R/PlotRangeSingleRun.R
In KarelMokany/AdaptR: Species distribution modelling incorporating genetic adaptation
#' Generate summary plot of range dynamics from a single run of AdpatR
#'
#' @param output.folder.path The directory where the output files are stored
#' @param run.name The name for the AdaptR simulation run
#' @return summary plots of the entire distribution over time
#' @examples
#' ## Complete example of the multiple steps in applying AdaptR using data example provided for Drosophila jambulina
#'
#' # 1. Set the filepath to the example data set
#' filepath.data <- system.file("extdata", package="AdaptR")
#'
#' # 2. Create text files to describe the file path to each input variable, and the output variable
#' write.table((file.path(filepath.data,"Tmax",paste0("Tmax",seq(1:159),".asc"))), file = file.path(filepath.data,"Tmax","Tmax_filenames.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )
#' write.table((file.path(filepath.data,"Habitat",paste0("Habitat",seq(1:159),".asc"))), file = file.path(filepath.data,"Habitat","Habitat_filenames.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )
#' write.table((file.path(filepath.data,"compact_grids",paste0("demo_compact_grids_T",seq(1:159)))), file = file.path(filepath.data,"compact_grids","demo_compact_grids_output_filenames.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )
#'
#' # 3. Run the grid compactor
#' CompactGrids(compactor.parameter.file.name = file.path(filepath.data,"species_inputs","Jambulina__grid_compactor_parameter_file.txt"),
#' ncols = 100,
#' nrows = 79,
#' n.env.vars = 2,
#' n.time.points = 159,
#' raw.env.grids.name.files = c(file.path(filepath.data,"Tmax","Tmax_filenames.txt"),file.path(filepath.data,"Habitat","Habitat_filenames.txt")),
#' output.env.name.file = file.path(filepath.data,"compact_grids","demo_compact_grids_output_filenames.txt"))
#'
#' # 4. Generate a dispersal kernel for the drosophila example
#' filepath.data <- system.file("extdata", package="AdaptR")
#' Dispersal_Neighbourhood(radius=5,
#' type="neg.power",
#' params=c(1,1),
#' output.name="Dispersal_relfile_L1_K1_rad5",
#' output.directory=file.path(filepath.data,"species_inputs"),
#' dispersal.plot=TRUE)
#'
#' # 5. Create text files to describe the file path to the compact grids
#' write.table(paste0("demo_compact_grids_T",rep(1:159, length.out=159)), file = file.path(filepath.data,"species_inputs","compact_series_names.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )
#'
#' # 6. Run the AdaptR model
#' AdaptR(run.name = "jambulina_test",
#' parameter.file.name = file.path(filepath.data,"outputs","jambulina_test_parameters.txt"),
#' ncols = 100,
#' nrows = 79,
#' output.folder.path = file.path(filepath.data,"outputs"),
#' verbose.outputs = FALSE,
#' n.time.points = 159,
#' n.env.vars = 2,
#' env.vars.names = c("MaxTemp", "Other_Maxent"),
#' env.grids.folder.path = file.path(filepath.data,"compact_grids"),
#' env.grids.name.file = file.path(filepath.data,"species_inputs","compact_series_names.txt"),
#' species.initial.grid = file.path(filepath.data,"species_inputs","demo_jambulia_initial_distribution.asc"),
#' minimum.survival.percentage = 5,
#' resident.population.weighting = 1000,
#' dispersal.neighbourhood.file = file.path(filepath.data,"species_inputs","Dispersal_relfile_L1_K1_rad5.dna"),
#' species.location.file = file.path(filepath.data,"species_inputs","demo_locations_out.txt"),
#' env.lower.thresholds = c(19.47,0.99),
#' env.upper.thresholds = c(37.94547,1.01),
#' env.low.adaptation = c(FALSE,FALSE),
#' env.high.adaptation = c(TRUE,FALSE),
#' adapt.limit = c(40,0),
#' heritability = c(0.53,0),
#' fitness.cost = c(0.05,0),
#' adapt.threshold.grids = c(FALSE,FALSE),
#' phenotypic.sd.grid = c(FALSE,FALSE),
#' phenotypic.sd.value = c(1.106,0),
#' plasticity = c(1.106,0))
#'
#' # 7. Map the predictions of the model (at the last time point)
#' map.single.run(output.folder.path = file.path(filepath.data,"outputs"),
#' run.name = "jambulina_test")
#'
#' # 8. Plot a summary of the model predictions over time
#' plot.range.single.run(output.folder.path = file.path(filepath.data,"outputs"),
#' run.name = "jambulina_test")
#'
#' @export
plot.range.single.run <-
function(output.folder.path = getwd(),
run.name)
{
##_____________________________________________________________________________________##
## First, let's run some sanity checks on the parameters
if(missing(run.name))
stop("Need to specify a name for this run of Adaptor.")
if(missing(output.folder.path))
warning("No output folder path specified: using working directory.")
if(!file.exists(output.folder.path))
stop("Output folder path invalid.") # check the files are there
temporal.occupancy.fname <- file.path(output.folder.path,paste0(run.name,"_occupancy_summary.txt"))
if(!file.exists(temporal.occupancy.fname))
stop("Output files with this run name were not found in the specified folder.")
