Package 'BayesX' reference manual

Title:	R Utilities Accompanying the Software Package BayesX
Description:	Functions for exploring and visualising estimation results obtained with BayesX, a free software for estimating structured additive regression models (<https://www.uni-goettingen.de/de/bayesx/550513.html>). In addition, functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX.
Authors:	Nikolaus Umlauf [aut, cre] , Thomas Kneib [aut], Nadja Klein [aut], Felix Heinzl [ctb], Andreas Brezger [ctb], Daniel Sabanes Bove [ctb]
Maintainer:	Nikolaus Umlauf <[email protected]>
License:	GPL-2 \| GPL-3
Version:	0.3-3
Built:	2024-09-14 06:26:36 UTC
Source:	CRAN

R Utilities Accompanying the Software Package BayesX

Description

This package provides functionality for exploring and visualising estimation results obtained with the software package BayesX for structured additive regression. It also provides functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX.

Author(s)

Nikolaus Umlauf, Thomas Kneib, Nadja Klein, Felix Heinzl, Andreas Brezger, Daniel Sabanes Bove

References

Belitz C, Brezger A, Kneib T, Lang S (2011). BayesX - Software for Bayesian Inference in Structured Additive Regression Models. Version 2.0.1. URL https://www.uni-goettingen.de/de/bayesx/550513.html.

Add Neighborhood Relations

Description

Adds a neighborhhod relationship between two given regions to a map object in graph format.

Usage

add.neighbor(map, region1, region2)
add.neighbor(map, region1, region2)

Arguments

`map`	Map object in graph format that should be modified.
`region1`, `region2`	Names of the regions that should be connected as neighbors.

Value

Returns an adjacency matrix that represents the neighborhood structure of map plus the new neighborhood relation in graph format.

Author(s)

Felix Heinzl, Thomas Kneib

Examples

germany <- read.gra(system.file("examples/germany.gra", package="BayesX"))
get.neighbor(germany, c("1001", "7339"))
germany <- add.neighbor(germany, "7339", "1001")
get.neighbor(germany, c("1001", "7339"))
germany <- read.gra(system.file("examples/germany.gra", package="BayesX"))
get.neighbor(germany, c("1001", "7339"))
germany <- add.neighbor(germany, "7339", "1001")
get.neighbor(germany, c("1001", "7339"))

Convert Boundary Format to Graph Format

Description

Converts a map in boundary format to a map in graph format.

Usage

bnd2gra(map)
bnd2gra(map)

Arguments

map

Map in boundary format that should be converted.

Value

Returns an adjacency matrix that represents the neighborhood structure of the map object in graph format.

Author(s)

Felix Heinzl, Thomas Kneib

References

BayesX Reference Manual. Available from https://www.uni-goettingen.de/de/bayesx/550513.html

Examples

tanzania.bnd <- read.bnd(system.file("examples/tanzania.bnd", package="BayesX"))
tanzania.gra <- bnd2gra(tanzania.bnd)
tanzania.bnd <- read.bnd(system.file("examples/tanzania.bnd", package="BayesX"))
tanzania.gra <- bnd2gra(tanzania.bnd)

Create map objects for some points and a given distance

Description

Creates a map object from a list of coordinates by treating observations within a certain distance as neighbors. The resulting neighborhood structure is stored in a map object in graph format while a map in boundary format is created to enable visualisation.

Usage

createxymap(x, y, districts=NULL, p=2, max.dist)
createxymap(x, y, districts=NULL, p=2, max.dist)

Arguments

`x`	Vector of x-coordinates of underlying points
`y`	Vector of y-coordinates of underlying points
`districts`	Either `NULL` or a vector of names for labeling points. If `districts=NULL`, points are labelled by index.
`p`	Any p-norm with p>=1 can be chosen as the distance measure with the Euclidian distance (`p=2`) being the default. For `p=Inf`, the maximum of coordinates is used. Except for `p=Inf`, calculations can fail for huge `p`.
`max.dist`	Value which determines the neighborship. Points whose distance is smaller or equal than `max.dist` are considered as neighbors.

Value

List of two elements: map object in graph format and map object in boundary format.

Author(s)

Felix Heinzl, Thomas Kneib

Examples

x <- c(3,3,5,9.5,11,11)
y <- c(2,4,4,6,4.5,5)
xymap <- createxymap(x, y, districts=c("A","B","C","D","E","F"), max.dist=2)
xymap$gra
drawmap(map=xymap$bnd)
x <- c(3,3,5,9.5,11,11)
y <- c(2,4,4,6,4.5,5)
xymap <- createxymap(x, y, districts=c("A","B","C","D","E","F"), max.dist=2)
xymap$gra
drawmap(map=xymap$bnd)

Delete Neighborhood Relations

Description

Adds the neighborhhod relationship between two given regions from a map object in graph format.

Usage

delete.neighbor(map, region1, region2)
delete.neighbor(map, region1, region2)

Arguments

`map`	Map object in graph format that should be modified.
`region1`, `region2`	Names of the regions that should no longer be regarded as neighbors.

Value

Returns an adjacency matrix that represents the neighborhood structure of map minus the deleted neighborhood relation in graph format.

