Nothing
R/TimeStratPetersenNonDiagErrorNPMarkAvail_fit.R
In BTSPAS: Bayesian Time-Stratified Population Analysis
Defines functions
TimeStratPetersenNonDiagErrorNPMarkAvail_fit
Documented in
TimeStratPetersenNonDiagErrorNPMarkAvail_fit
## 2021-10-23 CJS Added trunc.logitP to deal with extreme values of logitP during plotting
## 2020-12-15 CJS removed sampfrac from body of code
## 2020-12-15 CJS Fixed problem when specifyin u2==NA
## 2020-11-07 CJS Allowed user to specify prior for beta coefficient for logitP
## 2018-12-22 CJS add code to estimate mean movement vector (movep)
## 2018-12-19 CJS sampling fraction deprecated
## 2018-12-14 CJS bayesian p-value plots added
## 2018-12-06 CJS convert report to textConnections
## 2018-12-02 CJS convert trace plots to ggplot
## 2018-12-01 CJS converted acf, posterior plots to ggplot
## 2018-11-30 CJS Fixed problem of epsilon not being right length
## 2018-11-29 CJS Fixed problem of printing large results
## 2018-11-28 CJS remove reference to OpenBugs
## 2014-09-01 CJS conversion to jags
## 2012-08-30 CJS fixed problem in any() and all() in error checking with NAs
## 2011-02-21 CJS changed u2 to new.u2 in code for expanded.m2
## 2011-02-19 CJS First development
#' Wrapper (*_fit) to call the function to fit a Time Stratified Petersen Estimator
#' with NON Diagonal Entries with an non-parametric travel time and fall back
#'
#' Takes the number of marked fish released, the number of recaptures, and the
#' number of unmarked fish and uses Bayesian methods to fit a fit a spline
#' through the population numbers and a hierarchical model for the trap
#' efficiencies over time. The output is written to files and an MCMC object
#' is also created with samples from the posterior.
#'
#' Normally the user makes a call to the *_fit function which then calls the
#' fitting function.
#'
#' Use the \code{\link{TimeStratPetersenDiagError_fit}} function for cases
#' where recaptures take place ONLY in the stratum of release, i.e. the
#' diagonal case.
#'
#' The non-diagonal case fits a log-normal distribution for the travel time.
#' The *NP functions fit a non-parametric distribution for the travel times.
#' The *MarkAvail functions extend the *NP functions to allow for reductions in
#' mark availability because of fall back, immediate tagging mortality, etc.
#'
#' @template title
#' @template prefix
#' @template time
#' @template n1
#' @param m2 A numeric matrix of the number of fish released in stratum [i] and
#' recovered in [j-1] strata later. For example m2[3,5] is the number of
#' marked fish released in stratum 3 and recovered 4 strata later in stratum 7.
#' The first column is the number of marked fish recovered in the stratum of
#' release, i.e. 0 strata later. Use the
#' \code{\link{TimeStratPetersenDiagError_fit}} function for cases where
#' recaptures take place ONLY in the stratum of release, i.e. the diagonal
#' case.
#' @template u2.ND
#' @template sampfrac
#' @template jump.after
#' @template bad.n1
#' @template bad.m2
#' @template bad.u2
#' @template logitP.cov
#' @param logitP.fixed A numeric vector (could be null) of the time strata
#' where the logit(P) would be fixed. Typically, this is used when the capture
#' rates for some strata are 0 and logit(P) is set to -10 for these strata. The
#' fixed values are given in \code{logitP.fixed.values}
#' @param logitP.fixed.values A numerical vector (could be null) of the fixed
#' values for logit(P) at strata given by logitP.fixed. Typically this is used
#' when certain strata have a 0 capture rate and the fixed value is set to -10
#' which on the logit scale gives p[i] essentially 0. Don't specify values such
#' as -50 because numerical problems could occur in JAGS.
#' @param marked_available_n Information, usually from prior studies, on the
#' fraction of marks that will be available. The *_n and *_x are used to create
#' a "binomial" distribution for information on the marked availability. For
#' example, if *_n=66 and *_x=40, then you estimate that about 40/66=61\% of marks
#' are available and 39\% have dropped out or fallen back.
#' @param marked_available_x See marked_available_n
#' @template mcmc-parms
#' @template tauU.alpha.beta
#' @template taueU.alpha.beta
#' @template Delta.max
#' @template tauTT.alpha.beta
#' @template prior.beta.logitP.mean.sd
#' @template tauP.alpha.beta
#' @template run.prob
#' @template debug
#' @template InitialSeed
#' @template save.output.to.files
#' @template trunc.logitP
#'
#' @return An MCMC object with samples from the posterior distribution. A
#' series of graphs and text file are also created in the working directory.
#' @template author
#' @template references
#' @keywords ~models ~smooth
#' @examples
#'
#' ##---- See the vignettes for examples of how to use this package
#'
#' @export TimeStratPetersenNonDiagError_fit
#' @importFrom stats runif var sd
#' @export TimeStratPetersenNonDiagErrorNPMarkAvail_fit
TimeStratPetersenNonDiagErrorNPMarkAvail_fit<- function( title="TSPNDENP-avail", prefix="TSPNDENP-avail-",
time, n1, m2, u2, sampfrac=rep(1,length(u2)), jump.after=NULL,
bad.n1=c(), bad.m2=c(), bad.u2=c(),
logitP.cov=rep(1,length(u2)),
logitP.fixed=NULL, logitP.fixed.values=NULL,
marked_available_n, marked_available_x,
n.chains=3, n.iter=200000, n.burnin=100000, n.sims=2000,
tauU.alpha=1, tauU.beta=.05, taueU.alpha=1, taueU.beta=.05,
prior.beta.logitP.mean = c(logit(sum(m2,na.rm=TRUE)/sum(n1,na.rm=TRUE)),rep(0, ncol(as.matrix(logitP.cov))-1)),
prior.beta.logitP.sd = c(2, rep(10, ncol(as.matrix(logitP.cov))-1)),
tauP.alpha=.001, tauP.beta=.001,
Delta.max=NULL,tauTT.alpha=.1,tauTT.beta=.1,
run.prob=seq(0,1,.1), # what percentiles of run timing are wanted
debug=FALSE, debug2=FALSE,
InitialSeed=ceiling(stats::runif(1,min=0, max=1000000)),
save.output.to.files=TRUE,
trunc.logitP=15) {
## Fit a Time Stratified Petersen model with NON-diagonal entries and with smoothing on U allowing for random error
## and fall back after tagging. This is based on the Skeena River study, where only 40/66 (60%) acoustically tagged fish
## were observed above the canyon spot and hence 40% of tagged fish never migrated forward of their tagging release spot.
## This reduces the number of tagged fish available for recapture and so, if not accounted for, leads to
## positive biases in the estimates of abundance.
## This is the classical stratified Petersen model where the recoveries can take place for this and multiple
## strata later. Transisions of marked fish are modelled non-parametrically.
##
version <- '2021-11-02'
options(width=200)
## Input parameters are
## title - title for the analysis (character string)
## prefix - prefix used for files created with the analysis results
## this should be in standard Window's format, eg. JC-2002-ST-TSPNDE
## to which is appended various suffixes for plots etc (character string)
## time - vector of stratum numbers. For example, 9:38 would indicate that the
## Trinity River system sampled weeks 9 to 38.
## These values are never used in the analysis and only serve as labels for the weeks and for plotting purposes.
## They should be contiguous equally spaced values and be the same length as u2.
## n1, m2, u2 - the input data consisting of fish marked and released, recapture, and unmarked captured
## Note that m2 is a MATRIX. The first column are those fish recaptured in the stratum of release
## and the subsequent columns are those recoveries in later strata.
## This is expanded to the full matrix [i,j] for released released in stratum i and recovered in stratum j
## The vector u2 should be long enough to account for any fish that are recaptured later on
## from releases late in the season. The bottom right diagonal of m2 may be all zeros - that is ok
## Notice that length(u2) can be longer than length(n1)+nrow(m2).
## sampfrac - Deprecated. DO NOT USE.
## jump.after - in some cases, a single spline is still not flexible enough to cope with rapid
## changes in the run curve. For example, in the Trinity River project, a larger
## hatchery release occurs around stratum 14. This is a vector indicating the
## strata AFTER which the spline curve is allowed to jump.
## null or vector of arbitrary length.
## bad.n1 - list of stratum numbers where the value of n1 is suspect.
## bad.m2 - list of stratum numbers where the value of m2 is suspect.
## For example, the capture rate could be extremely low.
## These are set to NA prior to the call to JAGS
## bad.u2 - list of stratum numbers where the value of u2 is suspect.
## logitP.cov - matrix of covariates for logit(P). If the strata times are "missing" some values, an intercept is assumed
