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Forest Inventory Estimation and Analysis

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`FIESTA` Manual - Module Estimates

`FIESTA` Manual - Module Estimates
In FIESTA: Forest Inventory Estimation and Analysis 

library(knitr)
knitr::opts_chunk$set(message = F, warning = F)

# Sets up output folding
hooks = knitr::knit_hooks$get()
hook_foldable = function(type) {
  force(type)
  function(x, options) {
    res = hooks[[type]](x, options)

    if (isFALSE(options[[paste0("fold.", type)]])) return(res)

    paste0(
      "<details><summary>", type, "</summary>\n\n",
      res,
      "\n\n</details>"
    )
  }
}
knitr::knit_hooks$set(
  output = hook_foldable("output"),
  plot = hook_foldable("plot")
)

data.table::setDTthreads(2)

Overview

FIESTA's Estimation Modules combine multiple functions from FIESTA or other packages to generate estimates across a user-defined population(s) using different estimation strategies. Each module has an associated mod*pop function for compiling the population data and calculations, including adjustments for nonresponse and standardizing auxiliary data. The output form the mod*pop functions are input directly into the mod* estimation modules.

All Estimation Modules require similar inputs, including a set of population data output from associated mod*pop functions and other parameters defining filters and row and/or column domains. Refer to the following vignettes for specific examples.

Module Parameters {#input}

The parameters for FIESTA modules are organized by different categories based on population data and resulting estimates.

Estimates:

  1. Plot/condition filters to subset table records
  2. Tree information for tree estimates - modGBtree()
  3. Tree information for ratio estimates - modGBratio()
  4. Output table information
  5. Output table specifications
  6. Output table title(s)

Estimates {#estout}

Estimation Filters

Filters subset the area of the sample population for the desired output. Filters do no change the population data used for estimate (e.g., number of plots, strata weights). Some filters are defined internally, depending on the dataset, such as land area of interest (landarea) or inclusion of ACI (All Condition Inventory) data. Others are specified according to the input table, such as pfilter or cfilter. The nonsamp.filter is for removing nonsampled conditions and is applied, internally, unless otherwise stated. All filter expressions must be in R syntax.

Tree information for tree estimates - modGBtree()

Tree information is used for calculating estimates for derived tree data, such as basal area (BA), volume (e.g., VOLCFNET), or number of trees (i.e., TPA_UNADJ). All variables except number of tree variables are extrapolated to the acre plot size by multiplying by TPA_*).

Tree information for ratio estimates - modGBratio()

Tree information is used for calculating per-acre ratio estimates (numerator) and per-tree ratio estimates (numerator and denominator) from derived tree data, such as basal area (BA), volume (e.g., VOLCFNET), or number of trees (i.e., TPA_UNADJ). All variables except number of tree variables are extrapolated to the acre plot size by multiplying by TPA_*).

Table estimates

Estimates can be broken down by categorical domains for further detail about a population. These details are presented as rows or rows and columns in a matrix table, with a separate, associated sampling error for each cell in the table. If the area of interest (i.e., population) has more than one estimation unit (i.e., subpopulation), estimates may be presented by estimation unit or summed to the population.

Other

Output table options (table_opts)

The following parameters provide more detail about the presentation and aesthetics of the final output table, including: using code names instead of codes; applying specific ordering; adding categories not in the data set with 0 values; displaying percent standard errors and estimates together or separate; and rounding options. These parameters are set by supplying a list to the table_opts parameter. The possible parameters that can be set within the table_opts parameter can be seen by running help(table_options).

Note: rowlut/collut - There are several objectives for including rowlut/collut look-up tables: 1) to include descriptive names that match row/column codes in the input table; 2) to use number codes that match row/column names in the input table for ordering rows; 3) to add rows and/or columns with 0 values for consistency. No duplicate names are allowed.

Include 2 columns in the table: 1-the merging variable with same name as the variable in the input merge table; 2-the ordering or descriptive variable. If the ordering variable is the rowvar/colvar in the input table and the descriptive variable is in rowlut/collut, set row.orderby/col.orderby equal to rowvar/colvar. If the descriptive variable is the rowvar/colvar in the input table, and the ordering code variable is in rowlut/collut, set row.orderby/col.orderby equal to the variable name of the code variable in rowlut/collut.

Title parameters (title_opts)

If returntitle=TRUE, a default title is generated based on the estimation parameters above. The following parameters allow user-defined inputs for generating customized titles. These parameters are set by supplying a list to the title_opts parameter. The possible parameters that can be set within the title_opts parameter can be seen by running help(title_options).

