| Type: | Package |
| Title: | Association Among Disease Counts and Socio-Environmental Factors |
| Version: | 2.0.0 |
| Description: | Estimation of association between disease or death counts (e.g. COVID-19) and socio-environmental risk factors using a zero-inflated Bayesian spatiotemporal model. Non-spatiotemporal models and/or models without zero-inflation are also included for comparison. Functions to produce corresponding maps are also included. See Chakraborty et al. (2022) <doi:10.1007/s13253-022-00487-1> for more details on the method. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 2.10) |
| RoxygenNote: | 7.3.2 |
| Imports: | BayesLogit, boot, coda, dplyr, ggplot2, gt, gtsummary, maps, matrixcalc, MCMCpack, msm, reshape, spam, viridis |
| Suggests: | knitr, rmarkdown, CorrMixed, ape, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| URL: | https://github.com/SumanM47/BSTZINB |
| BugReports: | https://github.com/SumanM47/BSTZINB/issues |
| NeedsCompilation: | no |
| Packaged: | 2025-01-30 14:22:13 UTC; sum313 |
| Author: | Suman Majumder [cre, aut, cph], Yoon-Bae Jun [aut, cph], Sounak Chakraborty [ctb], Chae-Young Lim [ctb], Tanujit Dey [ctb] |
| Maintainer: | Suman Majumder <smajumd2@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-01-30 17:50:07 UTC |
Fit a Bayesian Negative Binomial Model
Description
Generate posterior samples for the parameters in a Bayesian Negative Binomial Model
Usage
BNB(y, X, A,
nchain=3, niter=100, nburn=20, nthin=1)
Arguments
y |
vector of counts, must be non-negative |
X |
matrix of covariates, numeric |
A |
adjacency matrix, numeric |
nchain |
positive integer, number of MCMC chains to be run |
niter |
positive integer, number of iterations in each chain |
nburn |
non-negative integer, number of iterations to be discarded as burn-in samples |
nthin |
positive integer, thinning interval |
Value
list of posterior samples of the parameters of the model
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res0 <- BNB(y, X, A, nchain=2, niter=100, nburn=20, nthin=1)
Fit a Bayesian Spatiotemporal Negative Binomial model
Description
Generate posterior samples for the parameters in a Bayesian Spatiotemporal Negative Binomial Model
Usage
BSTNB(y,X,A,
nchain=3,niter=100,nburn=20,nthin=1)
Arguments
y |
vector of counts, must be non-negative |
X |
matrix of covariates, numeric |
A |
adjacency matrix, numeric |
nchain |
positive integer, number of MCMC chains to be run |
niter |
positive integer, number of iterations in each chain |
nburn |
non-negative integer, number of iterations to be discarded as burn-in samples |
nthin |
positive integer, thinning interval |
Value
list of posterior samples of the parameters of the model
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res2 <- BSTNB(y, X, A, nchain=2, niter=100, nburn=20, nthin=1)
Fit a Bayesian Spatiotemporal Zero Inflated Negative Binomial model
Description
Generate posterior samples for the parameters in a Bayesian Spatiotemporal Zero Inflated Negative Binomial Model
Usage
BSTZINB(y,X,A,LinearT = TRUE,
nchain=3,niter=100,nburn=20,nthin=1)
Arguments
y |
vector of counts, must be non-negative |
X |
matrix of covariates, numeric |
A |
adjacency matrix, numeric |
LinearT |
logical, whether to fit a linear or non-linear temporal trend |
nchain |
positive integer, number of MCMC chains to be run |
niter |
positive integer, number of iterations in each chain |
nburn |
non-negative integer, number of iterations to be discarded as burn-in samples |
nthin |
positive integer, thinning interval |
Value
list of posterior samples of the parameters of the model
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res3 <- BSTZINB(y, X, A, LinearT=TRUE, nchain=2, niter=100, nburn=20, nthin=1)
Fit a Bayesian Zero Inflated Negative Binomial Model
Description
Generate posterior samples for the parameters in a Bayesian Zero Inflated Negative Binomial Model
Usage
BZINB(y,X,A,
nchain=3,niter=100,nburn=20,nthin=1)
Arguments
y |
vector of counts, must be non-negative |
X |
matrix of covariates, numeric |
A |
adjacency matrix, numeric |
nchain |
positive integer, number of MCMC chains to be run |
niter |
positive integer, number of iterations in each chain |
nburn |
non-negative integer, number of iterations to be discarded as burn-in samples |
nthin |
positive integer, thinning interval |
Value
list of posterior samples of the parameters of the model
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res1 <- BSTZINB(y, X, A, nchain=2, niter=100, nburn=20, nthin=1)
Summary Table for a fitted object
Description
Generates a short summary table for a fitted object using BSTP, BSTNB or BSTZINB function
Usage
ResultTableSummary(bstfit)
