R/OptimFunction_Single_covariate.R
In wpeterman/ResistanceGA: Optimize resistance surfaces using genetic algorithms
Defines functions
Resistance.Opt_single.cov
#' Optimize resistance surfaces individually with more than one predictor variable
#'
#' Optimize all resistance surfaces that are located in the same directory individually. This optimization function is designed to be called from GA
#'
#' @param PARM Parameters to transform conintuous surface or resistance values of categorical surface. A vector with parameters specified in the order of resistance surfaces.These values are selected during optimization if called within GA function.
#' @param Resistance Resistance surface to be optimized. This should be an R raster object. If not specified, the function will attempt to find the a resistance surface from \code{GA.inputs}
#' @param CS.inputs Object created from running \code{\link[ResistanceGA]{CS.prep}} function. Defined if optimizing using CIRCUITSCAPE
#' @param gdist.inputs Object created from running \code{\link[ResistanceGA]{gdist.prep}} function. Defined if optimizing using gdistance
#' @param jl.inputs Object created from running \code{\link[ResistanceGA]{jl.prep}} function. Defined if optimizing using CIRCUITSCAPE run in Julia
#' @param GA.inputs Object created from running \code{\link[ResistanceGA]{GA.prep}} function
#' @param Min.Max Define whether the optimization function should minimized ('min') or maximized ('max'). Default in 'max'
#' @param iter A counter for the number of surfaces that will be optimized
#' @param quiet Logical, if TRUE AICc and iteration duration will not be printed to the screen at the completion of each iteration.
#' @return Objective function value (either AIC, R2, or LL) from mixed effect model
#' @noRd
#' @author Bill Peterman <Peterman.73@@osu.edu>
Resistance.Opt_single.cov <-
function(PARM,
Resistance,
CS.inputs = NULL,
gdist.inputs = NULL,
jl.inputs = NULL,
GA.inputs,
Min.Max = 'max',
iter = NULL,
quiet = FALSE) {
t1 <- proc.time()[3]
if(is.null(iter)) {
iter <- 1
}
method <- GA.inputs$method
EXPORT.dir <- GA.inputs$Write.dir
select.trans <- GA.inputs$select.trans
R <- r <- Resistance
keep <- 1 # Indicator to keep or drop transformation
if (GA.inputs$surface.type[iter] == "cat") {
PARM <- PARM / min(PARM)
if(max(PARM) > GA.inputs$max.cat){
PARM <- SCALE.vector(PARM, 1, GA.inputs$max.cat)
}
parm <- PARM
df <-
data.frame(id = unique(r), PARM) # Data frame with original raster values and replacement values
# browser()
r <- subs(r, df)
} else {
## Else continuous surface
r <- SCALE(r, 0, 10)
# Set equation for continuous surface
equation <- floor(PARM[1]) # Parameter can range from 1-9.99
# Read in resistance surface to be optimized
SHAPE <- (PARM[2])
Max.SCALE <- (PARM[3])
if (equation %in% select.trans[[iter]]) {
# Apply specified transformation
rick.eq <- (equation == 2 ||
equation == 4 || equation == 6 || equation == 8)
if (rick.eq == TRUE & SHAPE > 5) {
equation <- 9
keep <- 0 # Drop transformation