## for the first element of the covariance matrix and 0 for the rest of the covariates.
## CAUTION - this MAY not be what you want to do. It is likely best to enter ALL strata
## if you have any covariates. The default, if not specified, is a constant (the mean logit)
## marked_available_n, marked_available_x Information on the movement forward rate. Treat this a binomial data
## which will be applied uniformly over all released. For example, use *_n=60 and *_x=40 to represent
## data from the telemetry study that had 40/60 tagged fish move forward. You can vary the precision of the
## estimate of the marked_availability_fraction by chaning the _n and _x values to create the illusion
## of better and worse information on the availability value.
##
## tauU.alpha, tauU.beta - parameters for the prior on variance in spline coefficients
## taueU.alpha, taueU.beta - parameters for the prior on variance in log(U) around fitted spline
# prior.beta.logitP.mean, prior.beta.logitP.sd - parameters for the prior on mean logit(P)'s [The intercept term]
# The other covariates are assigned priors of a mean of 0 and a sd of 30
## tauP.alpha, tauP.beta - parameters for the prior on 1/var of residual error in logit(P)'s
## Delta.max - maximum transition time for marked fish
## tauTT.alpha, tauTT.beta - parameters of the prior on 1/var of logit continuation ratio for travel times
## run.prob - percentiles of run timing wanted
## debug - if TRUE, then this is a test run with very small MCMC chains run to test out the data
# force input vectors to be vectors. Note that m2 is NOT a vector
time <- as.vector(time)
n1 <- as.vector(n1)
u2 <- as.vector(u2)
sampfrac <- as.vector(sampfrac)
## Do some basic error checking
## 1. Check that length of n1, m2, u2, sampfrac, time are consistent with each other.
## In the non-diagonal case, they don't have to match
if(length(n1)!=nrow(m2))
stop("***** ERROR ***** Length of n1 and number of rows of m2 must be equal. They are:",
length(n1)," ",nrow(u2),"\n")
if(!is.numeric(n1)){
cat("***** ERROR ***** n1 must be numeric. You have:",
paste(n1,collapse=", "),"\n")
return()}
if(any(is.na(n1))){
cat("***** ERROR ***** All values of n1 must not be missing. You have: ",
paste(n1,collapse=", "),"\n")
return()}
if(any(n1 < 0, na.rm=TRUE)){
cat("***** ERROR ***** All values of n1 must be non-negative. You have: ",
paste(n1,collapse=", "),"\n")
return()}
if(stats::var(c(length(u2),length(sampfrac),length(time)))>0)
stop("***** ERROR ***** Lengths of u2, sampfrac, time must all be equal. They are:",
length(u2),' ',length(sampfrac),' ',length(time),"\n")
if(length(logitP.cov) %% length(u2) != 0)
stop("***** ERROR ***** Dimension of covariate vector doesn't match length of u2. They are:",
length(u2),' ',length(logitP.cov),' ',dim(logitP.cov),"\n")
## 2. Check that rowsum of m2<= n1
if(any(apply(m2,1,sum, na.rm=TRUE)>n1))
stop("***** ERROR ***** m2[i,+] must be <= n1[i]. The arguments are \n n1:",paste(n1,collapse=","),
"\n m2:",paste(m2,collapse=","),"\n")
## 3. Elements of bad.m2 and jump.after must belong to time
if(!all(bad.n1 %in% time, na.rm=TRUE))
stop("***** ERROR ***** bad.n1 must be elements of strata identifiers. You entered \n bad.n1:",
paste(bad.n1,collapse=","),"\n Strata identifiers are \n time:",
paste(time,collapse=","),"\n")
if(!all(bad.m2 %in% time, na.rm=TRUE))
stop("***** ERROR ***** bad.m2 must be elements of strata identifiers. You entered \n bad.m2:",
paste(bad.m2,collapse=","),"\n Strata identifiers are \n time:",
paste(time,collapse=","),"\n")
if(!all(bad.u2 %in% time, na.rm=TRUE))
stop("***** ERROR ***** bad.u2 must be elements of strata identifiers. You entered \n bad.u2:",
paste(bad.u2,collapse=","),"\n Strata identifiers are \n time:",
paste(time,collapse=","), "\n")
if(!all(jump.after %in% time, na.rm=TRUE))
stop("***** ERROR ***** jump.after must be elements of strata identifiers. You entered \n jump.after:",
paste(jump.after,collapse=","),"\n Strata identifiers are \n time:",
paste(time,collapse=","), "\n")
# 4. check that index of logitP.fixed belong to time
if(!all(logitP.fixed %in% time, na.rm=TRUE)){
cat("***** ERROR ***** logitP.fixed must be elements of strata identifiers. You entered \n logitP.fixed:",
paste(logitP.fixed,collapse=","),"\n Strata identifiers are \n time:",
paste(time,collapse=","), "\n")
return()}
if(length(logitP.fixed)!=length(logitP.fixed.values)){
cat("***** ERROR ***** Lengths of logitP.fixed and logitP.fixed.values must all be equal. They are:",
length(logitP.fixed),length(logitP.fixed.values),"\n")
return()}
# 5. Check that some basic information on marked availability is given
if( is.na(marked_available_n) | is.na(marked_available_x) | marked_available_x > marked_available_n){
cat("***** ERROR ***** Bad marked_availability values. You entered:",marked_available_n," ",marked_available_x,"\n")
return()}
#7 check that the length of u2
if(length(u2) < length(n1) | length(u2) > (length(n1)+ ncol(m2)-1)){
cat("***** ERROR ***** Length(u2) must between length(n1) and length(n1)+ncol(m2) \n")
return()
}
# Check that that the prior.beta.logitP.mean and prior.beta.logitP.sd length=number of columns of covariates
logitP.cov <- as.matrix(logitP.cov)
if(!is.vector(prior.beta.logitP.mean) | !is.vector(prior.beta.logitP.sd)){
stop("prior.beta.logitP.mean and prior.beta.logitP.sd must be vectors")
}
if(!is.numeric(prior.beta.logitP.mean) | !is.numeric(prior.beta.logitP.sd)){
stop("prior.beta.logitP.mean and prior.beta.logitP.sd must be numeric")
}
if(length(prior.beta.logitP.mean) != ncol(logitP.cov) | length(prior.beta.logitP.sd) != ncol(logitP.cov)){
stop("prior.beta.logitP.mean and prior.beta.logitP.sd must be same length as number columns in covariate matrix")
}
# Deprecation of sampling fraction.
if(any(sampfrac != 1)){
cat("***** ERROR ***** Sampling fraction is deprecated for any values other than 1. DO NOT USE ANYMORE. ")
return()
}
## Define maximum travel time if not supplied by user
if(is.null(Delta.max)) Delta.max <- ncol(m2)-1
## Define output filename
results.filename <- paste(prefix,"-results.txt",sep="")
## Open sink to output file
stdout <- vector('character')
report <- textConnection('stdout', 'wr', local = TRUE)
sink(report)
cat(paste("Time Stratified Petersen with Non-Diagonal recaptures, error in smoothed U, non-parametric modelling of travel times, and incorporating mark availability- ", date()))
cat("\nVersion: ", version)
cat("\n\n", title, "Results \n\n")
## m2(i,+) < n1(i)
cat("*** Raw data *** (padded to match length of u2) \n")
jump.indicator <- rep(' ', length(u2))
jump.indicator[time %in% jump.after]<- '***'
ex.n1 <- c(n1, rep(NA, length(u2)-length(n1)))
ex.m2 <- rbind(m2,matrix(NA, nrow=length(u2)-length(n1), ncol=ncol(m2)))
temp<- data.frame(time=time, n1=ex.n1, m2=ex.m2, u2=u2, logitP.cov=logitP.cov, jump=jump.indicator)
print(temp)
cat("\n\n")
cat("*** Marked Availability prior information *** \n")
cat(" Set marked available n=", marked_available_n," with x=",marked_available_x,"\n\n\n")
## Print information about jump points
cat("Jump point are after strata: ", jump.after)
if(length(jump.after)==0) cat("none - A single spline is fit")
## Print information about delta max
cat("\nMaximum travel time (Delta.max): ",Delta.max)
cat("\nFixed logitP indices are: ", logitP.fixed)
if(length(logitP.fixed)==0) cat("none - NO fixed values")
cat("\nFixed logitP values are: ", logitP.fixed.values)
if(length(logitP.fixed)==0) cat("none - NO fixed values")
## Obtain the Pooled Petersen estimator prior to fixup of bad.n1, bad.m2, and bad.u2 values
cat("\n\n*** Pooled Petersen Estimate prior to fixing bad n1, m2, or u2 values CHECK - CHECK - CHECK - CHECK ***\n\n")
cat(" *** NOT ADJUSTED FOR MARK AVAILABILITY/Dropout/Fallback ***\n")
temp.n1 <- n1
temp.m2 <- m2
temp.u2 <- u2
cat("Total n1=", sum(temp.n1,na.rm=TRUE),"; m2=",sum(temp.m2,na.rm=TRUE),"; u2=",sum(temp.u2,na.rm=TRUE),"\n\n")
