spatsurv

Description

An R package for spatially correlated parametric proportional hazards survial analysis.

Usage

spatsurv

Format

An object of class logical of length 1.

Details

Index of help topics:

.onAttach               .onAttach function
B                       B function
Bspline.construct       Bspline.construct function
BsplineHaz              BsplineHaz function
CSplot                  CSplot function
Et_PP                   Et_PP function
EvalCov                 EvalCov function
ExponentialCovFct       ExponentialCovFct function
FFTgrid                 FFTgrid function
GammaFromY_SPDE         GammaFromY_SPDE function
GammafromY              GammafromY function
Independent             Independent function
MCE                     MCE function
NonSpatialLogLikelihood_or_gradient
                        NonSpatialLogLikelihood_or_gradient function
PsplineHaz              PsplineHaz function
QuadApprox              QuadApprox function
SPDE                    SPDE function
SPDEprec                SPDEprec function
SpikedExponentialCovFct
                        SpikedExponentialCovFct function
Summarise               Summarise function
TwoWayHazAdditive       TwoWayHazAdditive function
YFromGamma_SPDE         YFromGamma_SPDE function
YfromGamma              YfromGamma function
allocate                allocate function
alpha                   alpha function
baseHazST               baseHazST function
basehazard              basehazard function
basehazard.basehazardspec
                        basehazard.basehazardspec function
baselinehazard          baselinehazard function
baselinehazard_multiWay
                        baselinehazard_multiWay function
betapriorGauss          betapriorGauss function
blockDiag               A function to
boxplotRisk             boxplotRisk function
checkSurvivalData       checkSurvivalData function
circulant               circulant function
circulant.matrix        circulant.matrix function
circulant.numeric       circulant.numeric function
circulantij             circulantij function
covmodel                covmodel function
cumbasehazard           cumbasehazard function
cumbasehazard.basehazardspec
                        cumbasehazard.basehazardspec function
cumulativeBspline.construct
                        cumulativeBspline.construct function
density_PP              density_PP function
densityquantile         densityquantile function
densityquantile.basehazardspec
                        densityquantile.basehazardspec function
densityquantile_PP      densityquantile_PP function
derivindepGaussianprior
                        derivindepGaussianprior function
derivindepGaussianpriorST
                        derivindepGaussianpriorST function
derivpsplineprior       derivpsplineprior function
distinfo                distinfo function
distinfo.basehazardspec
                        distinfo.basehazardspec function
estimateY               estimateY function
etapriorGauss           etapriorGauss function
exponentialHaz          exponentialHaz function
fixParHaz               fixParHaz function
fixedpars               fixedpars function
fixmatrix               fixmatrix function
frailtylag1             frailtylag1 function
fs                      London Fire Brigade property
fstimes                 London Fire Brigade response times to dwelling
                        fires, 2009
gamma2risk              gamma2risk function
gencens                 gencens function
getBbasis               getBbasis function
getGrid                 getGrid function
getOptCellwidth         getOptCellwidth function
getbb                   getbb function
getcov                  getcov function
getgrd                  getgrd function
getleneta               getleneta function
getparranges            getparranges function
getsurvdata             getsurvdata function
gompertzHaz             gompertzHaz function
gradbasehazard          gradbasehazard function
gradbasehazard.basehazardspec
                        gradbasehazard.basehazardspec function
gradcumbasehazard       gradcumbasehazard function
gradcumbasehazard.basehazardspec
                        gradcumbasehazard.basehazardspec function
grid2spdf               grid2spdf function
grid2spix               grid2spix function
grid2spts               grid2spts function
gridY                   gridY function
gridY_polygonal         gridY_polygonal function
guess_t                 guess_t function
hasNext                 generic hasNext method
hasNext.iter            hasNext.iter function
hazard_PP               hazard_PP function
hazardexceedance        hazardexceedance function
hazardpars              hazardpars function
hessbasehazard          hessbasehazard function
hessbasehazard.basehazardspec
                        hessbasehazard.basehazardspec function
hesscumbasehazard       hesscumbasehazard function
hesscumbasehazard.basehazardspec
                        hesscumbasehazard.basehazardspec function
imputationModel         imputationModel function
indepGaussianprior      indepGaussianprior function
indepGaussianpriorST    indepGaussianpriorST function
inference.control       inference.control function
insert                  insert function
invtransformweibull     invtransformweibull function
is.burnin               is this a burn-in iteration?
is.retain               do we retain this iteration?
iteration               iteration number
logPosterior            logPosterior function
logPosterior_SPDE       logPosterior_SPDE function
logPosterior_gridded    logPosterior_gridded function
logPosterior_polygonal
                        logPosterior_polygonal function
loop.mcmc               loop over an iterator
makehamHaz              makehamHaz function
maxlikparamPHsurv       maxlikparamPHsurv function
mcmcLoop                iterator for MCMC loops
mcmcPriors              mcmcPriors function
mcmcProgressNone        null progress monitor
mcmcProgressPrint       printing progress monitor
mcmcProgressTextBar     text bar progress monitor
mcmcpars                mcmcpars function
midpts                  midpts function
multiWayHaz             multiWayHaz function
neighLocs               neighLocs function
neighOrder              neighOrder function
nextStep                next step of an MCMC chain
omegapriorGauss         omegapriorGauss function
omegapriorGaussST       omegapriorGaussST function
optifix                 optifix function
plot.FFTgrid            plot.FFTgrid function
plotsurv                plotsurv function
polyadd                 polyadd function
polymult                polymult function
posteriorcov            posteriorcov function
predict.mcmcspatsurv    predict.mcmcspatsurv function
print.mcmc              print.mcmc function
print.mcmcspatsurv      print.mcmcspatsurv function
print.mlspatsurv        print.mlspatsurv function
print.textSummary       print.textSummary function
priorposterior          priorposterior function
proposalVariance        proposalVariance function
proposalVariance_SPDE   proposalVariance_SPDE function
proposalVariance_gridded
                        proposalVariance_gridded function
proposalVariance_polygonal
                        proposalVariance_polygonal function
psplineRWprior          psplineRWprior function
psplineprior            psplineprior function
quantile.mcmcspatsurv   quantile.mcmcspatsurv function
quantile.mlspatsurv     quantile.mlspatsurv function
randompars              randompars function
reconstruct.bs          reconstruct.bs function
reconstruct.bs.coxph    reconstruct.bs.coxph function
reconstruct.bs.mcmcspatsurv
                        reconstruct.bs.mcmcspatsurv function
resetLoop               reset iterator
residuals.mcmcspatsurv
                        resuiduals.mcmcspatsurv function
rootWeibullHaz          rootWeibullHaz function
setTxtProgressBar2      set the progress bar
setupHazard             setupHazard function
setupPrecMatStruct      setupPrecMatStruct function
showGrid                showGrid function
simsurv                 simsurv function
spatialpars             spatialpars function
spatsurv-package        spatsurv
spatsurvVignette        spatsurvVignette function
summary.mcmc            summary.mcmc function
summary.mcmcspatsurv    summary.mcmcspatsurv function
surv3d                  Spatial Survival Plot in 3D
survival_PP             survival_PP function
survspat                survspat function
survspatNS              survspatNS function
textSummary             textSummary function
timevaryingPL           timevaryingPL function
tpowHaz                 tpowHaz function
transformweibull        transformweibull function
txtProgressBar2         A text progress bar with label
vcov.mcmcspatsurv       vcov.mcmcspatsurv function
vcov.mlspatsurv         vcov.mlspatsurv function
weibullHaz              weibullHaz function

Dependencies

The package spatsurv depends upon some other important contributions to CRAN in order to operate; their uses here are indicated:

survival, sp, spatstat, raster, iterators, RandomFields, fields, rgl, Matrix, stringr, RColorBrewer, geostatsp.

Citation

To cite use of spatsurv, the user may refer to the following work:

Benjamin M. Taylor and Barry S. Rowlingson (2017).
spatsurv: An R Package for Bayesian Inference with Spatial Survival Models.
Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

references

X

Author(s)

Benjamin Taylor, Health and Medicine, Lancaster University, Barry Rowlingson, Health and Medicine, Lancaster University

.onAttach function

Description

A function to print a welcome message on loading package

Usage

.onAttach(libname, pkgname)

Arguments

Value

...

B function

Description

A recursive function used in calculating the coefficients of a B-spline curve

Usage

B(x, i, j, knots)

Arguments

Value

a vector of polynomial coefficients

Bspline.construct function

Description

A function to construct a B-spline basis matrix for given data and basis coefficients. Used in evaluating the baseline hazard.