Author(s)

Felix Heinzl, Thomas Kneib

Examples

germany <- read.gra(system.file("examples/germany.gra", package="BayesX"))
get.neighbor(germany, c("7339"))
germany <- delete.neighbor(germany, "7339", "7141")
get.neighbor(germany, c("7339"))
germany <- read.gra(system.file("examples/germany.gra", package="BayesX"))
get.neighbor(germany, c("7339"))
germany <- delete.neighbor(germany, "7339", "7141")
get.neighbor(germany, c("7339"))

Drawing Geographical Information

Description

Visualises variables that are spatially aligned according to a given map object. Each of the regions in a map will be coloured accoring to the value of the variable.

Usage

drawmap(data, map, regionvar=2, plotvar=3, limits, cols="hcl", nrcolors=100, 
        swapcolors=FALSE, pcat=FALSE,
        hcl.par=list(h=c(120, 0), c=60, l=c(45,60), power=1.2), 
        hsv.par=list(s=1, v=1), legend=TRUE, drawnames=FALSE, cex.names=0.7, 
        cex.legend=0.7, mar.min=2, density=15, ...)drawmap(data, map, regionvar=2, plotvar=3, limits, cols="hcl", nrcolors=100, 
        swapcolors=FALSE, pcat=FALSE,
        hcl.par=list(h=c(120, 0), c=60, l=c(45,60), power=1.2), 
        hsv.par=list(s=1, v=1), legend=TRUE, drawnames=FALSE, cex.names=0.7, 
        cex.legend=0.7, mar.min=2, density=15, ...)

Arguments

`data`	Either the name of a file or a data frame containing the variables to be visualised. If missing, the map will be visualised without superposition of any further information
`map`	Map object containing the required boundary information (as obtained by a call to `read.bnd`
`regionvar`	Defines the variable specifying the geographical regions. Either the name of a variable in `data` or the index of the corresponding column.
`plotvar`	Defines the variable that should be visualised. Either the name of a variable in `data` or the index of the corresponding column.
`limits`	Restricts (or extends) the coloring scheme to a range of values.
`cols`	Color scheme to be employed. Could be either a vector of colors or one out of the following pre-defined schemes: `hcl`, `hsv`, `grey`
`nrcolors`	Number of colors (only meaningful when using one of the pre-defined colour schemes).
`swapcolors`	Reverse the order of colors (works also with user-specified colours but will be most usefule with the pre-defined schemes).
`pcat`	Option for the visualisation of posterior probabilities. In this case, a three-colour scheme representing significantly positive, insignificant and significantly negative values.
`hcl.par`	Parameters for the `hcl` colour scheme (see the documentation of `diverge_hcl` in package `vcd` for details).
`hsv.par`	Parameters for the `hsv` colour scheme (see the documentation of `hsv` for details).
`legend`	Should a legend be added to the figure?
`drawnames`	Adds the name of each region as a text label to the plot. In most cases the result will be confusing but may be useful when checking the validity of a map.
`cex.names`	Magnification to be used for the names (if `drawnames=TRUE`).
`cex.legend`	Magnification to be used for the legend.
`mar.min`	Controls the definition of boundaries. Could be either `NULL` for individual settings of `mar` or a value which defines `mar` as follows: The boundaries will be calculated according to the height to width ratio of the map with minimal boundary `mar.min`.
`density`	Regions without data will be visualised with diagonal stripes. `density` defines how dense the stripes should be.
`...`	Further arguments to be passed to the plot calls that visualise the region boundaries (probably not useful at all).

Author(s)

Felix Heinzl, Thomas Kneib, Andreas Brezger

Examples

germany <- read.bnd(system.file("examples/germany.bnd", package="BayesX"))
drawmap(map=germany)
drawmap(map=germany, drawnames=TRUE)

res <- read.table(system.file("examples/spatial_f_regions_spatial.res", 
                              package="BayesX"), header=TRUE)

drawmap(res, map=germany)
drawmap(res, map=germany, limits=c(-2,4))
drawmap(res, map=germany, regionvar="regions", plotvar="pmed")
drawmap(res, map=germany, legend=FALSE)
drawmap(res, map=germany, legend=FALSE, main="spatial effect")

drawmap(res, map=germany, cols="hsv")
drawmap(res, map=germany, swapcolors=TRUE, cols="hsv")
drawmap(res, map=germany, cols="grey")
drawmap(res, map=germany,
        cols=c('darkgreen','green','yellow','orange','red','darkred'))

drawmap(res, map=germany, pcat=TRUE, cols="hcl")
drawmap(res, map=germany, pcat=TRUE, cols="hsv")
drawmap(res, map=germany, pcat=TRUE, cols="grey")

drawmap(res, map=germany, nrcolors=10, cols="hcl")
drawmap(res, map=germany, nrcolors=10, cols="hsv")
drawmap(res, map=germany, nrcolors=10, cols="grey")

drawmap(res, map=germany, cols="hcl",
        hcl.par=list(h=c(0,120), c=60, l=c(45,90), power=1.2))
drawmap(res, map=germany, cols="hcl",
        hcl.par=list(h=c(300,120), c=60, l=c(45,90), power=1.2))
drawmap(res, map=germany, cols="hcl",
        hcl.par=list(h=c(40,260), c=60, l=c(45,90), power=1.2))
drawmap(res, map=germany, cols="hsv", hsv.par=list(s=0.7, v=0.7))
germany <- read.bnd(system.file("examples/germany.bnd", package="BayesX"))
drawmap(map=germany)
drawmap(map=germany, drawnames=TRUE)

res <- read.table(system.file("examples/spatial_f_regions_spatial.res", 
                              package="BayesX"), header=TRUE)

