Nothing
R/fitRMU_MHmcmc_p.R
In phenology: Tools to Manage a Parametric Function that Describes Phenology and More
Defines functions
fitRMU_MHmcmc_p
Documented in
fitRMU_MHmcmc_p
#' fitRMU_MHmcmc_p generates set of parameters to be used with fitRMU_MHmcmc()
#' @title Generates set of parameters to be used with fitRMU_MHmcmc()
#' @author Marc Girondot
#' @return A matrix with the parameters
#' @param result An object obtained after a fitRMU() fit
#' @param density Preset of density; can be dnorm, dunif, or dgamma
#' @param accept If TRUE, does not wait for user interaction
#' @family Fill gaps in RMU
#' @description Interactive or automatic script used to generate set of parameters to be
#' used with fitRMU_MHmcmc().\cr
#' If density="dgamma" is used, a uniform distribution is used for r, as
#' r can be negative.
#' @examples
#' \dontrun{
#' library("phenology")
#' RMU.name.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana",
#' "Galibi.Suriname",
#' "Irakumpapy.French.Guiana"),
#' se=c("se_Yalimapo.French.Guiana",
#' "se_Galibi.Suriname",
#' "se_Irakumpapy.French.Guiana"))
#' data.AtlanticW <- data.frame(Year=c(1990:2000),
#' Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA,
#' 6542, 5678, 1243, NA, 1566, 1566),
#' se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA,
#' 230, 129, 167, NA, 145, 20),
#' Galibi.Suriname=c(276, 275, 290, NA, 267,
#' 542, 678, NA, 243, 156, 123),
#' se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2,
#' 4.3, 2.3, NA, 10.3, 10.1, 8.9),
#' Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA,
#' 3542, 2678, 243, NA, 566, 566),
#' se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA,
#' 130, 29, 67, NA, 15, 20))
#'
#' cst <- fitRMU(data=data.AtlanticW, RMU.name=RMU.name.AtlanticW,
#' colname.year="Year", model.trend="Constant",
#' model.SD="Zero")
#' pMCMC <- fitRMU_MHmcmc_p(result=cst, accept=TRUE)
#' }
#' @export
fitRMU_MHmcmc_p <- function(result=stop("An output from fitRMU() must be provided"),
density="dunif", accept=FALSE) {
if (!inherits(result, "fitRMU")) {
stop("An output from fitRMU() must be provided")
}
# rownames(parametersMCMC)<-names(par)
if (length(density) != length(result$par)) {
density <- rep(density, length(result$par))[seq_along(result$par)]
}
parametersMCMC <- data.frame(Density=character(), Prior1=numeric(), Prior2=numeric(),
SDProp=numeric(), Min=numeric(), Max=numeric(), Init=numeric(), stringsAsFactors = FALSE)
for (indice.par in seq_along(result$par)) {
par <- result$par[indice.par]
nm <- names(par)
par <- unname(par)
SE <- unname(abs(result$SE[indice.par]))
# 9/4/2022
SE <- ifelse(is.na(SE), abs(par/10), SE)
# if (is.na(SE)) SE <- abs(par/10)
if (nm=="r") {
if (density[indice.par] == "dnorm") {
parametersMCMC_ec <- data.frame(Density="dnorm", Prior1=par, Prior2=SE,
SDProp=2, Min=min(-1, par-1),
Max=max(1, par+1), Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
if (density[indice.par] == "dgamma") {
warning("Gamma distribution is replaced by uniform distribution for r.")
