| Type: | Package |
| Title: | Bayesian Modeling: Estimate a Computer Model and Make Uncertain Predictions |
| Version: | 1.0.1 |
| Description: | An interface to the 'BaM' (Bayesian Modeling) engine, a 'Fortran'-based executable aimed at estimating a model with a Bayesian approach and using it for prediction, with a particular focus on uncertainty quantification. Classes are defined for the various building blocks of 'BaM' inference (model, data, error models, Markov Chain Monte Carlo (MCMC) samplers, predictions). The typical usage is as follows: (1) specify the model to be estimated; (2) specify the inference setting (dataset, parameters, error models...); (3) perform Bayesian-MCMC inference; (4) read, analyse and use MCMC samples; (5) perform prediction experiments. Technical details are available (in French) in Renard (2017) https://hal.science/hal-02606929v1. Examples of applications include Mansanarez et al. (2019) <doi:10.1029/2018WR023389>, Le Coz et al. (2021) <doi:10.1002/hyp.14169>, Perret et al. (2021) <doi:10.1029/2020WR027745>, Darienzo et al. (2021) <doi:10.1029/2020WR028607> and Perret et al. (2023) <doi:10.1061/JHEND8.HYENG-13101>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| URL: | https://github.com/BaM-tools/RBaM |
| BugReports: | https://github.com/BaM-tools/RBaM/issues |
| Depends: | R (≥ 4.0.0) |
| Imports: | ggplot2, gridExtra, R.utils, utils, grDevices, stats, rjson, rlang, tools, tidyr |
| Suggests: | rmarkdown |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2025-07-07 06:13:29 UTC; brenard |
| Author: | Benjamin Renard 
[aut, cre, cph],
INRAE [fnd],
Ministère de la Transition Ecologique - SCHAPI [fnd] |
| Maintainer: | Benjamin Renard <benjamin.renard@inrae.fr> |
| Repository: | CRAN |
| Date/Publication: | 2025-07-10 14:40:05 UTC |
Run BaM
Description
Run BaM.exe
Usage
BaM(
workspace,
mod,
data,
remnant = rep(list(remnantErrorModel()), mod$nY),
mcmc = mcmcOptions(),
cook = mcmcCooking(),
summary = mcmcSummary(),
residuals = residualOptions(),
pred = NULL,
doCalib = TRUE,
doPred = FALSE,
na.value = -9999,
run = TRUE,
preClean = FALSE,
dir.exe = .BAM_PATH,
name.exe = "BaM",
predMaster_fname = "Config_Pred_Master.txt"
)
Arguments
workspace |
Character, directory where config and result files are stored. |
mod |
model object, the model to be calibrated |
data |
dataset object, calibration data |
remnant |
list of remnantErrorModel objects. WARNING: make sure you use a list of length mod$nY (even if mod$nY=1!) |
mcmc |
mcmcOptions object, MCMC simulation number and options |
cook |
mcmcCooking object, properties of MCMC cooking (burn and slice) |
summary |
mcmcSummary object, properties of MCMC summary |
residuals |
residualOptions object, properties of residual analysis |
pred |
list of prediction objects, properties of prediction experiments |
doCalib |
Logical, do Calibration? (mcmc+cooking+summary+residuals) |
doPred |
Logical, do Prediction? |
na.value |
numeric, value used for NAs when writing the dataset in BaM format |
run |
Logical, run BaM? if FALSE, just write config files. |
preClean |
Logical, start by cleaning up workspace? Be careful, this will delete all files in the workspace, including old results! |
dir.exe |
Character, directory where BaM executable stands. |
name.exe |
Character, name of the executable without extension ('BaM' by default). |
predMaster_fname |
Character, name of configuration file pointing to all prediction experiments. |
Value
Nothing: just write config files and runs the executable.