drawmap(res, map=germany)
drawmap(res, map=germany, limits=c(-2,4))
drawmap(res, map=germany, regionvar="regions", plotvar="pmed")
drawmap(res, map=germany, legend=FALSE)
drawmap(res, map=germany, legend=FALSE, main="spatial effect")

drawmap(res, map=germany, cols="hsv")
drawmap(res, map=germany, swapcolors=TRUE, cols="hsv")
drawmap(res, map=germany, cols="grey")
drawmap(res, map=germany,
        cols=c('darkgreen','green','yellow','orange','red','darkred'))

drawmap(res, map=germany, pcat=TRUE, cols="hcl")
drawmap(res, map=germany, pcat=TRUE, cols="hsv")
drawmap(res, map=germany, pcat=TRUE, cols="grey")

drawmap(res, map=germany, nrcolors=10, cols="hcl")
drawmap(res, map=germany, nrcolors=10, cols="hsv")
drawmap(res, map=germany, nrcolors=10, cols="grey")

drawmap(res, map=germany, cols="hcl",
        hcl.par=list(h=c(0,120), c=60, l=c(45,90), power=1.2))
drawmap(res, map=germany, cols="hcl",
        hcl.par=list(h=c(300,120), c=60, l=c(45,90), power=1.2))
drawmap(res, map=germany, cols="hcl",
        hcl.par=list(h=c(40,260), c=60, l=c(45,90), power=1.2))
drawmap(res, map=germany, cols="hsv", hsv.par=list(s=0.7, v=0.7))

Extract MCMC samples from a BayesX results directory

Description

This is a convenience function to extract samples from a BayesX results directory, which processes the log file to e.g. convert the spline coefficients samples to function values samples.

Usage

extractSamples(directoryWithBasename, 
               logfile = file.path(dirname(directoryWithBasename), "log.txt"))
extractSamples(directoryWithBasename, 
               logfile = file.path(dirname(directoryWithBasename), "log.txt"))

Arguments

`directoryWithBasename`	The BayesX results directory with basename for the files (e.g. "results/test", if this was specified as outfile in BayesX for the bayesreg object)
`logfile`	The log file of the MCMC run, defaults to log.txt in the results directory.

Value

Returns a list with the extracted samples of effects and deviances as well as the prediction data.frame:

`<function name>`	for P-Splines, Random Walks and spatial effects: a list with mcmc objects 'functionSamples' and 'varianceSamples' containing the respective effects/function and variance parameter samples.
`FixedEffects`	an mcmc object of all fixed simple parametric effects
`RandomEffects`	if there is at least one random effect in the model, this is a list, with elements in the first hierarchy being the group ID names, and elements in the second hierarchy being the names of the covariates. The leafs are the mcmc objects 'functionSamples' and 'varianceSamples', as for the other non-fixed terms
`Deviance`	an mcmc object with the (unstandardized and saturated) deviance
`means`	if the option `predictmu` was used, this mcmc object contains the mean samples
`scale`	an mcmc object with the possible scale parameter samples
`lassoCoefficients`	an mcmc object with the possible lasso regression parameter samples
`ridgeCoefficients`	an mcmc object with the possible ridge regression parameter samples
`PredictMeans`	data.frame corresponding to the possible predictmean file in the BayesX directory

Additionally, entries for possibly remaining lasso or ridge variance parameters etc. are included in the return list.

Warning

You should be sure that only one MCMC run is saved in the given results directory in order to get sensible results out of this function.

Author(s)

Daniel Sabanes Bove, with contributions by Fabian Scheipl

Examples

## get the samples
samples <- extractSamples(file.path(system.file("examples/samples", package="BayesX"),
                                     "res"))
str(samples)

## check deviance convergence
plot(samples$Deviance)

## fixed parametric effects
plot(samples$FixedEffects)

## nonparametric effects:

## handy plot function to get means and pointwise credible intervals
nonpPlot <- function(samplesMatrix,
                     ...)
{
    x <- as.numeric(colnames(samplesMatrix))

    yMeans <- colMeans(samplesMatrix)
    yCredible <- t(apply(samplesMatrix,
                         MARGIN=2,
                         FUN=quantile,
                         prob=c(0.025, 0.975),
                         na.rm=TRUE))
    
    matplot(x, cbind(yMeans, yCredible),
            type="l",
            lty=c(1, 2, 2),
            lwd=c(2, 1, 1),
            col=c(1, 2, 2),
            ...)
}

nonpPlot(samples$f_x1$functionSamples,
         xlab=expression(x[1]),
         ylab=expression(hat(f)(x[1])))
nonpPlot(samples$f_x2$functionSamples,
         xlab=expression(x[2]),
         ylab=expression(hat(f)(x[2])))

## spatial effect
tanzania <- read.bnd(file=system.file("examples/tanzania.bnd", package="BayesX"))
drawmap(map=tanzania,
        data=
        with(samples$f_district,
             data.frame(name=colnames(functionSamples),
                        estimate=colMeans(functionSamples))),
        regionvar="name",
        plotvar="estimate")
## get the samples
samples <- extractSamples(file.path(system.file("examples/samples", package="BayesX"),
                                     "res"))
str(samples)

