuous = c("age"), Z_discrete = c("sex", "race"), data = dat.cov)
Z.name <- cov_m$New.Name
dat.cov <- cov_m$Updated.data
print(Z.name)
# example to normalize the continuous confounders and
# make dummy variables for categorical confoduners
dat.cov <- data.frame(
  age = c(1.5, 2.3, 3.1, 4.8, 5.2),
  sex = c(1, 2, 1, 2, 2),
  race = c(1, 2, 3, 4, 5)
)

# specify the confounder vector
Z.name <- c("age", "sex", "race")

# set levels and make the reference level first for categorical confounders
dat.cov$sex <- factor(dat.cov$sex, 1:2, c('Male', 'Female'))
dat.cov$race <- factor(dat.cov$race,1:5,c("NH-White", "NH-Black",
                                        "MexicanAmerican", "OtherRace", "Hispanic"))

# transform the confounder vector and check
cov_m <- confounder.trans(Z_continuous = c("age"), Z_discrete = c("sex", "race"), data = dat.cov)
Z.name <- cov_m$New.Name
dat.cov <- cov_m$Updated.data
print(Z.name)

plot interaction effect of two exposures

Description

plot interaction effect of two exposures

Usage

e.interaction.plot(fit, data, exp_1, exp_2)
e.interaction.plot(fit, data, exp_1, exp_2)

Arguments

`fit`	Fitted model from function 'plsi.lr.v1'
`data`	Original data set
`exp_1`	exposure name hoping to be checked
`exp_2`	exposure name hoping to be checked

Value

plot of interaction effect of two exposures with others at average level

Author(s)

Yuyan Wang

Examples


# example to plot interaction effect of two exposures
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot two exposures' interaction effect
e.interaction.plot(model_1, dat, "X4_a.tocopherol", "X3_g.tocopherol")
e.interaction.plot(model_1, dat, "X4_a.tocopherol", "X10_2.3.4.6.7.8.hxcdf")

# exchange exposures' names
e.interaction.plot(model_1, dat, "X8_3.3.4.4.5.pncb", "X6_PCB156")
e.interaction.plot(model_1, dat, "X6_PCB156", "X8_3.3.4.4.5.pncb")

# example to plot interaction effect of two exposures
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot two exposures' interaction effect
e.interaction.plot(model_1, dat, "X4_a.tocopherol", "X3_g.tocopherol")
e.interaction.plot(model_1, dat, "X4_a.tocopherol", "X10_2.3.4.6.7.8.hxcdf")

# exchange exposures' names
e.interaction.plot(model_1, dat, "X8_3.3.4.4.5.pncb", "X6_PCB156")
e.interaction.plot(model_1, dat, "X6_PCB156", "X8_3.3.4.4.5.pncb")

plot single exposure's main effect

Description

plot single exposure's main effect

Usage

e.main.plot(fit, data, exp_name)
e.main.plot(fit, data, exp_name)

Arguments

`fit`	Fitted model from function 'plsi.lr.v1'
`data`	Original data set
`exp_name`	exposure name hoping to be plotted

Value

plot of exposure's main effect with other exposures at average level 0

Author(s)

Yuyan Wang

Examples


# example to plot some exposure's main effect
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot some exposure's main effect
e.main.plot(model_1, dat, exp_name = c("X4_a.tocopherol"))
e.main.plot(model_1, dat, exp_name = c("X5_PCB99"))
e.main.plot(model_1, dat, exp_name = c("X10_2.3.4.6.7.8.hxcdf"))

# example to plot some exposure's main effect
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot some exposure's main effect
e.main.plot(model_1, dat, exp_name = c("X4_a.tocopherol"))
e.main.plot(model_1, dat, exp_name = c("X5_PCB99"))
e.main.plot(model_1, dat, exp_name = c("X10_2.3.4.6.7.8.hxcdf"))

plot interquartile effect of specific exposure based on quartile of other exposures

Description

plot interquartile effect of specific exposure based on quartile of other exposures

Usage

interquartile.quartile.plot(fit, data)
interquartile.quartile.plot(fit, data)

Arguments

`fit`	Fitted model from function 'plsi.lr.v1'
`data`	Original data set

Value

plot of main interquartile effect of exposure based on quartile of other exposures

Author(s)

Yuyan Wang

Examples


# example to interquartile effect based on quartile of other exposures
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot interquartile quartile
interquartile.quartile.plot(model_1, dat)

# example to interquartile effect based on quartile of other exposures
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot interquartile quartile
interquartile.quartile.plot(model_1, dat)

plot mixture's overall effect based on quantile of exposures

Description

plot mixture's overall effect based on quantile of exposures

Usage

mixture.overall.plot(fit, data)
mixture.overall.plot(fit, data)

Arguments

`fit`	Fitted model from function 'plsi.lr.v1'
`data`	Original data set

Value

plot of predicted outcomes based on quantile of exposures

Author(s)

Yuyan Wang

Examples


# example to plot mixture's overall effect
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot mixture overall effect
mixture.overall.plot(model_1, dat)

# example to plot mixture's overall effect
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot mixture overall effect
mixture.overall.plot(model_1, dat)

This is data from NHANES 2003–2004

Description

A data set containing outcome triglyceride, ten exposures, and three confounders.

Usage

nhanes
nhanes

Format

An object of class data.frame with 800 rows and 14 columns.