Usage

Bspline.construct(x, basis)

Arguments

Value

a basis matrix

BsplineHaz function

Description

A function to define a parametric proportional hazards model where the baseline hazard is modelled by a basis spline. This function returns an object inheriting class 'basehazardspec', list of functions 'distinfo', 'basehazard', 'gradbasehazard', 'hessbasehazard', 'cumbasehazard', 'gradcumbasehazard', 'hesscumbasehazard' and 'densityquantile'

Usage

BsplineHaz(times, knots = quantile(times), degree = 3, MLinits = NULL)

Arguments

Details

The distinfo function is used to provide basic distribution specific information to other spatsurv functions. The user is required to provide the following information in the returned list: npars, the number of parameters in this distribution; parnames, the names of the parameters; trans, the transformation scale on which the priors will be provided; itrans, the inverse transformation function that will be applied to the parameters before the hazard, and other functions are evaluated; jacobian, the derivative of the inverse transformation function with respect to each of the parameters; and hessian, the second derivatives of the inverse transformation function with respect to each of the parameters – note that currently the package spatsurv only allows the use of functions where the parameters are transformed independently.

The basehazard function is used to evaluate the baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradbasehazard function is used to evaluate the gradient of the baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hessbasehazard function is used to evaluate the Hessian of the baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The cumbasehazard function is used to evaluate the cumulative baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradcumbasehazard function is used to evaluate the gradient of the cumulative baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hesscumbasehazard function is used to evaluate the Hessian of the cumulative baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The densityquantile function is used to return quantiles of the density function. This is NOT REQUIRED for running the MCMC, merely for us in post-processing with the predict function where type is 'densityquantile'. In the case of the Weibull model for the baseline hazard, it can be shown that the q-th quantile is:

Value

an object inheriting class 'basehazardspec'

See Also

exponentialHaz, gompertzHaz, makehamHaz, weibullHaz

CSplot function

Description

A function to produce a diagnostic plot for model fit using the Cox-Snell residuals.

Usage

CSplot(mod, plot = TRUE, bw = FALSE, ...)

Arguments

Value

the x and y values used in the plot

Et_PP function

Description

A function to compute an individual's approximate expected survival time using numerical integration. Note this appears to be unstable; the function is based on R's integrate function. Not intended for general use (yet!).

Usage

Et_PP(inputs)

Arguments

Value

the expected survival time for the individual, obtained by numerical integration of the density function.

EvalCov function

Description

This function is used to evaluate the covariance function within the MCMC run. Not intended for general use.

Usage

EvalCov(cov.model, u, parameters)

Arguments

Value

method EvalCov

ExponentialCovFct function

Description

A function to declare and also evaluate an exponential covariance function.

Usage

ExponentialCovFct()

Value

the exponential covariance function

See Also

SpikedExponentialCovFct, covmodel

FFTgrid function

Description

A function to generate an FFT grid and associated quantities including cell dimensions, size of extended grid, centroids,

Usage

FFTgrid(spatialdata, cellwidth, ext, boundingbox = NULL)

Arguments

Value

a list

GammaFromY_SPDE function

Description

A function to go from Y to Gamma

Usage

GammaFromY_SPDE(Y, U, mu)

Arguments

Value

the value of Gamma for the given Y

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

2. Finn Lindgren, Havard Rue, Johan Lindstrom. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B 73(4)

GammafromY function

Description

A function to change Ys (spatially correlated noise) into Gammas (white noise). Used in the MALA algorithm.

Usage

GammafromY(Y, rootQeigs, mu)

Arguments

Value

Gamma

Independent function

Description

A function to declare and also evaluate an exponential covariance function.

Usage

Independent()

Value

the exponential covariance function

See Also

SpikedExponentialCovFct, covmodel

MCE function

Description

A function to compute Monte Carlo expectations from an object inheriting class mcmcspatsurv

Usage

MCE(object, fun)

Arguments

Value

the Monte Carlo mean of the function over the posterior.

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, hazardexceedance

NonSpatialLogLikelihood_or_gradient function

Description

A function to evaluate the log-likelihood of a non-spatial parametric proportional hazards model. Not intended for general use.

Usage

NonSpatialLogLikelihood_or_gradient(
  surv,
  X,
  beta,
  omega,
  control,
  loglikelihood,
  gradient
)

Arguments

Value

...

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

PsplineHaz function

Description

A function to define a parametric proportional hazards model where the baseline hazard is modelled by a basis spline and where the coefficients of the model follow a partially imporper random walk prior. This function returns an object inheriting class 'basehazardspec', list of functions 'distinfo', 'basehazard', 'gradbasehazard', 'hessbasehazard', 'cumbasehazard', 'gradcumbasehazard', 'hesscumbasehazard' and 'densityquantile'

Usage

PsplineHaz(times, knots = quantile(times), degree = 3, MLinits = NULL)

Arguments

Details

The distinfo function is used to provide basic distribution specific information to other spatsurv functions. The user is required to provide the following information in the returned list: npars, the number of parameters in this distribution; parnames, the names of the parameters; trans, the transformation scale on which the priors will be provided; itrans, the inverse transformation function that will be applied to the parameters before the hazard, and other functions are evaluated; jacobian, the derivative of the inverse transformation function with respect to each of the parameters; and hessian, the second derivatives of the inverse transformation function with respect to each of the parameters – note that currently the package spatsurv only allows the use of functions where the parameters are transformed independently.

The basehazard function is used to evaluate the baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradbasehazard function is used to evaluate the gradient of the baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hessbasehazard function is used to evaluate the Hessian of the baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The cumbasehazard function is used to evaluate the cumulative baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradcumbasehazard function is used to evaluate the gradient of the cumulative baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hesscumbasehazard function is used to evaluate the Hessian of the cumulative baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The densityquantile function is used to return quantiles of the density function. This is NOT REQUIRED for running the MCMC, merely for us in post-processing with the predict function where type is 'densityquantile'. In the case of the Weibull model for the baseline hazard, it can be shown that the q-th quantile is:

Value

an object inheriting class 'basehazardspec'

See Also

exponentialHaz, gompertzHaz, makehamHaz, weibullHaz

QuadApprox function

Description

A function to compute the second derivative of a function (of several real variables) using a quadratic approximation on a grid of points defined by the list argRanges. Also returns the local maximum.

Usage

QuadApprox(fun, npts, argRanges, plot = FALSE, ...)

Arguments

Value

a 2 by 2 matrix containing the curvature at the maximum and the (x,y) value at which the maximum occurs

SPDE function

Description

A function to declare and evaluate an SPDE covariance function.

Usage

SPDE(ord)

Arguments

Value

an covariance function based on the SPDE model

See Also

ExponentialCovFct, covmodel

SPDEprec function

Description

A function to used in entering elements into the precision matrix of an SPDE model. Not intended for general use.

Usage

SPDEprec(a, ord)

Arguments

Value

a function used for creating the precision matrix

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

2. Finn Lindgren, Havard Rue, Johan Lindstrom. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B 73(4)

SpikedExponentialCovFct function

Description

A function to declare and also evaluate a spiked exponential covariance function. This is an exponential covariance function with a nugget.

Usage

SpikedExponentialCovFct()

Value

the spiked exponential covariance function

See Also

ExponentialCovFct, covmodel

Summarise function

Description

A function to completely summarise the output of an object of class mcmcspatsurv.

Usage

Summarise(
  obj,
  digits = 3,
  scientific = -3,
  inclIntercept = FALSE,
  printmode = "LaTeX",
  displaymode = "console",
  ...
)

Arguments

Value

A text summary, that can be pasted into a LaTeX document and later edited.

TwoWayHazAdditive function

Description

A function to

Usage

TwoWayHazAdditive(bhlist, bhtime, bhfix, MLinits = NULL)

Arguments

Value

...

YFromGamma_SPDE function

Description

A function to go from Gamma to Y

Usage

YFromGamma_SPDE(gamma, U, mu)

Arguments

Value

the value of Y for the given Gamma

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

2. Finn Lindgren, Havard Rue, Johan Lindstrom. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B 73(4)

YfromGamma function

Description

A function to change Gammas (white noise) into Ys (spatially correlated noise). Used in the MALA algorithm.