## check deviance convergence
plot(samples$Deviance)

## fixed parametric effects
plot(samples$FixedEffects)

## nonparametric effects:

## handy plot function to get means and pointwise credible intervals
nonpPlot <- function(samplesMatrix,
                     ...)
{
    x <- as.numeric(colnames(samplesMatrix))

    yMeans <- colMeans(samplesMatrix)
    yCredible <- t(apply(samplesMatrix,
                         MARGIN=2,
                         FUN=quantile,
                         prob=c(0.025, 0.975),
                         na.rm=TRUE))
    
    matplot(x, cbind(yMeans, yCredible),
            type="l",
            lty=c(1, 2, 2),
            lwd=c(2, 1, 1),
            col=c(1, 2, 2),
            ...)
}

nonpPlot(samples$f_x1$functionSamples,
         xlab=expression(x[1]),
         ylab=expression(hat(f)(x[1])))
nonpPlot(samples$f_x2$functionSamples,
         xlab=expression(x[2]),
         ylab=expression(hat(f)(x[2])))

## spatial effect
tanzania <- read.bnd(file=system.file("examples/tanzania.bnd", package="BayesX"))
drawmap(map=tanzania,
        data=
        with(samples$f_district,
             data.frame(name=colnames(functionSamples),
                        estimate=colMeans(functionSamples))),
        regionvar="name",
        plotvar="estimate")

Combine Regions

Description

Combines a list of several regions of a map object in boundary format into a single region.

Usage

fuse(map, regions, name)
fuse(map, regions, name)

Arguments

`map`	Map object in boundary format that should be modified.
`regions`	Vector of regions to be combined
`name`	Name that should be given to the region arising from fusing the specified regions.

Value

Map object in boundary format with the specified regions combined.

Author(s)

Nadja Klein

Examples

## Not run: map <- read.bnd(system.file("examples/germany9301.bnd",
  package = "BayesX"))
drawmap(map = map, drawnames = TRUE)

## Vector of regions to be combined.
regions <- c("1056","1060","1061")

## New name of combined region.
newname <- "1"
newmap <- fuse(map,regions,newname)
drawmap(map = newmap, drawnames = TRUE)

## Vector of regions to be combined.
germany <- read.bnd(system.file("examples/germany.bnd", package="BayesX"))
drawmap(map = germany, drawnames = TRUE)
regions <- c("9371","9373","9374","9471","9472","9474","9574")

## New name of combined region.
newname <- "1"
newmap <- fuse(germany, regions, newname)
drawmap(map = newmap, drawnames = TRUE)

## End(Not run)## Not run: map <- read.bnd(system.file("examples/germany9301.bnd",
  package = "BayesX"))
drawmap(map = map, drawnames = TRUE)

## Vector of regions to be combined.
regions <- c("1056","1060","1061")

## New name of combined region.
newname <- "1"
newmap <- fuse(map,regions,newname)
drawmap(map = newmap, drawnames = TRUE)

## Vector of regions to be combined.
germany <- read.bnd(system.file("examples/germany.bnd", package="BayesX"))
drawmap(map = germany, drawnames = TRUE)
regions <- c("9371","9373","9374","9471","9472","9474","9574")

## New name of combined region.
newname <- "1"
newmap <- fuse(germany, regions, newname)
drawmap(map = newmap, drawnames = TRUE)

## End(Not run)

Compute Centroids of Polygons

Description

Computes all areas and centroids of the regions of a given map in boundary format.

Usage

get.centroids(map)
get.centroids(map)

Arguments

map

Map object in boundary format.

Value

Matrix of area and centroids.

Author(s)

Felix Heinzl, Thomas Kneib

Examples

germany <- read.bnd(system.file("examples/germany.bnd", package="BayesX"))
centroids <- get.centroids(germany)
centroids[1:10,]

plot(c(2100,3700),c(6800,8500),type="n", xlab="", ylab="")
for(i in 1:10){
   polygon(germany[[i]])
   region <- attr(germany,"names")[i]
   text(x=centroids[i,2]+50, y=centroids[i,3]+30, region, cex=0.7)
}   
points(centroids[1:10,2:3], col='red', pch=16)
germany <- read.bnd(system.file("examples/germany.bnd", package="BayesX"))
centroids <- get.centroids(germany)
centroids[1:10,]

plot(c(2100,3700),c(6800,8500),type="n", xlab="", ylab="")
for(i in 1:10){
   polygon(germany[[i]])
   region <- attr(germany,"names")[i]
   text(x=centroids[i,2]+50, y=centroids[i,3]+30, region, cex=0.7)
}   
points(centroids[1:10,2:3], col='red', pch=16)

Obtain Neighbors of Given Regions

Description

Extracts the neighbors of a number of regions from a map in graph format.

Usage

get.neighbor(map, regions)
get.neighbor(map, regions)

Arguments

`map`	Map object in graph format.
`regions`	Vector of names of regions for which the neighbors should be axtracted.

Value

A list of vectors containing the neighbors of the elements in regions.

Author(s)

Felix Heinzl, Thomas Kneib

Examples

germany <- read.gra(system.file("examples/germany.gra", package="BayesX"))
get.neighbor(germany, "1001")
get.neighbor(germany, c("1001", "7339"))
germany <- read.gra(system.file("examples/germany.gra", package="BayesX"))
get.neighbor(germany, "1001")
get.neighbor(germany, c("1001", "7339"))

Computing Highest Posterior Density (HPD) Intervals

Description

Compute approximate HPD intervals out of MCMC-samples in BayesX

Usage

hpd(data, alpha = 0.05, ...)
hpd.coda(data, alpha = 0.05)
hpd(data, alpha = 0.05, ...)
hpd.coda(data, alpha = 0.05)

Arguments

`data`	Either the name of a file or a data frame containing the sample.
`alpha`	A numeric scalar in the interval (0,1) such that 1 - alpha is the target probability content of the intervals.. The default is alpha = 0.05.
`...`	Further parameters to be passed to the internal call of `optim` and `integrate`.