}
if (equation == 1) {
r <- Inv.Rev.Monomolecular(r, parm = PARM)
EQ <- "Inverse-Reverse Monomolecular"
} else if (equation == 5) {
r <- Rev.Monomolecular(r, parm = PARM)
EQ <- "Reverse Monomolecular"
} else if (equation == 3) {
r <- Monomolecular(r, parm = PARM)
EQ <- "Monomolecular"
} else if (equation == 7) {
r <- Inv.Monomolecular(r, parm = PARM)
EQ <- "Inverse Monomolecular"
} else if (equation == 8) {
r <- Inv.Ricker(r, parm = PARM)
EQ <- "Inverse Ricker"
} else if (equation == 4) {
r <- Ricker(r, parm = PARM)
EQ <- "Ricker"
} else if (equation == 6) {
r <- Rev.Ricker(r, parm = PARM)
EQ <- "Reverse Ricker"
} else if (equation == 2) {
r <- Inv.Rev.Ricker(r, parm = PARM)
EQ <- "Inverse-Reverse Ricker"
} else {
r <- (r * 0) + 1 # Distance
EQ <- "Distance"
} # End if-else
} # Close select transformation
} # Close surface type if-else
## If a surface was reclassified or transformed, apply the following
if ((GA.inputs$surface.type[iter] == "cat") ||
(equation %in% select.trans[[iter]])) {
if(keep == 0) {
## Use -99999 as 'ga' maximizes
obj.func.opt <- -99999
} else {
rclmat <- matrix(c(1e-100, 1e-6, 1e-06, 1e6, Inf, 1e6, -1, 0, 1e6),
ncol = 3,
byrow = TRUE)
r <- reclassify(r, rclmat)
# CS ----------------------------------------------------------------------
if (!is.null(CS.inputs)) {
if(!exists('obj.func.opt')) {
CS.resist <- try(Run_CS(CS.inputs, r), TRUE)
}
if(exists('CS.resist') && isTRUE(class(CS.resist) == 'try-error')) {
obj.func.opt <- -99999
}
if(exists('CS.resist') && isTRUE(class(CS.resist) != 'try-error')) { # Continue with iteration
# writeRaster(
# x = r,
# filename = paste0(EXPORT.dir, File.name, ".asc"),
# overwrite = TRUE
# )
# CS.resist <-
# Run_CS2(
# CS.inputs,
# GA.inputs,
# r = r,
# # EXPORT.dir = GA.inputs$Write.dir,
# File.name = File.name
# )
dat <- CS.inputs$df
dat$cd <- scale(CS.resist)
fit.mod <- mlpe_rga(formula = CS.inputs$formula,
data = dat,
ZZ = CS.inputs$ZZ,
REML = FALSE)
# Run mixed effect model on each Circuitscape effective resistance
if(lme4::fixef(fit.mod)['cd'] < 0) {
obj.func.opt <- -99999
} else {
if (method == "AIC") {
obj.func <- suppressWarnings(AIC(
fit.mod
))
obj.func.opt <- obj.func * -1
} else if (method == "R2") {
obj.func <-
suppressWarnings(r.squaredGLMM(
fit.mod
))
obj.func.opt <- obj.func[[1]]
} else {
obj.func <- suppressWarnings(logLik(
fit.mod
))
obj.func.opt <- obj.func[[1]]
}
} # Positive parameter value
# if (method == "AIC") {
# obj.func <- suppressWarnings(AIC(
# MLPE.lmm2(
# resistance = CS.resist,
# response = CS.inputs$response,
# ID = CS.inputs$ID,
# ZZ = CS.inputs$ZZ,
# REML = FALSE
# )
# ))
# obj.func.opt <- obj.func * -1
# } else if (method == "R2") {
# obj.func <-
# suppressWarnings(r.squaredGLMM(
# MLPE.lmm2(
# resistance = CS.resist,
# response =
# CS.inputs$response,
# ID = CS.inputs$ID,
# ZZ = CS.inputs$ZZ,
# REML = FALSE
# )
# ))
# obj.func.opt <- obj.func[[1]]
#
# } else {
# obj.func <- suppressWarnings(logLik(
# MLPE.lmm2(
# resistance = CS.resist,
# response = CS.inputs$response,
# ID = CS.inputs$ID,
# ZZ = CS.inputs$ZZ,
# REML = FALSE
# )
# ))
# obj.func.opt <- obj.func[[1]]
# }
} # End Obj func ifelse
