Help for package groupWQS

Type:

Package

Title:

Grouped Weighted Quantile Sum Regression

Version:

0.0.3

Author:

David Wheeler, Matthew Carli

Maintainer:

Matthew Carli <carlimm@mymail.vcu.edu>

Description:

Fits weighted quantile sum (WQS) regressions for one or more chemical groups with continuous or binary outcomes. Wheeler D, Czarnota J.(2016) <doi:10.1289/isee.2016.4698>.

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.0.2

Depends:

R (≥ 3.2.1)

Imports:

Rsolnp, glm2, stats, graphics, MASS, rjags

Suggests:

knitr, rmarkdown, testthat

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2020-06-27 17:48:41 UTC; Matt Carli

Repository:

CRAN

Date/Publication:

2020-06-27 18:10:02 UTC

Simulated data of chemical concentrations and one continuous outcome variable

Description

Correlation and concentration patterns were loosely based on NHL data.

Usage

WQSdata

Format

A data frame with 1000 rows and 10 variables:

X1: a numeric vector
X2: a numeric vector
X3: a numeric vector
X4: a numeric vector
X5: a numeric vector
X6: a numeric vector
X7: a numeric vector
X8: a numeric vector
X9: a numeric vector
y: a numeric vector; the outcome variable

Grouped WQS Regression

Description

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

Usage

gwqs.fit(
  y,
  y.train = NULL,
  x,
  x.train = NULL,
  z = NULL,
  z.train = NULL,
  x.s,
  B = 100,
  n.quantiles = 4,
  pars = NULL,
  func,
  ineqLB = NULL,
  ineqUB = NULL,
  tol = 1e-06,
  delta = 1e-06
)

Arguments

y

A vector containing outcomes for validation.

y.train

A vector containing outcomes for training. If left as NULL the validation data will be used for training as well.

x

A matrix of component data for validation.

x.train

A matrix of component data for training. If left as NULL the validation data will be used for training as well.

z

A vector or matrix of covariates for validation.

z.train

A vector or matrix of covariates for training. If left as NULL the validation data will be used for training as well.

x.s

A vector of the number of components in each index.

B

The number of bootstrap samples, must be 1 or more.

n.quantiles

The number of quantiles to apply to data.

pars

A vector of initial values, listed in order: beta naught intercept and group index beta coefficients, individual chemical weight coefficients, and covariate coefficients.

func

The objective function to be used (must match outcome data type); currently only fun args "continuous" or "binary" are supported.

ineqLB

Vector of lower bounds for betas and weights, set to -2 by default.

ineqUB

Vector of upper bounds for betas and weights, set to 2 be default.

tol

Tolerance level for bootstrap convergence.

delta

Step size for bootstrap procedure.

Value

A list of 3 containing the GWQS estimate based on calculated weights, the GWQS model fit to validation data, and weight estimates

Examples

data("WQSdata")
group_list <- list(c("X1", "X2", "X3"), c("X4", "X7"), c("X5", "X6", "X9", "X8"))
x.s <- make.x.s(WQSdata, 3, group_list)
X <- make.X(WQSdata, 3, group_list)
Y <- WQSdata$y
results <- gwqs.fit(y = Y, x = X, x.s = x.s, B=1, func = "continuous")

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("WQSdata")
group_list <- list(c("X1", "X2", "X3"), c("X4", "X7"), c("X5", "X6", "X9", "X8"))
X <- make.X(WQSdata, 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("WQSdata")
group_list <- list(c("X1", "X2", "X3"), c("X4", "X7"), c("X5", "X6", "X9", "X8"))
x.s <- make.x.s(WQSdata, 3, group_list)
x.s

Simulated data of chemical concentrations and one binary outcome variable

Description

Data simulated to have .7 in-group correlation and .3 between-group correlation. There are three groups, 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 wqs.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)

Arguments

fit.object

The object that is returned by the wqs.fit function

group.names

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

Value

A plot for each group of the GWQS regression

Examples

data("WQSdata")
group_list <- list(c("X1", "X2", "X3"), c("X4", "X7"), c("X5", "X6", "X9", "X8"))
chem_groups <- c("PCBs", "Metals", "Insecticides")
x.s <- make.x.s(WQSdata, 3, group_list)
X <- make.X(WQSdata, 3, group_list)
Y <- WQSdata$y
results <- gwqs.fit(y = Y, x = X, x.s = x.s, B=1, func = "continuous")
weight.plot(results, chem_groups)