Details

hpd computes the HPD interval based on a kernel density estimate of the samples. hpd.coda computes the HPD interval with the function HPDinterval available in package coda.

Author(s)

Nadja Klein

Examples

res <- read.table(system.file("examples/nonparametric_f_x_pspline_sample.raw",
  package="BayesX"), header = TRUE)
hpd(res)
hpd.coda(res)
res <- read.table(system.file("examples/nonparametric_f_x_pspline_sample.raw",
  package="BayesX"), header = TRUE)
hpd(res)
hpd.coda(res)

Convert nb and gra format into each other

Description

Convert neighborhood structure objects of class "nb" from R-package spdep to graph objects of class "gra" from R-package BayesX and vice versa.

Usage

nb2gra(nbObject)
gra2nb(graObject)
nb2gra(nbObject)
gra2nb(graObject)

Arguments

`nbObject`	neighborhood structure object of class "nb"
`graObject`	graph object of class "gra"

Value

Equivalent object in the other format.

Author(s)

Daniel Sabanes Bove

Examples

## Not run: ## first nb to gra:
if(requireNamespace("sf") &
   requireNamespace("spdep")) {
  library("sf")
  library("spdep")

  columbus <- st_read(system.file("etc/shapes/columbus.shp",
    package = "spdep")[1])
  colNb <- poly2nb(columbus)
  ## ... here manual editing is possible ...
  ## then export to graph format
  colGra <- nb2gra(colNb)

  ## and save in BayesX file
  graFile <- tempfile()
  write.gra(colGra, file=graFile)

  ## now back from gra to nb:
  colGra <- read.gra(graFile)
  newColNb <- gra2nb(colGra)
  newColNb
  ## compare this with the original
  colNb
  ## only the call attribute does not match (which is OK):
  all.equal(newColNb, colNb,
    check.attributes=FALSE)
  attr(newColNb, "call")
  attr(colNb, "call")
}

## End(Not run)## Not run: ## first nb to gra:
if(requireNamespace("sf") &
   requireNamespace("spdep")) {
  library("sf")
  library("spdep")

  columbus <- st_read(system.file("etc/shapes/columbus.shp",
    package = "spdep")[1])
  colNb <- poly2nb(columbus)
  ## ... here manual editing is possible ...
  ## then export to graph format
  colGra <- nb2gra(colNb)

  ## and save in BayesX file
  graFile <- tempfile()
  write.gra(colGra, file=graFile)

  ## now back from gra to nb:
  colGra <- read.gra(graFile)
  newColNb <- gra2nb(colGra)
  newColNb
  ## compare this with the original
  colNb
  ## only the call attribute does not match (which is OK):
  all.equal(newColNb, colNb,
    check.attributes=FALSE)
  attr(newColNb, "call")
  attr(colNb, "call")
}

## End(Not run)

Convert sp and bnd format into each other

Description

Convert geographical information objects of class "SpatialPolygons" (or specializations) from R-package sp to objects of class "bnd" from R-package BayesX and vice versa.

Usage

sp2bnd(spObject, regionNames, height2width, epsilon)
bnd2sp(bndObject)
sp2bnd(spObject, regionNames, height2width, epsilon)
bnd2sp(bndObject)

Arguments

`spObject`	object of class "SpatialPolygons" (or specializations)
`regionNames`	character vector of region names (parallel to the Polygons list in spObject), defaults to the IDs
`height2width`	ratio of total height to width, defaults to the bounding box values
`epsilon`	how much can two polygons differ (in maximum squared Euclidean distance) and still match each other?, defaults to machine precision
`bndObject`	object of class "bnd"

Value

Equivalent object in the other format.

Author(s)

Daniel Sabanes Bove

Examples

## Not run: ## bnd to sp:
germany <- read.bnd(system.file("examples/germany2001.bnd", package="BayesX"))
spGermany <- bnd2sp(germany)

## plot the result together with the neighborhood graph
library(sp)
plot(spGermany)
library(spdep)
nbGermany <- poly2nb(spGermany)
plot(nbGermany, coords=coordinates(spGermany), add=TRUE)

## example with one region inside another
spExample <- spGermany[c("7211", "7235"), ]
plot(spExample)
plot(poly2nb(spExample), coords=coordinates(spExample), add=TRUE)

## now back from sp to bnd:
bndGermany <- sp2bnd(spGermany)
drawmap(map=bndGermany)

## compare names and number of polygons
stopifnot(identical(names(bndGermany),
                    names(germany)),
          identical(length(bndGermany),
                    length(germany)))

## compare contains-relations
surrounding <- attr(bndGermany, "surrounding")
whichInner <- which(sapply(surrounding, length) > 0L)
bndContainsData <- data.frame(inner=names(bndGermany)[whichInner],
                              outer=unlist(surrounding))

surrounding <- attr(germany, "surrounding")
whichInner <- which(sapply(surrounding, length) > 0L)
originalContainsData <- data.frame(inner=names(germany)[whichInner],
                                   outer=unlist(surrounding))

stopifnot(all(bndContainsData[order(bndContainsData$inner), ] ==
              originalContainsData[order(originalContainsData$inner), ]))

## End(Not run)## Not run: ## bnd to sp:
germany <- read.bnd(system.file("examples/germany2001.bnd", package="BayesX"))
spGermany <- bnd2sp(germany)

