Nothing
R/modMAtree.R
In FIESTA: Forest Inventory Estimation and Analysis
Defines functions
modMAtree
Documented in
modMAtree
#' Model-Assisted module - Generate model-assisted tree estimates.
#'
#' Generates tree estimates by estimation unit. Estimates are calculated from
#' McConville et al. (2018)'s mase R package.
#'
#' If variables are NULL, then it will prompt user to input variables.
#'
#' Necessary variables:\cr \tabular{llll}{ \tab \bold{Data} \tab
#' \bold{Variable} \tab \bold{Description}\cr \tab tree \tab tuniqueid \tab
#' Unique identifier for each plot, to link to pltassgn (e.g. PLT_CN).\cr \tab
#' \tab CONDID \tab Unique identifier of each condition on plot, to link to
#' cond. Set CONDID=1, if only 1 condition per plot.\cr \tab \tab TPA_UNADJ
#' \tab Number of trees per acre each sample tree represents (e.g., DESIGNCD=1:
#' TPA_UNADJ=6.018046 for trees on subplot; 74.965282 for trees on
#' microplot).\cr \tab cond \tab cuniqueid \tab Unique identifier for each
#' plot, to link to pltassgn (ex. PLT_CN).\cr \tab \tab CONDID \tab Unique
#' identifier of each condition on plot. Set CONDID=1, if only 1 condition per
#' plot.\cr \tab \tab CONDPROP_UNADJ \tab Unadjusted proportion of condition on
#' each plot. Set CONDPROP_UNADJ=1, if only 1 condition per plot.\cr \tab \tab
#' COND_STATUS_CD \tab Status of each forested condition on plot (i.e.
#' accessible forest, nonforest, water, etc.)\cr \tab \tab NF_COND_STATUS_CD
#' \tab If ACI=TRUE. Status of each nonforest condition on plot (i.e.
#' accessible nonforest, nonsampled nonforest)\cr \tab \tab SITECLCD \tab If
#' landarea=TIMBERLAND. Measure of site productivity.\cr \tab \tab RESERVCD
#' \tab If landarea=TIMBERLAND. Reserved status.\cr \tab \tab SUBPROP_UNADJ
#' \tab Unadjusted proportion of subplot conditions on each plot. Set
#' SUBPROP_UNADJ=1, if only 1 condition per subplot.\cr \tab \tab
#' MICRPROP_UNADJ \tab If microplot tree attributes. Unadjusted proportion of
#' microplot conditions on each plot. Set MICRPROP_UNADJ=1, if only 1 condition
#' per microplot.\cr \tab \tab MACRPROP_UNADJ \tab If macroplot tree
#' attributes. Unadjusted proportion of macroplot conditions on each plot. Set
#' MACRPROP_UNADJ=1, if only 1 condition per macroplot.\cr \tab pltassgn \tab
#' puniqueid \tab Unique identifier for each plot, to link to cond (ex. CN).\cr
#' \tab \tab STATECD \tab Identifies state each plot is located in.\cr \tab
#' \tab INVYR \tab Identifies inventory year of each plot.\cr \tab \tab
#' PLOT_STATUS_CD \tab Status of each plot (i.e. sampled, nonsampled). If not
#' included, all plots are assumed as sampled.\cr }
#'
#' Reference names are available for the following variables: \cr ADFORCD,
#' AGENTCD, CCLCD, DECAYCD, DSTRBCD, KINDCD, OWNCD, OWNGRPCD, FORTYPCD,
#' FLDTYPCD, FORTYPCDCALC, TYPGRPCD, FORINDCD, RESERVCD, LANDCLCD, STDSZCD,
#' FLDSZCD, PHYSCLCD, MIST_CL_CD, PLOT_STATUS_CD, STATECD, TREECLCD, TRTCD,
#' SPCD, SPGRPCD
#'
#' @param MApopdat List. Population data objects returned from modMApop().
#' @param MAmethod String. mase (i.e., model-assisted) method to use
#' ('greg', 'gregEN', 'ratio').
#' @param estvar String. Name of the tree-level estimate variable (e.g.,
#' 'VOLCFNET').
#' @param estvar.filter String. A tree-level filter for estvar. Must be R
#' syntax (e.g., 'STATUSCD == 1').
#' @param estseed String. Use seedling data only or add to tree data. Seedling
#' estimates are only for counts (estvar='TPA_UNADJ')-('none', 'only', 'add').
#' @param woodland String. If woodland = 'Y', include woodland tree species
#' where measured. If woodland = 'N', only include timber species. See
#' FIESTA::ref_species$WOODLAND ='Y/N'. If woodland = 'only', only include
#' woodland species.
#' @param landarea String. The condition-level filter for defining land area
#' ('ALL', 'FOREST', 'TIMBERLAND'). If landarea='FOREST', COND_STATUS_CD = 1;
#' if landarea='TIMBERLAND', SITECLCD in(1:6) & RESERVCD = 0.
#' @param pcfilter String. A filter for plot or cond attributes (including
#' pltassgn). Must be R logical syntax.
#' @param rowvar String. Optional. Name of domain variable to group estvar by
#' for rows in table output. Rowvar must be included in an input data frame
#' (i.e., plt, cond, tree). If no rowvar is included, an estimate is returned
#' for the total estimation unit. Include colvar for grouping by 2 variables.
#' @param colvar String. Optional. If rowvar != NULL, name of domain variable
#' to group estvar by for columns in table output. Colvar must be included in
#' an input data frame (i.e., plt, cond, tree).
#' @param prednames String vector. Name(s) of predictor variables to include in
#' model.
#' @param modelselect Logical. If TRUE, an elastic net regression model is fit
#' to the entire plot level data, and the variables selected in that model are
#' used for the proceeding estimation.
#' @param FIA Logical. If TRUE, the finite population term is removed from
#' estimator to match FIA estimates.
#' @param bootstrap Logical. If TRUE, returns bootstrap variance estimates,
#' otherwise uses Horvitz-Thompson estimator under simple random sampling
#' without replacement.
#' @param returntitle Logical. If TRUE, returns title(s) of the estimation
#' table(s).
#' @param savedata Logical. If TRUE, saves table(s) to outfolder.
#' @param table_opts List. See help(table_options()) for a list of
#' options.
#' @param title_opts List. See help(title_options()) for a list of options.
#' @param savedata_opts List. See help(savedata_options()) for a list
#' of options. Only used when savedata = TRUE.
#' @param gui Logical. If gui, user is prompted for parameters.
#' @param modelselect_bydomain Logical. If TRUE, modelselection will occur at
#' the domain level as specified by rowvar and/or colvar and not at the level of
#' the entire sample.
#' @param ... Parameters for modMApop() if MApopdat is NULL.
#' @return If FIA=TRUE or unitvar=NULL and colvar=NULL, one data frame is
#' returned with tree estimates and percent sample errors. Otherwise, a list is
#' returned with tree estimates in one data frame (est) and percent sample
#' errors in another data frame (est.pse). If rawdata=TRUE, another list is
#' returned including raw data used in the estimation process. If
#' addtitle=TRUE and returntitle=TRUE, the title for est/pse is returned. If
#' savedata=TRUE, all data frames are written to outfolder.
#'
#' \item{est}{ Data frame. Tree estimates by rowvar, colvar (and estimation
#' unit). If FIA=TRUE or one estimation unit and colvar=NULL, estimates and
#' percent sampling error are in one data frame. } \item{pse}{ Data frame.
#' Percent sampling errors for estimates by rowvar and colvar (and estimation
#' unit). } \item{titlelst}{ List with 1 or 2 string vectors. If
#' returntitle=TRUE a list with table title(s). The list contains one title if
#' est and pse are in the same table and two titles if est and pse are in
#' separate tables. } \item{raw}{ List of data frames. If rawdata=TRUE, a list
#' including: number of plots by plot status, if in dataset (plotsampcnt);
#' number of conditions by condition status (condsampcnt); data used for
#' post-stratification (stratdat); and 1-8 tables with calculated variables
#' used for processing estimates and percent sampling error for table cell
#' values and totals (See processing data below). }
#'
#' Raw data
#'
#' \item{plotsampcnt}{ Table. Number of plots by plot status (ex. sampled
#' forest on plot, sampled nonforest, nonsampled). } \item{condsampcnt}{ DF.
#' Number of conditions by condition status (forest land, nonforest land,
#' noncensus water, census water, nonsampled). }
#'
#' \item{stratdat}{ Data frame. Strata information by estimation unit. }
#' \tabular{lll}{ \tab \bold{Variable} \tab \bold{Description}\cr \tab ESTUNIT
#' \tab estimation unit\cr \tab STRATA \tab strata \cr \tab ACRES \tab area by
#' strata for estimation unit\cr \tab n.strata \tab number of plots in strata
#' (and estimation unit) \cr \tab n.total \tab number of plots for estimation
#' unit \cr \tab TOTACRES \tab total area for estimation unit \cr \tab strwt
#' \tab proportion of area (or number of plots) by strata (strata weight) \cr
#' \tab expfac.strata \tab expansion factor (in area unit (e.g., acres) by
#' strata (areavar/n.strata) \cr }
#'
#' \item{processing data}{ Data frames. Separate data frames containing
#' calculated variables used in estimation process. The number of processing
#' tables depends on the input parameters. The tables include: total by
#' estimation unit (unit.totest); rowvar totals (unit.rowest), and if colvar is
#' not NULL, colvar totals, (unit.colvar); and a combination of rowvar and
#' colvar (unit.grpvar). If FIA=TRUE, the raw data for the summed estimation
#' units are also included (totest, rowest, colest, grpest, respectively).