pp <- SimplePetersen(sum(temp.n1,na.rm=TRUE), sum(temp.m2,na.rm=TRUE), sum(temp.u2,na.rm=TRUE))
cat("Est U(total) not adjusted for fallback ", format(round(pp$U.est),big.mark=","),
" (SE ", format(round(pp$U.se), big.mark=","), ")\n")
cat("Est N(total) not adjusted for fallback ", format(round(pp$N.est),big.mark=","),
" (SE ", format(round(pp$N.se), big.mark=","), ")\n\n\n")
# adjustment for dropout
dr <- 1-marked_available_x/marked_available_n # dropout probability
se_dr <- sqrt(dr*(1-dr)/marked_available_n)
cat("\n\nAdjusting for fallback/dropout \n")
cat("Estimated dropout is", dr, "with se of ", se_dr, "\n")
# adjust the petersen estimator for drop out including the uncertainty in the dropout probability
pp.adj <- pp
pp.adj$N.est <- pp.adj$N.est * (1-dr)
pp.adj$N.se <- sqrt(pp$N.se^2 * se_dr^2+
pp$N.se^2 * (1-dr)^2 +
pp$N.est^2 * se_dr^2)
pp.adj$U.est <- pp.adj$U.est * (1-dr)
pp.adj$U.se <- sqrt(pp$U.se^2 * se_dr^2+
pp$U.se^2 * (1-dr)^2 +
pp$U.est^2 * se_dr^2)
cat("Est U(total) adjusting for dropout is ", format(round(pp.adj$U.est),big.mark=","),
" (SE ", format(round(pp.adj$U.se), big.mark=","), ")\n")
cat("Est N(total) adjusting for dropout is ", format(round(pp.adj$N.est),big.mark=","),
" (SE ", format(round(pp.adj$N.se), big.mark=","), ")\n\n\n")
## Test if pooling can be done
cat("*** Test if pooled Petersen is allowable. [Check if fraction captured equal] ***\n\n")
select <- (n1>0) & (!is.na(n1)) & (!is.na(apply(m2,1,sum)))
temp.n1 <- n1[select]
temp.m2 <- m2[select,]
test <- TestIfPool( temp.n1, apply(temp.m2,1,sum))
cat("(Large Sample) Chi-square test statistic ", test$chi$statistic," has p-value", test$chi$p.value,"\n\n")
temp <- cbind(time[1:length(n1)][select],test$chi$observed, round(test$chi$expected,1), round(test$chi$residuals^2,1))
colnames(temp) <- c('time','n1-m2*','m2*','E[n1-m2]','E[m2]','X2[n1-m2]','X2[m2]')
print(temp)
cat("\n Be cautious of using this test in cases of small expected values. \n\n")
## Adjust the data for the explicity bad values or other problems
new.time <- time
new.n1 <- n1
new.m2 <- m2
new.u2 <- u2
new.logitP.cov <- logitP.cov
## Set the bad values to missing
new.n1[time[1:length(n1)] %in% c(bad.n1, bad.m2)] <- 0
new.m2[time[1:length(n1)] %in% c(bad.m2, bad.n1)] <- 0
new.u2[time %in% bad.u2] <- NA
## Print out the revised data
cat("\n\n*** Revised data *** \n")
jump.indicator <- rep(' ', length(u2))
jump.indicator[time %in% jump.after]<- '***'
ex.n1 <- c(new.n1, rep(NA, length(new.u2)-length(new.n1)))
ex.m2 <- rbind(new.m2,matrix(NA, nrow=length(new.u2)-length(new.n1), ncol=ncol(new.m2)))
temp<- data.frame(time=new.time, n1=ex.n1, m2=ex.m2, u2=new.u2, logitP.cov=new.logitP.cov,
jump.after=jump.indicator)
print(temp)
cat("\n\n")
cat("*** Marked Availability prior information *** \n")
cat(" Set marked available n=", marked_available_n," with x=",marked_available_x,"\n\n\n")
## The NP analysis does not need the expanded m2 array, but this is
## needed late on. So, we'd better compute it here. The last column
## of this matrix will be the number of individuals from each
## stratum that are not recaptured.
##
expanded.m2 <- matrix(0, nrow=length(new.n1), ncol=length(new.u2)+1)
for(i in 1:length(new.n1)){
expanded.m2[i,1:length(new.u2)] <- c(rep(0,i-1),new.m2[i,],rep(0,length(new.u2)))[1:length(new.u2)]
expanded.m2[i,length(new.u2)+1] <- new.n1[i] - sum(new.m2[i,])
}
cat("*** Expanded m2 array with column sum and u2 ***\n\n")
save.max.print <- getOption("max.print")
options(max.print=.Machine$integer.max)
temp <- rbind(expanded.m2, apply(expanded.m2,2,sum, na.rm=TRUE))
rownames(temp)[nrow(temp)] <- 'Column totals'
temp <- rbind(temp, c(u2, rep(NA, ncol(expanded.m2)-length(u2)) ))
rownames(temp)[nrow(temp)] <- "Untagged (u2)"
temp <- rbind(temp, c(new.u2, rep(NA, ncol(expanded.m2)-length(new.u2)) ))
rownames(temp)[nrow(temp)] <- "Untagged - after fixups"
new.logitP.fixed <- rep(NA, length(new.u2))
new.logitP.fixed[match(logitP.fixed, time)] <- logitP.fixed.values
temp <- rbind(temp, c(new.logitP.fixed, rep(NA, ncol(expanded.m2)-length(new.u2)) ))
rownames(temp)[nrow(temp)] <- "Logit P fixed"
rownames(temp)[1:length(n1)] <- 1:length(n1)
print(temp)
options(max.print=save.max.print)
sink()
# some further checking on u2. Make sure that every columns where there are recoveries has a u2
# browser()
if( (length(u2)+1) <= (ncol(temp)-1)) {
if(any( temp["Column totals", (length(u2)+1):(ncol(temp)-1)] >0)){
cat("***** ERROR ***** Non-zero recoveries and u2 not available at end of experiment??? \n Check above matrix\n")
return()
}
}
sink(report, append=TRUE)
# assign the logitP fixed values etc.
new.logitP.fixed <- rep(NA, length(new.u2))
new.logitP.fixed[match(logitP.fixed, time)] <- logitP.fixed.values
## We do need to add the column of not recaptured counts to the m2
## array.
new.m2 <- cbind(new.m2,new.n1-apply(new.m2,1,sum))
## We construct a prior probability on the P(marks available) based on the information provided
## by assuming a beta prior that would give the binomial results
ma.p.alpha <- marked_available_x
ma.p.beta <- marked_available_n - marked_available_x
## Print out information on the prior distributions used
cat("\n\n*** Information on priors *** \n")
## 0) ma.p = p(marked_availability for subsequent recapture)
cat(" P(marked fish available for subsequent recapture) has beta(",ma.p.alpha,ma.p.beta,") which corresponds \n",
" to a mean of ", round(ma.p.alpha/(ma.p.alpha+ma.p.beta),2),' and sd of ',
round(sqrt(ma.p.alpha*ma.p.beta/(ma.p.alpha+ma.p.beta+1)/(ma.p.alpha+ma.p.beta)**2),3),"\n")
## 1) tauU = (variance of spline coefficients)^-1
cat(" Parameters for prior on tauU (variance in spline coefficients): ", tauU.alpha, tauU.beta,
" which corresponds to a mean/std dev of 1/var of:",
round(tauU.alpha/tauU.beta,2),round(sqrt(tauU.alpha/tauU.beta^2),2),"\n")
## 2) taueU = (variance of errors)^-1
cat(" Parameters for prior on taueU (variance of log(U) about spline): ",taueU.alpha, taueU.beta,
" which corresponds to a mean/std dev of 1/var of:",
round(taueU.alpha/taueU.beta,2),round(sqrt(taueU.alpha/taueU.beta^2),2),"\n")
## 3) prior.beta.logitP = priors for coefficients of covariates for logitP
cat(" Parameters for prior on beta.logitP[1] (intercept) (mean, sd): \n", cbind(round(prior.beta.logitP.mean,3), round(prior.beta.logitP.sd,5)),"\n")
## 4) tauP = (variance of capture probabilites conditional on covariates)^-1
cat(" Parameters for prior on tauP (residual variance of logit(P) after adjusting for covariates): ",tauP.alpha, tauP.beta,
" which corresponds to a mean/std dev of 1/var of:",
round(tauP.alpha/tauP.beta,2),round(sqrt(tauP.alpha/tauP.beta^2),2),"\n")
## 5) tauTT = (variance of continuation ratios for theta)^-1
cat(" Parameters for prior on tauTT (variance of continuation rations for travel times): ",tauTT.alpha, tauTT.beta,
" which corresponds to a mean/std dev of 1/var of:",
round(tauTT.alpha/tauTT.beta,2),round(sqrt(tauTT.alpha/tauTT.beta^2),2),"\n")
cat("\n\nInitial seed for this run is: ",InitialSeed, "\n")
sink()
if (debug2) {
cat("\nprior to formal call to TimeStratPetersenNonDiagError\n")
browser()
}
if (debug)
{results <- TimeStratPetersenNonDiagErrorNPMarkAvail(
title=title, prefix=prefix,
time=new.time, n1=new.n1, m2=new.m2, u2=new.u2,
jump.after=(1:length(u2))[time %in% jump.after],
logitP.cov=new.logitP.cov, logitP.fixed=new.logitP.fixed,
ma.p.alpha, ma.p.beta,
n.chains=3, n.iter=10000, n.burnin=5000, n.sims=500, # set to small values for debugging only
prior.beta.logitP.mean=prior.beta.logitP.mean,
prior.beta.logitP.sd =prior.beta.logitP.sd,
tauU.alpha=tauU.alpha, tauU.beta=tauU.beta,
taueU.alpha=taueU.alpha, taueU.beta=taueU.beta,
Delta.max=Delta.max,tauTT.alpha=tauTT.alpha,tauTT.beta=tauTT.beta,
debug=debug, debug2=debug2, InitialSeed=InitialSeed,
save.output.to.files=save.output.to.files)
} else #notice R syntax requires { before the else
{results <- TimeStratPetersenNonDiagErrorNPMarkAvail(