## plot the result together with the neighborhood graph
library(sp)
plot(spGermany)
library(spdep)
nbGermany <- poly2nb(spGermany)
plot(nbGermany, coords=coordinates(spGermany), add=TRUE)

## example with one region inside another
spExample <- spGermany[c("7211", "7235"), ]
plot(spExample)
plot(poly2nb(spExample), coords=coordinates(spExample), add=TRUE)

## now back from sp to bnd:
bndGermany <- sp2bnd(spGermany)
drawmap(map=bndGermany)

## compare names and number of polygons
stopifnot(identical(names(bndGermany),
                    names(germany)),
          identical(length(bndGermany),
                    length(germany)))

## compare contains-relations
surrounding <- attr(bndGermany, "surrounding")
whichInner <- which(sapply(surrounding, length) > 0L)
bndContainsData <- data.frame(inner=names(bndGermany)[whichInner],
                              outer=unlist(surrounding))

surrounding <- attr(germany, "surrounding")
whichInner <- which(sapply(surrounding, length) > 0L)
originalContainsData <- data.frame(inner=names(germany)[whichInner],
                                   outer=unlist(surrounding))

stopifnot(all(bndContainsData[order(bndContainsData$inner), ] ==
              originalContainsData[order(originalContainsData$inner), ]))

## End(Not run)

Computing and Plotting Autocorrelation Functions

Description

Computes and plot autocorrelation functions for samples obtained with MCMC in BayesX

Usage

plotautocor(data, ask = TRUE, lag.max=100, ...)
plotautocor(data, ask = TRUE, lag.max=100, ...)

Arguments

`data`	Either the name of a file or a data frame containing the sample.
`ask`	`plotautocor` will plot separate autocorrelation functions for each parameter. If `ask=TRUE`, the user will be prompted before showing the next plot.
`lag.max`	Maximum number of lags to be considered.
`...`	Further parameters to be passed to the internal call of `plot` such as `ylim`, etc.

Author(s)

Felix Heinzl, Thomas Kneib

Examples

res <- read.table(system.file("examples/nonparametric_f_x_pspline_sample.raw", 
                              package="BayesX"), header=TRUE)
plotautocor(res)
plotautocor(res, lag.max=50)
res <- read.table(system.file("examples/nonparametric_f_x_pspline_sample.raw", 
                              package="BayesX"), header=TRUE)
plotautocor(res)
plotautocor(res, lag.max=50)

Plotting Nonparametric Function Estimates

Description

Plots nonparametric function estimates obtained from BayesX

Usage

plotnonp(data, x = 2, y = c(3, 4, 5, 7, 8), ylim = NULL, 
         lty = c(1, 2, 3, 2, 3), cols = rep(1, length(y)), month, year, step=12, 
         xlab, ylab, ...)
plotnonp(data, x = 2, y = c(3, 4, 5, 7, 8), ylim = NULL, 
         lty = c(1, 2, 3, 2, 3), cols = rep(1, length(y)), month, year, step=12, 
         xlab, ylab, ...)

Arguments

`data`	Either the name of a file or a data frame containing the estimation results.
`x`	Defines the x-axis in the plot. Either the name of a variable in `data` or the index of the corresponding column.
`y`	Defines the variables to be plotted against `x`. May be either a vector of names of variables in `data` or the corresponding indices. The default choice corresponds to the point estimate plus two confidence bands.
`ylim`	Since `plotnonp` plots multiple y-variables, it automatically determines the appropriate `ylim` to make all curves visible. Argument `ylim` allows to override this default behaviour with fixed values.
`lty`	Vector of line types used for plotting (must have the same length as `y`). The default corresponds to solid lines for the point estimate and dashed and dotted lines for the confidence bands.
`cols`	Vector of colors used for plotting (must have the same length as `y`). Default are black lines.
`month`, `year`, `step`	Provide specific annotation for plotting estimation results for temporal variables. `month` and `year` define the minimum time point whereas step specifies the type of temporal data with `step=4`, `step=2` and `step=1` corresponding to quartely, half yearly and yearly data.
`xlab`, `ylab`	`plotnonp` constructs default labels that can be overwritten by these arguments
`...`	Further arguments to be passed to the interval call of `plot` such as `type`, etc.

Author(s)

Felix Heinzl, Andreas Brezger and Thomas Kneib

Examples


res <- read.table(system.file("examples/nonparametric_f_x_pspline.res", 
                              package="BayesX"), header=TRUE)
plotnonp(res)
plotnonp(res, x="x")
plotnonp(res, x="x", y="pmean")
plotnonp(res, x="x", y="pmed")
plotnonp(res, x="x", y="pmed", ylim=c(-2,2))
plotnonp(res, x="x", y=c("pmean", "pqu10", "pqu90"), lty=c(1,1,1), 
         col=c("red","blue","blue"))
plotnonp(res, xlab="some variable", ylab="f(some variable)", 
         main="Nonlinear effect of some variable", sub="penalised spline")

res <- read.table(system.file("examples/nonparametric2_f_time_pspline.res", 
                              package="BayesX"), header=TRUE)
plotnonp(res)
plotnonp(res, month=1, year=1980, step=12)

res <- res[1:18,]                                           
plotnonp(res, month=1, year=1980, step=12)
res <- read.table(system.file("examples/nonparametric_f_x_pspline.res", 
                              package="BayesX"), header=TRUE)
plotnonp(res)
plotnonp(res, x="x")
plotnonp(res, x="x", y="pmean")
plotnonp(res, x="x", y="pmed")
plotnonp(res, x="x", y="pmed", ylim=c(-2,2))
plotnonp(res, x="x", y=c("pmean", "pqu10", "pqu90"), lty=c(1,1,1), 
         col=c("red","blue","blue"))
plotnonp(res, xlab="some variable", ylab="f(some variable)", 
         main="Nonlinear effect of some variable", sub="penalised spline")

res <- read.table(system.file("examples/nonparametric2_f_time_pspline.res", 
                              package="BayesX"), header=TRUE)
plotnonp(res)
plotnonp(res, month=1, year=1980, step=12)

res <- res[1:18,]                                           
plotnonp(res, month=1, year=1980, step=12)