Usage

YfromGamma(Gamma, invrootQeigs, mu)

Arguments

Value

Y

allocate function

Description

A function to allocate coordinates to an observation whose spatial location is known to the regional level

Usage

allocate(poly, popden, survdat, pid, sid, n = 2, wid = 2000)

Arguments

Value

matrices x and y, both of size (number of observations in survdat x n) giving n potential candidate locations of points in the columns of x and y.

alpha function

Description

A function used in calculating the coefficients of a B-spline curve

Usage

alpha(i, j, knots, knotidx)

Arguments

Value

a vector

baseHazST function

Description

A function to

Usage

baseHazST(
  bh1 = NULL,
  survobj,
  t0,
  nbreaks = 5,
  breakmethod = "quantile",
  MLinits = NULL
)

Arguments

Value

...

basehazard function

Description

Generic function for computing the baseline hazard

Usage

basehazard(obj, ...)

Arguments

Value

method basehazard

See Also

basehazard.basehazardspec, exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

basehazard.basehazardspec function

Description

A function to retrieve the baseline hazard function

Usage

## S3 method for class 'basehazardspec'
basehazard(obj, ...)

Arguments

Value

a function returning the baseline hazard

See Also

exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

baselinehazard function

Description

A function to compute quantiles of the posterior baseline hazard or cumulative baseline hazard.

Usage

baselinehazard(
  x,
  t = NULL,
  n = 100,
  probs = c(0.025, 0.5, 0.975),
  cumulative = FALSE,
  plot = TRUE,
  bw = FALSE,
  ...
)

Arguments

Value

the vector of times and quantiles of the baseline or cumulative baseline hazard at those times

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

baselinehazard_multiWay function

Description

A function to

Usage

baselinehazard_multiWay(
  x,
  probs = c(0.025, 0.5, 0.975),
  cumulative = FALSE,
  plot = TRUE,
  joint = FALSE,
  xlims = NULL,
  ylims = NULL,
  ...
)

Arguments

Value

...

betapriorGauss function

Description

A function to define Gaussian priors for beta. This function simply stores a vector of means and standard deviations to be passed to the main MCMC function, survspat.

Usage

betapriorGauss(mean, sd)

Arguments

Value

an object of class "betapriorGauss"

See Also

survspat, betapriorGauss, omegapriorGauss, etapriorGauss, indepGaussianprior, derivindepGaussianprior

A function to

Description

A function to

Usage

blockDiag(matlist)

Arguments

Value

...

boxplotRisk function

Description

A function to

Usage

boxplotRisk(g2r)

Arguments

Value

...

checkSurvivalData function

Description

A function to check whether the survival data to be passed to survspat is in the correct format

Usage

checkSurvivalData(s)

Arguments

Value

if there are any issues with data format, these are returned with the data an error message explaining any issues with the data

circulant function

Description

generic function for constructing circulant matrices

Usage

circulant(x, ...)

Arguments

Value

method circulant

circulant.matrix function

Description

If x is a matrix whose columns are the bases of the sub-blocks of a block circulant matrix, then this function returns the block circulant matrix of interest.

Usage

## S3 method for class 'matrix'
circulant(x, ...)

Arguments

Value

If x is a matrix whose columns are the bases of the sub-blocks of a block circulant matrix, then this function returns the block circulant matrix of interest.

circulant.numeric function

Description

returns a circulant matrix with base x

Usage

## S3 method for class 'numeric'
circulant(x, ...)

Arguments

Value

a circulant matrix with base x

circulantij function

Description

A function to return the "idx" i.e. c(i,j) element of a circulant matrix with base "base".

Usage

circulantij(idx, base)

Arguments

Value

the ij element of the full circulant

covmodel function

Description

A function to define the spatial covariance model, see also ?CovarianceFct. Note that the parameters defined by the 'pars' argument are fixed, i.e. not estimated by the MCMC algorithm. To have spatsurv estimate these parameters, the user must construct a new covariance function to do so, stop("") see the spatsurv vignette.

Usage

covmodel(model, pars)

Arguments

Value

an object of class covmodel

cumbasehazard function

Description

Generic function for computing the cumulative baseline hazard

Usage

cumbasehazard(obj, ...)

Arguments

Value

method cumbasehazard

See Also

cumbasehazard.basehazardspec, exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

cumbasehazard.basehazardspec function

Description

A function to retrieve the cumulative baseline hazard function

Usage

## S3 method for class 'basehazardspec'
cumbasehazard(obj, ...)

Arguments

Value

a function returning the cumulative baseline hazard

See Also

exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

cumulativeBspline.construct function

Description

A function to construct the integral of a B-spline curve given data and basis coefficients. Used in evaluating the cumulative baseline hazard.

Usage

cumulativeBspline.construct(x, basis)

Arguments

Value

an object that allows the integral of a given B-spline curve to be computed

density_PP function

Description

A function to compute an individual's density function

Usage

density_PP(inputs)

Arguments

Value

the density function for the individual

densityquantile function

Description

Generic function for computing quantiles of the density function for a given baseline hazard. This may not be analytically tractable.

Usage

densityquantile(obj, ...)

Arguments

Value

method densityquantile

See Also

densityquantile.basehazardspec, exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

densityquantile.basehazardspec function

Description

A function to retrieve the quantiles of the density function

Usage

## S3 method for class 'basehazardspec'
densityquantile(obj, ...)

Arguments

Value

a function returning the density quantiles

See Also

exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

densityquantile_PP function

Description

A function to compute quantiles of the density function

Usage

densityquantile_PP(inputs)

Arguments

Value

quantiles of the density function for the individual

derivindepGaussianprior function

Description

A function for evaluating the first and second derivatives of the log of an independent Gaussian prior

Usage

derivindepGaussianprior(beta = NULL, omega = NULL, eta = NULL, priors)

Arguments

Value

returns the first and second derivatives of the prior

See Also

survspat, betapriorGauss, omegapriorGauss, etapriorGauss, indepGaussianprior, derivindepGaussianprior

derivindepGaussianpriorST function

Description

A function to

Usage

derivindepGaussianpriorST(beta = NULL, omega = NULL, eta = NULL, priors)

Arguments

Value

...

derivpsplineprior function

Description

A function for evaluating the first and second derivatives of the log of an independent Gaussian prior

Usage

derivpsplineprior(beta = NULL, omega = NULL, eta = NULL, priors)

Arguments

Value

returns the first and second derivatives of the prior

See Also

survspat, betapriorGauss, omegapriorGauss, etapriorGauss, indepGaussianprior, derivindepGaussianprior

distinfo function

Description

Generic function for returning information about the class of baseline hazard functions employed.

Usage

distinfo(obj, ...)

Arguments

Value

method distinfo

See Also

distinfo.basehazardspec, exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

distinfo.basehazardspec function

Description

A function to retrive information on the baseline hazard distribution of choice

Usage

## S3 method for class 'basehazardspec'
distinfo(obj, ...)

Arguments

Value

a function returning information on the baseline hazard distribution of choice

See Also

exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

estimateY function

Description

A function to get an initial estimate of Y, to be used in calibrating the MCMC. Not for general use

Usage

estimateY(X, betahat, omegahat, surv, control)

Arguments

Value

an estimate of Y, to be used in calibrating the MCMC

etapriorGauss function

Description

A function to define Gaussian priors for eta. This function simply stores a vector of means and standard deviations to be passed to the main MCMC function, survspat.

Usage

etapriorGauss(mean, sd)

Arguments

Value

an object of class "etapriorGauss"

See Also

survspat, betapriorGauss, omegapriorGauss, etapriorGauss, indepGaussianprior, derivindepGaussianprior

exponentialHaz function

Description

A function to define a parametric proportional hazards model where the baseline hazard is taken from the exponential model. This function returns an object inheriting class 'basehazardspec', list of functions 'distinfo', 'basehazard', 'gradbasehazard', 'hessbasehazard', 'cumbasehazard', 'gradcumbasehazard', 'hesscumbasehazard' and 'densityquantile'

Usage

exponentialHaz()

Details

The distinfo function is used to provide basic distribution specific information to other spatsurv functions. The user is required to provide the following information in the returned list: npars, the number of parameters in this distribution; parnames, the names of the parameters; trans, the transformation scale on which the priors will be provided; itrans, the inverse transformation function that will be applied to the parameters before the hazard, and other functions are evaluated; jacobian, the derivative of the inverse transformation function with respect to each of the parameters; and hessian, the second derivatives of the inverse transformation function with respect to each of the parameters – note that currently the package spatsurv only allows the use of functions where the parameters are transformed independently.