#' These tables do not included estimate proportions (nhat and nhat.var).
#'
#' The data frames include the following information: \tabular{lll}{ \tab
#' \bold{Variable} \tab \bold{Description}\cr \tab nhat \tab estimated
#' proportion of trees \cr \tab nhat.var \tab estimated variance of estimated
#' proportion of trees \cr \tab ACRES \tab total area for estimation unit \cr
#' \tab est \tab estimated area of trees nhat*ACRES \cr \tab est.var \tab
#' estimated variance of estimated area of trees nhat.var*areavar^2 \cr \tab
#' est.se \tab standard error of estimated area of trees sqrt(est.var) \cr \tab
#' est.cv \tab coefficient of variation of estimated area of trees est.se/est
#' \cr \tab pse \tab percent sampling error of estimate est.cv*100 \cr \tab
#' CI99left \tab left tail of 99 percent confidence interval for estimated area
#' \cr \tab CI99right \tab right tail of 99 percent confidence interval for
#' estimated area \cr \tab CI95left \tab left tail of 95 percent confidence
#' interval for estimated area \cr \tab CI95right \tab right tail of 95 percent
#' confidence interval for estimated area \cr \tab CI67left \tab left tail of
#' 67 percent confidence interval for estimated area \cr \tab CI67right \tab
#' right tail of 67 percent confidence interval for estimated area \cr } }
#'
#' Table(s) are also written to outfolder.
#' @note
#'
#' ADJUSTMENT FACTOR:\cr The adjustment factor is necessary to account for
#' nonsampled conditions. It is calculated for each estimation unit by strata.
#' by summing the unadjusted proportions of the subplot, microplot, and
#' macroplot (i.e. *PROP_UNADJ) and dividing by the number of plots in the
#' strata/estimation unit).
#'
#' An adjustment factor is determined for each tree based on the size of the
#' plot it was measured on. This is identified using TPA_UNADJ as follows:
#'
#' \tabular{llr}{ \tab \bold{PLOT SIZE} \tab \bold{TPA_UNADJ} \cr \tab SUBPLOT
#' \tab 6.018046 \cr \tab MICROPLOT \tab 74.965282 \cr \tab MACROPLOT \tab
#' 0.999188 \cr }
#'
#' If ACI=FALSE, only nonsampled forest conditions are accounted for in the
#' adjustment factor. \cr If ACI=TRUE, the nonsampled nonforest conditions are
#' removed as well and accounted for in adjustment factor. This is if you are
#' interested in estimates for all lands or nonforest lands in the
#' All-Condition-Inventory.
#'
#' sumunits:\cr An estimation unit is a population, or area of interest, with
#' known area and number of plots. Individual counties or combined
#' Super-counties are common estimation units for FIA. An estimation unit may
#' also be a subpopulation of a larger population (e.g., Counties within a
#' State). Subpopulations are mutually exclusive and independent within a
#' population, therefore estimated totals and variances are additive. For
#' example, State-level estimates are generated by summing estimates from all
#' subpopulations within the State (Bechtold and Patterson. 2005. Chapter 2).
#' Each plot must be assigned to only one estimation unit.
#'
#' stratcombine:\cr If MAmethod='PS', and stratcombine=TRUE, and less than 2
#' plots in any one estimation unit, all estimation units with 10 or less plots
#' are combined. The current method for combining is to group the estimation
#' unit with less than 10 plots with the estimation unit following in
#' consecutive order (numeric or alphabetical), restrained by survey unit
#' (UNITCD) if included in dataset, and continuing until the number of plots
#' equals 10. If there are no estimation units following in order, it is
#' combined with the estimation unit previous in order.
#'
#' autoxreduce:\cr If MAmethod='GREG', and autoxreduce=TRUE, and there is an
#' error because of multicolinearity, a variable reduction method is applied to
#' remove correlated variables. The method used is based on the
#' variance-inflation factor (vif) from a linear model. The vif estimates how
#' much the variance of each x variable is inflated due to mulitcolinearity in
#' the model.
#'
#' rowlut/collut:\cr 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:\cr 1-the merging variable with same name as
#' the variable in the input merge table;\cr 2-the ordering or descriptive
#' variable.\cr 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.
#'
#' UNITS:\cr The following variables are converted from pounds (from FIA
#' database) to short tons by multiplying the variable by 0.0005. DRYBIO_AG,
#' DRYBIO_BG, DRYBIO_WDLD_SPP, DRYBIO_SAPLING, DRYBIO_STUMP, DRYBIO_TOP,
#' DRYBIO_BOLE, DRYBIOT, DRYBIOM, DRYBIOTB, JBIOTOT, CARBON_BG, CARBON_AG
#'
#' MORTALITY:\cr For Interior-West FIA, mortality estimates are mainly based on
#' whether a tree has died within the last 5 years of when the plot was
#' measured. If a plot was remeasured, mortality includes trees that were alive
#' the previous visit but were dead in the next visit. If a tree was standing
#' the previous visit, but was not standing in the next visit, no diameter was
#' collected (DIA = NA) but the tree is defined as mortality.
#'
#' Common tree filters: \cr
#'
#' \tabular{llr}{ \tab \bold{FILTER} \tab \bold{DESCRIPTION} \cr \tab "STATUSCD
#' == 1" \tab Live trees \cr \tab "STATUSCD == 2" \tab Dead trees \cr \tab
#' "TPAMORT_UNADJ > 0" \tab Mortality trees \cr \tab "STATUSCD == 2 & DIA >=
#' 5.0" \tab Dead trees >= 5.0 inches diameter \cr \tab "STATUSCD == 2 &
#' AGENTCD == 30" \tab Dead trees from fire \cr }
#' @author Tracey S. Frescino
#' @references Kelly McConville, Becky Tang, George Zhu, Shirley Cheung, and
#' Sida Li (2018). mase: Model-Assisted Survey Estimation. R package version
#' 0.1.2 https://cran.r-project.org/package=mase
#' @keywords data
#' @examples
#' \donttest{
#' # Set up population dataset (see ?modMApop() for more information)
#' MApopdat <- modMApop(popTabs = list(tree = FIESTA::WYtree,
#' cond = FIESTA::WYcond),
#' pltassgn = FIESTA::WYpltassgn,
#' pltassgnid = "CN",
#' unitarea = FIESTA::WYunitarea,
#' unitvar = "ESTN_UNIT",
#' unitzonal = FIESTA::WYunitzonal,
#' prednames = c("dem", "tcc", "tpi", "tnt"),
#' predfac = "tnt")
#'
#' # Use GREG Estimator to Estimate cubic foot volume of live trees in our
#' # population
#' mod1 <- modMAtree(MApopdat = MApopdat,
#' MAmethod = "greg",
#' estvar = "VOLCFNET",
#' estvar.filter = "STATUSCD == 1")
#'
#' str(mod1)
#'
#' # Use GREG Elastic Net Estimator to Estimate basal area of live trees in our
#' # population
#' mod2 <- modMAtree(MApopdat = MApopdat,
#' MAmethod = "gregEN",
#' estvar = "BA",
#' estvar.filter = "STATUSCD == 1")
#'
#' str(mod2)
#' }
#' @export modMAtree
modMAtree <- function(MApopdat,
MAmethod,
estvar,
estvar.filter = NULL,
estseed = "none",
woodland = "Y",
landarea = "FOREST",
pcfilter = NULL,
rowvar = NULL,
colvar = NULL,
prednames = NULL,
modelselect = FALSE,
FIA = TRUE,
bootstrap = FALSE,
returntitle = FALSE,
savedata = FALSE,
table_opts = NULL,
title_opts = NULL,
savedata_opts = NULL,
gui = FALSE,
modelselect_bydomain = FALSE,
...){
##################################################################################
## DESCRIPTION:
## Generates model-assisted estimates by domain (and estimation unit)
##################################################################################
## CHECK GUI - IF NO ARGUMENTS SPECIFIED, ASSUME GUI=TRUE
if (nargs() == 0 && is.null(MApopdat)) {
gui <- TRUE
}
## If gui.. set variables to NULL
if (gui) {
tree=landarea=strvar=areavar <- NULL
if (!row.FIAname) row.FIAname <- NULL
if (!col.FIAname) col.FIAname <- NULL
}
## Set parameters
minplotnum <- 10
esttype="TREE"
parameters <- FALSE
returnlst <- list()
sumunits <- FALSE
## Set global variables
ONEUNIT=n.total=n.strata=strwt=TOTAL <- NULL
rawdata <- TRUE
##################################################################
## CHECK PARAMETER NAMES
##################################################################
## Check input parameters
input.params <- names(as.list(match.call()))[-1]