} # End CS Loop
# gdistance ------------------------------------------------------------
if (!is.null(gdist.inputs)) {
if(mean(r@data@values, na.rm = TRUE) == 0) { # Skip iteration
obj.func.opt <- -99999
}
if(!exists('obj.func.opt')) {
cd <- try(Run_gdistance(gdist.inputs, r), TRUE)
}
if(exists('cd') && isTRUE(class(cd) == 'try-error')) {
obj.func.opt <- -99999
}
if(exists('cd') && isTRUE(class(cd) != 'try-error')) { # Continue with iteration
dat <- gdist.inputs$df
dat$cd <- scale(c(cd))
fit.mod <- mlpe_rga(formula = gd ~ cd + (1 | pop),
data = dat,
ZZ = gdist.inputs$ZZ,
REML = FALSE)
if(lme4::fixef(fit.mod)['cd'] < 0) {
obj.func.opt <- -99999
} else {
if (method == "AIC") {
obj.func <- suppressWarnings(AIC(
fit.mod
))
obj.func.opt <- obj.func * -1
} else if (method == "R2") {
obj.func <-
suppressWarnings(r.squaredGLMM(
fit.mod
))
obj.func.opt <- obj.func[[1]]
} else {
obj.func <- suppressWarnings(logLik(
fit.mod
))
obj.func.opt <- obj.func[[1]]
}
} # Positive parameter value
} # End Keep loop
## ORIGINAL BELOW
# if(mean(r@data@values, na.rm = TRUE) == 0) { # Skip iteration
# obj.func.opt <- -99999
#
# }
#
# if(!exists('obj.func.opt')) {
# cd <- try(Run_gdistance(gdist.inputs, r), TRUE)
# }
#
# if(exists('cd') && isTRUE(class(cd) == 'try-error')) {
# obj.func.opt <- -99999
# rm(cd, r)
# gc()
# }
#
# if(exists('cd') && isTRUE(class(cd) != 'try-error')) { # Continue with iteration
# dat <- gdist.inputs$df
# dat$cd <- scale(c(cd))
# fit.mod <- mlpe_rga(formula = gdist.inputs$formula,
# data = dat,
# ZZ = gdist.inputs$ZZ,
# REML = FALSE)
#
# if(lme4::fixef(fit.mod)['cd'] < 0) {
# obj.func.opt <- -99999
# } else {
# if (method == "AIC") {
# obj.func <- suppressWarnings(AIC(
# fit.mod
# ))
# obj.func.opt <- obj.func * -1
# } else if (method == "R2") {
# obj.func <-
# suppressWarnings(r.squaredGLMM(
# fit.mod
# ))
# obj.func.opt <- obj.func[[1]]
#
# } else {
# obj.func <- suppressWarnings(logLik(
# fit.mod
# ))
# obj.func.opt <- obj.func[[1]]
# }
# } # Positive parameter value
# rm(cd, r)
# gc()
# } # Keep loop
} # End gdistance Loop
# Julia ------------------------------------------------------------
if (!is.null(jl.inputs)) {
# if(cellStats(r, "mean") == 0) { # Skip iteration
if(mean(r@data@values, na.rm = TRUE) == 0) { # Skip iteration
obj.func.opt <- -99999
}
if(!exists('obj.func.opt')) {
cd <- try(Run_CS.jl(jl.inputs, r), TRUE)
}
if(exists('cd') && isTRUE(class(cd) == 'try-error')) {
obj.func.opt <- -99999
}
if(exists('cd') && isTRUE(class(cd) != 'try-error')) { # Continue with iteration
dat <- jl.inputs$df
dat$cd <- scale(cd)
fit.mod <- mlpe_rga(formula = jl.inputs$formula,
data = dat,
ZZ = jl.inputs$ZZ,
REML = FALSE)
if(lme4::fixef(fit.mod)['cd'] < 0) {
obj.func.opt <- -99999
} else {
if (method == "AIC") {
obj.func <- suppressWarnings(AIC(
fit.mod
))
obj.func.opt <- obj.func * -1
} else if (method == "R2") {