}
parametersMCMC_ec <- data.frame(Density="dunif", Prior1=-2, Prior2=+2,
SDProp=2, Min=min(-2, par-2),
Max=max(2, par+2), Init=par,
row.names = nm, stringsAsFactors = FALSE)
}
}
if (substr(nm, 1, 2)=="T_") {
if (density[indice.par] == "dnorm") {
parametersMCMC_ec <- data.frame(Density="dnorm",
Prior1=par, Prior2=SE,
SDProp=par,
Min=0,
Max=par*2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
if (density[indice.par] == "dgamma") {
# Prior1 est shape=a
# Prior2 est rate=1/s
# Moyenne=a*s
# Var=a*s^2
# a=Moyenne/s=Moyenne*r
# Var=Moyenne/s*s^2
# Var=Moyenne*s
# s=Var/Moyenne
# r=Moyenne/Var
# a=Moyenne*r
r <- par/(SE^2)
a <- par*r
parametersMCMC_ec <- data.frame(Density="dgamma",
Prior1=a, Prior2=r,
SDProp=par,
Min=0,
Max=par * 2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
parametersMCMC_ec <- data.frame(Density="dunif",
Prior1=0, Prior2=par*2,
SDProp=par,
Min=0,
Max=par*2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
}
}
}
if (substr(nm, 1, 3)=="SD_") {
if (density[indice.par] == "dnorm") {
parametersMCMC_ec <- data.frame(Density="dnorm", Prior1=par, Prior2=SE,
SDProp=2, Min=0.01, Max=max(20, par+20), Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
if (density[indice.par] == "dgamma") {
# Prior1 est shape=a
# Prior2 est rate=1/s
# Moyenne=a*s
# Var=a*s^2
# a=Moyenne/s=Moyenne*r
# Var=Moyenne/s*s^2
# Var=Moyenne*s
# s=Var/Moyenne
# r=Moyenne/Var
# a=Moyenne*r
r <- par/(SE^2)
a <- par*r
parametersMCMC_ec <- data.frame(Density="dgamma",
Prior1=a, Prior2=r,
SDProp=par,
Min=0,
Max=par * 2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
parametersMCMC_ec <- data.frame(Density="dunif", Prior1=1E-5, Prior2=par+20,
SDProp=2, Min=1E-5, Max=max(20, par+20),
Init=par,
row.names = nm, stringsAsFactors = FALSE)
}
}
}
if (substr(nm, 1, 4)=="aSD_") {
if (density[indice.par] == "dnorm") {
parametersMCMC_ec <- data.frame(Density="dnorm",
Prior1=par, Prior2=SE,
SDProp=2, Min=1E-5, Max=max(20, par+20),
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
if (density[indice.par] == "dgamma") {
# Prior1 est shape=a
# Prior2 est rate=1/s
# Moyenne=a*s
# Var=a*s^2
# a=Moyenne/s=Moyenne*r
# Var=Moyenne/s*s^2
# Var=Moyenne*s
# s=Var/Moyenne
# r=Moyenne/Var
# a=Moyenne*r
r <- par/(SE^2)
a <- par*r
parametersMCMC_ec <- data.frame(Density="dgamma",
Prior1=a, Prior2=r,
SDProp=par,
Min=0,
Max=par * 2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
parametersMCMC_ec <- data.frame(Density="dunif",
Prior1=1E-5, Prior2=max(20, par+20),
SDProp=2, Min=1E-5, Max=max(20, par+20),
Init=par,
row.names = nm, stringsAsFactors = FALSE)
}
}
}
if (substr(nm, 1, 1)=="a" & substr(nm, 3, 3)=="_") {
if (density[indice.par] == "dnorm") {
parametersMCMC_ec <- data.frame(Density="dnorm",
Prior1=par, Prior2=SE,
SDProp=2, Min=0,
Max=max(20, par*2), Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
if (density[indice.par] == "dgamma") {
# Prior1 est shape=a
# Prior2 est rate=1/s
# Moyenne=a*s
# Var=a*s^2
# a=Moyenne/s=Moyenne*r
# Var=Moyenne/s*s^2
# Var=Moyenne*s
# s=Var/Moyenne
# r=Moyenne/Var
# a=Moyenne*r
r <- par/(SE^2)
a <- par*r
parametersMCMC_ec <- data.frame(Density="dgamma",
Prior1=a, Prior2=r,
SDProp=par,
Min=0,
Max=par * 2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
parametersMCMC_ec <- data.frame(Density="dunif",
Prior1=par/2,
Prior2=par*2,
SDProp=2, Min=par/2,
Max=par*2, Init=par,
row.names = nm, stringsAsFactors = FALSE)
}
}
}
parametersMCMC <- rbind(parametersMCMC, parametersMCMC_ec)
}
parameters <- parametersMCMC
if (accept) {
return(parameters)
} else {
repeat {
cat("Proposition:\n")
print(parameters)
cat("Name of the parameter to change or Enter to quit:\n")
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)==0) f <- "q"
if (f=="q") {
return(parameters)
} else {
variable <- which(f==names(par))
if (length(variable)==0) {
cat("The parameter does not exist:\n")
} else {
print(variable)
cat(paste("Change for the parameter ",names(par)[variable],":\n",sep=""))
cat(paste("Distribution of the prior (Enter for default ",parameters[variable, "Density"], "):", sep=""))
density<-scan(nmax=1, quiet=TRUE, what=character())
if (length(density)!=0) { parameters[variable, "Density"] <- density } else { density <- parameters[variable, "Density"] }
if (density == "dunif") {
cat(paste("Distribution of the prior, Minimum (Enter for default ",parameters[variable, "Prior1"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior1"] <- f
cat(paste("Distribution of the prior, Maximum (Enter for default ",parameters[variable, "Prior2"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior2"] <- f
} else {
if (density == "dnorm") {
cat(paste("Distribution of the prior, Mean (Enter for default ",parameters[variable, "Prior1"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior1"] <- f