formallst <- c(names(formals(modMAtree)),
names(formals(modMApop)))
if (!all(input.params %in% formallst)) {
miss <- input.params[!input.params %in% formallst]
stop("invalid parameter: ", toString(miss))
}
## Check parameter lists
pcheck.params(input.params, table_opts=table_opts, title_opts=title_opts,
savedata_opts=savedata_opts)
## Set savedata defaults
savedata_defaults_list <- formals(savedata_options)[-length(formals(savedata_options))]
for (i in 1:length(savedata_defaults_list)) {
assign(names(savedata_defaults_list)[[i]], savedata_defaults_list[[i]])
}
## Set user-supplied savedata values
if (length(savedata_opts) > 0) {
if (!savedata) {
message("savedata=FALSE with savedata parameters... no data are saved")
}
for (i in 1:length(savedata_opts)) {
if (names(savedata_opts)[[i]] %in% names(savedata_defaults_list)) {
assign(names(savedata_opts)[[i]], savedata_opts[[i]])
} else {
stop(paste("Invalid parameter: ", names(savedata_opts)[[i]]))
}
}
}
## Set table defaults
table_defaults_list <- formals(table_options)[-length(formals(table_options))]
for (i in 1:length(table_defaults_list)) {
assign(names(table_defaults_list)[[i]], table_defaults_list[[i]])
}
## Set user-supplied table values
if (length(table_opts) > 0) {
for (i in 1:length(table_opts)) {
if (names(table_opts)[[i]] %in% names(table_defaults_list)) {
assign(names(table_opts)[[i]], table_opts[[i]])
} else {
stop(paste("Invalid parameter: ", names(table_opts)[[i]]))
}
}
}
## Set title defaults
title_defaults_list <- formals(title_options)[-length(formals(title_options))]
for (i in 1:length(title_defaults_list)) {
assign(names(title_defaults_list)[[i]], title_defaults_list[[i]])
}
## Set user-supplied title values
if (length(title_opts) > 0) {
for (i in 1:length(title_opts)) {
if (names(title_opts)[[i]] %in% names(title_defaults_list)) {
assign(names(title_opts)[[i]], title_opts[[i]])
} else {
stop(paste("Invalid parameter: ", names(title_opts)[[i]]))
}
}
}
##################################################################
## CHECK PARAMETER INPUTS
##################################################################
## Check MAmethod
MAmethodlst <- c("greg", "gregEN", "ratio")
MAmethod <- pcheck.varchar(var2check=MAmethod, varnm="MAmethod", gui=gui,
checklst=MAmethodlst, caption="MAmethod", multiple=FALSE, stopifnull=TRUE)
if (MAmethod %in% c("greg", "gregEN")) {
predselectlst <- list()
}
list.items <- c("condx", "pltcondx", "cuniqueid", "condid",
"ACI.filter", "unitarea", "unitvar", "unitlut", "npixels",
"npixelvar", "plotsampcnt", "condsampcnt")
#if (MAmethod == "PS") {
# list.items <- c(list.items, "strvar")
#}
#if (MAmethod == "greg") {
# list.items <- c(list.items, "prednames")
#}
MApopdat <- pcheck.object(MApopdat, "MApopdat", list.items=list.items)
if (is.null(MApopdat)) return(NULL)
condx <- MApopdat$condx
pltcondx <- MApopdat$pltcondx
treex <- MApopdat$treex
seedx <- MApopdat$seedx
if (is.null(treex) && is.null(seedx)) {
stop("must include tree data for tree estimates")
}
cuniqueid <- MApopdat$cuniqueid
condid <- MApopdat$condid
tuniqueid <- MApopdat$tuniqueid
ACI.filter <- MApopdat$ACI.filter
unitarea <- MApopdat$unitarea
areavar <- MApopdat$areavar
areaunits <- MApopdat$areaunits
unitvar <- MApopdat$unitvar
unitlut <- MApopdat$unitlut
unitvars <- MApopdat$unitvars
npixels <- MApopdat$npixels
npixelvar <- MApopdat$npixelvar
expcondtab <- MApopdat$expcondtab
plotsampcnt <- MApopdat$plotsampcnt
condsampcnt <- MApopdat$condsampcnt
states <- MApopdat$states
invyrs <- MApopdat$invyrs
stratcombinelut <- MApopdat$stratcombinelut
predfac <- MApopdat$predfac
strvar <- MApopdat$strvar
adj <- MApopdat$adj
if (MAmethod %in% c("greg", "gregEN", "ratio")) {
if (is.null(prednames)) {
prednames <- MApopdat$prednames
} else {
if (!all(prednames %in% MApopdat$prednames)) {
if (any(prednames %in% MApopdat$predfac)) {
predfacnames <- prednames[prednames %in% MApopdat$predfac]
for (nm in predfacnames) {
prednames[prednames == nm] <- MApopdat$prednames[grepl(nm, MApopdat$prednames)]
}
} else {
stop("invalid prednames... must be in: ", toString(MApopdat$prednames))
}
}
}
}
########################################
## Check area units
########################################
unitchk <- pcheck.areaunits(unitarea=unitarea, areavar=areavar,
areaunits=areaunits, metric=metric)
unitarea <- unitchk$unitarea
areavar <- unitchk$areavar
areaunits <- unitchk$outunits
if (is.null(key(unitarea))) {
setkeyv(unitarea, unitvar)
}
###################################################################################
## Check parameters and apply plot and condition filters
###################################################################################
estdat <- check.estdata(esttype=esttype, pltcondf=pltcondx,
cuniqueid=cuniqueid, condid=condid, treex=treex, seedx=seedx,
estseed=estseed, woodland=woodland,
sumunits=sumunits, landarea=landarea,
ACI.filter=ACI.filter, pcfilter=pcfilter, allin1=allin1,
estround=estround, pseround=pseround, divideby=divideby,
addtitle=addtitle, returntitle=returntitle,
rawdata=rawdata, rawonly=rawonly, savedata=savedata,
outfolder=outfolder, overwrite_dsn=overwrite_dsn,
overwrite_layer=overwrite_layer, outfn.pre=outfn.pre,
outfn.date=outfn.date, append_layer=append_layer,
raw_fmt=raw_fmt, raw_dsn=raw_dsn, gui=gui)
if (is.null(estdat)) return(NULL)
pltcondf <- estdat$pltcondf
cuniqueid <- estdat$cuniqueid
treef <- estdat$treef
seedf <- estdat$seedf
tuniqueid <- estdat$tuniqueid
estseed <- estdat$estseed
woodland <- estdat$woodland
sumunits <- estdat$sumunits
landarea <- estdat$landarea
allin1 <- estdat$allin1
estround <- estdat$estround
pseround <- estdat$pseround
divideby <- estdat$divideby
addtitle <- estdat$addtitle
returntitle <- estdat$returntitle
rawdata <- estdat$rawdata
rawonly <- estdat$rawonly
savedata <- estdat$savedata
outfolder <- estdat$outfolder
overwrite_layer <- estdat$overwrite_layer
raw_fmt <- estdat$raw_fmt
raw_dsn <- estdat$raw_dsn
rawfolder <- estdat$rawfolder
if ("STATECD" %in% names(pltcondf)) {
states <- pcheck.states(sort(unique(pltcondf$STATECD)))
}
if ("INVYR" %in% names(pltcondf)) {
invyr <- sort(unique(pltcondf$INVYR))
}
###################################################################################
### GET ROW AND COLUMN INFO FROM condf
###################################################################################
rowcolinfo <- check.rowcol(gui=gui, esttype=esttype, treef=treef, seedf=seedf,
condf=pltcondf, cuniqueid=cuniqueid,
tuniqueid=tuniqueid, estseed=estseed,
rowvar=rowvar, colvar=colvar,
row.FIAname=row.FIAname, col.FIAname=col.FIAname,
row.orderby=row.orderby, col.orderby=col.orderby,
row.add0=row.add0, col.add0=col.add0,
title.rowvar=title.rowvar, title.colvar=title.colvar,
rowlut=rowlut, collut=collut, rowgrp=rowgrp,
rowgrpnm=rowgrpnm, rowgrpord=rowgrpord, landarea=landarea)
treef <- rowcolinfo$treef
seedf <- rowcolinfo$seedf
condf <- rowcolinfo$condf
uniquerow <- rowcolinfo$uniquerow
uniquecol <- rowcolinfo$uniquecol
domainlst <- rowcolinfo$domainlst
rowvar <- rowcolinfo$rowvar
colvar <- rowcolinfo$colvar
row.orderby <- rowcolinfo$row.orderby
col.orderby <- rowcolinfo$col.orderby
row.add0 <- rowcolinfo$row.add0
col.add0 <- rowcolinfo$col.add0
title.rowvar <- rowcolinfo$title.rowvar
title.colvar <- rowcolinfo$title.colvar
rowgrpnm <- rowcolinfo$rowgrpnm
title.rowgrp <- rowcolinfo$title.rowgrp
bytdom <- rowcolinfo$bytdom
tdomvar <- rowcolinfo$tdomvar
tdomvar2 <- rowcolinfo$tdomvar2
grpvar <- rowcolinfo$grpvar
#rm(rowcolinfo)
## Generate a uniquecol for estimation units
if (!sumunits && colvar == "NONE") {
uniquecol <- data.table(unitarea[[unitvar]])
setnames(uniquecol, unitvar)
uniquecol[[unitvar]] <- factor(uniquecol[[unitvar]])
}
#################################################################################
### GET ESTIMATION DATA FROM TREE TABLE
#################################################################################