The basehazard function is used to evaluate the baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradbasehazard function is used to evaluate the gradient of the baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hessbasehazard function is used to evaluate the Hessian of the baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The cumbasehazard function is used to evaluate the cumulative baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradcumbasehazard function is used to evaluate the gradient of the cumulative baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hesscumbasehazard function is used to evaluate the Hessian of the cumulative baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The densityquantile function is used to return quantiles of the density function. This is NOT REQUIRED for running the MCMC, merely for us in post-processing with the predict function where type is 'densityquantile'. In the case of the Weibull model for the baseline hazard, it can be shown that the q-th quantile is:

Value

an object inheriting class 'basehazardspec'

See Also

tpowHaz, gompertzHaz, makehamHaz, weibullHaz

fixParHaz function

Description

A function to

Usage

fixParHaz(bh, idx, fixval)

Arguments

Value

...

fixedpars function

Description

A function to return the mcmc chains for the covariate effects

Usage

fixedpars(x)

Arguments

Value

the beta mcmc chains

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

fixmatrix function

Description

!! THIS FUNCTION IS NOT INTENDED FOR GENERAL USE !!

Usage

fixmatrix(mat)

Arguments

Details

A function to fix up an estimated covariance matrix using a VERY ad-hoc method.

Value

the fixed matrix

frailtylag1 function

Description

A function to produce a plot of, and return, the lag 1 (or higher, see argument 'lag') autocorrelation for each of the spatially correlated frailty chains

Usage

frailtylag1(object, plot = TRUE, lag = 1, ...)

Arguments

Value

the lag 1 autocorrelation for each of the spatially correlated frailty chains

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

London Fire Brigade property

Description

London Fire Brigade property

Usage

data(fs)

Format

data.frame

Source

https://data.london.gov.uk/

References

https://data.london.gov.uk/,https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Examples

fire <- data(fs)

London Fire Brigade response times to dwelling fires, 2009

Description

London Fire Brigade response times to dwelling fires, 2009

Usage

data(fstimes)

Format

data.frame

Source

https://data.london.gov.uk/

References

https://data.london.gov.uk/,https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Examples

firetimes <- data(fstimes)

gamma2risk function

Description

A function to

Usage

gamma2risk(mod)

Arguments

Value

...

gencens function

Description

A function to generate observed times given a vector of true survival times and a vector of censoring times. Used in the simulation of survival data.

Usage

gencens(survtimes, censtimes, type = "right")

Arguments

Value

an object of class 'Surv', the censoring indicator is equal to 1 if the event is uncensored and 0 otherwise for right/left censored data, or for interval censored data, the indicator is 0 uncensored, 1 right censored, 2 left censored, or 3 interval censored.

getBbasis function

Description

A function returning the piecewise polynomial coefficients for a B-spline basis function i.e. the basis functions.

Usage

getBbasis(x, knots, degree, force = FALSE)

Arguments

Value

the knots and the piecewise polynomial coefficients for a B-spline basis function i.e. the basis functions.

getGrid function

Description

A function to extract and return the computational grid from a gridded analysis.

Usage

getGrid(mod, returnclass = "SpatialPolygonsDataFrame")

Arguments

Value

a SpatialPolygonsDataFrame in which Monte Carlo expectations can be stored and later plotted.

getOptCellwidth function

Description

A function to compute an optimal cellwidth close to an initial suggestion. This maximises the efficiency of the MCMC algorithm when in the control argument of the function survspat, the option gridded is set to TRUE

Usage

getOptCellwidth(dat, cellwidth, ext = 2, plot = TRUE, boundingbox = NULL)

Arguments

Value

the optimum cell width

getbb function

Description

A function to get the bounding box of a Spatial object

Usage

getbb(obj)

Arguments

Value

a SpatialPolygons object: the bounding box

getcov function

Description

A function to return the covariance from a model based on the randomFields covariance functions. Not intended for general use.

Usage

getcov(u, sigma, phi, model, pars)

Arguments

Value

this is just a wrapper for CovarianceFct

getgrd function

Description

A function to create a regular grid over an observation window in order to model the spatial randome effects as a Gaussian Markov random field.

Usage

getgrd(shape, cellwidth)

Arguments

Value

a SpatialPolygons object: the grid on which prediction of the spatial effects will occur

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

2. Finn Lindgren, Havard Rue, Johan Lindstrom. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B 73(4)

getleneta function

Description

A function to compute the length of eta

Usage

getleneta(cov.model)

Arguments

Value

the length of eta

getparranges function

Description

A function to extract parameter ranges for creating a grid on which to evaluate the log-posterior, used in calibrating the MCMC. This function is not intended for general use.

Usage

getparranges(priors, leneta, mult = 1.96)

Arguments

Value

an appropriate range used to calibrate the MCMC: the mean of the prior for eta plus/minus 1.96 times the standard deviation

getsurvdata function

Description

A function to return the survival data from an object of class mcmcspatsurv. This function is not intended for general use.

Usage

getsurvdata(x)

Arguments

Value

the survival data from an object of class mcmcspatsurv

gompertzHaz function

Description

A function to define a parametric proportional hazards model where the baseline hazard is taken from a Gompertz model. This function returns an object inheriting class 'basehazardspec', list of functions 'distinfo', 'basehazard', 'gradbasehazard', 'hessbasehazard', 'cumbasehazard', 'gradcumbasehazard', 'hesscumbasehazard' and 'densityquantile'

Usage

gompertzHaz()

Details

The distinfo function is used to provide basic distribution specific information to other spatsurv functions. The user is required to provide the following information in the returned list: npars, the number of parameters in this distribution; parnames, the names of the parameters; trans, the transformation scale on which the priors will be provided; itrans, the inverse transformation function that will be applied to the parameters before the hazard, and other functions are evaluated; jacobian, the derivative of the inverse transformation function with respect to each of the parameters; and hessian, the second derivatives of the inverse transformation function with respect to each of the parameters – note that currently the package spatsurv only allows the use of functions where the parameters are transformed independently.

The basehazard function is used to evaluate the baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradbasehazard function is used to evaluate the gradient of the baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hessbasehazard function is used to evaluate the Hessian of the baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The cumbasehazard function is used to evaluate the cumulative baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradcumbasehazard function is used to evaluate the gradient of the cumulative baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hesscumbasehazard function is used to evaluate the Hessian of the cumulative baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The densityquantile function is used to return quantiles of the density function. This is NOT REQUIRED for running the MCMC, merely for us in post-processing with the predict function where type is 'densityquantile'. In the case of the Weibull model for the baseline hazard, it can be shown that the q-th quantile is:

Value

an object inheriting class 'basehazardspec'

See Also

tpowHaz, exponentialHaz, makehamHaz, weibullHaz

gradbasehazard function

Description

Generic function for computing the gradient of the baseline hazard

Usage

gradbasehazard(obj, ...)

Arguments

Value

method gradbasehazard

See Also

gradbasehazard.basehazardspec, exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

gradbasehazard.basehazardspec function

Description

A function to retrieve the gradient of the baseline hazard function

Usage

## S3 method for class 'basehazardspec'
gradbasehazard(obj, ...)

Arguments

Value

a function returning the gradient of the baseline hazard

See Also

exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

gradcumbasehazard function

Description

Generic function for computing the gradient of the cumulative baseline hazard

Usage

gradcumbasehazard(obj, ...)

Arguments

Value

method gradcumbasehazard

See Also

gradcumbasehazard.basehazardspec, exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

gradcumbasehazard.basehazardspec function

Description

A function to retrieve the gradient of the cumulative baseline hazard function

Usage

## S3 method for class 'basehazardspec'
gradcumbasehazard(obj, ...)

Arguments

Value

a function returning the gradient of the cumulative baseline hazard

See Also

exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

grid2spdf function

Description

A function to convert a regular (x,y) grid of centroids into a SpatialPoints object

Usage

grid2spdf(xgrid, ygrid, proj4string = CRS(as.character(NA)))

Arguments

Value

a SpatialPolygonsDataFrame

grid2spix function

Description

A function to convert a regular (x,y) grid of centroids into a SpatialPixels object

Usage

grid2spix(xgrid, ygrid, proj4string = CRS(as.character(NA)))

Arguments

Value

a SpatialPixels object

grid2spts function

Description

A function to convert a regular (x,y) grid of centroids into a SpatialPoints object

Usage

grid2spts(xgrid, ygrid, proj4string = CRS(as.character(NA)))

Arguments

Value

a SpatialPoints object

gridY function

Description

A function to put estimated individual Y's onto a grid

Usage

gridY(Y, control)

Arguments

Value

...

gridY_polygonal function

Description

A function to put estimated individual Y's onto a grid

Usage

gridY_polygonal(Y, control)

Arguments

Value

...

guess_t function

Description

A function to get an initial guess of the failure time t, to be used in calibrating the MCMC. Not for general use

Usage

guess_t(surv)

Arguments

Value

a guess at the failure times

generic hasNext method

Description

test if an iterator has any more values to go

Usage

hasNext(obj)

Arguments

hasNext.iter function

Description

method for iter objects test if an iterator has any more values to go

Usage

## S3 method for class 'iter'
hasNext(obj)

Arguments

hazard_PP function

Description

A function to compute an individual's hazard function.