cat(paste("Distribution of the prior, Standard deviation (Enter for default ",parameters[variable, "Prior2"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior2"] <- f
} else {
cat(paste("Distribution of the prior, value 1 (Enter for default ",parameters[variable, "Prior1"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior1"] <- f
cat(paste("Distribution of the prior, value 2 (Enter for default ",parameters[variable, "Prior2"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior2"] <- f
}
}
cat(paste("SD of new proposition (Enter for default ",parameters[variable, "SDProp"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "SDProp"] <- f
cat(paste("Minimum for the parameter (default ",parameters[variable, "Min"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Min"] <- f
cat(paste("Maximum for the parameter (Enter for default ",parameters[variable, "Max"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Max"] <- f
cat(paste("Initial value (Enter for default ",parameters[variable, "Init"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Init"] <- f
}
}
}
}
for (i in 1:nrow(parameters)) {
if (parameters[i, "Density"]=="dunif") {
mn <- max(as.numeric(parameters[i, "Prior1"]), as.numeric(parameters[i, "Min"]))
} else {
mn <- as.numeric(parameters[i, "Min"])
mx <- as.numeric(parameters[i, "Max"])
}
if (findInterval(as.numeric(parameters[i, "Init"]), c(mn, mx)) != 1) {
parameters[i, "Init"] <- as.character(mn+(mx-mn)/2)
warning(paste("Initial value for parameter ", rownames(parameters)[i], " was out of range; It is corrected. Check it."))
}
}
}
Try the phenology package in your browser
Any scripts or data that you put into this service are public.
phenology documentation built on Sept. 11, 2024, 6:07 p.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 fitRMU_MHmcmc_p
Documented in fitRMU_MHmcmc_p
#' fitRMU_MHmcmc_p generates set of parameters to be used with fitRMU_MHmcmc()
#' @title Generates set of parameters to be used with fitRMU_MHmcmc()
#' @author Marc Girondot
#' @return A matrix with the parameters
#' @param result An object obtained after a fitRMU() fit
#' @param density Preset of density; can be dnorm, dunif, or dgamma
#' @param accept If TRUE, does not wait for user interaction
#' @family Fill gaps in RMU
#' @description Interactive or automatic script used to generate set of parameters to be
#' used with fitRMU_MHmcmc().\cr
#' If density="dgamma" is used, a uniform distribution is used for r, as
#' r can be negative.
#' @examples
#' \dontrun{
#' library("phenology")
#' RMU.name.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana",
#' "Galibi.Suriname",
#' "Irakumpapy.French.Guiana"),
#' se=c("se_Yalimapo.French.Guiana",
#' "se_Galibi.Suriname",
#' "se_Irakumpapy.French.Guiana"))
#' data.AtlanticW <- data.frame(Year=c(1990:2000),
#' Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA,
#' 6542, 5678, 1243, NA, 1566, 1566),
#' se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA,
#' 230, 129, 167, NA, 145, 20),
#' Galibi.Suriname=c(276, 275, 290, NA, 267,
#' 542, 678, NA, 243, 156, 123),
#' se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2,
#' 4.3, 2.3, NA, 10.3, 10.1, 8.9),
#' Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA,
#' 3542, 2678, 243, NA, 566, 566),
#' se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA,
#' 130, 29, 67, NA, 15, 20))
#'
#' cst <- fitRMU(data=data.AtlanticW, RMU.name=RMU.name.AtlanticW,
#' colname.year="Year", model.trend="Constant",
#' model.SD="Zero")
#' pMCMC <- fitRMU_MHmcmc_p(result=cst, accept=TRUE)
#' }
#' @export
fitRMU_MHmcmc_p <- function(result=stop("An output from fitRMU() must be provided"),
density="dunif", accept=FALSE) {
if (!inherits(result, "fitRMU")) {
stop("An output from fitRMU() must be provided")
}
# rownames(parametersMCMC)<-names(par)
if (length(density) != length(result$par)) {
density <- rep(density, length(result$par))[seq_along(result$par)]
}
parametersMCMC <- data.frame(Density=character(), Prior1=numeric(), Prior2=numeric(),
SDProp=numeric(), Min=numeric(), Max=numeric(), Init=numeric(), stringsAsFactors = FALSE)
for (indice.par in seq_along(result$par)) {
par <- result$par[indice.par]
nm <- names(par)
par <- unname(par)
SE <- unname(abs(result$SE[indice.par]))
# 9/4/2022
SE <- ifelse(is.na(SE), abs(par/10), SE)
# if (is.na(SE)) SE <- abs(par/10)
if (nm=="r") {
if (density[indice.par] == "dnorm") {
parametersMCMC_ec <- data.frame(Density="dnorm", Prior1=par, Prior2=SE,
SDProp=2, Min=min(-1, par-1),
Max=max(1, par+1), Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
if (density[indice.par] == "dgamma") {
warning("Gamma distribution is replaced by uniform distribution for r.")