Plotting Sampling Paths

Description

Plots sampling paths obtained with MCMC-sampling in BayesX

Usage

plotsample(data, ask = TRUE, ...)
plotsample.coda(data, ask = TRUE, ...)
plotsample(data, ask = TRUE, ...)
plotsample.coda(data, ask = TRUE, ...)

Arguments

`data`	Either the name of a file or a data frame containing the sample.
`ask`	`plotsample` will plot separate sampling paths for each parameter. If `ask=TRUE`, the user will be prompted before showing the next plot.
`...`	Further parameters to be passed to the internal call of `plot` such as `ylim`, etc.

Details

plotsample simply plots sampling paths while plotsampe.coda makes use of the plotting facilities available in package coda.

Author(s)

Felix Heinzl, Andreas Brezger, Thomas Kneib

Examples

res <- read.table(system.file("examples/nonparametric_f_x_pspline_sample.raw",
                              package="BayesX"), header=TRUE)
plotsample(res)
res <- read.table(system.file("examples/nonparametric_f_x_pspline_sample.raw",
                              package="BayesX"), header=TRUE)
plotsample(res)

Visualise Surface Estimates

Description

Visualises surface estimates obtained with BayesX.

Usage

plotsurf(data, x=2, y=3, z=4, mode=1, ticktype="detailed", 
         expand=0.75, d=100, theta=-30, phi=25, ...)
plotsurf(data, x=2, y=3, z=4, mode=1, ticktype="detailed", 
         expand=0.75, d=100, theta=-30, phi=25, ...)

Arguments

`data`	Either the name of a file or a data frame containing the estimation results.
`x`	Defines the x-axis in the plot. Either the name of a variable in `data` or the index of the corresponding column.
`y`	Defines the y-axis in the plot. Either the name of a variable in `data` or the index of the corresponding column.
`z`	Defines the z-axis in the plot. Either the name of a variable in `data` or the index of the corresponding column.
`mode`	`plotsurf` is mostly a convenient interface to the functions `persp` (`mode=1`), `image` (`mode=2`) and `contour` (`mode=3`).
`ticktype`, `expand`, `d`, `theta`, `phi`	Overwrite the default behaviour of `persp`
`...`	Further parameteres that are parsed to the internal call to `persp`, `image` or `contour`

Author(s)

Felix Heinzl, Thomas Kneib

Examples

res <- read.table(system.file("examples/surface_f_x1_x2_pspline.res", 
                              package="BayesX"), header=TRUE)

plotsurf(res)
plotsurf(res, mode=2)
plotsurf(res, mode=3)

plotsurf(res, x="x1", y="x2", z="pmed")

plotsurf(res, ticktype="simple")

plotsurf(res, main="3D-Plot", xlab="myx", ylab="myy", zlab="f(myx,myy)")
res <- read.table(system.file("examples/surface_f_x1_x2_pspline.res", 
                              package="BayesX"), header=TRUE)

plotsurf(res)
plotsurf(res, mode=2)
plotsurf(res, mode=3)

plotsurf(res, x="x1", y="x2", z="pmed")

plotsurf(res, ticktype="simple")

plotsurf(res, main="3D-Plot", xlab="myx", ylab="myy", zlab="f(myx,myy)")

Read Geographical Information in Boundary Format

Description

Reads the geographical information provided in a file in boundary format (see Ch. 5 of the BayesX Reference Manual) and stores it in a map object.

Usage

read.bnd(file, sorted=FALSE)
read.bnd(file, sorted=FALSE)

Arguments

`file`	Name of the boundary file to be read.
`sorted`	Should the regions be ordered by the numbers speciying the region names (`sorted=TRUE`)?

Value

Returns a list of polygons that form the map. Additional attributes are

`surrounding`	Parallel list where for each polygon, the name of a possible surrounding region is saved.
`height2width`	Ratio between height and width of the map. Allows customised drawing and storage in files by specifying the appropriate height and width.
`class`	Indicates whether the map is stored in boundary format (`bnd`) or graph format (`gra`). Maps returned by `read.bnd` are of class `bnd`

Author(s)

Daniel Sabanes Bove, Felix Heinzl, Thomas Kneib, Andreas Brezger

References

BayesX Reference Manual. Available from https://www.uni-goettingen.de/de/bayesx/550513.html

Examples

germany <- read.bnd(system.file("examples/germany.bnd", package="BayesX"))
drawmap(map=germany)
attributes(germany)

germany <- read.bnd(system.file("examples/germany2001.bnd", package="BayesX"))
drawmap(map=germany)
attributes(germany)
germany <- read.bnd(system.file("examples/germany.bnd", package="BayesX"))
drawmap(map=germany)
attributes(germany)

germany <- read.bnd(system.file("examples/germany2001.bnd", package="BayesX"))
drawmap(map=germany)
attributes(germany)

Read Geographical Information in Graph Format

Description

Reads the geographical information provided in a file in graph format (see Ch. 5 of the BayesX Reference Manual) and stores it in a map object.