Usage

hazard_PP(inputs)

Arguments

Value

the hazard function for the individual

hazardexceedance function

Description

A function to compute exceedance probabilities for the spatially correlated frailties.

Usage

hazardexceedance(threshold, direction = "upper")

Arguments

Value

a function that can be passed to the function MCE in order to compute the exceedance probabilities

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE,

hazardpars function

Description

A function to return the mcmc chains for the hazard function parameters

Usage

hazardpars(x)

Arguments

Value

the omega mcmc chains

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

hessbasehazard function

Description

Generic function for computing the hessian of the baseline hazard

Usage

hessbasehazard(obj, ...)

Arguments

Value

method hessbasehazard

See Also

hessbasehazard.basehazardspec, exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

hessbasehazard.basehazardspec function

Description

A function to retrieve the Hessian of the baseline hazard function

Usage

## S3 method for class 'basehazardspec'
hessbasehazard(obj, ...)

Arguments

Value

a function returning the Hessian of the baseline hazard

See Also

exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

hesscumbasehazard function

Description

Generic function for computing the Hessian of the cumulative baseline hazard

Usage

hesscumbasehazard(obj, ...)

Arguments

Value

method hesscumbasehazard

See Also

hesscumbasehazard.basehazardspec, exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

hesscumbasehazard.basehazardspec function

Description

A function to retrieve the hessian of the cumulative baseline hazard function

Usage

## S3 method for class 'basehazardspec'
hesscumbasehazard(obj, ...)

Arguments

Value

a function returning the hessian of the cumulative baseline hazard

See Also

exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz

imputationModel function

Description

A function to

Usage

imputationModel(formula, offset, covariateData, priors)

Arguments

Value

...

indepGaussianprior function

Description

A function for evaluating the log of an independent Gaussian prior for a given set of parameter values.

Usage

indepGaussianprior(beta = NULL, omega = NULL, eta = NULL, priors)

Arguments

Value

the log of the prior evaluated at the given parameter values

See Also

survspat, betapriorGauss, omegapriorGauss, etapriorGauss, indepGaussianprior, derivindepGaussianprior

indepGaussianpriorST function

Description

A function to

Usage

indepGaussianpriorST(beta = NULL, omega = NULL, eta = NULL, priors)

Arguments

Value

...

inference.control function

Description

A function to control inferential settings. This function is used to set parameters for more advanced use of spatsurv.

Usage

inference.control(
  gridded = FALSE,
  cellwidth = NULL,
  ext = 2,
  imputation = NULL,
  optimcontrol = NULL,
  hessian = FALSE,
  plotcal = FALSE,
  timeonlyMCMC = FALSE,
  nugget = FALSE,
  savenugget = FALSE,
  split = 0.5,
  logUsigma_priormean = 0,
  logUsigma_priorsd = 0.5,
  nis = NULL,
  olinfo = NULL
)

Arguments

Value

returns parameters to be used in the function survspat

See Also

survspat

insert function

Description

A function to

Usage

insert(pars, idx, val)

Arguments

Value

...

invtransformweibull function

Description

A function to transform estimates of the (alpha, lambda) parameters of the weibull baseline hazard function, so they are commensurate with R's inbuilt density functions, (shape, scale).

Usage

invtransformweibull(x)

Arguments

Value

the transformed parameters. For the weibull model, this transforms 'shape' 'scale' (see ?dweibull) to 'alpha' and 'lambda' for the MCMC

is this a burn-in iteration?

Description

if this mcmc iteration is in the burn-in period, return TRUE

Usage

is.burnin(obj)

Arguments

Value

TRUE or FALSE

do we retain this iteration?

Description

if this mcmc iteration is one not thinned out, this is true

Usage

is.retain(obj)

Arguments

Value

TRUE or FALSE

iteration number

Description

within a loop, this is the iteration number we are currently doing.

Usage

iteration(obj)

Arguments

Details

get the iteration number

Value

integer iteration number, starting from 1.

logPosterior function

Description

A function to evaluate the log-posterior of a spatial parametric proportional hazards model. Not intended for general use.

Usage

logPosterior(
  surv,
  X,
  beta,
  omega,
  eta,
  gamma,
  priors,
  cov.model,
  u,
  control,
  gradient = FALSE,
  hessian = FALSE
)

Arguments

Value

evaluates the log-posterior and the gradient and hessian, if required.

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

logPosterior_SPDE function

Description

A function to evaluate the log-posterior of a spatial parametric proportional hazards model. Not intended for general use.

Usage

logPosterior_SPDE(
  surv,
  X,
  beta,
  omega,
  eta,
  gamma,
  priors,
  cov.model,
  u,
  control,
  gradient = FALSE,
  hessian = FALSE
)

Arguments

Value

evaluates the log-posterior and the gradient and hessian, if required.

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

2. Finn Lindgren, Havard Rue, Johan Lindstrom. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B 73(4)

logPosterior_gridded function

Description

A function to evaluate the log-posterior of a spatial parametric proportional hazards model using gridded Y. Not intended for general use.

Usage

logPosterior_gridded(
  surv,
  X,
  beta,
  omega,
  eta,
  gamma,
  priors,
  cov.model,
  u,
  control,
  gradient = FALSE,
  hessian = FALSE
)

Arguments

Value

evaluates the log-posterior and the gradient and hessian, if required.

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

logPosterior_polygonal function

Description

A function to evaluate the log-posterior of a spatial parametric proportional hazards model. Not intended for general use.

Usage

logPosterior_polygonal(
  surv,
  X,
  beta,
  omega,
  eta,
  gamma,
  priors,
  cov.model,
  u,
  control,
  gradient = FALSE,
  hessian = FALSE
)

Arguments

Value

evaluates the log-posterior and the gradient and hessian, if required.

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

loop over an iterator

Description

useful for testing progress bars

Usage

loop.mcmc(object, sleep = 1)

Arguments

makehamHaz function

Description

A function to define a parametric proportional hazards model where the baseline hazard is taken from the Gompertz-Makeham model. This function returns an object inheriting class 'basehazardspec', list of functions 'distinfo', 'basehazard', 'gradbasehazard', 'hessbasehazard', 'cumbasehazard', 'gradcumbasehazard', 'hesscumbasehazard' and 'densityquantile'

Usage

makehamHaz()

Details

The distinfo function is used to provide basic distribution specific information to other spatsurv functions. The user is required to provide the following information in the returned list: npars, the number of parameters in this distribution; parnames, the names of the parameters; trans, the transformation scale on which the priors will be provided; itrans, the inverse transformation function that will be applied to the parameters before the hazard, and other functions are evaluated; jacobian, the derivative of the inverse transformation function with respect to each of the parameters; and hessian, the second derivatives of the inverse transformation function with respect to each of the parameters – note that currently the package spatsurv only allows the use of functions where the parameters are transformed independently.

The basehazard function is used to evaluate the baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradbasehazard function is used to evaluate the gradient of the baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hessbasehazard function is used to evaluate the Hessian of the baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The cumbasehazard function is used to evaluate the cumulative baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradcumbasehazard function is used to evaluate the gradient of the cumulative baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hesscumbasehazard function is used to evaluate the Hessian of the cumulative baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The densityquantile function is used to return quantiles of the density function. This is NOT REQUIRED for running the MCMC, merely for us in post-processing with the predict function where type is 'densityquantile'. In the case of the Weibull model for the baseline hazard, it can be shown that the q-th quantile is:

Value

an object inheriting class 'basehazardspec'

See Also

tpowHaz, exponentialHaz, gompertzHaz, weibullHaz

maxlikparamPHsurv function

Description

A function to get initial estimates of model parameters using maximum likelihood. Not intended for general purose use.

Usage

maxlikparamPHsurv(surv, X, control)

Arguments

Value

initial estimates of the parameters

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

iterator for MCMC loops

Description

control an MCMC loop with this iterator

Usage

mcmcLoop(N, burnin, thin, trim = TRUE, progressor = mcmcProgressPrint)

Arguments

mcmcPriors function

Description

A function to define priors for the MCMC.

