interpolatePoints: Interpolate output results

In DrylandEcology/rMultivariateMatching: Multivariate matching for site selection and interpolation using multi-dimensional datasets

Description

Use multivariate matching to interpolate simulation output results to high resolution maps

Usage

interpolatePoints(
  matches = NULL,
  output_results = NULL,
  exclude_poor_matches = TRUE,
  subset_cell_names = "subset_cell",
  quality_name = "matching_quality",
  matching_distance = 1.5,
  raster_template = NULL,
  plotraster = TRUE,
  saveraster = TRUE,
  filepath = getwd(),
  overwrite = FALSE
)

Arguments

matches	data frame. Output from multivarmatch or secondaryMatching functions.
output_results	data frame. Simulation output results for all simulated sites (Subset cells). The first column and the rownames should correspond to the unique identifiers for the Subsetcells. Importantly, these identifiers need to match the identifiers in the 'subset_cell' column of the 'matches' data frame.
exclude_poor_matches	boolean. Indicates whether poor matches (defined as Target cells that are more than the designated 'matching_distance' from their matched Subset cell) should be excluded from matching. Defaults to TRUE.
subset_cell_names	character. This is the name of the column in the 'matches' data frame that provides the unique identity of the Subset cells matched to each Target cell. Defaults to "subset_cell". When 'matches' is the output from secondaryMatching, this should be 'subset_cell_secondary'.
quality_name	character. This is the name of the column in the 'matches' data frame that provides the matching quality between the Subset cells and Target cells. Defaults to ""matching_quality"". When 'matches' is the output from secondaryMatching, this should be 'matching_quality_secondary'.
matching_distance	numeric. Gives the maximum allowable matching quality value (weighted Euclidean distance) between Target and Subset cells. Default value is 1.5.
raster_template	one of the raster layers used for input data.
plotraster	boolean. Indicates if raster should be plotted to a map. Defaults to TRUE.
saveraster	boolean. Indicates if a raster should be saved to file. Defaults to TRUE.
filepath	provides path for location where raster will be saved. Defaults to working directory.
overwrite	boolean. Indicates whether saving the rasters will be allowed to overwrite existing files with the same name. Defaults to FALSE.

Value

Raster files of interpolated output variables.

Author(s)

Rachel R. Renne

Examples

# Load targetcells data for Target Cells
data(targetcells)

# Create data frame of potential matching variables for Target Cells
allvars <- makeInputdata(targetcells)

# Subset to include only matching variables
matchingvars <- allvars[,c("cellnumbers","x","y","bioclim_01","bioclim_04",
                       "bioclim_09","bioclim_12","bioclim_15","bioclim_18")]

# Create raster_template
raster_template <- targetcells[[1]]

# Create vector of matching criteria
criteria <- c(0.7,42,3.3,66,5.4,18.4)


# For an example where subset cells were generated from kpoints function
# Find solution for k = 200
results1 <- kpoints(matchingvars,criteria = criteria,klist = 200,n_starts = 1,
                    min_area = 50,iter = 50,raster_template = raster_template)

# Get points from solution to kpoints algorithm
subsetcells <- results1$solutions[[1]]

# Find matches and calculate matching quality
quals <- multivarmatch(matchingvars, subsetcells, criteria = criteria,
                         matchingvars_id = "cellnumbers",
                         raster_template = raster_template,
                         subset_in_target = TRUE)

# Create toy data set of "output variables"
# There are really just climate variables from the 'targetcells' rasters,
# but we will treat them as output variables to illustrate the method
output_results <- allvars[rownames(subsetcells),
                         c("cellnumbers","bioclim_02","bioclim_03",
                         "bioclim_16","bioclim_17")]

# Interpolate simulation output to rasters
interpolatePoints(matches = quals, output_results = output_results,
                  exclude_poor_matches = TRUE,
                  subset_cell_names = "subset_cell",
                  quality_name = "matching_quality",
                  matching_distance = 1.5,
                  raster_template = raster_template,
                  plotraster = TRUE,
                  saveraster = FALSE,
                  overwrite = FALSE)


###################################
# For example where subset cells were not generated from kpoints function
# Remove previous subsetcells
rm(subsetcells)

# Get points from solution to kpoints algorithm
data(subsetcells)

# Pull results from subsetcells
output_results <- subsetcells[,c("site_ids","Dryprop","CwetWinter","CdrySummer",
                                 "Cwet8","Dryall","Dryany")]
rownames(output_results) <- output_results$site_ids

# Remove duplicates (representing cells with same climate but different soils--
# we want to match on climate only)
subsetcells <- subsetcells[!duplicated(subsetcells$site_id),]

# Pull out matching variables only, with site_id that identifies unique climate
subsetcells1 <- subsetcells[,c("site_id","X_WGS84","Y_WGS84","bioclim_01",
                           "bioclim_04","bioclim_09","bioclim_12",
                           "bioclim_15","bioclim_18")]

# Ensure that site_id will be values unique to subsetcells
subsetcells1$site_id <- paste0("00",subsetcells$site_id)

# Find matches and calculate matching quality
quals <- multivarmatch(matchingvars, subsetcells=subsetcells1,
                         criteria = criteria,
                         matchingvars_id = "cellnumbers",
                         subsetcells_id = "site_id",
                         raster_templat = targetcells[[1]],
                         subset_in_target = FALSE)

# Bring in matching variables for secondary matching
# Subset to include only secondaryvars
secondaryvars <- allvars[,c("cellnumbers","x","y","sand","clay")]

# Remove previous subsetcells
rm(subsetcells)

# Bring in secondary id variable from subsetcells
data(subsetcells)

# Remove duplicates (keeping only site-specific soils with site_ids ending
# in ".1").
subsetcells <- subsetcells[!duplicated(subsetcells$site_id),]

# Pull out matching variables only, with site_id that identifies unique climate
subsetcells <- subsetcells[,c("site_id","X_WGS84","Y_WGS84",
                              "sand","clay"),]

# Convert sand and clay to percentage from fraction
subsetcells$sand <- subsetcells$sand*100
subsetcells$clay <- subsetcells$clay*100

# Make sure subsetcell ids are unique
subsetcells$site_id <- paste0("00",subsetcells$site_id)

# Bring in "other" treatments
data(setsoiltypes)
other_treatments = setsoiltypes

# Calculate criteria
criteria = c((max(secondaryvars$sand,na.rm = TRUE)-
                 min(secondaryvars$sand,na.rm = TRUE))/10,
             (max(secondaryvars$clay,na.rm = TRUE)-
                 min(secondaryvars$clay,na.rm = TRUE))/10)

# Run secondary matching on soils data
quals2 <- secondaryMatching(secondaryvars = secondaryvars, matches = quals,
                            subsetcells=subsetcells,subsetcells_id = "site_id",
                            subset_in_target = FALSE, criteria = criteria,
                            raster_template = raster_template,
                            reference_treatment = "1",
                            other_treatments = other_treatments)

# Interpolate simulation output to rasters
interpolatePoints(matches = quals2, output_results = output_results,
                  exclude_poor_matches = TRUE,
                  subset_cell_names = "subset_cell_secondary",
                  quality_name = "matching_quality",
                  matching_distance = 1.5,
                  raster_template = raster_template,
                  plotraster = TRUE,
                  saveraster = FALSE,
                  overwrite = FALSE)

DrylandEcology/rMultivariateMatching documentation built on Dec. 17, 2021, 5:30 p.m.