Details

triglyceride: outcome triglyceride level, unite mg/dl
a1.trans.b.carotene: exposure: trans-b-carotene (ug/dL)
a5.Retinol: exposure: retinol (ug/dL)
a6.g.tocopherol: exposure: g-tocopherol (ug/dL)
a7.a.Tocopherol: exposure: a-tocopherol (ug/dL)
a10.PCB99: exposure: Polychlorinated Biphenyl (PCB) 99 Lipid Adj (ng/g)
a13.PCB156: exposure: Polychlorinated Biphenyl (PCB) 156 Lipid Adj (ng/g)
a19.PCB206: exposure: Polychlorinated Biphenyl (PCB) 206 Lipid Adj (ng/g)
a20.3.3.4.4.5.pncb: exposure: 3,3,4,4,5-Pentachlorobiphenyl (pncb) Lipid Adj (pg/g)
a21.1.2.3.4.7.8.hxcdf: exposure: 1,2,3,4,7,8-hxcdf Lipid Adj (pg/g)
a22.2.3.4.6.7.8.hxcdf: exposure: 2,3,4,6,7,8-hxcdf Lipid Adj (pg/g)
age: subject age at measurement
sex: subject sex
race: subject race

Author(s)

Yuyan Wang [email protected]

Source

https://www.cdc.gov/nchs/nhanes/index.htm

This is updated data from original data based on NHANES 2003–2004 survey

Description

A data set containing outcome triglyceride, re-named ten exposures, and transformed confounders.

Usage

nhanes.new
nhanes.new

Format

An object of class data.frame with 789 rows and 17 columns.

Details

triglyceride: outcome triglyceride level, unite mg/dl
X1_trans.b.carotene: renamed exposure: trans-b-carotene (ug/dL)
X2_retinol: renamed exposure: retinol (ug/dL)
X3_g.tocopherol: renamed exposure: g-tocopherol (ug/dL)
X4_a.tocopherol: renamed exposure: a-tocopherol (ug/dL)
X5_PCB99: renamed exposure: Polychlorinated Biphenyl (PCB) 99 Lipid Adj (ng/g)
X6_PCB156: renamed exposure: Polychlorinated Biphenyl (PCB) 156 Lipid Adj (ng/g)
X7_PCB206: renamed exposure: Polychlorinated Biphenyl (PCB) 206 Lipid Adj (ng/g)
X8_3.3.4.4.5.pncb: renamed exposure: 3,3,4,4,5-Pentachlorobiphenyl (pncb) Lipid Adj (pg/g)
X9_1.2.3.4.7.8.hxcdf: renamed exposure: 1,2,3,4,7,8-hxcdf Lipid Adj (pg/g)
X10_2.3.4.6.7.8.hxcdf: renamed exposure: 2,3,4,6,7,8-hxcdf Lipid Adj (pg/g)
AGE.c: rescaled continuous confounder: subject age at measurement
SEX.Female: categorical confounder dummy variable: subject sex as Female
RACE.NH.Black: categorical dummy variable: subject race as Non-Hispanic Black
RACE.MexicanAmerican: categorical dummy variable: subject race as Mexican American
RACE.OtherRace: categorical dummy variable: subject race as Other Races
RACE.Hispanic: categorical dummy variable: subject race as Hispanic

Author(s)

Yuyan Wang [email protected]

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

Arguments

`data`	A data set including all needed variables
`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)

# 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)

plot estimated single index coefficients

Description

plot estimated single index coefficients

Usage

si.coef.plot(si.coef.est)
si.coef.plot(si.coef.est)

Arguments

si.coef.est

A data set of estimated single index coefficients

Value

single index coefficient plot

Author(s)

Yuyan Wang

Examples


# example to plot estimated single index coefficients
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot estimated single index coefficients
si.coef.plot(model_1$si.coefficient)

# check estimated single index coefficients
model_1$si.coefficient

# example to plot estimated single index coefficients
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot estimated single index coefficients
si.coef.plot(model_1$si.coefficient)

# check estimated single index coefficients
model_1$si.coefficient

plot estimated single index function

Description

plot estimated single index function

Usage

si.fun.plot(si.ci)
si.fun.plot(si.ci)

Arguments

si.ci

A data set of estimated index and corresponding single index values

Value

Single index function plot

Author(s)

Yuyan Wang

Examples


# example to plot estimated single index function
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot single index function
si.fun.plot(model_1$si.fun)

# example to plot estimated single index function
data(nhanes.new)
dat <- nhanes.new

# specify variable names and parameters
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" )
spline.num = 5
spline.degree = 3
initial.random.num = 1

# run PLSI linear regression
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)

# plot single index function
si.fun.plot(model_1$si.fun)

Package 'EPLSIM'

Help Index

Transformation for confounder vector Z

Description

Usage

Arguments

Value

Author(s)

Examples

plot interaction effect of two exposures

Description

Usage

Arguments

Value

Author(s)

Examples

plot single exposure's main effect

Description

Usage

Arguments

Value

Author(s)

Examples

plot interquartile effect of specific exposure based on quartile of other exposures

Description

Usage

Arguments

Value

Author(s)

Examples

plot mixture's overall effect based on quantile of exposures

Description

Usage

Arguments

Value

Author(s)

Examples

This is data from NHANES 2003–2004

Description

Usage

Format

Details

Author(s)

Source

This is updated data from original data based on NHANES 2003–2004 survey

Description

Usage

Format

Details

Author(s)

Partial linear single index linear regression for scalar outcome

Description

Usage

Arguments

Value

Author(s)

Examples

plot estimated single index coefficients

Description

Usage

Arguments

Value

Author(s)

Examples

plot estimated single index function

Description

Usage

Arguments

Value

Author(s)

Examples