Usage

mcmcPriors(
  betaprior = NULL,
  omegaprior = NULL,
  etaprior = NULL,
  call = NULL,
  derivative = NULL
)

Arguments

Details

The package spatsurv only provides functionality for the built-in Gaussian priors. However, the choice of prior is extensible by the user by creating functions similar to the functions betapriorGauss, omegapriorGauss, etapriorGauss, indepGaussianprior and derivindepGaussianprior: the first three of which provide a mechanism for storing and retrieving the parameters of the priors; the fourth, a function for evaluating the log of the prior for a given set of parameter values; and the fifth, a function for evaluating the first and second derivatives of the log of the prior. It is assumed that parameters are a priori independent. The user interested in using other priors is encouraged to look at the structure of the five functions mentioned above.

Value

an object of class mcmcPriors

See Also

survspat, betapriorGauss, omegapriorGauss, etapriorGauss, indepGaussianprior, derivindepGaussianprior

null progress monitor

Description

a progress monitor that does nothing

Usage

mcmcProgressNone(mcmcloop)

Arguments

Value

a progress monitor

printing progress monitor

Description

a progress monitor that prints each iteration

Usage

mcmcProgressPrint(mcmcloop)

Arguments

Value

a progress monitor

text bar progress monitor

Description

a progress monitor that uses a text progress bar

Usage

mcmcProgressTextBar(mcmcloop)

Arguments

Value

a progress monitor

mcmcpars function

Description

A function for setting MCMC options.

Usage

mcmcpars(nits, burn, thin, inits = NULL, adaptivescheme = NULL)

Arguments

Value

mcmc parameters

midpts function

Description

A function to compute the midpoints of a vector

Usage

midpts(x)

Arguments

Value

the midpoints, a vector of length length(x)-1

multiWayHaz function

Description

A function to

Usage

multiWayHaz(bhlist, bhtime, bhfix, MLinits = NULL)

Arguments

Value

...

neighLocs function

Description

A function used in the computation of neighbours on non-rectangular grids. Not intended for general use.

Usage

neighLocs(coord, cellwidth, order)

Arguments

Value

coordinates of centroids of neighbours

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

2. Finn Lindgren, Havard Rue, Johan Lindstrom. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B 73(4)

neighOrder function

Description

A function to compute the order of a set of neighbours. Not intended for general use.

Usage

neighOrder(neighlocs)

Arguments

Value

the neighbour orders

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

2. Finn Lindgren, Havard Rue, Johan Lindstrom. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B 73(4)

next step of an MCMC chain

Description

just a wrapper for nextElem really.

Usage

nextStep(object)

Arguments

omegapriorGauss function

Description

A function to define Gaussian priors for omega. This function simply stores a vector of means and standard deviations to be passed to the main MCMC function, survspat.

Usage

omegapriorGauss(mean, sd)

Arguments

Value

an object of class "omegapriorGauss"

See Also

survspat, betapriorGauss, omegapriorGauss, etapriorGauss, indepGaussianprior, derivindepGaussianprior

omegapriorGaussST function

Description

A function to

Usage

omegapriorGaussST(basehaz, fmean, fsd, taumean, tausd, thetamean, thetasd)

Arguments

Value

...

optifix function

Description

optifix. Optimise with fixed parameters

Usage

optifix(
  par,
  fixed,
  fn,
  gr = NULL,
  ...,
  method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN"),
  lower = -Inf,
  upper = Inf,
  control = list(),
  hessian = FALSE
)

Arguments

Details

its like optim, but with fixed parameters.

specify a second argument 'fixed', a vector of TRUE/FALSE values. If TRUE, the corresponding parameter in fn() is fixed. Otherwise its variable and optimised over.

The return thing is the return thing from optim() but with a couple of extra bits - a vector of all the parameters and a vector copy of the 'fixed' argument.

Written by Barry Rowlingson <b.rowlingson@lancaster.ac.uk> October 2011

This file released under a CC By-SA license: http://creativecommons.org/licenses/by-sa/3.0/

and must retain the text: "Originally written by Barry Rowlingson" in comments.

Value

...

plot.FFTgrid function

Description

A function to

Usage

## S3 method for class 'FFTgrid'
plot(x, y = NULL, ...)

Arguments

Value

...

plotsurv function

Description

A function to produce a 2-D plot of right censored spatial survival data.

Usage

plotsurv(
  spp,
  ss,
  maxcex = 1,
  transform = identity,
  background = NULL,
  eventpt = 19,
  eventcol = "red",
  censpt = "+",
  censcol = "black",
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  add = FALSE,
  ...
)

Arguments

Value

Plots the survival data non-censored observations appear as dots and censored observations as crosses. The size of the dot is proportional to the observed time.

polyadd function

Description

A function to add two polynomials in the form of vectors of coefficients. The first element of the vector being the constant (order 0) term

Usage

polyadd(poly1, poly2)

Arguments

Value

the coefficients of the sum of poly1 and poly2

polymult function

Description

A function to multiply two polynomials in the form of vectors of coefficients. The first element of the vector being the constant (order 0) term

Usage

polymult(poly1, poly2)

Arguments

Value

the coefficients of the product of poly1 and poly2

posteriorcov function

Description

A function to produce a plot of the posterior covariance function with upper and lower quantiles.

Usage

posteriorcov(
  x,
  probs = c(0.025, 0.5, 0.975),
  rmax = NULL,
  n = 100,
  plot = TRUE,
  bw = FALSE,
  corr = FALSE,
  ...
)

Arguments

Value

produces a plot of the posterior spatial covariance function.

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, MCE, hazardexceedance

predict.mcmcspatsurv function

Description

A function to produce predictions from MCMC output. These could include quantiles of the individual density, survival or hazard functions or quantiles of the density function (if available analytically).

Usage

## S3 method for class 'mcmcspatsurv'
predict(
  object,
  type = "density",
  t = NULL,
  n = 110,
  indx = NULL,
  probs = c(0.025, 0.5, 0.975),
  plot = TRUE,
  pause = TRUE,
  bw = FALSE,
  ...
)

Arguments

Value

the required predictions

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, priorposterior, posteriorcov, MCE, hazardexceedance

print.mcmc function

Description

print method print an mcmc iterator's details

Usage

## S3 method for class 'mcmc'
print(x, ...)

Arguments

print.mcmcspatsurv function

Description

A function to print summary tables from an MCMC run

Usage

## S3 method for class 'mcmcspatsurv'
print(x, probs = c(0.5, 0.025, 0.975), digits = 3, scientific = -3, ...)

Arguments

Value

prints summary tables to the console

See Also

quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

print.mlspatsurv function

Description

A function to print summary tables from an MCMC run

Usage

## S3 method for class 'mlspatsurv'
print(x, probs = c(0.5, 0.025, 0.975), digits = 3, scientific = -3, ...)

Arguments

Value

prints summary tables to the console

See Also

quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

print.textSummary function

Description

A function to print summary tables from an MCMC run

Usage

## S3 method for class 'textSummary'
print(x, ...)

Arguments

Value

prints a text summary of 'x' to the console

priorposterior function

Description

A function to produce plots of the prior (which shows as a red line) and posterior (showing as a histogram)

Usage

priorposterior(
  x,
  breaks = 30,
  ylab = "Density",
  main = "",
  pause = TRUE,
  bw = FALSE,
  ...
)

Arguments

Value

plots of the prior (red line) and posterior (histogram).

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, posteriorcov, MCE, hazardexceedance

proposalVariance function

Description

A function to compute an approximate scaling matrix for the MCMC algorithm. Not intended for general use.

Usage

proposalVariance(
  X,
  surv,
  betahat,
  omegahat,
  Yhat,
  priors,
  cov.model,
  u,
  control
)

Arguments

Value

an estimate of eta and also an approximate scaling matrix for the MCMC

proposalVariance_SPDE function

Description

A function to compute an approximate scaling matrix for the MCMC algorithm. Not intended for general use.

Usage

proposalVariance_SPDE(
  X,
  surv,
  betahat,
  omegahat,
  Yhat,
  priors,
  cov.model,
  u,
  control
)

Arguments

Value

an estimate of eta and also an approximate scaling matrix for the MCMC

proposalVariance_gridded function

Description

A function to compute an approximate scaling matrix for the MCMC algorithm. Not intended for general use.

Usage

proposalVariance_gridded(
  X,
  surv,
  betahat,
  omegahat,
  Yhat,
  priors,
  cov.model,
  u,
  control
)

Arguments

Value

an estimate of eta and also an approximate scaling matrix for the MCMC

proposalVariance_polygonal function

Description

A function to compute an approximate scaling matrix for the MCMC algorithm. Not intended for general use.