Arguments
bstfit |
fitted object using the function BSTP, BSTNB or BSTZINB |
Value
summary table
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res3 <- BSTZINB(y, X, A, LinearT=TRUE, nchain=3, niter=100, nburn=20, nthin=1)
ResultTableSummary(res3)
Generate a summary table of the outputs all different methods given the data
Description
Fits BSTP, BSTNB and BSTZINB (with linear or non-linear temporal trend) to a given data and summarizes the results in a table
Usage
ResultTableSummary2(y,X,A,LinearT=FALSE,
nchain=3,niter=100,nburn=20,nthin=1)
Arguments
y |
vector of counts, must be non-negative |
X |
matrix of covariates, numeric |
A |
adjacency matrix, numeric |
LinearT |
logical, whether to fit a linear or non-linear temporal trend |
nchain |
positive integer, number of MCMC chains to be run |
niter |
positive integer, number of iterations in each chain |
nburn |
non-negative integer, number of iterations to be discarded as burn-in samples |
nthin |
positive integer, thinning interval |
Value
summary tables for the different methods
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
ResultTableSummary2(y, X, A, LinearT=TRUE, nchain=3, niter=100, nburn=20, nthin=1)
Time-trend curve over the study time domain for counties in the US
Description
Produce a time-trend curve over the study time domain for counties in the US
Usage
TimetrendCurve(bstfit,cname,vn=5,smooth.mode=TRUE,
cex.title=18, cex.lab=18, cex.legend=18)
Arguments
bstfit |
fitted object from BSTP, BSTNB or BSTZINB |
cname |
character vector of county names to use |
vn |
positive integer, number of sample counties to use |
smooth.mode |
logical, should splines be fitted to make it smooth |
cex.title |
Positive number to control the size of the text of the main title. Defaults to 18. |
cex.lab |
Positive number to control the size of the text in the axes labels. Defaults to 18. |
cex.legend |
Positive number to control the size of the text in the legend. Defaults to 18. |
Value
time-trend curves
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res3 <- BSTZINB(y, X, A, LinearT=TRUE, nchain=3, niter=100, nburn=20, nthin=1)
TimetrendCurve(res3,cname=countyname,vn=5,smooth.mode=TRUE,cex.title=18, cex.lab=12, cex.legend=12)
USAcities: A dataset containing state and county information for the cities in the United States
Description
Dataframe to be used internally to make maps and get county information
Usage
USAcities
Format
USAcities
A dataframe with 3232 rows and 4 columns: state_id (State abbreviation), county_name (County name), county_fips (FIPS codes for the counties) and population (County population).
Draw spatial maps of various quantities over regions in the US
Description
Creates a map of any given quantity (at a selected time or averaged over time) for regions in the US specified by state and county
Usage
USDmapCount(state.sel,dat,scol,tcol=NULL,tsel=NULL,cname,uplim=NULL)
Arguments
state.sel |
character vector giving the selected states |
dat |
data frame having named components: y - the necessary quantity (numeric), sid - the region indices, tid - the time indices |
scol |
column index of the spatial regions |
tcol |
(optional) column index of the time points |
tsel |
(optional) selected time point |
cname |
character vector of county names, must match those in USAcities |
uplim |
(optional) numeric, upper limit for the given quantity |
Value
spatial map of the required quantity over the specified region
Examples
data(simdat)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
USDmapCount(state.sel="IA",dat=simdat,scol=1,tcol=2,tsel=150,cname=countyname)
DIC for BSTNB or BNB fitted objects
Description
Computes DIC for a BSTNB or BNB fitted object
Usage
compute_NB_DIC(y,bstfit,lastit,nchain)
Arguments
y |
vector of counts, must be non-negative, the response used for fitting a BSTNB or BSTP model |
bstfit |
BSTNB or BNB fitted object |
lastit |
positive integer, size of the chain used to fit BSTZINB |
nchain |
positive integer, number of chains used to fit BSTZINB |
Value
DIC value
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res2 <- BSTNB(y, X, A, nchain=3, niter=100, nburn=20, nthin=1)
compute_NB_DIC(y,res2,lastit=(100-20)/1,nchain=3)
DIC for BSTZINB fitted objects
Description
Computes DIC for a BSTZINB fitted object
Usage
compute_ZINB_DIC(y,bstfit,lastit,nchain)
Arguments
y |
vector of counts, must be non-negative, the response used for fitting a BSTZINB model |
bstfit |
BSTZINB fitted object |
lastit |
positive integer, size of the chain used to fit BSTZINB |
nchain |
positive integer, number of chains used to fit BSTZINB |
Value
DIC value