}
parametersMCMC_ec <- data.frame(Density="dunif", Prior1=-2, Prior2=+2,
SDProp=2, Min=min(-2, par-2),
Max=max(2, par+2), Init=par,
row.names = nm, stringsAsFactors = FALSE)
}
}
if (substr(nm, 1, 2)=="T_") {
if (density[indice.par] == "dnorm") {
parametersMCMC_ec <- data.frame(Density="dnorm",
Prior1=par, Prior2=SE,
SDProp=par,
Min=0,
Max=par*2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
if (density[indice.par] == "dgamma") {
# Prior1 est shape=a
# Prior2 est rate=1/s
# Moyenne=a*s
# Var=a*s^2
# a=Moyenne/s=Moyenne*r
# Var=Moyenne/s*s^2
# Var=Moyenne*s
# s=Var/Moyenne
# r=Moyenne/Var
# a=Moyenne*r
r <- par/(SE^2)
a <- par*r
parametersMCMC_ec <- data.frame(Density="dgamma",
Prior1=a, Prior2=r,
SDProp=par,
Min=0,
Max=par * 2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
parametersMCMC_ec <- data.frame(Density="dunif",
Prior1=0, Prior2=par*2,
SDProp=par,
Min=0,
Max=par*2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
}
}
}
if (substr(nm, 1, 3)=="SD_") {
if (density[indice.par] == "dnorm") {
parametersMCMC_ec <- data.frame(Density="dnorm", Prior1=par, Prior2=SE,
SDProp=2, Min=0.01, Max=max(20, par+20), Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
if (density[indice.par] == "dgamma") {
# Prior1 est shape=a
# Prior2 est rate=1/s
# Moyenne=a*s
# Var=a*s^2
# a=Moyenne/s=Moyenne*r
# Var=Moyenne/s*s^2
# Var=Moyenne*s
# s=Var/Moyenne
# r=Moyenne/Var
# a=Moyenne*r
r <- par/(SE^2)
a <- par*r
parametersMCMC_ec <- data.frame(Density="dgamma",
Prior1=a, Prior2=r,
SDProp=par,
Min=0,
Max=par * 2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
parametersMCMC_ec <- data.frame(Density="dunif", Prior1=1E-5, Prior2=par+20,
SDProp=2, Min=1E-5, Max=max(20, par+20),
Init=par,
row.names = nm, stringsAsFactors = FALSE)
}
}
}
if (substr(nm, 1, 4)=="aSD_") {
if (density[indice.par] == "dnorm") {
parametersMCMC_ec <- data.frame(Density="dnorm",
Prior1=par, Prior2=SE,
SDProp=2, Min=1E-5, Max=max(20, par+20),
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
if (density[indice.par] == "dgamma") {
# Prior1 est shape=a
# Prior2 est rate=1/s
# Moyenne=a*s
# Var=a*s^2
# a=Moyenne/s=Moyenne*r
# Var=Moyenne/s*s^2
# Var=Moyenne*s
# s=Var/Moyenne
# r=Moyenne/Var
# a=Moyenne*r
r <- par/(SE^2)
a <- par*r
parametersMCMC_ec <- data.frame(Density="dgamma",
Prior1=a, Prior2=r,
SDProp=par,
Min=0,
Max=par * 2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
parametersMCMC_ec <- data.frame(Density="dunif",
Prior1=1E-5, Prior2=max(20, par+20),
SDProp=2, Min=1E-5, Max=max(20, par+20),
Init=par,
row.names = nm, stringsAsFactors = FALSE)
}
}
}
if (substr(nm, 1, 1)=="a" & substr(nm, 3, 3)=="_") {
if (density[indice.par] == "dnorm") {
parametersMCMC_ec <- data.frame(Density="dnorm",
Prior1=par, Prior2=SE,
SDProp=2, Min=0,
Max=max(20, par*2), Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
if (density[indice.par] == "dgamma") {
# Prior1 est shape=a
# Prior2 est rate=1/s
# Moyenne=a*s
# Var=a*s^2
# a=Moyenne/s=Moyenne*r
# Var=Moyenne/s*s^2
# Var=Moyenne*s
# s=Var/Moyenne
# r=Moyenne/Var
# a=Moyenne*r
r <- par/(SE^2)
a <- par*r
parametersMCMC_ec <- data.frame(Density="dgamma",
Prior1=a, Prior2=r,
SDProp=par,
Min=0,
Max=par * 2,
Init=par,
row.names = nm, stringsAsFactors = FALSE)
} else {