adjtree <- ifelse(adj %in% c("samp", "plot"), TRUE, FALSE)
treedat <- check.tree(gui=gui, treef=treef, seedf=seedf, estseed=estseed,
bycond=TRUE, condf=condf, bytdom=bytdom,
tuniqueid=tuniqueid, cuniqueid=cuniqueid,
esttype=esttype, estvarn=estvar, estvarn.filter=estvar.filter,
esttotn=TRUE, tdomvar=tdomvar, tdomvar2=tdomvar2,
adjtree=adjtree, metric=metric, woodland=woodland)
if (is.null(treedat)) return(NULL)
tdomdat <- treedat$tdomdat
if (rowvar != "TOTAL") {
#if (!row.add0) {
if (any(is.na(tdomdat[[rowvar]]))) {
if (!row.FIAname) {
rval <- ifelse (any(!is.na(tdomdat[[rowvar]]) & tdomdat[[rowvar]] == 0), max(tdomdat[[rowvar]], na.rm=TRUE), 0)
tdomdat[is.na(tdomdat[[rowvar]]), rowvar] <- rval
levels(uniquerow[[rowvar]]) <- c(levels(uniquerow[[rowvar]]), as.character(rval))
uniquerow[is.na(uniquerow[[rowvar]]), rowvar] <- as.character(rval)
} else {
tdomdat <- tdomdat[!is.na(tdomdat[[rowvar]]), ]
}
}
#}
if (colvar != "NONE") {
#if (!col.add0) {
if (any(is.na(tdomdat[[colvar]]))) {
if (!col.FIAname) {
cval <- ifelse (any(!is.na(tdomdat[[colvar]]) & tdomdat[[colvar]] == 0), max(tdomdat[[colvar]], na.rm=TRUE), 0)
tdomdat[is.na(tdomdat[[colvar]]), colvar] <- cval
levels(uniquecol[[colvar]]) <- c(levels(uniquecol[[colvar]]), as.character(cval))
uniquecol[is.na(uniquecol[[colvar]]), colvar] <- as.character(cval)
} else {
tdomdat <- tdomdat[!is.na(tdomdat[[colvar]]), ]
}
}
#}
}
}
## Merge tdomdat with condx
xchk <- check.matchclass(condx, tdomdat, c(cuniqueid, condid))
condx <- xchk$tab1
tdomdat <- xchk$tab2
tdomdat <- merge(condx, tdomdat, by=c(cuniqueid, condid), all.x=TRUE)
estvar <- treedat$estvar
estvar.name <- treedat$estvar.name
estvar.filter <- treedat$estvar.filter
tdomvarlst <- treedat$tdomvarlst
estunits <- treedat$estunits
#################################################################################
### GET TITLES FOR OUTPUT TABLES
#################################################################################
alltitlelst <- check.titles(dat=tdomdat, esttype=esttype, estseed=estseed,
sumunits=sumunits, title.main=title.main, title.ref=title.ref,
title.rowvar=title.rowvar, title.rowgrp=title.rowgrp,
title.colvar=title.colvar, title.unitvar=title.unitvar,
title.filter=title.filter, title.unitsn=estunits,
title.estvarn=title.estvar, unitvar=unitvar,
rowvar=rowvar, colvar=colvar, estvarn=estvar,
estvarn.filter=estvar.filter, addtitle=addtitle,
returntitle=returntitle, rawdata=rawdata,
states=states, invyrs=invyrs, landarea=landarea, pcfilter=pcfilter,
allin1=allin1, divideby=divideby, outfn.pre=outfn.pre)
title.unitvar <- alltitlelst$title.unitvar
title.est <- alltitlelst$title.est
title.pse <- alltitlelst$title.pse
title.estpse <- alltitlelst$title.estpse
title.ref <- alltitlelst$title.ref
outfn.estpse <- alltitlelst$outfn.estpse
outfn.param <- alltitlelst$outfn.param
if (rawdata) {
outfn.rawdat <- alltitlelst$outfn.rawdat
outfn.rawdat <- paste0(outfn.rawdat, "_modMA_mase", "_", MAmethod)
}
## Append name of package and method to outfile name
outfn.estpse <- paste0(outfn.estpse, "_modMA_mase", "_", MAmethod)
#################################################################################
## GENERATE ESTIMATES
#################################################################################
unit_totest=unit_rowest=unit_colest=unit_grpest=rowunit=totunit <- NULL
addtotal <- ifelse(((rowvar == "TOTAL" || length(unique(tdomdat[[rowvar]])) > 1) ||
(!is.null(tdomvarlst) && length(tdomvarlst) > 1)), TRUE, FALSE)
response <- estvar.name
estunits <- sort(unique(tdomdat[[unitvar]]))
masemethod <- ifelse(MAmethod == "PS", "postStrat",
ifelse(MAmethod == "greg", "greg",
ifelse(MAmethod == "gregEN", "gregElasticNet",
ifelse(MAmethod == "ratio", "ratioEstimator", "horvitzThompson"))))
message("generating estimates using mase::", masemethod, " function...\n")
predselect.overall <- NULL
if (MAmethod == "greg" && modelselect == T) {
# want to do variable selection on plot level data...
pltlvl <- tdomdat[ , lapply(.SD, sum, na.rm = TRUE),
by=c(unitvar, cuniqueid, "TOTAL", strvar, prednames),
.SDcols=response]
y <- pltlvl[[response]]
xsample <- pltlvl[ , prednames, with = F, drop = F]
# need to go means -> totals -> summed totals
xpop <- unitlut[ , c(unitvar, prednames), with = F, drop = F]
xpop_npix <- merge(xpop, npixels, by = unitvar, all.x = TRUE)
# multiply unitvar level population means by corresponding npixel values to get population level totals
xpop_npix[ ,2:ncol(xpop)] <- lapply(xpop_npix[ ,2:ncol(xpop)], function(x) xpop_npix[["npixels"]] * x)
# sum those values
xpop_totals <- colSums(xpop_npix[ ,2:ncol(xpop)])
# format xpop for mase input
xpop_totals <- data.frame(as.list(xpop_totals))
N <- sum(npixels[["npixels"]])
# since we want to do modelselection + get the coefficients, just use MAest.greg
coefs_select <- MAest.greg(y = y,
N = N,
x_sample = setDF(xsample),
x_pop = xpop_totals,
modelselect = TRUE)
predselect.overall <- coefs_select$predselect
prednames <- names(predselect.overall[ ,(!is.na(predselect.overall))[1,], with = F])
message(paste0("Predictors ", "[", paste0(prednames, collapse = ", "), "]", " were chosen in model selection.\n"))
}
if (!MAmethod %in% c("HT", "PS")) {
message("using the following predictors...", toString(prednames))
}
getweights <- ifelse(MAmethod %in% c("greg", "PS", "HT"), TRUE, FALSE)
getweights <- FALSE
if(bootstrap) {
if(MAmethod %in% c('greg', 'gregEN', 'ratio')) {
var_method <- "bootstrapSRS"
}
} else {
if(MAmethod %in% c('greg', 'gregEN', 'ratio')) {
var_method <- "LinHTSRS"
}
}
# if (addtotal) {
## Get total estimate and merge area
tdomdattot <- tdomdat[, lapply(.SD, sum, na.rm=TRUE),
by=c(unitvar, cuniqueid, "TOTAL", strvar, prednames), .SDcols=response]
unit_totestlst <- lapply(estunits, MAest.unit,
dat=tdomdattot, cuniqueid=cuniqueid,
unitlut=unitlut, unitvar=unitvar, esttype=esttype,
MAmethod=MAmethod, strvar=strvar, prednames=prednames,
domain="TOTAL", response=response, npixels=npixels,
FIA=FIA, modelselect=modelselect_bydomain, getweights=getweights,
var_method=var_method)
unit_totest <- do.call(rbind, sapply(unit_totestlst, '[', "unitest"))
unit_weights <- do.call(rbind, sapply(unit_totestlst, '[', "weights"))
unit_weights$areaweights <- unit_weights$weights * sum(unitarea[[areavar]])
if (MAmethod %in% c("greg", "gregEN")) {
predselectlst$totest <- do.call(rbind, sapply(unit_totestlst, '[', "predselect"))
}
tabs <- check.matchclass(unitarea, unit_totest, unitvar)
unitarea <- tabs$tab1
unit_totest <- tabs$tab2
setkeyv(unit_totest, unitvar)
unit_totest <- unit_totest[unitarea, nomatch=0]
if (totals) {
unit_totest <- getpse(unit_totest, areavar=areavar, esttype=esttype)
} else {
unit_totest <- getpse(unit_totest, esttype=esttype)
}
# }
## Get row, column, cell estimate and merge area if row or column in cond table
if (rowvar != "TOTAL") {
tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE),
by=c(unitvar, cuniqueid, rowvar, strvar, prednames), .SDcols=response]
unit_rowestlst <- lapply(estunits, MAest.unit,
dat=tdomdatsum, cuniqueid=cuniqueid,
unitlut=unitlut, unitvar=unitvar, esttype=esttype,
MAmethod=MAmethod, strvar=strvar, prednames=prednames,
domain=rowvar, response=response, npixels=npixels,
FIA=FIA, modelselect=modelselect_bydomain, getweights=getweights,
var_method=var_method)
unit_rowest <- do.call(rbind, sapply(unit_rowestlst, '[', "unitest"))
if (MAmethod %in% c("greg", "gregEN")) {
predselectlst$rowest <- do.call(rbind, sapply(unit_totestlst, '[', "predselect"))
}
if (colvar != "NONE") {
tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE),
by=c(unitvar, cuniqueid, colvar, strvar, prednames), .SDcols=response]
unit_colestlst <- lapply(estunits, MAest.unit,
dat=tdomdatsum, cuniqueid=cuniqueid,
unitlut=unitlut, unitvar=unitvar, esttype=esttype,
MAmethod=MAmethod, strvar=strvar, prednames=prednames,
domain=colvar, response=response, npixels=npixels,
