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acffunc()
calculate correlation for residuals by block
AICh()
Function to calculate AICh (Hardin and Hilbe 2013)
anova(<gamMRSea> )
Anova Tables for gamMRSea Models
bootstrap.orig.data()
Obtaining a data frame of bootstrapped data using resamples
checkfactorlevelcounts()
Factor level response check
This function checks that there are some non-zero counts in each level of each factor variable for consideration in a model
choose.radii()
Function to choose the radii for the CReSS local radial basis function
create.bootcount.data()
Aggregate bootstrapped distance data into count data
create.bootstrap.data()
Create bootstrap data for non-parametric bootstrapping
create.count.data()
Aggregate distance data into count data
create.NHAT()
Estimated number of individuals for each detection
cv.gamMRSea()
Cross-validation for gamMRSea Models
dis.data.de
Line transect data with decrease post-impact
dis.data.no
Line transect data with no post-impact consequence
dis.data.re
Line transect data with redistribution post-impact
do.bootstrap.cress()
Bootstrapping function without model selection using CReSS/SALSA for fitting the second stage count model
do.bootstrap.cress.robust.beta()
Bootstrapping function without model selection for a model of class 'gamMRSea' and beta family
do.bootstrap.cress.robust()
Bootstrapping function without model selection for a model of class 'gamMRSea'
drop.step_2d()
Function that tries dropping knots to find an improvement in fit
exchange.step_2d()
Function for exchanging knot locations and re-fitting model to find best one
fit.thinPlate_2d()
Function to fit a local radial basis function (CReSS) as a two dimensional smooth
gamMRSea()
gamMRSea model function
generateNoise()
Function to generate noisy data
getCVids()
IDs for running cross validation
getDifferences()
Identify any significant differences between predicted data before an impact event and predicted data after an impact event
getDispersion()
dispersion parameter
getEmpDistribution()
Function to generate the empirical distribution of the runs test statistic, given some data and a model.
getGeoDist()
Function to calculate geodesic distances
getKnotgrid()
Generate a grid of knot locations to run SALSA2D.
getPlotdimensions()
find the plotting dimensions for quilt.plot when using a regular grid
getRadiiChoices()
Function for obtaining a sequence of range parameters for the CReSS smoother
getRadiiChoices.vario()
Function for obtaining a sequence of range parameters for the CReSS smoother
getRadiiSequence()
Function for obtaining a sequence of range parameters for the bivariate CReSS smoother
improve.step_2d()
Function to move knots to neighbours to see if there is any improvement in fit
knotgrid.ns
Knot grid data for nearshore example
knotgrid.off
Knot grid data for offshore example
LocalRadialFunction()
Function for creating an Gaussian basis function for a spatial smooth using the CReSS method.
LRF.e()
Function for creating an Exponential basis function for a spatial smooth using the CReSS method.
LRF.g()
Function for creating an Gaussian basis function for a spatial smooth using the CReSS method.
make.gamMRSea()
Function to make model of class gamMRSea
makeBootCIs()
Calculate percentile confidence intervals from a matrix of bootstrapped predictions
makeDists()
Make Euclidean distance matrices for use in CReSS and SALSA model frameworks
makesplineParams()
Constructing an object of spline parameters
MRSea
MRSea
ns.data.de
Nearshore data with decrease post-impact
ns.data.no
Nearshore data with no effect of impact
ns.data.re
Nearshore data with redistribution post-impact
ns.predict.data.de
Prediction grid data for nearshore post-impact decrease
ns.predict.data.no
Prediction grid data for nearshore with no effect of impact
ns.predict.data.re
Prediction grid data for nearshore post-impact redistribution
nysted.analysisdata
Nysted Data
nysted.coast
Polygon of the Nysted coastline
nysted.predictdata
Prediction grid data for Nysted Data
nysted.studybnd
Polygon of the outline of the Nysted study area
plotacf()
run functions to create acf matrix and plot the results
plotCumRes()
Calculate cumulative residuals and plot.
plotMeanVar()
Functions to create a Mean-Variance plot for checking the distribution assumptions of the mean and the variance. Distributions available are Gaussian, Poisson, QuasiPoisson, Gamma and Tweedie.
plotRunsProfile()
Calculate runs test and plot profile plot. The output is a plot of runs profiles (with p-value to indicate level of correlation)
predict.data.de
Prediction grid data for post-impact decrease
predict.data.no
Prediction grid data for no post-impact consequence
predict.data.re
Prediction grid data for post-impact redistribution
predict(<gamMRSea> )
Function for making predictions for a model containing a CReSS basis (two dimensional local smooth).
QICb()
Function to calculate QICb
qposbinom()
qposbinom function
qzibinom()
qzibinom function from the VGAM package
return.reg.spline.fit.2d()
Wrapper function for running SALSA2D
return.reg.spline.fit()
Code for adaptively spacing knots for a given covariate.
rpois.od()
Generating overdispersed poisson data
runACF()
run functions to create acf matrix and plot the results
runDiagnostics()
functions to create observed vs fitted and fitted vs scaled pearsons residual plots
runInfluence()
Assessing the influece of each correlated block on both the precision of the parameter estimates (COVRATIO statistics) and the sensitivity of model predictions (PRESS statistics).
runPartialPlots()
Plot partial plots for each of the variables listed in factorlist.in or varlist.in.
runSALSA1D()
Running SALSA for continuous one-dimensional covariates.
runSALSA2D()
Running SALSA for a spatial smooth with a CReSS basis
runsTest()
Runs Test for Randomness
rzibinom()
rzibinom function from the VGAM package
selectFctrKnots()
Select candidate knots for multi-level factor interaction
selectFctrStartk()
Select starting knots for multi-level factor interaction
summary(<gamMRSea> )
Summarising model fits from models fitted using the MRSea package.
summaryshortnames()
Shortening names in summary object
thinModels()
function to thin the number of models
timeInfluenceCheck()
Timing check to see how long it will take to run runInfluence.
which.bin()
Determining the distance bin