obj.func <-
suppressWarnings(r.squaredGLMM(
fit.mod
))
obj.func.opt <- obj.func[[1]]
} else {
obj.func <- suppressWarnings(logLik(
fit.mod
))
obj.func.opt <- obj.func[[1]]
}
} # Positive parameter value
} # End Keep loop
} # End Julia Loop
} # End drop Loop
if(!exists('obj.func.opt')) {
obj.func.opt <- -99999
}
rt <- proc.time()[3] - t1
if (quiet == FALSE) {
cat(paste0("\t", "Iteration took ", round(rt, digits = 2), " seconds"), "\n")
# cat(paste0("\t", EQ,"; ",round(SHAPE,digits=2),"; ", round(Max.SCALE,digits=2)),"\n")
cat(paste0("\t", method, " = ", round(obj.func.opt, 4)), "\n", "\n")
if (!is.null(iter)) {
if (GA.inputs$surface.type[iter] != "cat") {
cat(paste0("\t", EQ, " | Shape = ", PARM[2], " | Max = ", PARM[3]),
"\n",
"\n")
}
}
}
} else { # End transformation loop
## Use -99999 as 'ga' maximizes
obj.func.opt <- -99999
rt <- proc.time()[3] - t1
if (quiet == FALSE) {
cat(paste0("\t", "Iteration took ", round(rt, digits = 2), " seconds"), "\n")
cat(paste0("\t", method, " = ", obj.func.opt, "\n"))
if (!is.null(iter)) {
if (GA.inputs$surface.type[iter] != "cat") {
cat(paste0("EXCLUDED TRANSFORMATION", "\n", "\n"))
}
}
}
}
# rm(r)
gc()
if(!is.null(GA.inputs$opt.digits)) {
obj.func.opt <- round(obj.func.opt, GA.inputs$opt.digits)
return(obj.func.opt)
} else {
return(obj.func.opt)
}
}
wpeterman/ResistanceGA documentation built on Nov. 20, 2023, 11:50 p.m.
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Defines functions Resistance.Opt_single.cov
#' Optimize resistance surfaces individually with more than one predictor variable
#'
#' Optimize all resistance surfaces that are located in the same directory individually. This optimization function is designed to be called from GA
#'
#' @param PARM Parameters to transform conintuous surface or resistance values of categorical surface. A vector with parameters specified in the order of resistance surfaces.These values are selected during optimization if called within GA function.
#' @param Resistance Resistance surface to be optimized. This should be an R raster object. If not specified, the function will attempt to find the a resistance surface from \code{GA.inputs}
#' @param CS.inputs Object created from running \code{\link[ResistanceGA]{CS.prep}} function. Defined if optimizing using CIRCUITSCAPE
#' @param gdist.inputs Object created from running \code{\link[ResistanceGA]{gdist.prep}} function. Defined if optimizing using gdistance
#' @param jl.inputs Object created from running \code{\link[ResistanceGA]{jl.prep}} function. Defined if optimizing using CIRCUITSCAPE run in Julia
#' @param GA.inputs Object created from running \code{\link[ResistanceGA]{GA.prep}} function
#' @param Min.Max Define whether the optimization function should minimized ('min') or maximized ('max'). Default in 'max'
#' @param iter A counter for the number of surfaces that will be optimized
#' @param quiet Logical, if TRUE AICc and iteration duration will not be printed to the screen at the completion of each iteration.