Examples
# Fitting a rating curve - see https://github.com/BaM-tools/RBaM
workspace=tempdir()
D=dataset(X=SauzeGaugings['H'],Y=SauzeGaugings['Q'],Yu=SauzeGaugings['uQ'],data.dir=workspace)
# Parameters of the low flow section control: activation stage k, coefficient a and exponent c
k1=parameter(name='k1',init=-0.5,prior.dist='Uniform',prior.par=c(-1.5,0))
a1=parameter(name='a1',init=50,prior.dist='LogNormal',prior.par=c(log(50),1))
c1=parameter(name='c1',init=1.5,prior.dist='Gaussian',prior.par=c(1.5,0.05))
# Parameters of the high flow channel control: activation stage k, coefficient a and exponent c
k2=parameter(name='k2',init=1,prior.dist='Gaussian',prior.par=c(1,1))
a2=parameter(name='a2',init=100,prior.dist='LogNormal',prior.par=c(log(100),1))
c2=parameter(name='c2',init=1.67,prior.dist='Gaussian',prior.par=c(1.67,0.05))
# Define control matrix: columns are controls, rows are stage ranges.
controlMatrix=rbind(c(1,0),c(0,1))
# Stitch it all together into a model object
M=model(ID='BaRatin',
nX=1,nY=1, # number of input/output variables
par=list(k1,a1,c1,k2,a2,c2), # list of model parameters
xtra=xtraModelInfo(object=controlMatrix)) # use xtraModelInfo() to pass the control matrix
# Call BaM to write configuration files. To actually run BaM, use run=TRUE,
# but BaM executable needs to be downloaded first (use downloadBaM())
BaM(workspace=workspace,mod=M,data=D,run=FALSE)
Meyras Gaugings
Description
Stage-discharge gaugings from the hydrometric station 'the Ardèche River at Meyras'. See https://en.wikipedia.org/wiki/Ardèche_(river) for a description of the river See https://doi.org/10.1029/2018WR023389 for an article using this dataset
Usage
MeyrasGaugings
Format
A data frame with 104 rows and 4 variables:
- h
Stage (m)
- Q
Discharge (m3/s)
- uQ
Discharge uncertainty (m3/s) expressed as a standard deviation
- Period
Stability period on which a single rating curve can be used
Sauze Gaugings
Description
Stage-discharge gaugings from the hydrometric station 'the Ardèche River at Sauze-St-Martin'. See https://en.wikipedia.org/wiki/Ardèche_(river) for a description of the river See https://hal.science/hal-00934237 for an article using this dataset
Usage
SauzeGaugings
Format
A data frame with 38 rows and 3 variables:
- H
Stage (m)
- Q
Discharge (m3/s)
- uQ
Discharge uncertainty (m3/s) expressed as a standard deviation
Add double quotes to a string or to each element of a string vector
Description
Add double quotes to a string or to each element of a string vector
Usage
addQuotes(txt)
Arguments
txt |
Character vector |
Value
The double-quoted character vector
Check a formula make sense within a given namespace
Description
Check a formula make sense within a given namespace
Usage
checkFormula(f, namespace)
Arguments
f |
Character string, formula |
namespace |
Character vector, namespace (i.e. names of known variables) |
Value
A list with the following fields:
- ok
Does the formula make sense?
- mess
message
dataset object constructor.
Description
Creates a new instance of a 'dataset' object
Usage
dataset(
X,
Y,
data.dir = getwd(),
data.fname = "CalibrationData.txt",
fname = "Config_Data.txt",
Xu = NULL,
Xb = NULL,
Xb.indx = NULL,
Yu = NULL,
Yb = NULL,
Yb.indx = NULL,
VAR.indx = NULL
)
Arguments
X |
data frame, observed input variables. |
Y |
data frame, observed output variables (same number of rows as X). |
data.dir |
Character, directory where a copy of the dataset will be written if required. Default is the current working directory, but you may prefer to use the BaM workspace. |
data.fname |
Character, data file name. |
fname |
Character, configuration file name. |
Xu |
data frame, random uncertainty in X, expressed as a standard deviation. Same dimension as X. |
Xb |
data frame, systematic uncertainty in X, expressed as a standard deviation. Same dimension as X. |
Xb.indx |
data frame, index of systematic errors in X. Same dimension as X. |
Yu |
data frame, random uncertainty in Y, expressed as a standard deviation. Same dimension as Y. |
Yb |
data frame, systematic uncertainty in Y, expressed as a standard deviation. Same dimension as Y. |
Yb.indx |
data frame, index of systematic errors in Y. Same dimension as Y. |
VAR.indx |
data frame, indices used for defining how VAR parameters vary. |
Value
An object of class 'dataset'.