title=title, prefix=prefix,
time=new.time, n1=new.n1, m2=new.m2, u2=new.u2,
jump.after=(1:length(u2))[time %in% jump.after],
logitP.cov=new.logitP.cov, logitP.fixed=new.logitP.fixed,
ma.p.alpha, ma.p.beta,
n.chains=n.chains, n.iter=n.iter, n.burnin=n.burnin, n.sims=n.sims,
prior.beta.logitP.mean=prior.beta.logitP.mean,
prior.beta.logitP.sd =prior.beta.logitP.sd,
tauU.alpha=tauU.alpha, tauU.beta=tauU.beta,
taueU.alpha=taueU.alpha, taueU.beta=taueU.beta,
Delta.max=Delta.max,tauTT.alpha=tauTT.alpha,tauTT.beta=tauTT.beta,
debug=debug, debug2=debug2, InitialSeed=InitialSeed,
save.output.to.files=save.output.to.files)
}
results$PP$using.all.data <-pp
results$PP$using.all.data.fallback <- pp.adj
results$dr <- data.frame(est=dr, se=se_dr)
## Now to create the various summary tables of the results
Nstrata.rel <- length(n1)
Nstrata.cap <- ncol(expanded.m2) -1 ## don't forget that last column of m2 is number of fish never seen
# A plot of the observered log(U) on the log scale, and the final mean log(U)
plot.df <- data.frame(time =new.time)
plot.df$logUi <-log( c((new.u2[1:Nstrata.rel]+1)*(new.n1+2)/(apply(expanded.m2[,1:Nstrata.cap],1,sum)+1), rep(NA, length(u2)-Nstrata.rel)))
# extract the fitted U values
results.row.names <- rownames(results$summary)
etaU.row.index <- grep("etaU", results.row.names)
etaU<- results$summary[etaU.row.index,]
plot.df$logU = etaU[,"mean"]
plot.df$logUlcl = etaU[,"2.5%"]
plot.df$logUucl = etaU[,"97.5%"]
# extract the spline values
logUne.row.index <- grep("logUne", results.row.names)
logUne<- results$summary[logUne.row.index,"mean"]
plot.df$spline <- results$summary[logUne.row.index,"mean"]
# add limits to the plot to avoid non-monotone secondary axis problems with extreme values
plot.df$logUi <- pmax(-10 , pmin(20, plot.df$logUi))
plot.df$logU <- pmax(-10 , pmin(20, plot.df$logU ))
plot.df$logUlcl <- pmax(-10 , pmin(20, plot.df$logUlcl ))
plot.df$logUucl <- pmax(-10 , pmin(20, plot.df$logUucl ))
plot.df$spline <- pmax(-10 , pmin(20, plot.df$spline))
fit.plot <- ggplot(data=plot.df, aes_(x=~time))+
ggtitle(title, subtitle="Fitted spline curve with 95% credible intervals for estimated log(U[i])")+
geom_point(aes_(y=~logUi), color="red", shape=1)+ # open circle
xlab("Time Index\nOpen/closed circles - initial and final estimates")+
ylab("log(U[i]) + 95% credible interval")+
geom_point(aes_(y=~logU), color="black", shape=19)+
geom_line (aes_(y=~logU), color="black")+
geom_errorbar(aes_(ymin=~logUlcl, ymax=~logUucl), width=.1)+
geom_line(aes_(y=~spline),linetype="dashed")+
scale_x_continuous(breaks=seq(min(plot.df$time,na.rm=TRUE),max(plot.df$time, na.rm=TRUE),2))+
scale_y_continuous(sec.axis = sec_axis(~ exp(.), name="U + 95% credible interval",
breaks=c(1,10,20,50,
100,200,500,
1000,2000,5000,
10000,20000, 50000,
100000,200000, 500000,
1000000,2000000,5000000,10000000),
labels = scales::comma))
if(save.output.to.files)ggsave(plot=fit.plot, filename=paste(prefix,"-fit.pdf",sep=""), height=6, width=10, units="in")
results$plots$fit.plot <- fit.plot
## acf plot
logitP.plot <- plot_logitP(title=title, time=new.time, n1=new.n1, m2=expanded.m2, u2=new.u2,
logitP.cov=new.logitP.cov, results=results,
trunc.logitP=trunc.logitP)
if(save.output.to.files)ggsave(plot=logitP.plot, filename=paste(prefix,"-logitP.pdf",sep=""), height=6, width=10, units="in")
results$plots$logitP.plot <- logitP.plot
## Look at autocorrelation function for Utot
mcmc.sample <- data.frame(parm="Utot", sample=results$sims.matrix[,"Utot"], stringsAsFactors=FALSE)
acf.Utot.plot <- plot_acf(mcmc.sample)
if(save.output.to.files)ggsave(plot=acf.Utot.plot, filename=paste(prefix,"-Utot-acf.pdf",sep=""), height=4, width=6, units="in")
results$plots$acf.Utot.plot <- acf.Utot.plot
## Look at the shape of the posterior distribution browser()
mcmc.sample1 <- data.frame(parm="Utot", sample=results$sims.matrix[,"Utot"], stringsAsFactors=FALSE)
mcmc.sample2 <- data.frame(parm="Ntot", sample=results$sims.matrix[,"Ntot"], stringsAsFactors=FALSE)
mcmc.sample <- rbind(mcmc.sample1, mcmc.sample2)
post.UNtot.plot <- plot_posterior(mcmc.sample)
post.UNtot.plot
if(save.output.to.files)ggsave(plot=post.UNtot.plot, filename=paste(prefix,"-UNtot-posterior.pdf",sep=""),
height=ifelse(length(unique(mcmc.sample$parm))<=2,4,6), width=6, units="in")
results$plots$post.UNtot.plot <- post.UNtot.plot
## Bayesian P-values
discrep <-PredictivePosterior.TSPNDENPMarkAvail(new.n1, expanded.m2, new.u2,
new.logitP.fixed,
expit(results$sims.list$logitP),
round(results$sims.list$U),
results$sims.list$Theta,
results$sims.list$ma.p,
Delta.max)
gof <- PredictivePosteriorPlot.TSPNDE (discrep)
if(save.output.to.files)ggsave(gof[[1]],filename=paste(prefix,"-GOF.pdf",sep=""), height=8, width=8, units="in", dpi=300 )
results$plots$gof <- gof
# create traceplots of logU, U, and logitP (along with R value) to look for non-convergence
# the plot_trace will return a list of plots (one for each page as needed)
varnames <- names(results$sims.array[1,1,]) # extract the names of the variables
# Trace plots of logitP
trace.plot <- plot_trace(title=title, results=results, parms_to_plot=varnames[grep("^logitP", varnames)])
if(save.output.to.files){
pdf(file=paste(prefix,"-trace-logitP.pdf",sep=""))
plyr::l_ply(trace.plot, function(x){plot(x)})
dev.off()
}
results$plots$trace.logitP.plot <- trace.plot
# now for the traceplots of logU (etaU), Utot, and Ntot
trace.plot <- plot_trace(title=title, results=results, parms_to_plot=varnames[c(grep("Utot",varnames), grep("Ntot",varnames), grep("^etaU", varnames))])
if(save.output.to.files){
pdf(file=paste(prefix,"-trace-logU.pdf",sep=""))
plyr::l_ply(trace.plot, function(x){plot(x)})
dev.off()
}
results$plots$trace.logU.plot <- trace.plot
sink(report, append=TRUE)
## Global summary of results
cat("\n\n*** Summary of MCMC results *** \n\n")
save.max.print <- getOption("max.print")
options(max.print=.Machine$integer.max)
print(results, digits.summary=3)#, max=.Machine$integer.max)
options(max.print=save.max.print)
cat("\n\n*** Alternate DIC computation based on p_D = var(deviance)/2 \n")
results.row.names <- rownames(results$summary)
deviance.row.index<- grep("deviance", results.row.names)
deviance <- results$summary[deviance.row.index,]
p.D <- deviance["sd"]^2/2
dic <- deviance["mean"]+p.D
cat(" D-bar: ", deviance["mean"],"; var(dev): ", deviance["sd"]^2,
"; p.D: ", p.D, "; DIC: ", dic)
## Summary of population sizes
cat("\n\n\n\n*** Summary of Unmarked Population Size ***\n")
temp<- results$summary[ grep("Utot", rownames(results$summary)),]
old.Rhat <- temp["Rhat"]
temp<- formatC(temp, big.mark=",", format="d")
temp["Rhat"] <- formatC(old.Rhat,digits=2,format="f",flag="#")
print(temp, quote=FALSE)
cat("\n\n*** Summary of Total Population Size *** \n")
temp<- results$summary[ grep("Ntot", rownames(results$summary)),]
old.Rhat <- temp["Rhat"]
temp<- formatC(temp, big.mark=",", format="d")
temp["Rhat"] <- formatC(old.Rhat,digits=2,format="f",flag="#")
print(temp, quote=FALSE)
cat("\n\n\n\n*** Summary of Quantiles of Run Timing *** \n")
cat( " This is based on the sample weeks provided and the U[i] values \n")
q <- RunTime(time=time, U=results$sims.list$U, prob=run.prob)
temp <- rbind(apply(q,2,mean), apply(q,2,sd))
rownames(temp) <- c("Mean", "Sd")
print(round(temp,2))
# Add the runtiming to the output object
results$runTime <- temp
cat("\n\n")
cat(paste("*** end of fit *** ", date()))
sink()
# save the report to a files?
if(save.output.to.files)writeLines(stdout, results.filename)
results$report <- stdout
## add some of the raw data to the bugs object for simplicity in referencing it later
results$data <- list( time=time, n1=n1, m2=m2, u2=u2,
jump.after=jump.after,
bad.n1=bad.n1, bad.m2=bad.m2, bad.u2=bad.u2,
logitP.cov=logitP.cov,
version=version, date_run=date(),title=title)
return(results)
} ## end of function
Try the BTSPAS package in your browser
Any scripts or data that you put into this service are public.