Usage

proposalVariance_polygonal(
  X,
  surv,
  betahat,
  omegahat,
  Yhat,
  priors,
  cov.model,
  u,
  control
)

Arguments

Value

an estimate of eta and also an approximate scaling matrix for the MCMC

psplineRWprior function

Description

A function to define Gaussian priors for omega. This function simply stores a vector of means and standard deviations to be passed to the main MCMC function, survspat.

Usage

psplineRWprior(taumean, tausd, basehaz, order = 2)

Arguments

Value

an object of class "omegapriorGauss"

See Also

survspat, betapriorGauss, omegapriorGauss, etapriorGauss, indepGaussianprior, derivindepGaussianprior

psplineprior function

Description

A function for evaluating the log of an independent Gaussian prior for a given set of parameter values.

Usage

psplineprior(beta = NULL, omega = NULL, eta = NULL, priors)

Arguments

Value

the log of the prior evaluated at the given parameter values

See Also

survspat, betapriorGauss, omegapriorGauss, etapriorGauss, indepGaussianprior, derivindepGaussianprior

quantile.mcmcspatsurv function

Description

A function to extract quantiles of the parameters from an mcmc run

Usage

## S3 method for class 'mcmcspatsurv'
quantile(x, probs = c(0.025, 0.5, 0.975), ...)

Arguments

Value

quantiles of model parameters

See Also

print.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

quantile.mlspatsurv function

Description

A function to extract quantiles of the parameters from an mcmc run

Usage

## S3 method for class 'mlspatsurv'
quantile(x, probs = c(0.025, 0.5, 0.975), ...)

Arguments

Value

quantiles of model parameters

See Also

print.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

randompars function

Description

A function to return the mcmc chains for the spatially correlated frailties

Usage

randompars(x)

Arguments

Value

the Y mcmc chains

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

reconstruct.bs function

Description

Generic function for reconstructing B-spline covariate effects. See ?reconstruct.bs.mcmcspatsurv and ?reconstruct.bs.coxph

Usage

reconstruct.bs(mod, ...)

Arguments

Value

method reconstruct.bs

reconstruct.bs.coxph function

Description

When bs(varname) has been used in the formula of a coxph model, this function can be used to reconstruct the predicted relative risk of that parameter over time.

Usage

## S3 method for class 'coxph'
reconstruct.bs(
  mod,
  varname,
  fun = NULL,
  probs = c(0.025, 0.975),
  bw = FALSE,
  xlab = NULL,
  ylab = NULL,
  plot = TRUE,
  ...
)

Arguments

Value

median, upper and lower confidence bands for the effect of varname over time; the funciton also produces a plot.

reconstruct.bs.mcmcspatsurv function

Description

When bs(varname) has been used in the formula of a model, this function can be used to reconstruct the posterior relative risk of that parameter over time.

Usage

## S3 method for class 'mcmcspatsurv'
reconstruct.bs(
  mod,
  varname,
  probs = c(0.025, 0.975),
  bw = FALSE,
  xlab = NULL,
  ylab = NULL,
  plot = TRUE,
  ...
)

Arguments

Value

median, upper and lower confidence bands for the effect of varname over time; the funciton also produces a plot.

reset iterator

Description

call this to reset an iterator's state to the initial

Usage

resetLoop(obj)

Arguments

resuiduals.mcmcspatsurv function

Description

A function to compute Cox-Snell / modeified Cox-Snell / Martingale or Deviance residuals

Usage

## S3 method for class 'mcmcspatsurv'
residuals(object, type = "Cox-Snell", ...)

Arguments

Value

the residuals

rootWeibullHaz function

Description

A function to define a parametric proportional hazards model where the baseline hazard is taken from the Weibull model. This function returns an object inheriting class 'basehazardspec', list of functions 'distinfo', 'basehazard', 'gradbasehazard', 'hessbasehazard', 'cumbasehazard', 'gradcumbasehazard', 'hesscumbasehazard' and 'densityquantile'

Usage

rootWeibullHaz(MLinits = NULL)

Arguments

Details

The distinfo function is used to provide basic distribution specific information to other spatsurv functions. The user is required to provide the following information in the returned list: npars, the number of parameters in this distribution; parnames, the names of the parameters; trans, the transformation scale on which the priors will be provided; itrans, the inverse transformation function that will be applied to the parameters before the hazard, and other functions are evaluated; jacobian, the derivative of the inverse transformation function with respect to each of the parameters; and hessian, the second derivatives of the inverse transformation function with respect to each of the parameters – note that currently the package spatsurv only allows the use of functions where the parameters are transformed independently.

The basehazard function is used to evaluate the baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradbasehazard function is used to evaluate the gradient of the baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hessbasehazard function is used to evaluate the Hessian of the baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The cumbasehazard function is used to evaluate the cumulative baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradcumbasehazard function is used to evaluate the gradient of the cumulative baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hesscumbasehazard function is used to evaluate the Hessian of the cumulative baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The densityquantile function is used to return quantiles of the density function. This is NOT REQUIRED for running the MCMC, merely for us in post-processing with the predict function where type is 'densityquantile'. In the case of the Weibull model for the baseline hazard, it can be shown that the q-th quantile is:

Value

an object inheriting class 'basehazardspec'

See Also

tpowHaz, exponentialHaz, gompertzHaz, makehamHaz

set the progress bar

Description

update a text progress bar. See help(txtProgressBar) for more info.

Usage

setTxtProgressBar2(pb, value, title = NULL, label = NULL)

Arguments

setupHazard function

Description

A function to set up the baseline hazard, cumulative hazard and derivative functions for use in evaluating the log posterior. This fucntion is not intended for general use.

Usage

setupHazard(dist, pars, grad = FALSE, hess = FALSE)

Arguments

Value

a list of functions used in evaluating the log posterior

setupPrecMatStruct function

Description

A function to set up the computational grid and precision matrix structure for SPDE models.

Usage

setupPrecMatStruct(shape, cellwidth, no)

Arguments

Value

the computational grid and a function for constructing the precision matrix

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

2. Finn Lindgren, Havard Rue, Johan Lindstrom. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B 73(4)

showGrid function

Description

A function to show the grid that will be used for a given cellwidth

Usage

showGrid(dat, cellwidth, ext = 2, boundingbox = NULL)

Arguments

Value

a plot showing the grid and the data. Ideally the data should only just fit inside the grid.

simsurv function

Description

A function to simulate spatial parametric proportional hazards model. The function works by simulating candidate survival times using MCMC in parallel for each individual based on each individual's covariates and the common parameter effects, beta.

Usage

simsurv(
  X = cbind(age = runif(100, 5, 50), sex = rbinom(100, 1, 0.5), cancer = rbinom(100, 1,
    0.2)),
  beta = c(0.0296, 0.0261, 0.035),
  omega = 1,
  dist = exponentialHaz(),
  coords = matrix(runif(2 * nrow(X)), nrow(X), 2),
  cov.parameters = c(1, 0.1),
  cov.model = ExponentialCovFct(),
  mcmc.control = mcmcpars(nits = 1e+05, burn = 10000, thin = 90),
  savechains = TRUE
)

Arguments

Value

in list element 'survtimes', a vector of simulated survival times (the last simulated value from the MCMC chains) in list element 'T' the MCMC chains

See Also

covmodel, survspat, tpowHaz, exponentialHaz, gompertzHaz, makehamHaz, weibullHaz

spatialpars function

Description

A function to return the mcmc chains for the spatial covariance function parameters

Usage

spatialpars(x)

Arguments

Value

the eta mcmc chains

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

spatsurvVignette function

Description

Display the introductory vignette for the spatsurv package.

Usage

spatsurvVignette()

Value

displays the vignette by calling browseURL

summary.mcmc function

Description

summary of an mcmc iterator print out values of an iterator and reset it. DONT call this in a loop that uses this iterator - it will reset it. And break.