FIA=FIA, modelselect=modelselect_bydomain, var_method=var_method)
unit_colest <- do.call(rbind, sapply(unit_colestlst, '[', "unitest"))
if (MAmethod %in% c("greg", "gregEN")) {
predselectlst$colest <- do.call(rbind, sapply(unit_colestlst, '[', "predselect"))
}
tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE),
by=c(unitvar, cuniqueid, grpvar, strvar, prednames), .SDcols=response]
tdomdatsum[, grpvar := do.call(paste, c(.SD, sep="#")), .SDcols=grpvar]
unit_grpestlst <- lapply(estunits, MAest.unit,
dat=tdomdatsum, cuniqueid=cuniqueid,
unitlut=unitlut, unitvar=unitvar, esttype=esttype,
MAmethod=MAmethod, strvar=strvar, prednames=prednames,
domain="grpvar", response=response, npixels=npixels,
FIA=FIA, modelselect=modelselect_bydomain, var_method=var_method)
unit_grpest <- do.call(rbind, sapply(unit_grpestlst, '[', "unitest"))
if (MAmethod %in% c("greg", "gregEN")) {
predselectlst$grpest <- do.call(rbind, sapply(unit_grpestlst, '[', "predselect"))
}
if (any(unit_grpest$grpvar == "NA#NA")) {
unit_grpest <- unit_grpest[unit_grpest$grpvar != "NA#NA", ]
}
unit_grpest[, c(rowvar, colvar) := tstrsplit(grpvar, "#", fixed=TRUE)]
}
}
###############################################################################
## Check add0 and Add area
###############################################################################
if (!sumunits && nrow(unitarea) > 1) col.add0 <- TRUE
if (!is.null(unit_rowest)) {
unit_rowest <- add0unit(x=unit_rowest, xvar=rowvar, uniquex=uniquerow,
unitvar=unitvar, xvar.add0=row.add0)
tabs <- check.matchclass(unitarea, unit_rowest, unitvar)
unitarea <- tabs$tab1
unit_rowest <- tabs$tab2
if (!is.null(row.orderby) && row.orderby != "NONE") {
setorderv(unit_rowest, c(row.orderby))
}
setkeyv(unit_rowest, unitvar)
unit_rowest <- unit_rowest[unitarea, nomatch=0]
if (totals) {
unit_rowest <- getpse(unit_rowest, areavar=areavar, esttype=esttype)
} else {
unit_rowest <- getpse(unit_rowest, esttype=esttype)
}
setkeyv(unit_rowest, c(unitvar, rowvar))
}
if (!is.null(unit_colest)) {
unit_colest <- add0unit(x=unit_colest, xvar=colvar, uniquex=uniquecol,
unitvar=unitvar, xvar.add0=col.add0)
tabs <- check.matchclass(unitarea, unit_colest, unitvar)
unitarea <- tabs$tab1
unit_colest <- tabs$tab2
if (!is.null(col.orderby) && col.orderby != "NONE") {
setorderv(unit_colest, c(col.orderby))
}
setkeyv(unit_colest, unitvar)
unit_colest <- unit_colest[unitarea, nomatch=0]
if (totals) {
unit_colest <- getpse(unit_colest, areavar=areavar, esttype=esttype)
} else {
unit_colest <- getpse(unit_colest, esttype=esttype)
}
setkeyv(unit_colest, c(unitvar, colvar))
}
if (!is.null(unit_grpest)) {
unit_grpest <- add0unit(x=unit_grpest, xvar=rowvar, uniquex=uniquerow,
unitvar=unitvar, xvar.add0=row.add0, xvar2=colvar, uniquex2=uniquecol,
xvar2.add0=col.add0)
tabs <- check.matchclass(unitarea, unit_grpest, unitvar)
unitarea <- tabs$tab1
unit_grpest <- tabs$tab2
if (!is.null(row.orderby) && row.orderby != "NONE") {
if (!is.null(col.orderby) && col.orderby != "NONE") {
setorderv(unit_grpest, c(row.orderby, col.orderby))
} else {
setorderv(unit_grpest, c(row.orderby))
}
} else if (!is.null(col.orderby) && col.orderby != "NONE") {
setorderv(unit_grpest, c(col.orderby))
}
setkeyv(unit_grpest, unitvar)
unit_grpest <- unit_grpest[unitarea, nomatch=0]
if (totals) {
unit_grpest <- getpse(unit_grpest, areavar=areavar, esttype=esttype)
} else {
unit_grpest <- getpse(unit_grpest, esttype=esttype)
}
setkeyv(unit_grpest, c(unitvar, rowvar, colvar))
}
###############################################################################
## GENERATE OUTPUT TABLES
###############################################################################
message("getting output...")
estnm <- "est"
tabs <- est.outtabs(esttype=esttype, sumunits=sumunits, areavar=areavar,
unitvar=unitvar, unitvars=unitvars, unit_totest=unit_totest,
unit_rowest=unit_rowest, unit_colest=unit_colest, unit_grpest=unit_grpest,
rowvar=rowvar, colvar=colvar, uniquerow=uniquerow, uniquecol=uniquecol,
rowgrp=rowgrp, rowgrpnm=rowgrpnm, rowunit=rowunit, totunit=totunit,
allin1=allin1, savedata=savedata, addtitle=addtitle,
title.ref=title.ref, title.colvar=title.colvar,
title.rowvar=title.rowvar, title.rowgrp=title.rowgrp,
title.unitvar=title.unitvar, title.estpse=title.estpse,
title.est=title.est, title.pse=title.pse, rawdata=rawdata,
outfn.estpse=outfn.estpse, outfolder=outfolder, outfn.date=outfn.date,
overwrite=overwrite_layer, estnm=estnm,
estround=estround, pseround=pseround, divideby=divideby,
returntitle=returntitle, estnull=estnull, psenull=psenull)
est2return <- tabs$tabest
pse2return <- tabs$tabpse
if (!is.null(est2return)) {
returnlst$est <- est2return
}
if (!is.null(pse2return)) {
returnlst$pse <- pse2return
}
if (returntitle) {
returnlst$titlelst <- alltitlelst
}
if (rawdata) {
rawdat <- tabs$rawdat
rawdat$domdat <- setDF(tdomdat)
#rawdat$expcondtab <- unit_weights
rawdat$plotweights <- unit_weights
rawdat$estvar <- estvar.name
rawdat$estvar.filter <- estvar.filter
if (savedata) {
if (!is.null(title.estpse)) {
title.raw <- paste(title.estpse, title.ref)
} else {
title.raw <- title.est
}
for (i in 1:length(rawdat)) {
tabnm <- names(rawdat[i])
if (!tabnm %in% c(estvar, prednames)) {
rawtab <- rawdat[[i]]
outfn.rawtab <- paste0(outfn.rawdat, "_", tabnm)
if (tabnm %in% c("plotsampcnt", "condsampcnt", "stratcombinelut")) {
write2csv(rawtab, outfolder=rawfolder, outfilenm=outfn.rawtab,
outfn.date=outfn.date, overwrite=overwrite_layer)
} else if (is.data.frame(rawtab)) {
if (raw_fmt != "csv") {
out_layer <- tabnm
} else {
out_layer <- outfn.rawtab
}
datExportData(rawtab,
savedata_opts=list(outfolder=rawfolder,
out_fmt=raw_fmt,
out_dsn=raw_dsn,
out_layer=out_layer,
overwrite_layer=overwrite_layer,
append_layer=append_layer,
add_layer=TRUE))
}
}
}
}
rawdat$module <- "MA"
rawdat$esttype <- "TREE"
rawdat$MAmethod <- MAmethod
rawdat$predselect.overall <- predselect.overall
rawdat$predselectlst <- predselectlst
rawdat$estvar <- estvar
rawdat$estvar.filter <- estvar.filter
if (!is.null(rowvar)) rawdat$rowvar <- rowvar
if (!is.null(colvar)) rawdat$colvar <- colvar
rawdat$areaunits <- areaunits
rawdat$estunits <- estunits
returnlst$raw <- rawdat
}
if ("STATECD" %in% names(pltcondf)) {
returnlst$statecd <- sort(unique(pltcondf$STATECD))
}
if ("INVYR" %in% names(pltcondf)) {
returnlst$invyr <- sort(unique(pltcondf$INVYR))
}
return(returnlst)
}
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Defines functions modMAtree
Documented in modMAtree
#' Model-Assisted module - Generate model-assisted tree estimates.
#'
#' Generates tree estimates by estimation unit. Estimates are calculated from
#' McConville et al. (2018)'s mase R package.
#'
#' If variables are NULL, then it will prompt user to input variables.
#'
#' Necessary variables:\cr \tabular{llll}{ \tab \bold{Data} \tab
#' \bold{Variable} \tab \bold{Description}\cr \tab tree \tab tuniqueid \tab
#' Unique identifier for each plot, to link to pltassgn (e.g. PLT_CN).\cr \tab
#' \tab CONDID \tab Unique identifier of each condition on plot, to link to
#' cond. Set CONDID=1, if only 1 condition per plot.\cr \tab \tab TPA_UNADJ
#' \tab Number of trees per acre each sample tree represents (e.g., DESIGNCD=1:
#' TPA_UNADJ=6.018046 for trees on subplot; 74.965282 for trees on
#' microplot).\cr \tab cond \tab cuniqueid \tab Unique identifier for each
#' plot, to link to pltassgn (ex. PLT_CN).\cr \tab \tab CONDID \tab Unique
#' identifier of each condition on plot. Set CONDID=1, if only 1 condition per
#' plot.\cr \tab \tab CONDPROP_UNADJ \tab Unadjusted proportion of condition on
#' each plot. Set CONDPROP_UNADJ=1, if only 1 condition per plot.\cr \tab \tab
#' COND_STATUS_CD \tab Status of each forested condition on plot (i.e.
#' accessible forest, nonforest, water, etc.)\cr \tab \tab NF_COND_STATUS_CD
#' \tab If ACI=TRUE. Status of each nonforest condition on plot (i.e.