BTSPAS documentation built on Oct. 25, 2021, 9:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions TimeStratPetersenNonDiagErrorNPMarkAvail_fit
Documented in TimeStratPetersenNonDiagErrorNPMarkAvail_fit
## 2021-10-23 CJS Added trunc.logitP to deal with extreme values of logitP during plotting
## 2020-12-15 CJS removed sampfrac from body of code
## 2020-12-15 CJS Fixed problem when specifyin u2==NA
## 2020-11-07 CJS Allowed user to specify prior for beta coefficient for logitP
## 2018-12-22 CJS add code to estimate mean movement vector (movep)
## 2018-12-19 CJS sampling fraction deprecated
## 2018-12-14 CJS bayesian p-value plots added
## 2018-12-06 CJS convert report to textConnections
## 2018-12-02 CJS convert trace plots to ggplot
## 2018-12-01 CJS converted acf, posterior plots to ggplot
## 2018-11-30 CJS Fixed problem of epsilon not being right length
## 2018-11-29 CJS Fixed problem of printing large results
## 2018-11-28 CJS remove reference to OpenBugs
## 2014-09-01 CJS conversion to jags
## 2012-08-30 CJS fixed problem in any() and all() in error checking with NAs
## 2011-02-21 CJS changed u2 to new.u2 in code for expanded.m2
## 2011-02-19 CJS First development
#' Wrapper (*_fit) to call the function to fit a Time Stratified Petersen Estimator
#' with NON Diagonal Entries with an non-parametric travel time and fall back
#'
#' Takes the number of marked fish released, the number of recaptures, and the
#' number of unmarked fish and uses Bayesian methods to fit a fit a spline
#' through the population numbers and a hierarchical model for the trap
#' efficiencies over time. The output is written to files and an MCMC object
#' is also created with samples from the posterior.
#'
#' Normally the user makes a call to the *_fit function which then calls the
#' fitting function.
#'
#' Use the \code{\link{TimeStratPetersenDiagError_fit}} function for cases
#' where recaptures take place ONLY in the stratum of release, i.e. the
#' diagonal case.
#'
#' The non-diagonal case fits a log-normal distribution for the travel time.
#' The *NP functions fit a non-parametric distribution for the travel times.
#' The *MarkAvail functions extend the *NP functions to allow for reductions in
#' mark availability because of fall back, immediate tagging mortality, etc.
#'
#' @template title
#' @template prefix
#' @template time
#' @template n1
#' @param m2 A numeric matrix of the number of fish released in stratum [i] and
#' recovered in [j-1] strata later. For example m2[3,5] is the number of
#' marked fish released in stratum 3 and recovered 4 strata later in stratum 7.
#' The first column is the number of marked fish recovered in the stratum of
#' release, i.e. 0 strata later. Use the
#' \code{\link{TimeStratPetersenDiagError_fit}} function for cases where
#' recaptures take place ONLY in the stratum of release, i.e. the diagonal
#' case.
#' @template u2.ND
#' @template sampfrac
#' @template jump.after
#' @template bad.n1
#' @template bad.m2
#' @template bad.u2
#' @template logitP.cov
#' @param logitP.fixed A numeric vector (could be null) of the time strata
#' where the logit(P) would be fixed. Typically, this is used when the capture
#' rates for some strata are 0 and logit(P) is set to -10 for these strata. The
#' fixed values are given in \code{logitP.fixed.values}
#' @param logitP.fixed.values A numerical vector (could be null) of the fixed
#' values for logit(P) at strata given by logitP.fixed. Typically this is used
#' when certain strata have a 0 capture rate and the fixed value is set to -10
#' which on the logit scale gives p[i] essentially 0. Don't specify values such
#' as -50 because numerical problems could occur in JAGS.
#' @param marked_available_n Information, usually from prior studies, on the
#' fraction of marks that will be available. The *_n and *_x are used to create
#' a "binomial" distribution for information on the marked availability. For
#' example, if *_n=66 and *_x=40, then you estimate that about 40/66=61\% of marks
#' are available and 39\% have dropped out or fallen back.
#' @param marked_available_x See marked_available_n
#' @template mcmc-parms
#' @template tauU.alpha.beta
#' @template taueU.alpha.beta
#' @template Delta.max
#' @template tauTT.alpha.beta
#' @template prior.beta.logitP.mean.sd
#' @template tauP.alpha.beta
#' @template run.prob
#' @template debug
#' @template InitialSeed
#' @template save.output.to.files
#' @template trunc.logitP
#'
#' @return An MCMC object with samples from the posterior distribution. A
#' series of graphs and text file are also created in the working directory.
#' @template author
#' @template references
#' @keywords ~models ~smooth
#' @examples
#'
#' ##---- See the vignettes for examples of how to use this package
#'
#' @export TimeStratPetersenNonDiagError_fit
#' @importFrom stats runif var sd
#' @export TimeStratPetersenNonDiagErrorNPMarkAvail_fit
TimeStratPetersenNonDiagErrorNPMarkAvail_fit<- function( title="TSPNDENP-avail", prefix="TSPNDENP-avail-",
time, n1, m2, u2, sampfrac=rep(1,length(u2)), jump.after=NULL,
bad.n1=c(), bad.m2=c(), bad.u2=c(),
logitP.cov=rep(1,length(u2)),
logitP.fixed=NULL, logitP.fixed.values=NULL,
marked_available_n, marked_available_x,
n.chains=3, n.iter=200000, n.burnin=100000, n.sims=2000,
tauU.alpha=1, tauU.beta=.05, taueU.alpha=1, taueU.beta=.05,
prior.beta.logitP.mean = c(logit(sum(m2,na.rm=TRUE)/sum(n1,na.rm=TRUE)),rep(0, ncol(as.matrix(logitP.cov))-1)),
prior.beta.logitP.sd = c(2, rep(10, ncol(as.matrix(logitP.cov))-1)),
tauP.alpha=.001, tauP.beta=.001,
Delta.max=NULL,tauTT.alpha=.1,tauTT.beta=.1,
run.prob=seq(0,1,.1), # what percentiles of run timing are wanted
debug=FALSE, debug2=FALSE,
InitialSeed=ceiling(stats::runif(1,min=0, max=1000000)),
save.output.to.files=TRUE,
trunc.logitP=15) {
## Fit a Time Stratified Petersen model with NON-diagonal entries and with smoothing on U allowing for random error
## and fall back after tagging. This is based on the Skeena River study, where only 40/66 (60%) acoustically tagged fish
## were observed above the canyon spot and hence 40% of tagged fish never migrated forward of their tagging release spot.
## This reduces the number of tagged fish available for recapture and so, if not accounted for, leads to
## positive biases in the estimates of abundance.
## This is the classical stratified Petersen model where the recoveries can take place for this and multiple
## strata later. Transisions of marked fish are modelled non-parametrically.
##
version <- '2021-11-02'
options(width=200)
## Input parameters are
## title - title for the analysis (character string)
## prefix - prefix used for files created with the analysis results
## this should be in standard Window's format, eg. JC-2002-ST-TSPNDE
## to which is appended various suffixes for plots etc (character string)
## time - vector of stratum numbers. For example, 9:38 would indicate that the
## Trinity River system sampled weeks 9 to 38.
## These values are never used in the analysis and only serve as labels for the weeks and for plotting purposes.
## They should be contiguous equally spaced values and be the same length as u2.
## n1, m2, u2 - the input data consisting of fish marked and released, recapture, and unmarked captured
## Note that m2 is a MATRIX. The first column are those fish recaptured in the stratum of release
## and the subsequent columns are those recoveries in later strata.
## This is expanded to the full matrix [i,j] for released released in stratum i and recovered in stratum j
## The vector u2 should be long enough to account for any fish that are recaptured later on
## from releases late in the season. The bottom right diagonal of m2 may be all zeros - that is ok
## Notice that length(u2) can be longer than length(n1)+nrow(m2).
## sampfrac - Deprecated. DO NOT USE.
## jump.after - in some cases, a single spline is still not flexible enough to cope with rapid
## changes in the run curve. For example, in the Trinity River project, a larger
## hatchery release occurs around stratum 14. This is a vector indicating the
## strata AFTER which the spline curve is allowed to jump.
## null or vector of arbitrary length.
## bad.n1 - list of stratum numbers where the value of n1 is suspect.
## bad.m2 - list of stratum numbers where the value of m2 is suspect.
## For example, the capture rate could be extremely low.
## These are set to NA prior to the call to JAGS
## bad.u2 - list of stratum numbers where the value of u2 is suspect.
## logitP.cov - matrix of covariates for logit(P). If the strata times are "missing" some values, an intercept is assumed