##_____________________________________________________________________________________##
# read in the data
R1 = read.table(temporal.occupancy.fname, header=TRUE)
# work out how many adaptive environment variables we have
if(ncol(R1)>4)
{
n.env <- (ncol(R1)-4)/2
# Now loop through the adaptive env variables and plot them
for(i.env in 1:n.env)
{
par(mfrow=c(3,1), mar=c(0,4,0.5,1), mgp =c(2.5,0.5,0),las=1)
# Range RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR
plot(Cells_Occupied/1000 ~ Time, data=R1, type="l", ylim=c(0,((max(R1$Cells_Occupied)*1.1))/1000), ylab="Range Grid Cells (x1000)", axes=F)
polygon(x=c(R1$Time,rev(R1$Time)), y=c(R1$Cells_Occupied/1000,rev(R1$Cells_Occupied/1000)), col="lightgrey",border="lightgrey" )
lines(Cells_Occupied/1000 ~ Time, data=R1)
box()
axis(side=2, at=round(seq(0,(max(R1$Cells_Occupied)*1.1)/1000,len=7),1), las=1 )
# Adaptive tolerance trait value AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
Z.min<-min(R1[,(3+(i.env*2))])
Z.max<-max(R1[,(3+(i.env*2))])
Z.range<-Z.max-Z.min
Z.buffer<-0.1*Z.range
plot(R1[,(3+(i.env*2))] ~ Time, data=R1, type="l", ylim=c((Z.min-Z.buffer),(Z.max+Z.buffer)), ylab="Mean Env tolerance", axes=F)
polygon(x=c(R1$Time,rev(R1$Time)), y=c(R1[,(3+(i.env*2))],rev(R1[,(3+(i.env*2))])), col="lightgrey",border="lightgrey" )
lines(R1[,(3+(i.env*2))] ~ Time, data=R1)
box()
axis(side=2, at=round(seq((Z.min-Z.buffer),(Z.max+Z.buffer),len=7),1), las=1 )
# Adaptive tolerance trait variance VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV
vp.min<-min(R1[,(4+(i.env*2))])
vp.max<-max(R1[,(4+(i.env*2))])
vp.range<-vp.max-vp.min
vp.buffer<-0.1*vp.range
par(mar=c(3.5,4,0.5,1))
plot(R1[,(4+(i.env*2))] ~ Time, data=R1, type="l", ylim=c((vp.min-vp.buffer),(vp.max+vp.buffer)), ylab="Variance Env. Tol.", axes=F)
polygon(x=c(R1$Time,rev(R1$Time)), y=c(R1[,(4+(i.env*2))],rev(R1[,(4+(i.env*2))])), col="lightgrey",border="lightgrey" )
lines(R1[,(4+(i.env*2))] ~ Time, data=R1)
box()
axis(side=2, at=round(seq((vp.min-vp.buffer),(vp.max+vp.buffer),len=7),1), las=1 )
axis(side=1, at=round(seq(0,nrow(R1),len=10),0), labels=as.integer(seq(min(R1$Time),max(R1$Time),len=10)), las=1, cex=0.5 )
}#end for i.env
} # end if ncol(R1)>4
else # if no adaptive env grids, just plot the range
{
# Range RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR
plot(Cells_Occupied/1000 ~ Time, data=R1, type="l", ylim=c(0,((max(R1$Cells_Occupied)*1.1))/1000), ylab="Range Grid Cells (x1000)", axes=F)
polygon(x=c(R1$Time,rev(R1$Time)), y=c(R1$Cells_Occupied/1000,rev(R1$Cells_Occupied/1000)), col="lightgrey",border="lightgrey" )
lines(Cells_Occupied/1000 ~ Time, data=R1)
box()
axis(side=2, at=round(seq(0,(max(R1$Cells_Occupied)*1.1)/1000,len=7),1), las=1 )
axis(side=1, at=round(seq(0,nrow(R1),len=10),0), labels=as.integer(seq(min(R1$Time),max(R1$Time),len=10)), las=1, cex=0.5 )
}# end else ncol(R1)>4
} # End plot.range.single.run function
#############################################################################################
KarelMokany/AdaptR documentation built on May 8, 2019, 4:48 p.m.