#' accessible nonforest, nonsampled nonforest)\cr \tab \tab SITECLCD \tab If
#' landarea=TIMBERLAND. Measure of site productivity.\cr \tab \tab RESERVCD
#' \tab If landarea=TIMBERLAND. Reserved status.\cr \tab \tab SUBPROP_UNADJ
#' \tab Unadjusted proportion of subplot conditions on each plot. Set
#' SUBPROP_UNADJ=1, if only 1 condition per subplot.\cr \tab \tab
#' MICRPROP_UNADJ \tab If microplot tree attributes. Unadjusted proportion of
#' microplot conditions on each plot. Set MICRPROP_UNADJ=1, if only 1 condition
#' per microplot.\cr \tab \tab MACRPROP_UNADJ \tab If macroplot tree
#' attributes. Unadjusted proportion of macroplot conditions on each plot. Set
#' MACRPROP_UNADJ=1, if only 1 condition per macroplot.\cr \tab pltassgn \tab
#' puniqueid \tab Unique identifier for each plot, to link to cond (ex. CN).\cr
#' \tab \tab STATECD \tab Identifies state each plot is located in.\cr \tab
#' \tab INVYR \tab Identifies inventory year of each plot.\cr \tab \tab
#' PLOT_STATUS_CD \tab Status of each plot (i.e. sampled, nonsampled). If not
#' included, all plots are assumed as sampled.\cr }
#'
#' Reference names are available for the following variables: \cr ADFORCD,
#' AGENTCD, CCLCD, DECAYCD, DSTRBCD, KINDCD, OWNCD, OWNGRPCD, FORTYPCD,
#' FLDTYPCD, FORTYPCDCALC, TYPGRPCD, FORINDCD, RESERVCD, LANDCLCD, STDSZCD,
#' FLDSZCD, PHYSCLCD, MIST_CL_CD, PLOT_STATUS_CD, STATECD, TREECLCD, TRTCD,
#' SPCD, SPGRPCD
#'
#' @param MApopdat List. Population data objects returned from modMApop().
#' @param MAmethod String. mase (i.e., model-assisted) method to use
#' ('greg', 'gregEN', 'ratio').
#' @param estvar String. Name of the tree-level estimate variable (e.g.,
#' 'VOLCFNET').
#' @param estvar.filter String. A tree-level filter for estvar. Must be R
#' syntax (e.g., 'STATUSCD == 1').
#' @param estseed String. Use seedling data only or add to tree data. Seedling
#' estimates are only for counts (estvar='TPA_UNADJ')-('none', 'only', 'add').
#' @param woodland String. If woodland = 'Y', include woodland tree species
#' where measured. If woodland = 'N', only include timber species. See
#' FIESTA::ref_species$WOODLAND ='Y/N'. If woodland = 'only', only include
#' woodland species.
#' @param landarea String. The condition-level filter for defining land area
#' ('ALL', 'FOREST', 'TIMBERLAND'). If landarea='FOREST', COND_STATUS_CD = 1;
#' if landarea='TIMBERLAND', SITECLCD in(1:6) & RESERVCD = 0.
#' @param pcfilter String. A filter for plot or cond attributes (including
#' pltassgn). Must be R logical syntax.
#' @param rowvar String. Optional. Name of domain variable to group estvar by
#' for rows in table output. Rowvar must be included in an input data frame
#' (i.e., plt, cond, tree). If no rowvar is included, an estimate is returned
#' for the total estimation unit. Include colvar for grouping by 2 variables.
#' @param colvar String. Optional. If rowvar != NULL, name of domain variable
#' to group estvar by for columns in table output. Colvar must be included in
#' an input data frame (i.e., plt, cond, tree).
#' @param prednames String vector. Name(s) of predictor variables to include in
#' model.
#' @param modelselect Logical. If TRUE, an elastic net regression model is fit
#' to the entire plot level data, and the variables selected in that model are
#' used for the proceeding estimation.
#' @param FIA Logical. If TRUE, the finite population term is removed from
#' estimator to match FIA estimates.
#' @param bootstrap Logical. If TRUE, returns bootstrap variance estimates,
#' otherwise uses Horvitz-Thompson estimator under simple random sampling
#' without replacement.
#' @param returntitle Logical. If TRUE, returns title(s) of the estimation
#' table(s).
#' @param savedata Logical. If TRUE, saves table(s) to outfolder.
#' @param table_opts List. See help(table_options()) for a list of
#' options.
#' @param title_opts List. See help(title_options()) for a list of options.
#' @param savedata_opts List. See help(savedata_options()) for a list
#' of options. Only used when savedata = TRUE.
#' @param gui Logical. If gui, user is prompted for parameters.
#' @param modelselect_bydomain Logical. If TRUE, modelselection will occur at
#' the domain level as specified by rowvar and/or colvar and not at the level of
#' the entire sample.
#' @param ... Parameters for modMApop() if MApopdat is NULL.
#' @return If FIA=TRUE or unitvar=NULL and colvar=NULL, one data frame is
#' returned with tree estimates and percent sample errors. Otherwise, a list is
#' returned with tree estimates in one data frame (est) and percent sample
#' errors in another data frame (est.pse). If rawdata=TRUE, another list is
#' returned including raw data used in the estimation process. If
#' addtitle=TRUE and returntitle=TRUE, the title for est/pse is returned. If
#' savedata=TRUE, all data frames are written to outfolder.
#'
#' \item{est}{ Data frame. Tree estimates by rowvar, colvar (and estimation
#' unit). If FIA=TRUE or one estimation unit and colvar=NULL, estimates and
#' percent sampling error are in one data frame. } \item{pse}{ Data frame.
#' Percent sampling errors for estimates by rowvar and colvar (and estimation
#' unit). } \item{titlelst}{ List with 1 or 2 string vectors. If
#' returntitle=TRUE a list with table title(s). The list contains one title if
#' est and pse are in the same table and two titles if est and pse are in
#' separate tables. } \item{raw}{ List of data frames. If rawdata=TRUE, a list
#' including: number of plots by plot status, if in dataset (plotsampcnt);
#' number of conditions by condition status (condsampcnt); data used for
#' post-stratification (stratdat); and 1-8 tables with calculated variables
#' used for processing estimates and percent sampling error for table cell
#' values and totals (See processing data below). }
#'
#' Raw data
#'
#' \item{plotsampcnt}{ Table. Number of plots by plot status (ex. sampled
#' forest on plot, sampled nonforest, nonsampled). } \item{condsampcnt}{ DF.
#' Number of conditions by condition status (forest land, nonforest land,
#' noncensus water, census water, nonsampled). }
#'
#' \item{stratdat}{ Data frame. Strata information by estimation unit. }
#' \tabular{lll}{ \tab \bold{Variable} \tab \bold{Description}\cr \tab ESTUNIT
#' \tab estimation unit\cr \tab STRATA \tab strata \cr \tab ACRES \tab area by
#' strata for estimation unit\cr \tab n.strata \tab number of plots in strata
#' (and estimation unit) \cr \tab n.total \tab number of plots for estimation
#' unit \cr \tab TOTACRES \tab total area for estimation unit \cr \tab strwt
#' \tab proportion of area (or number of plots) by strata (strata weight) \cr
#' \tab expfac.strata \tab expansion factor (in area unit (e.g., acres) by
#' strata (areavar/n.strata) \cr }
#'
#' \item{processing data}{ Data frames. Separate data frames containing
#' calculated variables used in estimation process. The number of processing
#' tables depends on the input parameters. The tables include: total by
#' estimation unit (unit.totest); rowvar totals (unit.rowest), and if colvar is
#' not NULL, colvar totals, (unit.colvar); and a combination of rowvar and
#' colvar (unit.grpvar). If FIA=TRUE, the raw data for the summed estimation
#' units are also included (totest, rowest, colest, grpest, respectively).
#' These tables do not included estimate proportions (nhat and nhat.var).
#'
#' The data frames include the following information: \tabular{lll}{ \tab
#' \bold{Variable} \tab \bold{Description}\cr \tab nhat \tab estimated
#' proportion of trees \cr \tab nhat.var \tab estimated variance of estimated
#' proportion of trees \cr \tab ACRES \tab total area for estimation unit \cr
#' \tab est \tab estimated area of trees nhat*ACRES \cr \tab est.var \tab
#' estimated variance of estimated area of trees nhat.var*areavar^2 \cr \tab
#' est.se \tab standard error of estimated area of trees sqrt(est.var) \cr \tab
#' est.cv \tab coefficient of variation of estimated area of trees est.se/est
#' \cr \tab pse \tab percent sampling error of estimate est.cv*100 \cr \tab
#' CI99left \tab left tail of 99 percent confidence interval for estimated area
#' \cr \tab CI99right \tab right tail of 99 percent confidence interval for
#' estimated area \cr \tab CI95left \tab left tail of 95 percent confidence
#' interval for estimated area \cr \tab CI95right \tab right tail of 95 percent
#' confidence interval for estimated area \cr \tab CI67left \tab left tail of
#' 67 percent confidence interval for estimated area \cr \tab CI67right \tab
#' right tail of 67 percent confidence interval for estimated area \cr } }
#'
#' Table(s) are also written to outfolder.
#' @note
#'
#' ADJUSTMENT FACTOR:\cr The adjustment factor is necessary to account for
#' nonsampled conditions. It is calculated for each estimation unit by strata.
#' by summing the unadjusted proportions of the subplot, microplot, and
#' macroplot (i.e. *PROP_UNADJ) and dividing by the number of plots in the
#' strata/estimation unit).
#'
#' An adjustment factor is determined for each tree based on the size of the
#' plot it was measured on. This is identified using TPA_UNADJ as follows:
#'
#' \tabular{llr}{ \tab \bold{PLOT SIZE} \tab \bold{TPA_UNADJ} \cr \tab SUBPLOT
#' \tab 6.018046 \cr \tab MICROPLOT \tab 74.965282 \cr \tab MACROPLOT \tab
#' 0.999188 \cr }
#'
#' If ACI=FALSE, only nonsampled forest conditions are accounted for in the
#' adjustment factor. \cr If ACI=TRUE, the nonsampled nonforest conditions are
#' removed as well and accounted for in adjustment factor. This is if you are
#' interested in estimates for all lands or nonforest lands in the
#' All-Condition-Inventory.