#' @return Objective function value (either AIC, R2, or LL) from mixed effect model
#' @noRd
#' @author Bill Peterman <Peterman.73@@osu.edu>
Resistance.Opt_single.cov <-
function(PARM,
Resistance,
CS.inputs = NULL,
gdist.inputs = NULL,
jl.inputs = NULL,
GA.inputs,
Min.Max = 'max',
iter = NULL,
quiet = FALSE) {
t1 <- proc.time()[3]
if(is.null(iter)) {
iter <- 1
}
method <- GA.inputs$method
EXPORT.dir <- GA.inputs$Write.dir
select.trans <- GA.inputs$select.trans
R <- r <- Resistance
keep <- 1 # Indicator to keep or drop transformation
if (GA.inputs$surface.type[iter] == "cat") {
PARM <- PARM / min(PARM)
if(max(PARM) > GA.inputs$max.cat){
PARM <- SCALE.vector(PARM, 1, GA.inputs$max.cat)
}
parm <- PARM
df <-
data.frame(id = unique(r), PARM) # Data frame with original raster values and replacement values
# browser()
r <- subs(r, df)
} else {
## Else continuous surface
r <- SCALE(r, 0, 10)
# Set equation for continuous surface
equation <- floor(PARM[1]) # Parameter can range from 1-9.99
# Read in resistance surface to be optimized
SHAPE <- (PARM[2])
Max.SCALE <- (PARM[3])
if (equation %in% select.trans[[iter]]) {
# Apply specified transformation
rick.eq <- (equation == 2 ||
equation == 4 || equation == 6 || equation == 8)
if (rick.eq == TRUE & SHAPE > 5) {
equation <- 9
keep <- 0 # Drop transformation
}
if (equation == 1) {
r <- Inv.Rev.Monomolecular(r, parm = PARM)
EQ <- "Inverse-Reverse Monomolecular"
} else if (equation == 5) {
r <- Rev.Monomolecular(r, parm = PARM)
EQ <- "Reverse Monomolecular"
} else if (equation == 3) {
r <- Monomolecular(r, parm = PARM)
EQ <- "Monomolecular"
} else if (equation == 7) {
r <- Inv.Monomolecular(r, parm = PARM)
EQ <- "Inverse Monomolecular"
} else if (equation == 8) {
r <- Inv.Ricker(r, parm = PARM)
EQ <- "Inverse Ricker"
} else if (equation == 4) {
r <- Ricker(r, parm = PARM)
EQ <- "Ricker"
} else if (equation == 6) {
r <- Rev.Ricker(r, parm = PARM)
EQ <- "Reverse Ricker"
} else if (equation == 2) {
r <- Inv.Rev.Ricker(r, parm = PARM)
EQ <- "Inverse-Reverse Ricker"
} else {
r <- (r * 0) + 1 # Distance
EQ <- "Distance"
} # End if-else
} # Close select transformation
} # Close surface type if-else
## If a surface was reclassified or transformed, apply the following
if ((GA.inputs$surface.type[iter] == "cat") ||
(equation %in% select.trans[[iter]])) {
if(keep == 0) {
## Use -99999 as 'ga' maximizes
obj.func.opt <- -99999
} else {
rclmat <- matrix(c(1e-100, 1e-6, 1e-06, 1e6, Inf, 1e6, -1, 0, 1e6),
ncol = 3,
byrow = TRUE)
r <- reclassify(r, rclmat)
# CS ----------------------------------------------------------------------
if (!is.null(CS.inputs)) {
if(!exists('obj.func.opt')) {
CS.resist <- try(Run_CS(CS.inputs, r), TRUE)
}
if(exists('CS.resist') && isTRUE(class(CS.resist) == 'try-error')) {
obj.func.opt <- -99999
}
if(exists('CS.resist') && isTRUE(class(CS.resist) != 'try-error')) { # Continue with iteration