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#' Generate summary plot of range dynamics from a single run of AdpatR
#'
#' @param output.folder.path The directory where the output files are stored
#' @param run.name The name for the AdaptR simulation run
#' @return summary plots of the entire distribution over time
#' @examples
#' ## Complete example of the multiple steps in applying AdaptR using data example provided for Drosophila jambulina
#'
#' # 1. Set the filepath to the example data set
#' filepath.data <- system.file("extdata", package="AdaptR")
#'
#' # 2. Create text files to describe the file path to each input variable, and the output variable
#' write.table((file.path(filepath.data,"Tmax",paste0("Tmax",seq(1:159),".asc"))), file = file.path(filepath.data,"Tmax","Tmax_filenames.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )
#' write.table((file.path(filepath.data,"Habitat",paste0("Habitat",seq(1:159),".asc"))), file = file.path(filepath.data,"Habitat","Habitat_filenames.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )
#' write.table((file.path(filepath.data,"compact_grids",paste0("demo_compact_grids_T",seq(1:159)))), file = file.path(filepath.data,"compact_grids","demo_compact_grids_output_filenames.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )
#'
#' # 3. Run the grid compactor
#' CompactGrids(compactor.parameter.file.name = file.path(filepath.data,"species_inputs","Jambulina__grid_compactor_parameter_file.txt"),
#' ncols = 100,
#' nrows = 79,
#' n.env.vars = 2,
#' n.time.points = 159,
#' raw.env.grids.name.files = c(file.path(filepath.data,"Tmax","Tmax_filenames.txt"),file.path(filepath.data,"Habitat","Habitat_filenames.txt")),
#' output.env.name.file = file.path(filepath.data,"compact_grids","demo_compact_grids_output_filenames.txt"))
#'
#' # 4. Generate a dispersal kernel for the drosophila example
#' filepath.data <- system.file("extdata", package="AdaptR")
#' Dispersal_Neighbourhood(radius=5,
#' type="neg.power",
#' params=c(1,1),
#' output.name="Dispersal_relfile_L1_K1_rad5",
#' output.directory=file.path(filepath.data,"species_inputs"),
#' dispersal.plot=TRUE)
#'
#' # 5. Create text files to describe the file path to the compact grids
#' write.table(paste0("demo_compact_grids_T",rep(1:159, length.out=159)), file = file.path(filepath.data,"species_inputs","compact_series_names.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )
#'
#' # 6. Run the AdaptR model
#' AdaptR(run.name = "jambulina_test",
#' parameter.file.name = file.path(filepath.data,"outputs","jambulina_test_parameters.txt"),
#' ncols = 100,
#' nrows = 79,
#' output.folder.path = file.path(filepath.data,"outputs"),
#' verbose.outputs = FALSE,
#' n.time.points = 159,
#' n.env.vars = 2,
#' env.vars.names = c("MaxTemp", "Other_Maxent"),
#' env.grids.folder.path = file.path(filepath.data,"compact_grids"),
#' env.grids.name.file = file.path(filepath.data,"species_inputs","compact_series_names.txt"),
#' species.initial.grid = file.path(filepath.data,"species_inputs","demo_jambulia_initial_distribution.asc"),
#' minimum.survival.percentage = 5,
#' resident.population.weighting = 1000,
#' dispersal.neighbourhood.file = file.path(filepath.data,"species_inputs","Dispersal_relfile_L1_K1_rad5.dna"),
#' species.location.file = file.path(filepath.data,"species_inputs","demo_locations_out.txt"),
#' env.lower.thresholds = c(19.47,0.99),
#' env.upper.thresholds = c(37.94547,1.01),
#' env.low.adaptation = c(FALSE,FALSE),
#' env.high.adaptation = c(TRUE,FALSE),
#' adapt.limit = c(40,0),
#' heritability = c(0.53,0),
#' fitness.cost = c(0.05,0),
#' adapt.threshold.grids = c(FALSE,FALSE),
#' phenotypic.sd.grid = c(FALSE,FALSE),
#' phenotypic.sd.value = c(1.106,0),
#' plasticity = c(1.106,0))
#'
#' # 7. Map the predictions of the model (at the last time point)
#' map.single.run(output.folder.path = file.path(filepath.data,"outputs"),
#' run.name = "jambulina_test")
#'
#' # 8. Plot a summary of the model predictions over time
#' plot.range.single.run(output.folder.path = file.path(filepath.data,"outputs"),
#' run.name = "jambulina_test")
#'
#' @export
plot.range.single.run <-
function(output.folder.path = getwd(),
run.name)
{
##_____________________________________________________________________________________##
## First, let's run some sanity checks on the parameters
if(missing(run.name))
stop("Need to specify a name for this run of Adaptor.")
if(missing(output.folder.path))
warning("No output folder path specified: using working directory.")
if(!file.exists(output.folder.path))
stop("Output folder path invalid.") # check the files are there
temporal.occupancy.fname <- file.path(output.folder.path,paste0(run.name,"_occupancy_summary.txt"))
if(!file.exists(temporal.occupancy.fname))
stop("Output files with this run name were not found in the specified folder.")