#'
#' sumunits:\cr An estimation unit is a population, or area of interest, with
#' known area and number of plots. Individual counties or combined
#' Super-counties are common estimation units for FIA. An estimation unit may
#' also be a subpopulation of a larger population (e.g., Counties within a
#' State). Subpopulations are mutually exclusive and independent within a
#' population, therefore estimated totals and variances are additive. For
#' example, State-level estimates are generated by summing estimates from all
#' subpopulations within the State (Bechtold and Patterson. 2005. Chapter 2).
#' Each plot must be assigned to only one estimation unit.
#'
#' stratcombine:\cr If MAmethod='PS', and stratcombine=TRUE, and less than 2
#' plots in any one estimation unit, all estimation units with 10 or less plots
#' are combined. The current method for combining is to group the estimation
#' unit with less than 10 plots with the estimation unit following in
#' consecutive order (numeric or alphabetical), restrained by survey unit
#' (UNITCD) if included in dataset, and continuing until the number of plots
#' equals 10. If there are no estimation units following in order, it is
#' combined with the estimation unit previous in order.
#'
#' autoxreduce:\cr If MAmethod='GREG', and autoxreduce=TRUE, and there is an
#' error because of multicolinearity, a variable reduction method is applied to
#' remove correlated variables. The method used is based on the
#' variance-inflation factor (vif) from a linear model. The vif estimates how
#' much the variance of each x variable is inflated due to mulitcolinearity in
#' the model.
#'
#' rowlut/collut:\cr 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:\cr 1-the merging variable with same name as
#' the variable in the input merge table;\cr 2-the ordering or descriptive
#' variable.\cr 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.
#'
#' UNITS:\cr The following variables are converted from pounds (from FIA
#' database) to short tons by multiplying the variable by 0.0005. DRYBIO_AG,
#' DRYBIO_BG, DRYBIO_WDLD_SPP, DRYBIO_SAPLING, DRYBIO_STUMP, DRYBIO_TOP,
#' DRYBIO_BOLE, DRYBIOT, DRYBIOM, DRYBIOTB, JBIOTOT, CARBON_BG, CARBON_AG
#'
#' MORTALITY:\cr For Interior-West FIA, mortality estimates are mainly based on
#' whether a tree has died within the last 5 years of when the plot was
#' measured. If a plot was remeasured, mortality includes trees that were alive
#' the previous visit but were dead in the next visit. If a tree was standing
#' the previous visit, but was not standing in the next visit, no diameter was
#' collected (DIA = NA) but the tree is defined as mortality.
#'
#' Common tree filters: \cr
#'
#' \tabular{llr}{ \tab \bold{FILTER} \tab \bold{DESCRIPTION} \cr \tab "STATUSCD
#' == 1" \tab Live trees \cr \tab "STATUSCD == 2" \tab Dead trees \cr \tab
#' "TPAMORT_UNADJ > 0" \tab Mortality trees \cr \tab "STATUSCD == 2 & DIA >=
#' 5.0" \tab Dead trees >= 5.0 inches diameter \cr \tab "STATUSCD == 2 &
#' AGENTCD == 30" \tab Dead trees from fire \cr }
#' @author Tracey S. Frescino
#' @references Kelly McConville, Becky Tang, George Zhu, Shirley Cheung, and
#' Sida Li (2018). mase: Model-Assisted Survey Estimation. R package version
#' 0.1.2 https://cran.r-project.org/package=mase
#' @keywords data
#' @examples
#' \donttest{
#' # Set up population dataset (see ?modMApop() for more information)
#' MApopdat <- modMApop(popTabs = list(tree = FIESTA::WYtree,
#' cond = FIESTA::WYcond),
#' pltassgn = FIESTA::WYpltassgn,
#' pltassgnid = "CN",
#' unitarea = FIESTA::WYunitarea,
#' unitvar = "ESTN_UNIT",
#' unitzonal = FIESTA::WYunitzonal,
#' prednames = c("dem", "tcc", "tpi", "tnt"),
#' predfac = "tnt")
#'
#' # Use GREG Estimator to Estimate cubic foot volume of live trees in our
#' # population
#' mod1 <- modMAtree(MApopdat = MApopdat,
#' MAmethod = "greg",
#' estvar = "VOLCFNET",
#' estvar.filter = "STATUSCD == 1")
#'
#' str(mod1)
#'
#' # Use GREG Elastic Net Estimator to Estimate basal area of live trees in our
#' # population
#' mod2 <- modMAtree(MApopdat = MApopdat,
#' MAmethod = "gregEN",
#' estvar = "BA",
#' estvar.filter = "STATUSCD == 1")
#'
#' str(mod2)
#' }
#' @export modMAtree
modMAtree <- function(MApopdat,
MAmethod,
estvar,
estvar.filter = NULL,
estseed = "none",
woodland = "Y",
landarea = "FOREST",
pcfilter = NULL,
rowvar = NULL,
colvar = NULL,
prednames = NULL,
modelselect = FALSE,
FIA = TRUE,
bootstrap = FALSE,
returntitle = FALSE,
savedata = FALSE,
table_opts = NULL,
title_opts = NULL,
savedata_opts = NULL,
gui = FALSE,
modelselect_bydomain = FALSE,
...){
##################################################################################
## DESCRIPTION:
## Generates model-assisted estimates by domain (and estimation unit)
##################################################################################
## CHECK GUI - IF NO ARGUMENTS SPECIFIED, ASSUME GUI=TRUE
if (nargs() == 0 && is.null(MApopdat)) {
gui <- TRUE
}
## If gui.. set variables to NULL
if (gui) {
tree=landarea=strvar=areavar <- NULL
if (!row.FIAname) row.FIAname <- NULL
if (!col.FIAname) col.FIAname <- NULL
}
## Set parameters
minplotnum <- 10
esttype="TREE"
parameters <- FALSE
returnlst <- list()
sumunits <- FALSE
## Set global variables
ONEUNIT=n.total=n.strata=strwt=TOTAL <- NULL
rawdata <- TRUE
##################################################################
## CHECK PARAMETER NAMES
##################################################################
## Check input parameters
input.params <- names(as.list(match.call()))[-1]
formallst <- c(names(formals(modMAtree)),
names(formals(modMApop)))
if (!all(input.params %in% formallst)) {
miss <- input.params[!input.params %in% formallst]
stop("invalid parameter: ", toString(miss))
}
## Check parameter lists
pcheck.params(input.params, table_opts=table_opts, title_opts=title_opts,
savedata_opts=savedata_opts)
## Set savedata defaults
savedata_defaults_list <- formals(savedata_options)[-length(formals(savedata_options))]
for (i in 1:length(savedata_defaults_list)) {
assign(names(savedata_defaults_list)[[i]], savedata_defaults_list[[i]])
}
## Set user-supplied savedata values
if (length(savedata_opts) > 0) {
if (!savedata) {
message("savedata=FALSE with savedata parameters... no data are saved")
}
for (i in 1:length(savedata_opts)) {
if (names(savedata_opts)[[i]] %in% names(savedata_defaults_list)) {
assign(names(savedata_opts)[[i]], savedata_opts[[i]])
} else {
stop(paste("Invalid parameter: ", names(savedata_opts)[[i]]))
}
}
}
## Set table defaults
table_defaults_list <- formals(table_options)[-length(formals(table_options))]
for (i in 1:length(table_defaults_list)) {
assign(names(table_defaults_list)[[i]], table_defaults_list[[i]])
}
## Set user-supplied table values
if (length(table_opts) > 0) {
for (i in 1:length(table_opts)) {
if (names(table_opts)[[i]] %in% names(table_defaults_list)) {
assign(names(table_opts)[[i]], table_opts[[i]])
} else {
stop(paste("Invalid parameter: ", names(table_opts)[[i]]))
}
}
}
## Set title defaults
title_defaults_list <- formals(title_options)[-length(formals(title_options))]
for (i in 1:length(title_defaults_list)) {
assign(names(title_defaults_list)[[i]], title_defaults_list[[i]])
}
## Set user-supplied title values
if (length(title_opts) > 0) {
for (i in 1:length(title_opts)) {
if (names(title_opts)[[i]] %in% names(title_defaults_list)) {
assign(names(title_opts)[[i]], title_opts[[i]])
} else {
stop(paste("Invalid parameter: ", names(title_opts)[[i]]))
}
}
}
##################################################################
## CHECK PARAMETER INPUTS
##################################################################
## Check MAmethod
MAmethodlst <- c("greg", "gregEN", "ratio")
MAmethod <- pcheck.varchar(var2check=MAmethod, varnm="MAmethod", gui=gui,
checklst=MAmethodlst, caption="MAmethod", multiple=FALSE, stopifnull=TRUE)