Examples
X=data.frame(input1=rnorm(100),input2=rnorm(100))
Y=data.frame(output=X$input1+0.8*X$input2+0.1*rnorm(100))
workspace=tempdir()
d <- dataset(X=X,Y=Y,data.dir=workspace)
densityPlot
Description
returns a histogram+density ggplot (or a list thereof if several columns in sim)
Usage
densityPlot(sim, xlab = "values", col = "black")
Arguments
sim |
vector or matrix or data frame, MCMC simulations |
xlab |
Character, label of x-axis to be used if sim has no names |
col |
Color |
Value
A ggplot (or a list thereof if several columns in sim)
Examples
# Create Monte Carlo samples
n=1000
sim=data.frame(p1=rnorm(n),p2=rlnorm(n),p3=runif(n))
# create density plot for each component
figures=densityPlot(sim)
BaM downloader
Description
Download BaM executable
Usage
downloadBaM(
destFolder,
url = NULL,
os = Sys.info()["sysname"],
quiet = FALSE,
...
)
Arguments
destFolder |
character string, folder where BaM executable will be downloaded. |
url |
character string, the url from which BaM should be downloaded. When NULL, the url is determined automatically by using GitHub API to determine the latest release and the file corresponding to the OS. |
os |
character string, operating system, e.g. 'Linux', 'Windows' or 'Darwin'. |
quiet |
logical, if TRUE, suppress status messages. |
... |
arguments passed to function 'download.file' |
Value
nothing - just download the file.
Examples
try(downloadBaM(destFolder=tempdir()))
Was BaM executable found?
Description
Was BaM executable found?
Usage
foundBaM(exedir, exename)
Arguments
exedir |
directory where the executable should be searched |
exename |
name of the executable |
Value
a logical, TRUE if the exe was found, false otherwise
Bathymetry interpreter
Description
Compute Area A(h), width w(h) and wet perimeter P(h) from a bathymetry profile (a,z).
Usage
getAwPfromBathy(
bathy,
hgrid = seq(min(bathy[, 2]), max(bathy[, 2]), diff(range(bathy[, 2]))/1000),
segmentLength = sum(sqrt(apply(apply(bathy, 2, diff)^2, 1, sum)))/1000
)
Arguments
bathy |
data frame, 2 columns containing abscissa (increasing values) and stage. |
hgrid |
numeric vector, grid of h values where A, w and P are computed. By default 1000 values in the range of bathymetry's z. |
segmentLength |
numeric, segment length for bathymetry subsampling. By default 1/1000 of the total bathymetry's perimeter. |
Value
A 4-column dataframe containing h, A(h), w(h) and P(h)
Examples
bathy=data.frame(a=c(0,0,0,1,2,2,4,6,8),h=c(3,2,0,-0.5,0,2,2.0001,2.3,3))
plot(bathy,type='l')
df=getAwPfromBathy(bathy)
plot(df$h,df$A,type='l')
plot(df$h,df$w,type='l')
plot(df$h,df$P,type='l')
BaM catalogue
Description
Distributions and models available in BaM
Usage
getCatalogue(printOnly = FALSE)
Arguments
printOnly |
Logical, should the catalogue be returned or only printed? |
Value
If printOnly==FALSE, a list with the following fields:
- distributions
available univariate distributions.
- models
available models.
Examples
catalogue <- getCatalogue()
getCatalogue(printOnly=TRUE)
Get object names
Description
getNames from an object or a list of objects having a $name field (e.g. parameters)
Usage
getNames(loo, name = "name")
Arguments
loo |
List Of Objects |
name |
character, string denoting the name field |
Value
A character vector containing names
Examples
pars <- list(parameter(name='par1',init=0),
parameter(name='par2',init=0),
parameter(name='Third parameter',init=0))
getNames(pars)
Get parameter names
Description
Get parameter names for a distribution d
Usage
getParNames(d)
Arguments
d |
Character (possibly vector), distribution (possibly distributions) |
Value
A character vector with parameter names.
Examples
parnames <- getParNames('GEV')
npar <- length(getParNames('Gumbel'))
dataset tester
Description
Is an object of class 'dataset'?
Usage
is.dataset(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'dataset', FALSE otherwise.
mcmcCooking tester
Description
Is an object of class 'mcmcCooking'?
Usage
is.mcmcCooking(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'mcmcCooking', FALSE otherwise.
mcmcOptions tester
Description
Is an object of class 'mcmcOptions'?
Usage
is.mcmcOptions(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'mcmcOptions', FALSE otherwise.
mcmcSummary tester
Description
Is an object of class 'mcmcSummary'?