##_____________________________________________________________________________________##
# read in the data
R1 = read.table(temporal.occupancy.fname, header=TRUE)
# work out how many adaptive environment variables we have
if(ncol(R1)>4)
{
n.env <- (ncol(R1)-4)/2
# Now loop through the adaptive env variables and plot them
for(i.env in 1:n.env)
{
par(mfrow=c(3,1), mar=c(0,4,0.5,1), mgp =c(2.5,0.5,0),las=1)
# Range RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR
plot(Cells_Occupied/1000 ~ Time, data=R1, type="l", ylim=c(0,((max(R1$Cells_Occupied)*1.1))/1000), ylab="Range Grid Cells (x1000)", axes=F)
polygon(x=c(R1$Time,rev(R1$Time)), y=c(R1$Cells_Occupied/1000,rev(R1$Cells_Occupied/1000)), col="lightgrey",border="lightgrey" )
lines(Cells_Occupied/1000 ~ Time, data=R1)
box()
axis(side=2, at=round(seq(0,(max(R1$Cells_Occupied)*1.1)/1000,len=7),1), las=1 )
# Adaptive tolerance trait value AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
Z.min<-min(R1[,(3+(i.env*2))])
Z.max<-max(R1[,(3+(i.env*2))])
Z.range<-Z.max-Z.min
Z.buffer<-0.1*Z.range
plot(R1[,(3+(i.env*2))] ~ Time, data=R1, type="l", ylim=c((Z.min-Z.buffer),(Z.max+Z.buffer)), ylab="Mean Env tolerance", axes=F)
polygon(x=c(R1$Time,rev(R1$Time)), y=c(R1[,(3+(i.env*2))],rev(R1[,(3+(i.env*2))])), col="lightgrey",border="lightgrey" )
lines(R1[,(3+(i.env*2))] ~ Time, data=R1)
box()
axis(side=2, at=round(seq((Z.min-Z.buffer),(Z.max+Z.buffer),len=7),1), las=1 )
# Adaptive tolerance trait variance VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV
vp.min<-min(R1[,(4+(i.env*2))])
vp.max<-max(R1[,(4+(i.env*2))])
vp.range<-vp.max-vp.min
vp.buffer<-0.1*vp.range
par(mar=c(3.5,4,0.5,1))
plot(R1[,(4+(i.env*2))] ~ Time, data=R1, type="l", ylim=c((vp.min-vp.buffer),(vp.max+vp.buffer)), ylab="Variance Env. Tol.", axes=F)
polygon(x=c(R1$Time,rev(R1$Time)), y=c(R1[,(4+(i.env*2))],rev(R1[,(4+(i.env*2))])), col="lightgrey",border="lightgrey" )
lines(R1[,(4+(i.env*2))] ~ Time, data=R1)
box()
axis(side=2, at=round(seq((vp.min-vp.buffer),(vp.max+vp.buffer),len=7),1), las=1 )
axis(side=1, at=round(seq(0,nrow(R1),len=10),0), labels=as.integer(seq(min(R1$Time),max(R1$Time),len=10)), las=1, cex=0.5 )
}#end for i.env
} # end if ncol(R1)>4
else # if no adaptive env grids, just plot the range
{
# Range RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR
plot(Cells_Occupied/1000 ~ Time, data=R1, type="l", ylim=c(0,((max(R1$Cells_Occupied)*1.1))/1000), ylab="Range Grid Cells (x1000)", axes=F)
polygon(x=c(R1$Time,rev(R1$Time)), y=c(R1$Cells_Occupied/1000,rev(R1$Cells_Occupied/1000)), col="lightgrey",border="lightgrey" )
lines(Cells_Occupied/1000 ~ Time, data=R1)
box()
axis(side=2, at=round(seq(0,(max(R1$Cells_Occupied)*1.1)/1000,len=7),1), las=1 )
axis(side=1, at=round(seq(0,nrow(R1),len=10),0), labels=as.integer(seq(min(R1$Time),max(R1$Time),len=10)), las=1, cex=0.5 )
}# end else ncol(R1)>4
} # End plot.range.single.run function
#############################################################################################
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