## for the first element of the covariance matrix and 0 for the rest of the covariates.
## CAUTION - this MAY not be what you want to do. It is likely best to enter ALL strata
## if you have any covariates. The default, if not specified, is a constant (the mean logit)
## marked_available_n, marked_available_x Information on the movement forward rate. Treat this a binomial data
## which will be applied uniformly over all released. For example, use *_n=60 and *_x=40 to represent
## data from the telemetry study that had 40/60 tagged fish move forward. You can vary the precision of the
## estimate of the marked_availability_fraction by chaning the _n and _x values to create the illusion
## of better and worse information on the availability value.
##
## tauU.alpha, tauU.beta - parameters for the prior on variance in spline coefficients
## taueU.alpha, taueU.beta - parameters for the prior on variance in log(U) around fitted spline
# prior.beta.logitP.mean, prior.beta.logitP.sd - parameters for the prior on mean logit(P)'s [The intercept term]
# The other covariates are assigned priors of a mean of 0 and a sd of 30
## tauP.alpha, tauP.beta - parameters for the prior on 1/var of residual error in logit(P)'s
## Delta.max - maximum transition time for marked fish
## tauTT.alpha, tauTT.beta - parameters of the prior on 1/var of logit continuation ratio for travel times
## run.prob - percentiles of run timing wanted
## debug - if TRUE, then this is a test run with very small MCMC chains run to test out the data
# force input vectors to be vectors. Note that m2 is NOT a vector
time <- as.vector(time)
n1 <- as.vector(n1)
u2 <- as.vector(u2)
sampfrac <- as.vector(sampfrac)
## Do some basic error checking
## 1. Check that length of n1, m2, u2, sampfrac, time are consistent with each other.
## In the non-diagonal case, they don't have to match
if(length(n1)!=nrow(m2))
stop("***** ERROR ***** Length of n1 and number of rows of m2 must be equal. They are:",
length(n1)," ",nrow(u2),"\n")
if(!is.numeric(n1)){
cat("***** ERROR ***** n1 must be numeric. You have:",
paste(n1,collapse=", "),"\n")
return()}
if(any(is.na(n1))){
cat("***** ERROR ***** All values of n1 must not be missing. You have: ",
paste(n1,collapse=", "),"\n")
return()}
if(any(n1 < 0, na.rm=TRUE)){
cat("***** ERROR ***** All values of n1 must be non-negative. You have: ",
paste(n1,collapse=", "),"\n")
return()}
if(stats::var(c(length(u2),length(sampfrac),length(time)))>0)
stop("***** ERROR ***** Lengths of u2, sampfrac, time must all be equal. They are:",
length(u2),' ',length(sampfrac),' ',length(time),"\n")
if(length(logitP.cov) %% length(u2) != 0)
stop("***** ERROR ***** Dimension of covariate vector doesn't match length of u2. They are:",
length(u2),' ',length(logitP.cov),' ',dim(logitP.cov),"\n")
## 2. Check that rowsum of m2<= n1
if(any(apply(m2,1,sum, na.rm=TRUE)>n1))
stop("***** ERROR ***** m2[i,+] must be <= n1[i]. The arguments are \n n1:",paste(n1,collapse=","),
"\n m2:",paste(m2,collapse=","),"\n")
## 3. Elements of bad.m2 and jump.after must belong to time
if(!all(bad.n1 %in% time, na.rm=TRUE))
stop("***** ERROR ***** bad.n1 must be elements of strata identifiers. You entered \n bad.n1:",
paste(bad.n1,collapse=","),"\n Strata identifiers are \n time:",
paste(time,collapse=","),"\n")
if(!all(bad.m2 %in% time, na.rm=TRUE))
stop("***** ERROR ***** bad.m2 must be elements of strata identifiers. You entered \n bad.m2:",
paste(bad.m2,collapse=","),"\n Strata identifiers are \n time:",
paste(time,collapse=","),"\n")
if(!all(bad.u2 %in% time, na.rm=TRUE))
stop("***** ERROR ***** bad.u2 must be elements of strata identifiers. You entered \n bad.u2:",
paste(bad.u2,collapse=","),"\n Strata identifiers are \n time:",
paste(time,collapse=","), "\n")
if(!all(jump.after %in% time, na.rm=TRUE))
stop("***** ERROR ***** jump.after must be elements of strata identifiers. You entered \n jump.after:",
paste(jump.after,collapse=","),"\n Strata identifiers are \n time:",
paste(time,collapse=","), "\n")
# 4. check that index of logitP.fixed belong to time
if(!all(logitP.fixed %in% time, na.rm=TRUE)){
cat("***** ERROR ***** logitP.fixed must be elements of strata identifiers. You entered \n logitP.fixed:",
paste(logitP.fixed,collapse=","),"\n Strata identifiers are \n time:",
paste(time,collapse=","), "\n")
return()}
if(length(logitP.fixed)!=length(logitP.fixed.values)){
cat("***** ERROR ***** Lengths of logitP.fixed and logitP.fixed.values must all be equal. They are:",
length(logitP.fixed),length(logitP.fixed.values),"\n")
return()}
# 5. Check that some basic information on marked availability is given
if( is.na(marked_available_n) | is.na(marked_available_x) | marked_available_x > marked_available_n){
cat("***** ERROR ***** Bad marked_availability values. You entered:",marked_available_n," ",marked_available_x,"\n")
return()}
#7 check that the length of u2
if(length(u2) < length(n1) | length(u2) > (length(n1)+ ncol(m2)-1)){
cat("***** ERROR ***** Length(u2) must between length(n1) and length(n1)+ncol(m2) \n")
return()
}
# Check that that the prior.beta.logitP.mean and prior.beta.logitP.sd length=number of columns of covariates
logitP.cov <- as.matrix(logitP.cov)
if(!is.vector(prior.beta.logitP.mean) | !is.vector(prior.beta.logitP.sd)){
stop("prior.beta.logitP.mean and prior.beta.logitP.sd must be vectors")
}
if(!is.numeric(prior.beta.logitP.mean) | !is.numeric(prior.beta.logitP.sd)){
stop("prior.beta.logitP.mean and prior.beta.logitP.sd must be numeric")
}
if(length(prior.beta.logitP.mean) != ncol(logitP.cov) | length(prior.beta.logitP.sd) != ncol(logitP.cov)){
stop("prior.beta.logitP.mean and prior.beta.logitP.sd must be same length as number columns in covariate matrix")
}
# Deprecation of sampling fraction.
if(any(sampfrac != 1)){
cat("***** ERROR ***** Sampling fraction is deprecated for any values other than 1. DO NOT USE ANYMORE. ")
return()
}
## Define maximum travel time if not supplied by user
if(is.null(Delta.max)) Delta.max <- ncol(m2)-1
## Define output filename
results.filename <- paste(prefix,"-results.txt",sep="")
## Open sink to output file
stdout <- vector('character')
report <- textConnection('stdout', 'wr', local = TRUE)
sink(report)
cat(paste("Time Stratified Petersen with Non-Diagonal recaptures, error in smoothed U, non-parametric modelling of travel times, and incorporating mark availability- ", date()))
cat("\nVersion: ", version)
cat("\n\n", title, "Results \n\n")
## m2(i,+) < n1(i)
cat("*** Raw data *** (padded to match length of u2) \n")
jump.indicator <- rep(' ', length(u2))
jump.indicator[time %in% jump.after]<- '***'
ex.n1 <- c(n1, rep(NA, length(u2)-length(n1)))
ex.m2 <- rbind(m2,matrix(NA, nrow=length(u2)-length(n1), ncol=ncol(m2)))
temp<- data.frame(time=time, n1=ex.n1, m2=ex.m2, u2=u2, logitP.cov=logitP.cov, jump=jump.indicator)
print(temp)
cat("\n\n")
cat("*** Marked Availability prior information *** \n")
cat(" Set marked available n=", marked_available_n," with x=",marked_available_x,"\n\n\n")
## Print information about jump points
cat("Jump point are after strata: ", jump.after)
if(length(jump.after)==0) cat("none - A single spline is fit")
## Print information about delta max
cat("\nMaximum travel time (Delta.max): ",Delta.max)
cat("\nFixed logitP indices are: ", logitP.fixed)
if(length(logitP.fixed)==0) cat("none - NO fixed values")
cat("\nFixed logitP values are: ", logitP.fixed.values)
if(length(logitP.fixed)==0) cat("none - NO fixed values")
## Obtain the Pooled Petersen estimator prior to fixup of bad.n1, bad.m2, and bad.u2 values
cat("\n\n*** Pooled Petersen Estimate prior to fixing bad n1, m2, or u2 values CHECK - CHECK - CHECK - CHECK ***\n\n")
cat(" *** NOT ADJUSTED FOR MARK AVAILABILITY/Dropout/Fallback ***\n")
temp.n1 <- n1
temp.m2 <- m2
temp.u2 <- u2
cat("Total n1=", sum(temp.n1,na.rm=TRUE),"; m2=",sum(temp.m2,na.rm=TRUE),"; u2=",sum(temp.u2,na.rm=TRUE),"\n\n")