Usage
is.mcmcSummary(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'mcmcSummary', FALSE otherwise.
model tester
Description
Is an object of class 'model'?
Usage
is.model(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'model', FALSE otherwise.
parameter tester
Description
Is an object of class 'parameter'?
Usage
is.parameter(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'parameter', FALSE otherwise.
parameter_VAR tester
Description
Is an object of class 'parameter_VAR'?
Usage
is.parameter_VAR(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'parameter_VAR', FALSE otherwise.
prediction tester
Description
Is an object of class 'prediction'?
Usage
is.prediction(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'prediction', FALSE otherwise.
remnantErrorModel tester
Description
Is an object of class 'remnantErrorModel'?
Usage
is.remnantErrorModel(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'remnantErrorModel', FALSE otherwise.
residualOptions tester
Description
Is an object of class 'residualOptions'?
Usage
is.residualOptions(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'residualOptions', FALSE otherwise.
runOptions tester
Description
Is an object of class 'runOptions'?
Usage
is.runOptions(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'runOptions', FALSE otherwise.
xtraModelInfo tester
Description
Is an object of class 'xtraModelInfo'?
Usage
is.xtraModelInfo(o)
Arguments
o |
Object, an object. |
Value
A logical equal to TRUE if class(o)== 'xtraModelInfo', FALSE otherwise.
mcmcCooking constructor.
Description
Creates a new instance of a 'mcmcCooking' object
Usage
mcmcCooking(
fname = "Config_Cooking.txt",
result.fname = "Results_Cooking.txt",
burn = 0.5,
nSlim = 10
)
Arguments
fname |
Character, configuration file name. |
result.fname |
Character, result file name. |
burn |
numeric, burn factor, >=0 and <1. 0.4 means the first 40 percent of MCMC samples are discarded). |
nSlim |
Integer, slimming period: 10 means only one MCMC sample every 10 is kept (after burning). |
Value
An object of class 'mcmcCooking'.
Examples
m <- mcmcCooking()
mcmcOptions object constructor.
Description
Creates a new instance of a 'mcmcOptions' object
Usage
mcmcOptions(
fname = "Config_MCMC.txt",
result.fname = "Results_MCMC.txt",
nAdapt = 100,
nCycles = 100,
minMoveRate = 0.1,
maxMoveRate = 0.5,
downMult = 0.9,
upMult = 1.1,
multFactor = 0.1,
manualMode = FALSE,
thetaStd = 9999,
gammaStd = list(9999)
)
Arguments
fname |
Character, configuration file name. |
result.fname |
Character, result file name. |
nAdapt |
Integer, adaptation period: jump sizes are increased/decreased every Nadapt iterations to comply with the desired moving rates. |
nCycles |
Integer, number of adaptation cycles (total number of iterations is hence Nadapt * Ncycles). |
minMoveRate |
Numeric in (0;1), lower bound for the desired move rate interval. |
maxMoveRate |
Numeric in (0;1), upper bound for the desired move rate interval. |
downMult |
Numeric in (0:1), multiplication factor used to decrease jump size when move rate is too low. |
upMult |
Numeric (>1, avoid 1/dowMult) multiplication factor used to increase jump size when move rate is too high. |
multFactor |
Numeric >0, multiplicative factor to set initial jump standard deviations to multFactor*|initValue| (AUTO mode). |
manualMode |
logical, should jump standard deviations be entered manually? |
thetaStd |
Numeric vector (>0), jump standard deviations for model parameters theta (MANUAL mode). |
gammaStd |
list of numeric vectors (>0), size = number of output variables of the model. Jump standard deviations for structural error parameters gamma of each output variable (MANUAL mode). |
Value
An object of class 'mcmcOptions'.
Examples
m <- mcmcOptions()
mcmcSummary constructor.
Description
Creates a new instance of a 'mcmcSummary' object
Usage
mcmcSummary(
fname = "Config_Summary.txt",
result.fname = "Results_Summary.txt",
DIC.fname = "Results_DIC.txt",
xtendedMCMC.fname = ""
)
Arguments
fname |
Character, configuration file name. |
result.fname |
Character, summary file name. |
DIC.fname |
Character, DIC file name. Not computed if empty string. |
xtendedMCMC.fname |
Character, xtended MCMC file name. Not written if empty string. |
Value
An object of class 'mcmcSummary'.
Examples
m <- mcmcSummary()
model object constructor.