Usage

read.gra(file, sorted=FALSE)
read.gra(file, sorted=FALSE)

Arguments

`file`	Name of the graph file to be read.
`sorted`	Should the regions be ordered by the numbers speciying the region names (`sorted=TRUE`)?

Value

Returns an adjacency matrix that represents the neighborhood structure defined in the graph file. Additional attributes are

`dim`	Dimension of the (square) adjacency matrix.
`dimnames`	List of region names corresponding to rows and columns of the adjacency matrix.
`class`	Indicates whether the map is stored in boundary format (`bnd`) or graph format (`gra`). Maps returned by `read.gra` are of class `gra`

Author(s)

Thomas Kneib, Felix Heinzl

References

BayesX Reference Manual. Available from https://www.uni-goettingen.de/de/bayesx/550513.html

Examples

germany <- read.gra(system.file("examples/germany.gra", package="BayesX"))
attributes(germany)
germany <- read.gra(system.file("examples/germany.gra", package="BayesX"))
attributes(germany)

convert a shape-file into a boundary object

Description

Converts the geographical information provided in a shape-file into a boundary object (see Ch. 5 of the Reference Manual)

Usage

shp2bnd(shpname, regionnames, check.is.in = TRUE)
shp2bnd(shpname, regionnames, check.is.in = TRUE)

Arguments

`shpname`	Base filename of the shape-file (including path)
`regionnames`	Either a vector of region names or the name of the variable in the dbf-file representing these names
`check.is.in`	Test whether some regions are surrounded by other regions (FALSE speeds up the execution time but may result in a corrupted bnd-file)

Value

Returns a boundary object, i.e. a list of polygons that form the map. See read.bnd for more information on the format.

Author(s)

Felix Heinzl, Daniel Sabanes Bove, Thomas Kneib with contributions by Michael Hoehle and Frank Sagerer

References

BayesX Reference Manual. Available from https://www.uni-goettingen.de/de/bayesx/550513.html

Examples

## read shapefile into bnd object
shpName <- sub(pattern="(.*)\\.dbf", replacement="\\1",
               x=system.file("examples/northamerica_adm0.dbf",
                             package="BayesX")) 
north <- shp2bnd(shpname=shpName, regionnames="COUNTRY")

## draw the map
drawmap(map=north)

## compare with shipped bnd file
shippedBnd <- read.bnd(system.file("examples/northamerica.bnd", package="BayesX"))
stopifnot(all.equal(north, shippedBnd))
## read shapefile into bnd object
shpName <- sub(pattern="(.*)\\.dbf", replacement="\\1",
               x=system.file("examples/northamerica_adm0.dbf",
                             package="BayesX")) 
north <- shp2bnd(shpname=shpName, regionnames="COUNTRY")

## draw the map
drawmap(map=north)

## compare with shipped bnd file
shippedBnd <- read.bnd(system.file("examples/northamerica.bnd", package="BayesX"))
stopifnot(all.equal(north, shippedBnd))

Round Boundary Information

Description

Rounds the boundary information in a map object in boundary format to a specified precision.

Usage

smooth.bnd(map, digits = 2, scale = 1)
smooth.bnd(map, digits = 2, scale = 1)

Arguments

`map`	Map object in boundary format that should be modified.
`digits`	Number of digits to round to.
`scale`	Scaling factor that should be applied for rounding. For example, with `scale=0.1` all polygons are divided by 10 before rounding.

Value

Map object in boundary format rounded to the specified precision.

Author(s)

Felix Heinzl, Thomas Kneib

Saving Maps in Boundary Format

Description

Writes the information of a map object to a file (in boundary format)

Usage

write.bnd(map, file, replace = FALSE)
write.bnd(map, file, replace = FALSE)

Arguments

`map`	Map object ot be saved (should be in boundary format).
`file`	Name of the file to write to
`replace`	Should an existing file be overwritten with the new version?

Author(s)

Thomas Kneib, Felix Heinzl

References

BayesX Reference Manual. Available from https://www.uni-goettingen.de/de/bayesx/550513.html

Saving Maps in Graph Format

Description

Writes the information of a map object to a file (in graph format)

Usage

write.gra(map, file, replace = FALSE)
write.gra(map, file, replace = FALSE)

Arguments

`map`	Map object ot be saved (should be in graph format, see `bnd2gra` for the conversion of boundary format to graph format).
`file`	Name of the file to write to
`replace`	Should an existing file be overwritten with the new version?

Author(s)

Thomas Kneib, Felix Heinzl

References

BayesX Reference Manual. Available from https://www.uni-goettingen.de/de/bayesx/550513.html

Package 'BayesX'

Help Index

R Utilities Accompanying the Software Package BayesX

Description

Author(s)

References

Add Neighborhood Relations

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Convert Boundary Format to Graph Format

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Create map objects for some points and a given distance

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Delete Neighborhood Relations

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Drawing Geographical Information

Description

Usage

Arguments

Author(s)

See Also

Examples

Extract MCMC samples from a BayesX results directory

Description

Usage

Arguments

Value

Warning

Author(s)

Examples

Combine Regions

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Compute Centroids of Polygons

Description

Usage

Arguments

Value

Author(s)

Examples

Obtain Neighbors of Given Regions

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Computing Highest Posterior Density (HPD) Intervals

Description

Usage