Skip to contents

Optimize Plot Design Based on Optimal Correlations

optimize.plot.design.Rd

Generation of interactive heatmaps graphically represent the optimal correlations between variables estimated from field data, and metrics derived from TLS data. These data must be derived from any of the three different plot designs currently available (circular fixed area, k-tree and angle-count) and correspond to plots with incremental values for the plot design parameter (radius, k and BAF, respectively). In addition, correlation measures that are currently admissible are Pearson's correlation coefficient and/or Spearman's rho.

Usage

optimize.plot.design(correlations,
                     variables = c("N", "G", "V", "d", "dg", "d.0", "h", "h.0"),
                     dir.result = NULL)

Arguments

correlations

List including the optimal correlations between field estimations and TLS metrics. The structure and format must be analogous to the opt.correlations element in the output returned by the correlations function. In particular, the list must include at least one of the following named elements:

  • pearson: list containing the optimal Pearson's correlations, and the names of the TLS metrics to which they correspond. It must include at least one of the following named elements:

    • fixed.area.plot: data frame containing the optimal Pearson's correlations, and the names of the TLS metrics to which they correspond, under circular fixed area plot design. Each row will correspond to a radius value, and the data frame will include the following columns:

      • radius: radius (m) of the simulated plots used for computing the estimated correlations.

      • Columns ‘<x>.cor’ and ‘<x>.metric’: the former, numeric column(s) containing optimal Pearson's correlations between ‘<x>’, a field estimate, and the available TLS metrics when correlations function was executed; and the latter, character column(s) containing names of the TLS metrics to which they correspond.

      If fixed.area.plot is included in pearson element, it must contain at least the radius column and a (‘<x>.cor’, ‘<x>.metric’) pair of columns corresponding to the same field estimation.

    • k.tree.plot: data frame including the optimal Pearson's correlations and the names of the TLS metrics to which they correspond, under the k-tree plot design. Each row will correspond to a k value, and the following columns will be included:

      • k: number of trees (trees) of the simulated plots used for computing the estimated correlations.

      • Columns ‘<x>.cor’ and ‘<x>.metric’: same description and format as indicated in correlations$pearson$fixed.area.plot element.

      If k.tree.plot is included in pearson element, it must contain at least k column, and a (‘<x>.cor’, ‘<x>.metric’) pair of columns corresponding to the same field estimation.

    • angle.count.plot: data frame including the optimal Pearson's correlations and the names of the TLS metrics to which they correspond, for the angle-count plot design. Each row will correspond to a BAF value, and the data frame will include the following columns:

      • BAF: BAF (\({m}^{2}/ha\)) of the simulated plots used to compute the estimated correlations.

      • Columns ‘<x>.cor’ and ‘<x>.metric’: same description and format as indicated in correlations$pearson$fixed.area.plot element.

      If the angle.count.plot is included in the pearson element, it must include at least the BAF column and a (‘<x>.cor’, ‘<x>.metric’) pair of columns corresponding to the same field estimation.

  • spearman: list containing the optimal Spearman's correlations, and the names of the TLS metrics to which they correspond. The structure and format will be analogous to that indicated for the previous element but with optimal Pearson's correlations replaced by Spearman's correlations.

variables

Optional character vector naming field estimations whose optimal correlations will be represented graphically in the heatmaps generated during the execution. If this argument is specified by the user, it must include at least one of the following character strings: “N”, “G”, “V”, “d”, “dg”, “dgeom”, “dharm”, “d.0”, “dg.0”, “dgeom.0”, “dharm.0”, “h”, “hg”, “hgeom”, “hharm”, “h.0”, “hg.0”, “hgeom.0”, or “hharm.0”. If this argument is not specified by the user, it will be set to c("N", "G", "V", "d", "dg", "d.0", "h", "h.0") by default. In both cases, all data frames in the correlations argument must have at least the (‘<x>.cor’, ‘<x>.metric’) pairs corresponding to the field estimations specified in the variables argument.

dir.result

Optional character string naming the absolute path of an existing directory where files described in ‘Output Files’ section will be saved. .Platform$file.sep must be used as the path separator in dir.result. If this argument is not specified by the user, it will be set to NULL by default and, as consequence, the current working directory of the R process will be assigned to dir.result during the execution.

Details

This function represents graphically, by means of interactive heatmaps, the strongest correlations (positive or negative) for each plot design and size simulated, between the estimated variables based on field data specified in the variables argument, and metrics derived from TLS data, under circular fixed area, k-tree and/or angle-count plot designs.

Two correlation measures are implemented at present: Pearson’s correlation coefficient and Spearman’s rho. Hence, only optimal correlations based on correlations arguments will be taken into account during the execution.

For each correlation measure and plot design, at least one no missing value for optimal correlations must be represented; otherwise, execution will be stopped, and an error message will appear. In addition, at least two different no missing values for optimal correlations are required to ensure that the colour palette is correctly applied when the heatmap is generated.

Value

Invisible NULL.

Output Files

During the execution, interactive heatmaps graphically representing optimal correlations values between field estimations and TLS metrics are created and saved in dir.result directory by means of the saveWidget function in the htmlwidgets package. The widgets generated allow users to consult optimal correlations values and TLS metrics to which they correspond directly on the plots, to zoom and scroll, and so on. The pattern used for naming these files is opt.correlations.<plot design>.<method>.html, where <plot design> equals “fixed.area.plot”, “k.tree.plot” or “angle.count.plot” according to plot design, and <method> equals “pearson” or “spearman” according to correlation measure.

Note

This function is key to choosing the best possible plot design (in terms of correlation measures) considering all variables of interest before establishing definitive sampling design.

Author

Juan Alberto Molina-Valero, María José Ginzo Villamayor, Manuel Antonio Novo Pérez, Adela Martínez-Calvo, Juan Gabriel Álvarez-González, Fernando Montes and César Pérez-Cruzado.

See also

correlations.

Examples

# \donttest{

# Load field estimations and TLS metrics corresponding to Rioja data set

data("Rioja.simulations")


# Compute correlations between field estimations and TLS metrics corresponding
# to Rioja example, and select optimal correlations results

corr <- correlations(simulations = simulations,
                     variables = c("N", "G", "d", "dg", "dgeom","dharm",
                                   "d.0", "dg.0", "dgeom.0", "dharm.0", "h",
                                   "hg", "hgeom", "hharm", "h.0", "hg.0",
                                   "hgeom.0", "hharm.0"),
                     save.result = FALSE)
#> Computing correlations for fixed area plots
#>  (372.16 secs)
#> Computing correlations for k-tree plots
#>  (94.56 secs)
#> Computing correlations for angle-count plots
#>  (73.34 secs)

opt.corr <- corr$opt.correlations


# Establish directory where optimal correlations heatmaps corresponding to Rioja
# example will be saved. For instance, current working directory

dir.result <- getwd()


# Generate heatmaps for optimal correlations between field estimations and TLS
# metrics corresponding to Rioja example
# Optimal Pearson's and Spearman's correlations for variables by default

# optimize.plot.design(correlations = opt.corr, dir.result = dir.result)


# Optimal Pearson's and Spearman's correlations for variables 'N' and 'G'

optimize.plot.design(correlations = opt.corr, variables = c("N", "G"),
                     dir.result = dir.result)
#> Plotting heatmap(s) for optimal Pearson's correlations 
#>  (6.57 secs)
#> Plotting heatmap(s) for optimal Spearman's correlations 
#>  (5.5 secs)


  # }