Description
Creates a new instance of a 'model' object
Usage
model(
fname = "Config_Model.txt",
ID = "Linear",
nX = 1,
nY = 1,
par = list(parameter("Xeffect", 1, prior.dist = "FlatPrior")),
xtra = xtraModelInfo()
)
Arguments
fname |
Character, configuration file name. |
ID |
Character, model ID. Type 'getCatalogue()' for available models. |
nX |
Integer, number of input variables. |
nY |
Integer, number of output variables. |
par |
list of parameter objects, parameters of the model. |
xtra |
xtraModelInfo object. |
Value
An object of class 'model'.
Examples
# defaut linear regression model Y=aX+b
mod <- model()
# BaRatin model for a single-control rating curve Y=a(X-b)^c
mod <- model(ID='BaRatin',nX=1,nY=1,
par=list(parameter('a',10,prior.dist='LogNormal',prior.par=c(log(10),0.1)),
parameter('b',-1,prior.dist='Gaussian',prior.par=c(-1,1)),
parameter('c',5/3,prior.dist='Gaussian',prior.par=c(5/3,0.05))),
xtra=xtraModelInfo(object=matrix(1,nrow=1,ncol=1)))
parameter object constructor.
Description
Creates a new instance of a 'parameter' object
Usage
parameter(name, init, prior.dist = "FlatPrior", prior.par = NULL)
Arguments
name |
character, parameter name. |
init |
numeric, initial guess. |
prior.dist |
character, prior distribution. |
prior.par |
numeric vector, prior parameters |
Value
An object of class 'parameter'.
Examples
p <- parameter(name='par',init=0,prior.dist='Gaussian',prior.par=c(0,1))
Varying parameter object constructor.
Description
Creates a new instance of a 'parameter_VAR' object
Usage
parameter_VAR(
name,
index,
d,
init,
prior.dist = rep("FlatPrior", length(init)),
prior.par = rep(list(NULL), length(init))
)
Arguments
name |
character, parameter name. |
index |
character, name of column in VAR.indx (see ?dataset) containing the index for this varying parameter |
d |
dataset object, the dataset containing (amongst other things) the index above |
init |
numeric vector, initial guesses for each instance of the VAR parameter. |
prior.dist |
character vector, prior distribution for each instance of the VAR parameter. |
prior.par |
list of numeric vectors, prior parameters for each instance of the VAR parameter |
Value
An object of class 'parameter_VAR'.
Examples
X=data.frame(input1=rnorm(100),input2=rnorm(100))
Y=data.frame(output=X$input1+0.8*X$input2+0.1*rnorm(100))
VAR.indx=data.frame(indx=c(rep(1,50),rep(2,50)))
workspace=tempdir()
d <- dataset(X=X,Y=Y,data.dir=workspace,VAR.indx=VAR.indx)
p <- parameter_VAR(name='par',index='indx',d=d,
init=c(-1,1,2),
prior.dist=c('Gaussian','FlatPrior','Triangle'),
prior.par=list(c(-1,1),NULL,c(2,0,5)))
prediction object constructor.
Description
Creates a new instance of a 'prediction' object
Usage
prediction(
X,
spagFiles,
data.dir = getwd(),
data.fnames = paste0("X", 1:length(X), ".pred"),
fname = paste0("Config_Pred_", paste0(sample(c(letters, LETTERS, 0:9), 6), collapse =
""), ".txt"),
doParametric = FALSE,
doStructural = rep(FALSE, length(spagFiles)),
transposeSpag = TRUE,
priorNsim = NULL,
envFiles = paste0(tools::file_path_sans_ext(spagFiles), ".env"),
consoleProgress = TRUE,
spagFiles_state = NULL,
transposeSpag_state = TRUE,
envFiles_state = switch(is.null(spagFiles_state) + 1,
paste0(tools::file_path_sans_ext(spagFiles_state), ".env"), NULL),
parSamples = NULL
)
Arguments
X |
data frame or list of dataframes / matrices, representing the values taken by the input variables.