# writeRaster(
# x = r,
# filename = paste0(EXPORT.dir, File.name, ".asc"),
# overwrite = TRUE
# )
# CS.resist <-
# Run_CS2(
# CS.inputs,
# GA.inputs,
# r = r,
# # EXPORT.dir = GA.inputs$Write.dir,
# File.name = File.name
# )
dat <- CS.inputs$df
dat$cd <- scale(CS.resist)
fit.mod <- mlpe_rga(formula = CS.inputs$formula,
data = dat,
ZZ = CS.inputs$ZZ,
REML = FALSE)
# Run mixed effect model on each Circuitscape effective resistance
if(lme4::fixef(fit.mod)['cd'] < 0) {
obj.func.opt <- -99999
} else {
if (method == "AIC") {
obj.func <- suppressWarnings(AIC(
fit.mod
))
obj.func.opt <- obj.func * -1
} else if (method == "R2") {
obj.func <-
suppressWarnings(r.squaredGLMM(
fit.mod
))
obj.func.opt <- obj.func[[1]]
} else {
obj.func <- suppressWarnings(logLik(
fit.mod
))
obj.func.opt <- obj.func[[1]]
}
} # Positive parameter value
# if (method == "AIC") {
# obj.func <- suppressWarnings(AIC(
# MLPE.lmm2(
# resistance = CS.resist,
# response = CS.inputs$response,
# ID = CS.inputs$ID,
# ZZ = CS.inputs$ZZ,
# REML = FALSE
# )
# ))
# obj.func.opt <- obj.func * -1
# } else if (method == "R2") {
# obj.func <-
# suppressWarnings(r.squaredGLMM(
# MLPE.lmm2(
# resistance = CS.resist,
# response =
# CS.inputs$response,
# ID = CS.inputs$ID,
# ZZ = CS.inputs$ZZ,
# REML = FALSE
# )
# ))
# obj.func.opt <- obj.func[[1]]
#
# } else {
# obj.func <- suppressWarnings(logLik(
# MLPE.lmm2(
# resistance = CS.resist,
# response = CS.inputs$response,
# ID = CS.inputs$ID,
# ZZ = CS.inputs$ZZ,
# REML = FALSE
# )
# ))
# obj.func.opt <- obj.func[[1]]
# }
} # End Obj func ifelse
} # End CS Loop
# gdistance ------------------------------------------------------------
if (!is.null(gdist.inputs)) {
if(mean(r@data@values, na.rm = TRUE) == 0) { # Skip iteration
obj.func.opt <- -99999
}
if(!exists('obj.func.opt')) {
cd <- try(Run_gdistance(gdist.inputs, r), TRUE)
}
if(exists('cd') && isTRUE(class(cd) == 'try-error')) {
obj.func.opt <- -99999
}
if(exists('cd') && isTRUE(class(cd) != 'try-error')) { # Continue with iteration
dat <- gdist.inputs$df
dat$cd <- scale(c(cd))
fit.mod <- mlpe_rga(formula = gd ~ cd + (1 | pop),
data = dat,
ZZ = gdist.inputs$ZZ,
REML = FALSE)
if(lme4::fixef(fit.mod)['cd'] < 0) {
obj.func.opt <- -99999
} else {
if (method == "AIC") {
obj.func <- suppressWarnings(AIC(
fit.mod
))
obj.func.opt <- obj.func * -1
} else if (method == "R2") {
obj.func <-
suppressWarnings(r.squaredGLMM(
fit.mod
))
obj.func.opt <- obj.func[[1]]
} else {
obj.func <- suppressWarnings(logLik(
fit.mod
))
obj.func.opt <- obj.func[[1]]
}
} # Positive parameter value
} # End Keep loop
