mixture.overall.plot: Plot mixture's overall effect based on quantile of exposures...

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

Plot mixture's overall effect based on quantile of exposures from PLSIM

Description

Plot mixture's overall effect based on quantile of exposures from PLSIM

Usage

mixture.overall.plot(fit, data, type = c("linear", "logistic", "log"))

Arguments

fit	Fitted model from plsi.lr.auto(), plsi.logistic.auto(), or plsi.log.auto()
data	Original data set
type	Which outcome scale to plot. "linear" (default) plots the predicted (continuous) outcome, for a fit from plsi.lr.auto(). "logistic" plots the predicted probability, for a fit from plsi.logistic.auto(). "log" plots the predicted count, for a fit from plsi.log.auto().

Value

Plot of predicted outcomes based on quantile of exposures

Author(s)

Yuyan Wang

Examples

## Not run: 
# example to plot mixture's overall effect of continuous outcome
data(nhanes.new)
data <- nhanes.new

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" )

k <- 10
bs <- "cr"
initial.random.num <- 1
seed = 2026

model_lr_auto <- plsi.lr.auto(data = data, Y.name = Y.name, X.name = X.name, Z.name = Z.name,
                      k = k, bs = bs, initial.random.num = initial.random.num, seed = seed)

# plot mixture overall effect of predicted (continuous) outcome
mixture.overall.plot(model_lr_auto, data, type = "linear")

## End(Not run)

EPLSIM documentation built on Nov. 5, 2026, 1:08 a.m.