|
spagFiles |
Character vector (size nY, the number of output variables). Name of the files containing the spaghettis for each output variable. NOTE: provide file names only, not full paths. Using a '.spag' extension is a good practice. |
data.dir |
Character, directory where a copies of the dataset X will be written if required (1 file per variable in X). Default is the current working directory, but you may prefer to use the BaM workspace. |
data.fnames |
Character, data file names. |
fname |
Character, configuration file name. |
doParametric |
Logical, propagate parametric uncertainty? If FALSE, maxpost parameters are used. |
doStructural |
Logical, propagate structural uncertainty for each output variable? (size nY) |
transposeSpag |
Logical. If FALSE, spaghettis are written horizontally (row-wise), otherwise they will be transposed so that each spaghetti is a column. |
priorNsim |
Integer, number of samples from the prior distribution for 'prior prediction' experiments. If negative or NULL (default), posterior samples are used. |
envFiles |
Character vector (size nY, the number of output variables). Name of the files containing the envelops (e.g. prediction intervals) computed from the spaghettis for each output variable. By default, same name as spaghetti files but with a '.env' extension. If NULL, envelops are not computed. |
consoleProgress |
Logical, print progress in BaM.exe console? |
spagFiles_state |
Character vector (size nState, the number of state variables), same as spagFiles but for states rather than outputs. If NULL, states are not predicted. Note that only parametric uncertainty is propagated for state variables since they are not observed. Consequently, structural uncertainty = 0 and total uncertainty = parametric uncertainty. |
transposeSpag_state |
Logical. Same as transposeSpag, but for states rather than outputs. |
envFiles_state |
Character vector (size nState, the number of state variables), same as envFiles, but for states rather than outputs. |
parSamples |
data frame, parameter samples that will replace the MCMC-generated one for this prediction. |
Value
An object of class 'prediction'.
Examples
#--------------
# Example using the twoPopulations dataset, containing 101 values for
# 3 input variables (time t, temperature at site 1 T1, temperature at site 2 T2)
# and 2 output variables (population at site 1 P1, population at site 2 P2).
pred=prediction(X=twoPopulations[,1:3],spagFiles=c('P1.spag','P2.spag'))
#--------------
# Alternative example showing how to propagate uncertainty in some of
# the input variables (here, temperatures T1 and T2)
# Create 100 noisy replicates for T1, representing uncertainty
T1rep=matrix(rnorm(101*100,mean=twoPopulations$T1,sd=0.1),nrow=101,ncol=100)
# Same for T2
T2rep=matrix(rnorm(101*100,mean=twoPopulations$T2,sd=0.1),nrow=101,ncol=100)
# Create prediction object
pred=prediction(X=list(twoPopulations$t,T1rep,T2rep),spagFiles=c('P1.spag','P2.spag'))
Write a character vector (typically output of toString() to file)
Description
Write a character vector (typically output of toString() to file)
Usage
quickWrite(txt, dir, fname)
Arguments
txt |
Character vector, text to be written |
dir |
Character, directory where file is written. The directory is created if needed. dir should end with a path seperator (not checked here). |
fname |
Character, file name |
Value
Nothing
MCMC Reader
Description
Read raw MCMC samples, return cooked (burnt & sliced) ones
Usage
readMCMC(
file = "Results_Cooking.txt",
burnFactor = 0,
slimFactor = 1,
sep = "",
reportFile = NULL,
panelPerCol = 10,
panelHeight = 3,
panelWidth = 23/panelPerCol
)
Arguments
file |
Character, full path to MCMC file. |
burnFactor |
Numeric, burn factor. 0.1 means the first 10 are discarded. |
slimFactor |
Integer, slim factor. 10 means that only one iteration every 10 is kept. |
sep |
Character, separator used in MCMC file. |
reportFile |
Character, full path to pdf report file, not created if NULL |
panelPerCol |
Integer, max number of panels per column |
panelHeight |
Numeric, height of each panel |
panelWidth |
Numeric, width of each panel |
Value
A data frame containing the cooked mcmc samples.
Examples
# Create Monte Carlo samples and write them to file
n=4000
sim=data.frame(p1=rnorm(n),p2=rlnorm(n),p3=runif(n))
workspace=tempdir()
write.table(sim,file=file.path(workspace,'MCMC.txt'),row.names=FALSE)
# Read file, burn the first half and keep every other row
M=readMCMC(file=file.path(workspace,'MCMC.txt'),burnFactor=0.5,slimFactor=2)
dim(M)
remnantErrorModel object constructor.