parametersMCMC_ec <- data.frame(Density="dunif",
Prior1=par/2,
Prior2=par*2,
SDProp=2, Min=par/2,
Max=par*2, Init=par,
row.names = nm, stringsAsFactors = FALSE)
}
}
}
parametersMCMC <- rbind(parametersMCMC, parametersMCMC_ec)
}
parameters <- parametersMCMC
if (accept) {
return(parameters)
} else {
repeat {
cat("Proposition:\n")
print(parameters)
cat("Name of the parameter to change or Enter to quit:\n")
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)==0) f <- "q"
if (f=="q") {
return(parameters)
} else {
variable <- which(f==names(par))
if (length(variable)==0) {
cat("The parameter does not exist:\n")
} else {
print(variable)
cat(paste("Change for the parameter ",names(par)[variable],":\n",sep=""))
cat(paste("Distribution of the prior (Enter for default ",parameters[variable, "Density"], "):", sep=""))
density<-scan(nmax=1, quiet=TRUE, what=character())
if (length(density)!=0) { parameters[variable, "Density"] <- density } else { density <- parameters[variable, "Density"] }
if (density == "dunif") {
cat(paste("Distribution of the prior, Minimum (Enter for default ",parameters[variable, "Prior1"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior1"] <- f
cat(paste("Distribution of the prior, Maximum (Enter for default ",parameters[variable, "Prior2"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior2"] <- f
} else {
if (density == "dnorm") {
cat(paste("Distribution of the prior, Mean (Enter for default ",parameters[variable, "Prior1"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior1"] <- f
cat(paste("Distribution of the prior, Standard deviation (Enter for default ",parameters[variable, "Prior2"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior2"] <- f
} else {
cat(paste("Distribution of the prior, value 1 (Enter for default ",parameters[variable, "Prior1"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior1"] <- f
cat(paste("Distribution of the prior, value 2 (Enter for default ",parameters[variable, "Prior2"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Prior2"] <- f
}
}
cat(paste("SD of new proposition (Enter for default ",parameters[variable, "SDProp"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "SDProp"] <- f
cat(paste("Minimum for the parameter (default ",parameters[variable, "Min"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Min"] <- f
cat(paste("Maximum for the parameter (Enter for default ",parameters[variable, "Max"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Max"] <- f
cat(paste("Initial value (Enter for default ",parameters[variable, "Init"], "):", sep=""))
f<-scan(nmax=1, quiet=TRUE, what=character())
if (length(f)!=0) parameters[variable, "Init"] <- f
}
}
}
}
for (i in 1:nrow(parameters)) {
if (parameters[i, "Density"]=="dunif") {
mn <- max(as.numeric(parameters[i, "Prior1"]), as.numeric(parameters[i, "Min"]))
} else {
mn <- as.numeric(parameters[i, "Min"])
mx <- as.numeric(parameters[i, "Max"])
}
if (findInterval(as.numeric(parameters[i, "Init"]), c(mn, mx)) != 1) {
parameters[i, "Init"] <- as.character(mn+(mx-mn)/2)
warning(paste("Initial value for parameter ", rownames(parameters)[i], " was out of range; It is corrected. Check it."))
}
}
}
Try the phenology 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.