Usage

## S3 method for class 'mcmc'
summary(object, ...)

Arguments

summary.mcmcspatsurv function

Description

A function to return summary tables from an MCMC run

Usage

## S3 method for class 'mcmcspatsurv'
summary(object, probs = c(0.5, 0.025, 0.975), ...)

Arguments

Value

summary tables to the console

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, vcov.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

Spatial Survival Plot in 3D

Description

Do a 3d plot of spatial survival data

Usage

surv3d(
  spp,
  ss,
  lwd = 2,
  lcol = "black",
  lalpha = 1,
  pstyle = c("point", "text"),
  psize = c(20, 10),
  pcol = c("red", "black"),
  ptext = c("X", ""),
  palpha = 1,
  title = "Spatial Survival",
  basegrid = TRUE,
  baseplane = TRUE
)

Arguments

Details

Uses rgl graphics to make a spinny zoomy plot

Value

nothing

Author(s)

Barry S Rowlingson

Examples

## Not run: 
require(sp)
require(survival)
d = data.frame(
  x=runif(40)*1.5,
  y = runif(40),
  age=as.integer(20+30*runif(40)),
  sex = sample(c("M","F"),40,TRUE)
)
coordinates(d)=~x+y
d$surv = Surv(as.integer(5+20*runif(40)),runif(40)>.9)
clear3d();surv3d(d,d$surv,baseplane=TRUE,basegrid=TRUE)
clear3d();surv3d(d,d$surv,baseplane=TRUE,basegrid=TRUE,pstyle="t",lalpha=0.5,lwd=3,palpha=1)

## End(Not run)

survival_PP function

Description

A function to compute an individual's survival function

Usage

survival_PP(inputs)

Arguments

Value

the survival function for the individual

survspat function

Description

A function to run a Bayesian analysis on censored spatial survial data assuming a proportional hazards model using an adaptive Metropolis-adjusted Langevin algorithm.

Usage

survspat(
  formula,
  data,
  dist,
  cov.model,
  mcmc.control,
  priors,
  shape = NULL,
  ids = list(shpid = NULL, dataid = NULL),
  control = inference.control(gridded = FALSE),
  boundingbox = NULL
)

Arguments

Value

an object inheriting class 'mcmcspatsurv' for which there exist methods for printing, summarising and making inference from.

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

See Also

tpowHaz, exponentialHaz, gompertzHaz, makehamHaz, weibullHaz, covmodel, ExponentialCovFct, SpikedExponentialCovFct, mcmcpars, mcmcPriors, inference.control

survspatNS function

Description

A function to perform maximun likelihood inference for non-spatial survival data.

Usage

survspatNS(formula, data, dist, control = inference.control())

Arguments

Value

an object inheriting class 'mcmcspatsurv' for which there exist methods for printing, summarising and making inference from.

References

1. Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.

See Also

tpowHaz, exponentialHaz, gompertzHaz, makehamHaz, weibullHaz, covmodel, ExponentialCovFct, SpikedExponentialCovFct, mcmcpars, mcmcPriors, inference.control

textSummary function

Description

A function to print a text description of the inferred paramerers beta and eta from a call to the function lgcpPredictSpatialPlusPars, lgcpPredictAggregateSpatialPlusPars, lgcpPredictSpatioTemporalPlusPars or lgcpPredictMultitypeSpatialPlusPars

Usage

textSummary(
  obj,
  digits = 3,
  scientific = -3,
  inclIntercept = FALSE,
  printmode = "LaTeX",
  ...
)

Arguments

Value

A text summary, that can be pasted into a LaTeX document and later edited.

timevaryingPL function

Description

A function to

Usage

timevaryingPL(
  formula,
  t0,
  t,
  delta,
  dist,
  data,
  ties = "Efron",
  optimcontrol = NULL
)

Arguments

Value

...

tpowHaz function

Description

A function to define a parametric proportional hazards model where the baseline hazard is taken from the 'powers of t' model. This function returns an object inheriting class 'basehazardspec', list of functions 'distinfo', 'basehazard', 'gradbasehazard', 'hessbasehazard', 'cumbasehazard', 'gradcumbasehazard', 'hesscumbasehazard' and 'densityquantile'

Usage

tpowHaz(powers)

Arguments

Details

The distinfo function is used to provide basic distribution specific information to other spatsurv functions. The user is required to provide the following information in the returned list: npars, the number of parameters in this distribution; parnames, the names of the parameters; trans, the transformation scale on which the priors will be provided; itrans, the inverse transformation function that will be applied to the parameters before the hazard, and other functions are evaluated; jacobian, the derivative of the inverse transformation function with respect to each of the parameters; and hessian, the second derivatives of the inverse transformation function with respect to each of the parameters – note that currently the package spatsurv only allows the use of functions where the parameters are transformed independently.

The basehazard function is used to evaluate the baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradbasehazard function is used to evaluate the gradient of the baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hessbasehazard function is used to evaluate the Hessian of the baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The cumbasehazard function is used to evaluate the cumulative baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradcumbasehazard function is used to evaluate the gradient of the cumulative baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hesscumbasehazard function is used to evaluate the Hessian of the cumulative baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The densityquantile function is used to return quantiles of the density function. This is NOT REQUIRED for running the MCMC, merely for us in post-processing with the predict function where type is 'densityquantile'. In the case of the Weibull model for the baseline hazard, it can be shown that the q-th quantile is:

Value

an object inheriting class 'basehazardspec'

See Also

exponentialHaz, gompertzHaz, makehamHaz, weibullHaz

transformweibull function

Description

A function to back-transform estimates of the parameters of the weibull baseline hazard function, so they are commensurate with R's inbuilt density functions. Transforms from (shape, scale) to (alpha, lambda)

Usage

transformweibull(x)

Arguments

Value

the transformed parameters. For the weibull model, this is the back-transform from 'alpha' and 'lambda' to 'shape' 'scale' (see ?dweibull).

A text progress bar with label

Description

This is the base txtProgressBar but with a little modification to implement the label parameter for style=3. For full info see txtProgressBar

Usage

txtProgressBar2(
  min = 0,
  max = 1,
  initial = 0,
  char = "=",
  width = NA,
  title = "",
  label = "",
  style = 1
)

Arguments

vcov.mcmcspatsurv function

Description

A function to return the variance covariance matrix of the parameters beta, omega and eta

Usage

## S3 method for class 'mcmcspatsurv'
vcov(object, ...)

Arguments

Value

the variance covariance matrix of the parameters beta, omega and eta

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

vcov.mlspatsurv function

Description

A function to return the variance covariance matrix of the parameters beta, omega and eta

Usage

## S3 method for class 'mlspatsurv'
vcov(object, ...)

Arguments

Value

the variance covariance matrix of the parameters beta, omega and eta

See Also

print.mcmcspatsurv, quantile.mcmcspatsurv, summary.mcmcspatsurv, frailtylag1, spatialpars, hazardpars, fixedpars, randompars, baselinehazard, predict.mcmcspatsurv, priorposterior, posteriorcov, MCE, hazardexceedance

weibullHaz function

Description

A function to define a parametric proportional hazards model where the baseline hazard is taken from the Weibull model. This function returns an object inheriting class 'basehazardspec', list of functions 'distinfo', 'basehazard', 'gradbasehazard', 'hessbasehazard', 'cumbasehazard', 'gradcumbasehazard', 'hesscumbasehazard' and 'densityquantile'

Usage

weibullHaz(MLinits = NULL)

Arguments

Details

The distinfo function is used to provide basic distribution specific information to other spatsurv functions. The user is required to provide the following information in the returned list: npars, the number of parameters in this distribution; parnames, the names of the parameters; trans, the transformation scale on which the priors will be provided; itrans, the inverse transformation function that will be applied to the parameters before the hazard, and other functions are evaluated; jacobian, the derivative of the inverse transformation function with respect to each of the parameters; and hessian, the second derivatives of the inverse transformation function with respect to each of the parameters – note that currently the package spatsurv only allows the use of functions where the parameters are transformed independently.

The basehazard function is used to evaluate the baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradbasehazard function is used to evaluate the gradient of the baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hessbasehazard function is used to evaluate the Hessian of the baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The cumbasehazard function is used to evaluate the cumulative baseline hazard function for the distribution of interest. It returns a function that accepts as input a vector of times, t and returns a vector.

The gradcumbasehazard function is used to evaluate the gradient of the cumulative baseline hazard function with respect to the parameters, this typically returns a vector. It returns a function that accepts as input a vector of times, t, and returns a matrix.

The hesscumbasehazard function is used to evaluate the Hessian of the cumulative baseline hazard function. It returns a function that accepts as input a vector of times, t and returns a list of hessian matrices corresponding to each t.

The densityquantile function is used to return quantiles of the density function. This is NOT REQUIRED for running the MCMC, merely for us in post-processing with the predict function where type is 'densityquantile'. In the case of the Weibull model for the baseline hazard, it can be shown that the q-th quantile is:

Value

an object inheriting class 'basehazardspec'

See Also

tpowHaz, exponentialHaz, gompertzHaz, makehamHaz