Saving data specifications (savedata_opts)

If savedata=TRUE, output is saved using default settings. The following parameters specify different options for saving output. These parameters are set by supplying a list to the savedata_opts parameter. The possible parameters that can be set within the savedata_opts parameter can be seen by running help(savedata_options).

Output values from FIESTA modules

Estimates with percent sampling error for the row domain (and column domain) specified by the input parameters. This can be in the form of one table or two separate tables, depending on the number of domains and on allin1 parameter.

A list object with one or more of the following components. If savedata=TRUE, all output data frames are written to outfolder.

Raw data used for producing estimates (If rawdata=TRUE, raw$):

The raw data includes the domain-level data set used for estimation and separate data frames with calculated variables used in estimation process. The number of processing tables depends on the input parameters. The tables include:

nonratio <- data.frame(Variable = c("nhat", "nhat.var", "est", "est.var"), Description = c("Estimated proportion", "Variance estimate of estimated proportion", "Estimated acres { nhat * ACRES }", "Variance estimate of estimated acres { nhat * ACRES^2 }"), stringsAsFactors = FALSE)

ratio <- data.frame(Variable = c("nhat", "nhat.var", "dhat", "dhat.var", "covar", "estn", "estd", "estn.var", "estn.se", "estn.cv", "estn.pse", "estd.var", "estd.se", "estd.cv", "estd.pse", "est.covar", "rhat", "rhat.var", "rhat.se", "rhat.cv", "est", "est.var"), Description = c("Estimated proportion of land, for numerator", "Variance estimate of estimated proportion of land, for numerator", "Estimated proportion of land, for denominator", "Variance estimate of estimated proportion of land, for denominator", "Covariance of estimated proportion of numerator and denominator", "Estimated acres, for numerator", "Estimated acres, for denominator", "Variance estimate of estimate acres, for numerator", "Standard error estimated acres, for numerator", "Coefficient of variation of estimated acres, for numerator", "Percent sampling error of estimate, for numerator", "Variance estimate of estimate acres, for denominator", "Standard error estimated acres, for denominator", "Coefficient of variation of estimated acres, for denominator", "Percent sampling error of estimate, for denominator", "Covariance of estimated acres of numerator and denominator", "Ratio of estimated proportions (numerator/denominator)", "Variance of ratio of estimated proportions", "Standard error of ratio of estimated proportions { rhat.se/rhat }", "Coefficient of variation of ratio of estimated proportions { sqrt(rhat.var) }", "Estimated percent cover of land { rhat*100 }", "Variance of estimated percent cover of land { rhat.var*100^2 }"), stringsAsFactors = FALSE)

all <- data.frame(Variable = c("NBRPLT.gt0", "ACRES", "est.se", "est.cv", "pse", "CI99left", "CI99right", "CI95left", "CI95right", "CI68left", "CI68right"), Description = c("Number of nonzero plots used in estimate", "Total acres for estimation unit", "Standard error of estimated acres { sqrt(est.var) }", "Coefficient of variation of estimated acres { est.se/est }", "Percent sampling error of estimate { est.cv * 100 }", "Left tail of 99% confidence interval for estimate { est - (2.58*est.se) }", "Right tail of 99% confidence interval for estimate { est + (2.58*est.se) }", "Left tail of 95% confidence interval for estimate { est - (1.96*est.se) }", "Right tail of 95% confidence interval for estimate { est + (1.96*est.se) }", "Left tail of 68% confidence interval for estimate { est - (0.97*est.se) }", "Right tail of 68% confidence interval for estimate { est + (0.97*est.se) }"), stringsAsFactors = FALSE)



kable(nonratio,
  format = "pandoc",   # default
  caption = "Description of variables in nonratio tables.",
  col.names = names(nonratio),
  row.names = FALSE,
  align = c("l"),       # align = c("c", "c", "c", "r")
  # padding = 2         # inner spacing
) 


kable(ratio,
  format = "pandoc",   # default
  caption = "Description of variables in nonratio tables.",
  col.names = names(ratio),
  row.names = FALSE,
  align = c("l"),       # align = c("c", "c", "c", "r")
  # padding = 2         # inner spacing
) 


kable(all,
  format = "pandoc",   # default
  caption = "Description of variables in nonratio and ratio tables.",
  col.names = names(all),
  row.names = FALSE,
  align = c("l"),       # align = c("c", "c", "c", "r")
  # padding = 2         # inner spacing
)

Reference Tables in FIESTA

There are several reference tables stored in FIESTA, including table variable descriptions, code definitions, and estimation categories.

getdomain() getestvar()



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FIESTA documentation built on Nov. 22, 2023, 1:07 a.m.

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