pp <- SimplePetersen(sum(temp.n1,na.rm=TRUE), sum(temp.m2,na.rm=TRUE), sum(temp.u2,na.rm=TRUE))
cat("Est U(total) not adjusted for fallback ", format(round(pp$U.est),big.mark=","),
" (SE ", format(round(pp$U.se), big.mark=","), ")\n")
cat("Est N(total) not adjusted for fallback ", format(round(pp$N.est),big.mark=","),
" (SE ", format(round(pp$N.se), big.mark=","), ")\n\n\n")
# adjustment for dropout
dr <- 1-marked_available_x/marked_available_n # dropout probability
se_dr <- sqrt(dr*(1-dr)/marked_available_n)
cat("\n\nAdjusting for fallback/dropout \n")
cat("Estimated dropout is", dr, "with se of ", se_dr, "\n")
# adjust the petersen estimator for drop out including the uncertainty in the dropout probability
pp.adj <- pp
pp.adj$N.est <- pp.adj$N.est * (1-dr)
pp.adj$N.se <- sqrt(pp$N.se^2 * se_dr^2+
pp$N.se^2 * (1-dr)^2 +
pp$N.est^2 * se_dr^2)
pp.adj$U.est <- pp.adj$U.est * (1-dr)
pp.adj$U.se <- sqrt(pp$U.se^2 * se_dr^2+
pp$U.se^2 * (1-dr)^2 +
pp$U.est^2 * se_dr^2)
cat("Est U(total) adjusting for dropout is ", format(round(pp.adj$U.est),big.mark=","),
" (SE ", format(round(pp.adj$U.se), big.mark=","), ")\n")
cat("Est N(total) adjusting for dropout is ", format(round(pp.adj$N.est),big.mark=","),
" (SE ", format(round(pp.adj$N.se), big.mark=","), ")\n\n\n")
## Test if pooling can be done
cat("*** Test if pooled Petersen is allowable. [Check if fraction captured equal] ***\n\n")
select <- (n1>0) & (!is.na(n1)) & (!is.na(apply(m2,1,sum)))
temp.n1 <- n1[select]
temp.m2 <- m2[select,]
test <- TestIfPool( temp.n1, apply(temp.m2,1,sum))
cat("(Large Sample) Chi-square test statistic ", test$chi$statistic," has p-value", test$chi$p.value,"\n\n")
temp <- cbind(time[1:length(n1)][select],test$chi$observed, round(test$chi$expected,1), round(test$chi$residuals^2,1))
colnames(temp) <- c('time','n1-m2*','m2*','E[n1-m2]','E[m2]','X2[n1-m2]','X2[m2]')
print(temp)
cat("\n Be cautious of using this test in cases of small expected values. \n\n")
## Adjust the data for the explicity bad values or other problems
new.time <- time
new.n1 <- n1
new.m2 <- m2
new.u2 <- u2
new.logitP.cov <- logitP.cov
## Set the bad values to missing
new.n1[time[1:length(n1)] %in% c(bad.n1, bad.m2)] <- 0
new.m2[time[1:length(n1)] %in% c(bad.m2, bad.n1)] <- 0
new.u2[time %in% bad.u2] <- NA
## Print out the revised data
cat("\n\n*** Revised data *** \n")
jump.indicator <- rep(' ', length(u2))
jump.indicator[time %in% jump.after]<- '***'
ex.n1 <- c(new.n1, rep(NA, length(new.u2)-length(new.n1)))
ex.m2 <- rbind(new.m2,matrix(NA, nrow=length(new.u2)-length(new.n1), ncol=ncol(new.m2)))
temp<- data.frame(time=new.time, n1=ex.n1, m2=ex.m2, u2=new.u2, logitP.cov=new.logitP.cov,
jump.after=jump.indicator)
print(temp)
cat("\n\n")
cat("*** Marked Availability prior information *** \n")
cat(" Set marked available n=", marked_available_n," with x=",marked_available_x,"\n\n\n")
## The NP analysis does not need the expanded m2 array, but this is
## needed late on. So, we'd better compute it here. The last column
## of this matrix will be the number of individuals from each
## stratum that are not recaptured.
##
expanded.m2 <- matrix(0, nrow=length(new.n1), ncol=length(new.u2)+1)
for(i in 1:length(new.n1)){
expanded.m2[i,1:length(new.u2)] <- c(rep(0,i-1),new.m2[i,],rep(0,length(new.u2)))[1:length(new.u2)]
expanded.m2[i,length(new.u2)+1] <- new.n1[i] - sum(new.m2[i,])
}
cat("*** Expanded m2 array with column sum and u2 ***\n\n")
save.max.print <- getOption("max.print")
options(max.print=.Machine$integer.max)
temp <- rbind(expanded.m2, apply(expanded.m2,2,sum, na.rm=TRUE))
rownames(temp)[nrow(temp)] <- 'Column totals'
temp <- rbind(temp, c(u2, rep(NA, ncol(expanded.m2)-length(u2)) ))
rownames(temp)[nrow(temp)] <- "Untagged (u2)"
temp <- rbind(temp, c(new.u2, rep(NA, ncol(expanded.m2)-length(new.u2)) ))
rownames(temp)[nrow(temp)] <- "Untagged - after fixups"
new.logitP.fixed <- rep(NA, length(new.u2))
new.logitP.fixed[match(logitP.fixed, time)] <- logitP.fixed.values
temp <- rbind(temp, c(new.logitP.fixed, rep(NA, ncol(expanded.m2)-length(new.u2)) ))
rownames(temp)[nrow(temp)] <- "Logit P fixed"
rownames(temp)[1:length(n1)] <- 1:length(n1)
print(temp)
options(max.print=save.max.print)
sink()
# some further checking on u2. Make sure that every columns where there are recoveries has a u2
# browser()
if( (length(u2)+1) <= (ncol(temp)-1)) {
if(any( temp["Column totals", (length(u2)+1):(ncol(temp)-1)] >0)){
cat("***** ERROR ***** Non-zero recoveries and u2 not available at end of experiment??? \n Check above matrix\n")
return()
}
}
sink(report, append=TRUE)
# assign the logitP fixed values etc.
new.logitP.fixed <- rep(NA, length(new.u2))
new.logitP.fixed[match(logitP.fixed, time)] <- logitP.fixed.values
## We do need to add the column of not recaptured counts to the m2
## array.
new.m2 <- cbind(new.m2,new.n1-apply(new.m2,1,sum))
## We construct a prior probability on the P(marks available) based on the information provided
## by assuming a beta prior that would give the binomial results
ma.p.alpha <- marked_available_x
ma.p.beta <- marked_available_n - marked_available_x
## Print out information on the prior distributions used
cat("\n\n*** Information on priors *** \n")
## 0) ma.p = p(marked_availability for subsequent recapture)
cat(" P(marked fish available for subsequent recapture) has beta(",ma.p.alpha,ma.p.beta,") which corresponds \n",
" to a mean of ", round(ma.p.alpha/(ma.p.alpha+ma.p.beta),2),' and sd of ',
round(sqrt(ma.p.alpha*ma.p.beta/(ma.p.alpha+ma.p.beta+1)/(ma.p.alpha+ma.p.beta)**2),3),"\n")
## 1) tauU = (variance of spline coefficients)^-1
cat(" Parameters for prior on tauU (variance in spline coefficients): ", tauU.alpha, tauU.beta,
" which corresponds to a mean/std dev of 1/var of:",
round(tauU.alpha/tauU.beta,2),round(sqrt(tauU.alpha/tauU.beta^2),2),"\n")
## 2) taueU = (variance of errors)^-1
cat(" Parameters for prior on taueU (variance of log(U) about spline): ",taueU.alpha, taueU.beta,
" which corresponds to a mean/std dev of 1/var of:",
round(taueU.alpha/taueU.beta,2),round(sqrt(taueU.alpha/taueU.beta^2),2),"\n")
## 3) prior.beta.logitP = priors for coefficients of covariates for logitP
cat(" Parameters for prior on beta.logitP[1] (intercept) (mean, sd): \n", cbind(round(prior.beta.logitP.mean,3), round(prior.beta.logitP.sd,5)),"\n")
## 4) tauP = (variance of capture probabilites conditional on covariates)^-1
cat(" Parameters for prior on tauP (residual variance of logit(P) after adjusting for covariates): ",tauP.alpha, tauP.beta,
" which corresponds to a mean/std dev of 1/var of:",
round(tauP.alpha/tauP.beta,2),round(sqrt(tauP.alpha/tauP.beta^2),2),"\n")
## 5) tauTT = (variance of continuation ratios for theta)^-1
cat(" Parameters for prior on tauTT (variance of continuation rations for travel times): ",tauTT.alpha, tauTT.beta,
" which corresponds to a mean/std dev of 1/var of:",
round(tauTT.alpha/tauTT.beta,2),round(sqrt(tauTT.alpha/tauTT.beta^2),2),"\n")
cat("\n\nInitial seed for this run is: ",InitialSeed, "\n")
sink()
if (debug2) {
cat("\nprior to formal call to TimeStratPetersenNonDiagError\n")
browser()
}
if (debug)
{results <- TimeStratPetersenNonDiagErrorNPMarkAvail(
title=title, prefix=prefix,
time=new.time, n1=new.n1, m2=new.m2, u2=new.u2,
jump.after=(1:length(u2))[time %in% jump.after],
logitP.cov=new.logitP.cov, logitP.fixed=new.logitP.fixed,
ma.p.alpha, ma.p.beta,
n.chains=3, n.iter=10000, n.burnin=5000, n.sims=500, # set to small values for debugging only
prior.beta.logitP.mean=prior.beta.logitP.mean,
prior.beta.logitP.sd =prior.beta.logitP.sd,
tauU.alpha=tauU.alpha, tauU.beta=tauU.beta,
taueU.alpha=taueU.alpha, taueU.beta=taueU.beta,
Delta.max=Delta.max,tauTT.alpha=tauTT.alpha,tauTT.beta=tauTT.beta,
debug=debug, debug2=debug2, InitialSeed=InitialSeed,
save.output.to.files=save.output.to.files)
} else #notice R syntax requires { before the else
{results <- TimeStratPetersenNonDiagErrorNPMarkAvail(
