uhcsimXvalid: uhcsimXvalid
In aaarchmiller/uhcplots: Used-Habitat Calibration Plots (UHC Plots)

Description Usage Arguments Details Value See Also Examples

uhcsimXvalid samples, randomly, locations from non-stratified test data using k-fold cross validation.

1 2	uhcsimXvalid(data_sim, k_folds, nsims, nused, navail, ncovariates, model_form, z_colnames, xmat_colnames)

`data_sim`	The data set to be used in cross-validation
`k_folds`	The number of bins to split data into
`nsims`	The number of simulations (M) used to create the UHC plot.
`nused`	The number of used locations in the data set.
`navail`	The number of available locations in the data set (ie not used)
`ncovariates`	The number of covariates or p in the model
`model_form`	The model in the form: "y~x1+x2+...+xp" (including quotations)
`z_colnames`	The column names of the environmental characteristics to be plotted with `uhcdensplot`. Takes the form: c("x1","x2",...,"xp")
`xmat_colnames`	The column names of the environmental characteristics that were used as predictors in the model_form. Takes the form: c("x1","x2",...,"xp")

This function samples, randomly, locations from non-stratified test data and returns an array of dimension nsims x nused_test x p (where p is the number of predictors to be validated)

An array of dimensions nsims x nused_test x p.

Full archive of the data and code necessary to replicate the manuscript at http://doi.org/10.13020/D6T590.

# Simulate data using uhcplots::uhcdatasimulator
data_sim <- uhcdatasimulator(nused = 100,
                            navail = 10000,
                            betas = c(0.5, -1) ,
                            corx = 0,
                            ntemp = 100000,
                            example = "missing predictor")

#Set up parameters

# Number of used and available locations in dataset
used <- data_sim[data_sim$y==1,];  nused <- nrow(used)
avail <- data_sim[data_sim$y==0,]; navail <- nrow(avail)

## k-fold cross validation

#**Step 1** Partition data into k-fold bins
k_folds <- 10
nused_k <- nused/k_folds
navail_k <- navail/k_folds
data_sim$k <- dismo::kfold(data_sim, k = k_folds, by = data_sim$y)
table(data_sim$k)

#**Step 2** Fit a model (in this case, missing precipitation)

# Set up some parameters
nsims <- 1000 # M from paper;
ncovariates <- 2;
model_form_missingP <- "y~elev"
z_colnames <- c("elev","precip")
xmat_colnames_missingP <- c("elev")

# Create an array to store chosen location covariates for each simulation
# and each k-fold "test" data bin
x_sim_choice_missingP <- array(NA, dim = c(nsims, nused, ncovariates))
# Get predicted distribution of covariates for each fold, k:
x_sims_choice_missingP <- uhcplots::uhcsimXvalid(data_sim = data_sim,
                                                k_folds = k_folds,
                                                nsims = nsims,
                                                nused = nused,
                                                navail = navail,
                                                ncovariates = ncovariates,
                                                model_form = model_form_missingP,
                                                z_colnames = z_colnames,
                                                xmat_colnames = xmat_colnames_missingP)
# **Step 3** Density calculations
denscalc_elev_missingP <- uhcdenscalc(rand_sims = x_sims_choice_missingP[,,1],
                                     dat = used[,"elev"],
                                     avail = avail[,"elev"],
                                     gridsize = 500)
denscalc_prec_missingP <- uhcdenscalc(rand_sims = x_sims_choice_missingP[,,2],
                                     dat = used[,"precip"],
                                     avail = avail[,"precip"],
                                     gridsize = 500)
# **Step 4** Create UHC plots for each covariate
opar<-par()
layout(matrix(c(1,2,3,3), ncol=2, byrow=TRUE), heights=c(4,1))
par(mai=rep(0.5, 4))
uhcdensplot(densdat = denscalc_elev_missingP$densdat,
           densrand = denscalc_elev_missingP$densrand,
           includeAvail = TRUE,
           densavail = denscalc_elev_missingP$densavail,includeLegend = FALSE)
mtext(outer=FALSE, side=1, line = 3, "Elevation")

uhcdensplot(densdat = denscalc_prec_missingP$densdat,
           densrand = denscalc_prec_missingP$densrand,
           includeAvail = TRUE,
           densavail = denscalc_prec_missingP$densavail,
           includeLegend = FALSE)
mtext(outer=FALSE, side=1, line= 3, "Precipitation")
par(mai=c(0,0,0,0))
plot.new()
legend("center",c("Available", "Used", "Predicted"), lty = c(1,2,1),
     col = c("black", "red", "grey"), bty = "n", lwd = c(1,1,5))
par<-opar