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res3 <- BSTZINB(y, X, A, LinearT=TRUE, nchain=3, niter=100, nburn=20, nthin=1)
compute_ZINB_DIC(y,res3,lastit=(100-20)/1,nchain=3)
convergence test for parameters in the fitted objects
Description
Conducts a test of convergence for a given parameter in the fitted objects using the posterior samples for the said parameter
Usage
conv.test(params,nchain=3,thshold=1.96)
Arguments
params |
numeric matrix of dimension 2 (iterations x number of parameters, single chain) or 3 (iterations x number of parameters x chain, multiple chains) of posterior samples |
nchain |
positive integer, number of chains used to fit BSTZINB, BSTNB or BSTP |
thshold |
positive scalar, the threshold for testing the convergence. Defaults to 1.96 |
Value
logical vector indicating whether convergence was achieved or not
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res3 <- BSTZINB(y, X, A, LinearT=TRUE, nchain=3, niter=100, nburn=20, nthin=1)
conv.test(res3$Alpha,nchain=3)
county.adjacency: A dataframe containing neighborhood information for counties in the US
Description
Data set containing neighborhood information for counties in the US, to be used to create adjacency matrices
Usage
county.adjacency
Format
county.adjacency
A dataframe with 22200 rows and 4 columns
Adjacency matrix for counties of one or many states in the United States
Description
Creates the adjacency matrix for the supplied counties within the United States using the available neighborhood information
Usage
get_adj_mat(county.adjacency,Countyvec,Statevec)
Arguments
county.adjacency |
data frame containing the neighborhood information for the counties of the entire US |
Countyvec |
character vector containing the names of the counties for which the adjacency matrix is to be computed |
Statevec |
character vector containing the names of the states the supplied counties belong to |
Value
the corresponding adjacency matrix
Examples
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
Bar plot for time-averaged log-q estimates over quantile-representative counties (descending order)
Description
Produce a descending order of bar plot for time-averaged log-q estimates over quantile-representative counties
Usage
qRankPar(state.set,cname,bstfit,vn=12,
cex.title=18, cex.lab=18, cex.legend=18)
Arguments
state.set |
character vector of set of states on which the the graphics is to be made |
cname |
character vector of the names of the counties |
bstfit |
the fitted data for BSTP, BSTNB or BSTZINB |
vn |
positive integer, number of sample counties to display |
cex.title |
Positive number to control the size of the text of the main title. Defaults to 18. |
cex.lab |
Positive number to control the size of the text in the axes labels. Defaults to 18. |
cex.legend |
Positive number to control the size of the text in the legend. Defaults to 18. |
Value
bar graph
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res3 <- BSTZINB(y, X, A, LinearT=TRUE, nchain=3, niter=100, nburn=20, nthin=1)
qRankPar(state.set=c("IA"),cname=countyname,bstfit=res3,vn=12,
cex.title=18, cex.lab=12, cex.legend=12)
Bar plot for time-averaged log-q estimates over top ranking counties (descending order)
Description
Produce a descending order of bar plot for time-averaged log-q estimates over top ranking counties
Usage
qRankParTop(state.set,cname,bstfit,vn=12,
cex.title=18, cex.lab=18, cex.legend=18)
Arguments
state.set |
character vector of set of states on which the the graphics is to be made |
cname |
character vector of the names of the counties |
bstfit |
the fitted data for BSTP, BSTNB or BSTZINB |
vn |
positive integer, number of sample counties to display |
cex.title |
Positive number to control the size of the text of the main title. Defaults to 18. |
cex.lab |
Positive number to control the size of the text in the axes labels. Defaults to 18. |
cex.legend |
Positive number to control the size of the text in the legend. Defaults to 18. |
Value
bar graph
Examples
data(simdat)
y <- simdat$y
X <- cbind(simdat$V1,simdat$x)
data(county.adjacency)
data(USAcities)
IAcities <- subset(USAcities,state_id=="IA")
countyname <- unique(IAcities$county_name)
A <- get_adj_mat(county.adjacency,countyname,c("IA"))
res3 <- BSTZINB(y, X, A, LinearT=TRUE, nchain=3, niter=100, nburn=20, nthin=1)
qRankParTop(state.set=c("IA"),cname=countyname,bstfit=res3,vn=12,
cex.title=18, cex.lab=12, cex.legend=12)
simdat: A simulated dataset containing response and covariates with region and time information
Description
Synthetic dataframe to be used for examples and trial runs
Usage
simdat
Format
simdat
A dataframe with 2376 rows and 5 columns: sid (region ID), tid (timepoint), y (count response), V1 (intercept), and x (covariate).