title=title, prefix=prefix,
time=new.time, n1=new.n1, m2=new.m2, u2=new.u2,
jump.after=(1:length(u2))[time %in% jump.after],
logitP.cov=new.logitP.cov, logitP.fixed=new.logitP.fixed,
ma.p.alpha, ma.p.beta,
n.chains=n.chains, n.iter=n.iter, n.burnin=n.burnin, n.sims=n.sims,
prior.beta.logitP.mean=prior.beta.logitP.mean,
prior.beta.logitP.sd =prior.beta.logitP.sd,
tauU.alpha=tauU.alpha, tauU.beta=tauU.beta,
taueU.alpha=taueU.alpha, taueU.beta=taueU.beta,
Delta.max=Delta.max,tauTT.alpha=tauTT.alpha,tauTT.beta=tauTT.beta,
debug=debug, debug2=debug2, InitialSeed=InitialSeed,
save.output.to.files=save.output.to.files)
}
results$PP$using.all.data <-pp
results$PP$using.all.data.fallback <- pp.adj
results$dr <- data.frame(est=dr, se=se_dr)
## Now to create the various summary tables of the results
Nstrata.rel <- length(n1)
Nstrata.cap <- ncol(expanded.m2) -1 ## don't forget that last column of m2 is number of fish never seen
# A plot of the observered log(U) on the log scale, and the final mean log(U)
plot.df <- data.frame(time =new.time)
plot.df$logUi <-log( c((new.u2[1:Nstrata.rel]+1)*(new.n1+2)/(apply(expanded.m2[,1:Nstrata.cap],1,sum)+1), rep(NA, length(u2)-Nstrata.rel)))
# extract the fitted U values
results.row.names <- rownames(results$summary)
etaU.row.index <- grep("etaU", results.row.names)
etaU<- results$summary[etaU.row.index,]
plot.df$logU = etaU[,"mean"]
plot.df$logUlcl = etaU[,"2.5%"]
plot.df$logUucl = etaU[,"97.5%"]
# extract the spline values
logUne.row.index <- grep("logUne", results.row.names)
logUne<- results$summary[logUne.row.index,"mean"]
plot.df$spline <- results$summary[logUne.row.index,"mean"]
# add limits to the plot to avoid non-monotone secondary axis problems with extreme values
plot.df$logUi <- pmax(-10 , pmin(20, plot.df$logUi))
plot.df$logU <- pmax(-10 , pmin(20, plot.df$logU ))
plot.df$logUlcl <- pmax(-10 , pmin(20, plot.df$logUlcl ))
plot.df$logUucl <- pmax(-10 , pmin(20, plot.df$logUucl ))
plot.df$spline <- pmax(-10 , pmin(20, plot.df$spline))
fit.plot <- ggplot(data=plot.df, aes_(x=~time))+
ggtitle(title, subtitle="Fitted spline curve with 95% credible intervals for estimated log(U[i])")+
geom_point(aes_(y=~logUi), color="red", shape=1)+ # open circle
xlab("Time Index\nOpen/closed circles - initial and final estimates")+
ylab("log(U[i]) + 95% credible interval")+
geom_point(aes_(y=~logU), color="black", shape=19)+
geom_line (aes_(y=~logU), color="black")+
geom_errorbar(aes_(ymin=~logUlcl, ymax=~logUucl), width=.1)+
geom_line(aes_(y=~spline),linetype="dashed")+
scale_x_continuous(breaks=seq(min(plot.df$time,na.rm=TRUE),max(plot.df$time, na.rm=TRUE),2))+
scale_y_continuous(sec.axis = sec_axis(~ exp(.), name="U + 95% credible interval",
breaks=c(1,10,20,50,
100,200,500,
1000,2000,5000,
10000,20000, 50000,
100000,200000, 500000,
1000000,2000000,5000000,10000000),
labels = scales::comma))
if(save.output.to.files)ggsave(plot=fit.plot, filename=paste(prefix,"-fit.pdf",sep=""), height=6, width=10, units="in")
results$plots$fit.plot <- fit.plot
## acf plot
logitP.plot <- plot_logitP(title=title, time=new.time, n1=new.n1, m2=expanded.m2, u2=new.u2,
logitP.cov=new.logitP.cov, results=results,
trunc.logitP=trunc.logitP)
if(save.output.to.files)ggsave(plot=logitP.plot, filename=paste(prefix,"-logitP.pdf",sep=""), height=6, width=10, units="in")
results$plots$logitP.plot <- logitP.plot
## Look at autocorrelation function for Utot
mcmc.sample <- data.frame(parm="Utot", sample=results$sims.matrix[,"Utot"], stringsAsFactors=FALSE)
acf.Utot.plot <- plot_acf(mcmc.sample)
if(save.output.to.files)ggsave(plot=acf.Utot.plot, filename=paste(prefix,"-Utot-acf.pdf",sep=""), height=4, width=6, units="in")
results$plots$acf.Utot.plot <- acf.Utot.plot
## Look at the shape of the posterior distribution browser()
mcmc.sample1 <- data.frame(parm="Utot", sample=results$sims.matrix[,"Utot"], stringsAsFactors=FALSE)
mcmc.sample2 <- data.frame(parm="Ntot", sample=results$sims.matrix[,"Ntot"], stringsAsFactors=FALSE)
mcmc.sample <- rbind(mcmc.sample1, mcmc.sample2)
post.UNtot.plot <- plot_posterior(mcmc.sample)
post.UNtot.plot
if(save.output.to.files)ggsave(plot=post.UNtot.plot, filename=paste(prefix,"-UNtot-posterior.pdf",sep=""),
height=ifelse(length(unique(mcmc.sample$parm))<=2,4,6), width=6, units="in")
results$plots$post.UNtot.plot <- post.UNtot.plot
## Bayesian P-values
discrep <-PredictivePosterior.TSPNDENPMarkAvail(new.n1, expanded.m2, new.u2,
new.logitP.fixed,
expit(results$sims.list$logitP),
round(results$sims.list$U),
results$sims.list$Theta,
results$sims.list$ma.p,
Delta.max)
gof <- PredictivePosteriorPlot.TSPNDE (discrep)
if(save.output.to.files)ggsave(gof[[1]],filename=paste(prefix,"-GOF.pdf",sep=""), height=8, width=8, units="in", dpi=300 )
results$plots$gof <- gof
# create traceplots of logU, U, and logitP (along with R value) to look for non-convergence
# the plot_trace will return a list of plots (one for each page as needed)
varnames <- names(results$sims.array[1,1,]) # extract the names of the variables
# Trace plots of logitP
trace.plot <- plot_trace(title=title, results=results, parms_to_plot=varnames[grep("^logitP", varnames)])
if(save.output.to.files){
pdf(file=paste(prefix,"-trace-logitP.pdf",sep=""))
plyr::l_ply(trace.plot, function(x){plot(x)})
dev.off()
}
results$plots$trace.logitP.plot <- trace.plot
# now for the traceplots of logU (etaU), Utot, and Ntot
trace.plot <- plot_trace(title=title, results=results, parms_to_plot=varnames[c(grep("Utot",varnames), grep("Ntot",varnames), grep("^etaU", varnames))])
if(save.output.to.files){
pdf(file=paste(prefix,"-trace-logU.pdf",sep=""))
plyr::l_ply(trace.plot, function(x){plot(x)})
dev.off()
}
results$plots$trace.logU.plot <- trace.plot
sink(report, append=TRUE)
## Global summary of results
cat("\n\n*** Summary of MCMC results *** \n\n")
save.max.print <- getOption("max.print")
options(max.print=.Machine$integer.max)
print(results, digits.summary=3)#, max=.Machine$integer.max)
options(max.print=save.max.print)
cat("\n\n*** Alternate DIC computation based on p_D = var(deviance)/2 \n")
results.row.names <- rownames(results$summary)
deviance.row.index<- grep("deviance", results.row.names)
deviance <- results$summary[deviance.row.index,]
p.D <- deviance["sd"]^2/2
dic <- deviance["mean"]+p.D
cat(" D-bar: ", deviance["mean"],"; var(dev): ", deviance["sd"]^2,
"; p.D: ", p.D, "; DIC: ", dic)
## Summary of population sizes
cat("\n\n\n\n*** Summary of Unmarked Population Size ***\n")
temp<- results$summary[ grep("Utot", rownames(results$summary)),]
old.Rhat <- temp["Rhat"]
temp<- formatC(temp, big.mark=",", format="d")
temp["Rhat"] <- formatC(old.Rhat,digits=2,format="f",flag="#")
print(temp, quote=FALSE)
cat("\n\n*** Summary of Total Population Size *** \n")
temp<- results$summary[ grep("Ntot", rownames(results$summary)),]
old.Rhat <- temp["Rhat"]
temp<- formatC(temp, big.mark=",", format="d")
temp["Rhat"] <- formatC(old.Rhat,digits=2,format="f",flag="#")
print(temp, quote=FALSE)
cat("\n\n\n\n*** Summary of Quantiles of Run Timing *** \n")
cat( " This is based on the sample weeks provided and the U[i] values \n")
q <- RunTime(time=time, U=results$sims.list$U, prob=run.prob)
temp <- rbind(apply(q,2,mean), apply(q,2,sd))
rownames(temp) <- c("Mean", "Sd")
print(round(temp,2))
# Add the runtiming to the output object
results$runTime <- temp
cat("\n\n")
cat(paste("*** end of fit *** ", date()))
sink()
# save the report to a files?
if(save.output.to.files)writeLines(stdout, results.filename)
results$report <- stdout
## add some of the raw data to the bugs object for simplicity in referencing it later
results$data <- list( time=time, n1=n1, m2=m2, u2=u2,
jump.after=jump.after,
bad.n1=bad.n1, bad.m2=bad.m2, bad.u2=bad.u2,
logitP.cov=logitP.cov,
version=version, date_run=date(),title=title)
return(results)
} ## end of function
Try the BTSPAS package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.