## ORIGINAL BELOW
# if(mean(r@data@values, na.rm = TRUE) == 0) { # Skip iteration
# obj.func.opt <- -99999
#
# }
#
# if(!exists('obj.func.opt')) {
# cd <- try(Run_gdistance(gdist.inputs, r), TRUE)
# }
#
# if(exists('cd') && isTRUE(class(cd) == 'try-error')) {
# obj.func.opt <- -99999
# rm(cd, r)
# gc()
# }
#
# if(exists('cd') && isTRUE(class(cd) != 'try-error')) { # Continue with iteration
# dat <- gdist.inputs$df
# dat$cd <- scale(c(cd))
# fit.mod <- mlpe_rga(formula = gdist.inputs$formula,
# data = dat,
# ZZ = gdist.inputs$ZZ,
# REML = FALSE)
#
# if(lme4::fixef(fit.mod)['cd'] < 0) {
# obj.func.opt <- -99999
# } else {
# if (method == "AIC") {
# obj.func <- suppressWarnings(AIC(
# fit.mod
# ))
# obj.func.opt <- obj.func * -1
# } else if (method == "R2") {
# obj.func <-
# suppressWarnings(r.squaredGLMM(
# fit.mod
# ))
# obj.func.opt <- obj.func[[1]]
#
# } else {
# obj.func <- suppressWarnings(logLik(
# fit.mod
# ))
# obj.func.opt <- obj.func[[1]]
# }
# } # Positive parameter value
# rm(cd, r)
# gc()
# } # Keep loop
} # End gdistance Loop
# Julia ------------------------------------------------------------
if (!is.null(jl.inputs)) {
# if(cellStats(r, "mean") == 0) { # Skip iteration
if(mean(r@data@values, na.rm = TRUE) == 0) { # Skip iteration
obj.func.opt <- -99999
}
if(!exists('obj.func.opt')) {
cd <- try(Run_CS.jl(jl.inputs, r), TRUE)
}
if(exists('cd') && isTRUE(class(cd) == 'try-error')) {
obj.func.opt <- -99999
}
if(exists('cd') && isTRUE(class(cd) != 'try-error')) { # Continue with iteration
dat <- jl.inputs$df
dat$cd <- scale(cd)
fit.mod <- mlpe_rga(formula = jl.inputs$formula,
data = dat,
ZZ = jl.inputs$ZZ,
REML = FALSE)
if(lme4::fixef(fit.mod)['cd'] < 0) {
obj.func.opt <- -99999
} else {
if (method == "AIC") {
obj.func <- suppressWarnings(AIC(
fit.mod
))
obj.func.opt <- obj.func * -1
} else if (method == "R2") {
obj.func <-
suppressWarnings(r.squaredGLMM(
fit.mod
))
obj.func.opt <- obj.func[[1]]
} else {
obj.func <- suppressWarnings(logLik(
fit.mod
))
obj.func.opt <- obj.func[[1]]
}
} # Positive parameter value
} # End Keep loop
} # End Julia Loop
} # End drop Loop
if(!exists('obj.func.opt')) {
obj.func.opt <- -99999
}
rt <- proc.time()[3] - t1
if (quiet == FALSE) {
cat(paste0("\t", "Iteration took ", round(rt, digits = 2), " seconds"), "\n")
# cat(paste0("\t", EQ,"; ",round(SHAPE,digits=2),"; ", round(Max.SCALE,digits=2)),"\n")
cat(paste0("\t", method, " = ", round(obj.func.opt, 4)), "\n", "\n")
if (!is.null(iter)) {
if (GA.inputs$surface.type[iter] != "cat") {
cat(paste0("\t", EQ, " | Shape = ", PARM[2], " | Max = ", PARM[3]),
"\n",
"\n")
}
}
}
} else { # End transformation loop
## Use -99999 as 'ga' maximizes
obj.func.opt <- -99999
rt <- proc.time()[3] - t1
if (quiet == FALSE) {
cat(paste0("\t", "Iteration took ", round(rt, digits = 2), " seconds"), "\n")
cat(paste0("\t", method, " = ", obj.func.opt, "\n"))
if (!is.null(iter)) {
if (GA.inputs$surface.type[iter] != "cat") {
cat(paste0("EXCLUDED TRANSFORMATION", "\n", "\n"))
}
}
}
}
# rm(r)
gc()
if(!is.null(GA.inputs$opt.digits)) {
obj.func.opt <- round(obj.func.opt, GA.inputs$opt.digits)
return(obj.func.opt)
} else {
return(obj.func.opt)
}
}
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