Description
Creates a new instance of a 'remnantErrorModel' object
Usage
remnantErrorModel(
fname = "Config_RemnantSigma.txt",
funk = "Linear",
par = list(parameter("g1", 1, prior.dist = "FlatPrior+"), parameter("g2", 0.1,
prior.dist = "FlatPrior+"))
)
Arguments
fname |
Character, configuration file name. |
funk |
Character, function f used in remnant sdev = f(Ysim). Available: 'Constant', 'Proportional', 'Linear' (default), 'Exponential', 'Gaussian'. |
par |
list of parameter objects, parameters of the function above. respectively, npar= 1,1,2,3,3 |
Value
An object of class 'remnantErrorModel'.
Examples
r <- remnantErrorModel()
residualOptions constructor.
Description
Creates a new instance of a 'residualOptions' object
Usage
residualOptions(
fname = "Config_Residuals.txt",
result.fname = "Results_Residuals.txt"
)
Arguments
fname |
Character, configuration file name. |
result.fname |
Character, result file name. |
Value
An object of class 'residualOptions'.
Examples
r <- residualOptions()
Run BaM executable
Description
Run BaM executable
Usage
runExe(exedir, exename, workspace)
Arguments
exedir |
directory of the executable |
exename |
name of the executable |
workspace |
full path to workspace |
Value
an error code (0 for success)
runOptions constructor.
Description
Creates a new instance of a 'runOptions' object
Usage
runOptions(
fname = "Config_RunOptions.txt",
doMCMC = TRUE,
doSummary = TRUE,
doResiduals = TRUE,
doPrediction = FALSE
)
Arguments
fname |
Character, configuration file name. |
doMCMC |
logical, do MCMC sampling? |
doSummary |
logical, do MCMC summarizing? |
doResiduals |
logical, do residuals analysis? |
doPrediction |
logical, do prediction experiments? |
Value
An object of class 'runOptions'.
Examples
o <- runOptions()
Path to BaM
Description
Set path to BaM executable
Usage
setPathToBaM(dir.exe, quiet = FALSE)
Arguments
dir.exe |
character string, folder where BaM executable is located. A NULL value resets BaM directory to 'unknown' by removing the config folder where it was stored on your computer (use 'tools::R_user_dir(package="RBaM",which="config")' to locate this folder). |
quiet |
logical, if TRUE, suppress status messages. |
Value
nothing - just write a config file.
Examples
setPathToBaM(dir.exe=tempdir())
dataset to string
Description
Convert an object of class 'dataset' into a ready-to-write vector of string
Usage
## S3 method for class 'dataset'
toString(x, ...)
Arguments
x |
dataset object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
X=data.frame(input1=rnorm(100),input2=rnorm(100))
Y=data.frame(output=X$input1+0.8*X$input2+0.1*rnorm(100))
workspace=tempdir()
d <- dataset(X=X,Y=Y,data.dir=workspace)
toString(d)
mcmcCooking to string
Description
Convert an object of class 'mcmcCooking' into a ready-to-write vector of string
Usage
## S3 method for class 'mcmcCooking'
toString(x, ...)
Arguments
x |
mcmcCooking object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
toString(mcmcCooking())
mcmcOptions to string
Description
Convert an object of class 'mcmcOptions' into a ready-to-write vector of string
Usage
## S3 method for class 'mcmcOptions'
toString(x, ...)
Arguments
x |
mcmcOptions object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
toString(mcmcOptions())
mcmcSummary to string
Description
Convert an object of class 'mcmcSummary' into a ready-to-write vector of string
Usage
## S3 method for class 'mcmcSummary'
toString(x, ...)
Arguments
x |
mcmcSummary object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
toString(mcmcSummary())
model to string
Description
Convert an object of class 'model' into a ready-to-write vector of string
Usage
## S3 method for class 'model'
toString(x, ...)
Arguments
x |
model object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
toString(model())
parameter to string
Description
Convert an object of class 'parameter' into a ready-to-write vector of string
Usage
## S3 method for class 'parameter'
toString(x, ...)
Arguments
x |
parameter object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
p <- parameter(name='par',init=0,prior.dist='Gaussian',prior.par=c(0,1))
toString(p)
parameter_VAR to string
Description
Convert an object of class 'parameter_VAR' into a ready-to-write vector of string
Usage
## S3 method for class 'parameter_VAR'
toString(x, ...)
