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Summarise by plot the posterior distribution of AGB values

Source: R/summaryByPlot.R
summaryByPlot.Rd

This function summarises the matrix AGB_val given by the function AGBmonteCarlo() by plot.

Usage

summaryByPlot(AGB_val, plot, drawPlot = FALSE)

Arguments

AGB_val

Either the matrix resulting from the AGBmonteCarlo() function (AGB_simu element of the list), or simply the output of the AGBmonteCarlo() function itself.

plot

Vector corresponding to the plots code (plots ID)

drawPlot

A logical indicating whether the graphic should be displayed or not

Value

a data frame where:

  • plot: the code of the plot

  • AGB: AGB value at the plot level

  • Cred_2.5: the 2.5\

  • Cred_97.5: the 97.5\

Details

If some trees belong to an unknown plot (i.e. NA value in the plot arguments), their AGB values are randomly assigned to a plot at each iteration of the AGB monte Carlo approach.

Examples


# Load a database
data(NouraguesHD)
data(NouraguesTrees)

# Modelling height-diameter relationship
HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2", bayesian = FALSE)

# Retrieving wood density values
# \donttest{
  NouraguesWD <- getWoodDensity(NouraguesTrees$Genus, NouraguesTrees$Species,
                                stand = NouraguesTrees$plotId)
#> Your taxonomic table contains 409 taxa
#> Warning: 142 taxa don't match the Global Wood Density Database V2. You may provide 'family' to match wood density estimates at family level.
# }

# Propagating errors
# \donttest{
  resultMC <- AGBmonteCarlo(
    D = NouraguesTrees$D, WD = NouraguesWD$meanWD,
    errWD = NouraguesWD$sdWD, HDmodel = HDmodel )
  
  # The summary by plot
  summaryByPlot(AGB_val = resultMC$AGB_simu, plot = NouraguesTrees$Plot)
#>   plot      AGB Cred_2.5 Cred_97.5
#> 1  201 439.5721 399.0864  493.1228
#> 2  204 500.8183 456.9948  552.6980
#> 3  213 399.1548 358.5400  445.7011
#> 4  223 271.5084 246.8512  300.2784
# }