Help for package BayesGWQS

Type:

Package

Title:

Bayesian Grouped Weighted Quantile Sum Regression

Version:

0.1.1

Author:

David Wheeler, Matthew Carli

Maintainer:

Matthew Carli <carlimm@mymail.vcu.edu>

Description:

Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more chemical groups with binary outcomes. Wheeler DC et al. (2019) <doi:10.1016/j.sste.2019.100286>.

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.1.1

Depends:

R (≥ 4.0.0)

SystemRequirements:

JAGS

Imports:

coda, stats, rjags, stringr, plyr

Suggests:

testthat

NeedsCompilation:

Packaged:

2022-01-20 22:32:42 UTC; Matt Carli

Repository:

CRAN

Date/Publication:

2022-01-20 22:42:54 UTC

Bayesian Grouped WQS Regression

Description

This function fits a Bayesian grouped weighted quantile sum (BGWQS) regression model.

Usage

bgwqs.fit(
  y,
  x,
  z,
  x.s,
  n.quantiles = 4,
  working.dir,
  n.chains = 1,
  n.iter = 10000,
  n.burnin = 5000,
  n.thin = 1,
  n.adapt = 500,
  DIC = FALSE
)

Arguments

y

A vector containing outcomes.

x

A matrix of component data.

z

A vector or matrix of controlling covariates.

x.s

A vector of the number of components in each index.

n.quantiles

The number of quantiles to apply to the component data.

working.dir

A file path to the directory.

n.chains

The number of Markov chains; must be a positive integer.

n.iter

The number of total iterations per chain, including burn in.

n.burnin

The number of iterations to discard at the beginning.

n.thin

The thinning rate; must be a positive integer.

n.adapt

The number of adaption iterations.

DIC

Logical; whether or not the user desires the function to return DIC.

Value

A list which includes BUGS output, sample chains post-burnin, and convergence test results.

Examples

## Not run: 
data("simdata")
group_list <- list(c("pcb_118", "pcb_138", "pcb_153", "pcb_180", "pcb_192"),
                   c("as", "cu", "pb", "sn"),
                   c("carbaryl", "propoxur", "methoxychlor", "diazinon", "chlorpyrifos"))
x.s <- make.x.s(simdata, 3, group_list)
X <- make.X(simdata, 3, group_list)
Y <- simdata$Y
work_dir <- tempdir()
results <- bgwqs.fit(y = Y, x = X, x.s = x.s, n.quantiles=4,
                     working.dir = work_dir,
                     n.chains = 1, n.iter = 10000, n.burnin = 5000, n.thin = 1, n.adapt = 500)


## End(Not run)

Forms matrix of components

Description

This function returns a matrix of component variables, X. The user can specify the desired chemicals and order by creating a list of string vectors, each vector containing the variable names of all desired elements of that group.

Usage

make.X(df, num.groups, groups)

Arguments

df

A dataframe containing named component variables

num.groups

An integer representing the number of component groups desired

groups

A list, each item in the list being a string vector of variable names for one component group

Value

A matrix of component variables

Examples

data("simdata")
group_list <- list(c("pcb_118", "pcb_138", "pcb_153", "pcb_180", "pcb_192"),
                   c("as", "cu", "pb", "sn"),
                   c("carbaryl", "propoxur", "methoxychlor", "diazinon", "chlorpyrifos"))
X <- make.X(simdata, 3, group_list)
X

Forms component group ID vector of X

Description

This function returns a vector which lets WQS.fit know the size and order of groups in X

Usage

make.x.s(df, num.groups, groups)

Arguments

df

A dataframe containing named component variables

num.groups

An integer representing the number of component groups desired

groups

A list, each item in the list being a string vector of variable names for one component group

Value

A vector of integers, each integer relating how many columns are in each group

Examples

data("simdata")
group_list <- list(c("pcb_118", "pcb_138", "pcb_153", "pcb_180", "pcb_192"),
                   c("as", "cu", "pb", "sn"),
                   c("carbaryl", "propoxur", "methoxychlor", "diazinon", "chlorpyrifos"))
x.s <- make.x.s(simdata, 3, group_list)
x.s

Simulated data of chemical concentrations and one binary outcome variable

Description

Data were simulated to have 0.7 in-group correlation and 0.3 between-group correlation. There are three groups, with the third being significantly correlated to the outcome variable.

Usage

simdata

Format

A data frame with 1000 rows and 15 variables:

pcb_118: a numeric vector; part of group 1
pcb_138: a numeric vector; part of group 1
pcb_153: a numeric vector; part of group 1
pcb_180: a numeric vector; part of group 1
pcb_192: a numeric vector; part of group 1
as: a numeric vector; part of group 2
cu: a numeric vector; part of group 2
pb: a numeric vector; part of group 2
sn: a numeric vector; part of group 2
carbaryl: a numeric vector; part of group 3
propoxur: a numeric vector; part of group 3
methoxychlor: a numeric vector; part of group 3
diazinon: a numeric vector; part of group 3
chlorpyrifos: a numeric vector; part of group 3
Y: a numeric vector; the outcome variable

Generates Plots of weights by group

Description

This function takes the object created by the bgwqs.fit function and a vector of group names and generates a random forest variable importance plot for each group. The weights in each group are listed in descending order.

Usage

weight.plot(fit.object, group.names, group.list, x.s)

Arguments

fit.object

The object that is returned by the bgwqs.fit function

group.names

A string vector containing the name of each group included in the BGWQS regression. Will be used for plot titles.

group.list

A list, each item in the list being a string vector of variable names for one component group.

x.s

A vector of the number of components in each index.

Value

A plot for each group of the BGWQS regression