Arguments
x |
parameter_VAR object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
X=data.frame(input1=rnorm(100),input2=rnorm(100))
Y=data.frame(output=X$input1+0.8*X$input2+0.1*rnorm(100))
VAR.indx=data.frame(indx=c(rep(1,50),rep(2,50)))
workspace=tempdir()
d <- dataset(X=X,Y=Y,data.dir=workspace,VAR.indx=VAR.indx)
p <- parameter_VAR(name='par',index='indx',d=d,
init=c(-1,1,2),
prior.dist=c('Gaussian','FlatPrior','Triangle'),
prior.par=list(c(-1,1),NULL,c(2,0,5)))
toString(p)
prediction to string
Description
Convert an object of class 'prediction' into a ready-to-write vector of string
Usage
## S3 method for class 'prediction'
toString(x, ...)
Arguments
x |
prediction object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
pred=prediction(X=twoPopulations[,1:3],spagFiles=c('P1.spag','P2.spag'))
toString(pred)
remnantErrorModel to string
Description
Convert an object of class 'remnantErrorModel' into a ready-to-write vector of string
Usage
## S3 method for class 'remnantErrorModel'
toString(x, ...)
Arguments
x |
remnantErrorModel object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
toString(remnantErrorModel())
residualOptions to string
Description
Convert an object of class 'residualOptions' into a ready-to-write vector of string
Usage
## S3 method for class 'residualOptions'
toString(x, ...)
Arguments
x |
residualOptions object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
toString(residualOptions())
runOptions to string
Description
Convert an object of class 'runOptions' into a ready-to-write vector of string
Usage
## S3 method for class 'runOptions'
toString(x, ...)
Arguments
x |
runOptions object, object to be converted. |
... |
Optional arguments. |
Value
A string ready to be printed or written.
Examples
toString(runOptions())
Write value-comment pairs into string vector
Description
Write value-comment pairs into string vector
Usage
toString_engine(val, comment, addQuote = TRUE)
Arguments
val |
List, object/list of values to be printed |
comment |
Character vector, associated comments (same length as val) |
addQuote |
Logical, ass double quotes to strings? |
Value
A character vector, ready to be printed of written to file
MCMC reporting
Description
2DO (adapt from STooDs): Generate pdf report files summarizing mcmc samples
Usage
tracePlot(sim, ylab = "values", keep = NULL, col = "black", psize = 0.5)
Arguments
sim |
vector or matrix or data frame, MCMC simulations |
ylab |
Character, label of y-axis to be used if sim has no names |
keep |
Integer vector, indices of samples to be kept in cooked MCMC sample |
col |
Color |
psize |
Numeric, point size |
Details
tracePlot
returns a trace plot ggplot (or a list thereof if several columns in sim)
Value
A ggplot (or a list thereof if several columns in sim)
Examples
# Create Monte Carlo samples
n=1000
sim=data.frame(p1=rnorm(n),p2=rlnorm(n),p3=runif(n))
# create trace plot for each component
figures=tracePlot(sim)
Evolution of two populations
Description
Size of two populations of the same species put in two different environments, as a function of time and local temperature.
Input variables: time t, temperature at site 1 T1, temperature at site 2 T2.
Output variables: population size at site 1 P1, population size at site 2 P2.
Data are synthetically generated from a logistic model.
Usage
twoPopulations
Format
An object of class data.frame with 101 rows and 5 columns.
violinPlot
Description
returns a violinplot ggplot
Usage
violinPlot(sim, ylab = "values", col = "black")
Arguments
sim |
vector or matrix or data frame, MCMC simulations |
ylab |
Character, label of y-axis |
col |
Color |
Value
A ggplot
Examples
# Create Monte Carlo samples
n=1000
sim=data.frame(p1=rnorm(n),p2=rlnorm(n),p3=runif(n))
# create violin plot comparing all components
figure=violinPlot(sim)
Write prediction inputs
Description
Write input data of the prediction into files
Usage
writePredInputs(o)
Arguments
o |
prediction object |
Value
nothing - just write to files.
Examples
temp=tempdir()
pred=prediction(X=twoPopulations[,1:3],spagFiles=c('P1.spag','P2.spag'),
data.dir=temp)
writePredInputs(pred)
xtraModelInfo constructor.
Description
Creates a new instance of a 'xtraModelInfo' object containing extra model information
Usage
xtraModelInfo(fname = "Config_Xtra.txt", object = NULL)
Arguments
fname |
Character, configuration file name. |
object |
any R object containing xtra model info - typically a list of stuff. The content and meaning of 'object' is completely model-specific. |
Value
An object of class 'xtraModelInfo'.
Examples
x <- xtraModelInfo()