if (MAmethod %in% c("greg", "gregEN")) {
predselectlst <- list()
}
list.items <- c("condx", "pltcondx", "cuniqueid", "condid",
"ACI.filter", "unitarea", "unitvar", "unitlut", "npixels",
"npixelvar", "plotsampcnt", "condsampcnt")
#if (MAmethod == "PS") {
# list.items <- c(list.items, "strvar")
#}
#if (MAmethod == "greg") {
# list.items <- c(list.items, "prednames")
#}
MApopdat <- pcheck.object(MApopdat, "MApopdat", list.items=list.items)
if (is.null(MApopdat)) return(NULL)
condx <- MApopdat$condx
pltcondx <- MApopdat$pltcondx
treex <- MApopdat$treex
seedx <- MApopdat$seedx
if (is.null(treex) && is.null(seedx)) {
stop("must include tree data for tree estimates")
}
cuniqueid <- MApopdat$cuniqueid
condid <- MApopdat$condid
tuniqueid <- MApopdat$tuniqueid
ACI.filter <- MApopdat$ACI.filter
unitarea <- MApopdat$unitarea
areavar <- MApopdat$areavar
areaunits <- MApopdat$areaunits
unitvar <- MApopdat$unitvar
unitlut <- MApopdat$unitlut
unitvars <- MApopdat$unitvars
npixels <- MApopdat$npixels
npixelvar <- MApopdat$npixelvar
expcondtab <- MApopdat$expcondtab
plotsampcnt <- MApopdat$plotsampcnt
condsampcnt <- MApopdat$condsampcnt
states <- MApopdat$states
invyrs <- MApopdat$invyrs
stratcombinelut <- MApopdat$stratcombinelut
predfac <- MApopdat$predfac
strvar <- MApopdat$strvar
adj <- MApopdat$adj
if (MAmethod %in% c("greg", "gregEN", "ratio")) {
if (is.null(prednames)) {
prednames <- MApopdat$prednames
} else {
if (!all(prednames %in% MApopdat$prednames)) {
if (any(prednames %in% MApopdat$predfac)) {
predfacnames <- prednames[prednames %in% MApopdat$predfac]
for (nm in predfacnames) {
prednames[prednames == nm] <- MApopdat$prednames[grepl(nm, MApopdat$prednames)]
}
} else {
stop("invalid prednames... must be in: ", toString(MApopdat$prednames))
}
}
}
}
########################################
## Check area units
########################################
unitchk <- pcheck.areaunits(unitarea=unitarea, areavar=areavar,
areaunits=areaunits, metric=metric)
unitarea <- unitchk$unitarea
areavar <- unitchk$areavar
areaunits <- unitchk$outunits
if (is.null(key(unitarea))) {
setkeyv(unitarea, unitvar)
}
###################################################################################
## Check parameters and apply plot and condition filters
###################################################################################
estdat <- check.estdata(esttype=esttype, pltcondf=pltcondx,
cuniqueid=cuniqueid, condid=condid, treex=treex, seedx=seedx,
estseed=estseed, woodland=woodland,
sumunits=sumunits, landarea=landarea,
ACI.filter=ACI.filter, pcfilter=pcfilter, allin1=allin1,
estround=estround, pseround=pseround, divideby=divideby,
addtitle=addtitle, returntitle=returntitle,
rawdata=rawdata, rawonly=rawonly, savedata=savedata,
outfolder=outfolder, overwrite_dsn=overwrite_dsn,
overwrite_layer=overwrite_layer, outfn.pre=outfn.pre,
outfn.date=outfn.date, append_layer=append_layer,
raw_fmt=raw_fmt, raw_dsn=raw_dsn, gui=gui)
if (is.null(estdat)) return(NULL)
pltcondf <- estdat$pltcondf
cuniqueid <- estdat$cuniqueid
treef <- estdat$treef
seedf <- estdat$seedf
tuniqueid <- estdat$tuniqueid
estseed <- estdat$estseed
woodland <- estdat$woodland
sumunits <- estdat$sumunits
landarea <- estdat$landarea
allin1 <- estdat$allin1
estround <- estdat$estround
pseround <- estdat$pseround
divideby <- estdat$divideby
addtitle <- estdat$addtitle
returntitle <- estdat$returntitle
rawdata <- estdat$rawdata
rawonly <- estdat$rawonly
savedata <- estdat$savedata
outfolder <- estdat$outfolder
overwrite_layer <- estdat$overwrite_layer
raw_fmt <- estdat$raw_fmt
raw_dsn <- estdat$raw_dsn
rawfolder <- estdat$rawfolder
if ("STATECD" %in% names(pltcondf)) {
states <- pcheck.states(sort(unique(pltcondf$STATECD)))
}
if ("INVYR" %in% names(pltcondf)) {
invyr <- sort(unique(pltcondf$INVYR))
}
###################################################################################
### GET ROW AND COLUMN INFO FROM condf
###################################################################################
rowcolinfo <- check.rowcol(gui=gui, esttype=esttype, treef=treef, seedf=seedf,
condf=pltcondf, cuniqueid=cuniqueid,
tuniqueid=tuniqueid, estseed=estseed,
rowvar=rowvar, colvar=colvar,
row.FIAname=row.FIAname, col.FIAname=col.FIAname,
row.orderby=row.orderby, col.orderby=col.orderby,
row.add0=row.add0, col.add0=col.add0,
title.rowvar=title.rowvar, title.colvar=title.colvar,
rowlut=rowlut, collut=collut, rowgrp=rowgrp,
rowgrpnm=rowgrpnm, rowgrpord=rowgrpord, landarea=landarea)
treef <- rowcolinfo$treef
seedf <- rowcolinfo$seedf
condf <- rowcolinfo$condf
uniquerow <- rowcolinfo$uniquerow
uniquecol <- rowcolinfo$uniquecol
domainlst <- rowcolinfo$domainlst
rowvar <- rowcolinfo$rowvar
colvar <- rowcolinfo$colvar
row.orderby <- rowcolinfo$row.orderby
col.orderby <- rowcolinfo$col.orderby
row.add0 <- rowcolinfo$row.add0
col.add0 <- rowcolinfo$col.add0
title.rowvar <- rowcolinfo$title.rowvar
title.colvar <- rowcolinfo$title.colvar
rowgrpnm <- rowcolinfo$rowgrpnm
title.rowgrp <- rowcolinfo$title.rowgrp
bytdom <- rowcolinfo$bytdom
tdomvar <- rowcolinfo$tdomvar
tdomvar2 <- rowcolinfo$tdomvar2
grpvar <- rowcolinfo$grpvar
#rm(rowcolinfo)
## Generate a uniquecol for estimation units
if (!sumunits && colvar == "NONE") {
uniquecol <- data.table(unitarea[[unitvar]])
setnames(uniquecol, unitvar)
uniquecol[[unitvar]] <- factor(uniquecol[[unitvar]])
}
#################################################################################
### GET ESTIMATION DATA FROM TREE TABLE
#################################################################################
adjtree <- ifelse(adj %in% c("samp", "plot"), TRUE, FALSE)
treedat <- check.tree(gui=gui, treef=treef, seedf=seedf, estseed=estseed,
bycond=TRUE, condf=condf, bytdom=bytdom,
tuniqueid=tuniqueid, cuniqueid=cuniqueid,
esttype=esttype, estvarn=estvar, estvarn.filter=estvar.filter,
esttotn=TRUE, tdomvar=tdomvar, tdomvar2=tdomvar2,
adjtree=adjtree, metric=metric, woodland=woodland)
if (is.null(treedat)) return(NULL)
tdomdat <- treedat$tdomdat
if (rowvar != "TOTAL") {
#if (!row.add0) {
if (any(is.na(tdomdat[[rowvar]]))) {
if (!row.FIAname) {
rval <- ifelse (any(!is.na(tdomdat[[rowvar]]) & tdomdat[[rowvar]] == 0), max(tdomdat[[rowvar]], na.rm=TRUE), 0)
tdomdat[is.na(tdomdat[[rowvar]]), rowvar] <- rval
levels(uniquerow[[rowvar]]) <- c(levels(uniquerow[[rowvar]]), as.character(rval))
uniquerow[is.na(uniquerow[[rowvar]]), rowvar] <- as.character(rval)
} else {
tdomdat <- tdomdat[!is.na(tdomdat[[rowvar]]), ]
}
}
#}
if (colvar != "NONE") {
#if (!col.add0) {
if (any(is.na(tdomdat[[colvar]]))) {
if (!col.FIAname) {
cval <- ifelse (any(!is.na(tdomdat[[colvar]]) & tdomdat[[colvar]] == 0), max(tdomdat[[colvar]], na.rm=TRUE), 0)
tdomdat[is.na(tdomdat[[colvar]]), colvar] <- cval
levels(uniquecol[[colvar]]) <- c(levels(uniquecol[[colvar]]), as.character(cval))
uniquecol[is.na(uniquecol[[colvar]]), colvar] <- as.character(cval)
} else {
tdomdat <- tdomdat[!is.na(tdomdat[[colvar]]), ]
}
}
#}
}
}
## Merge tdomdat with condx
xchk <- check.matchclass(condx, tdomdat, c(cuniqueid, condid))
condx <- xchk$tab1
tdomdat <- xchk$tab2
tdomdat <- merge(condx, tdomdat, by=c(cuniqueid, condid), all.x=TRUE)
estvar <- treedat$estvar
estvar.name <- treedat$estvar.name
estvar.filter <- treedat$estvar.filter
tdomvarlst <- treedat$tdomvarlst
estunits <- treedat$estunits
#################################################################################
### GET TITLES FOR OUTPUT TABLES
#################################################################################
alltitlelst <- check.titles(dat=tdomdat, esttype=esttype, estseed=estseed,
sumunits=sumunits, title.main=title.main, title.ref=title.ref,
title.rowvar=title.rowvar, title.rowgrp=title.rowgrp,
title.colvar=title.colvar, title.unitvar=title.unitvar,
title.filter=title.filter, title.unitsn=estunits,
title.estvarn=title.estvar, unitvar=unitvar,
rowvar=rowvar, colvar=colvar, estvarn=estvar,
estvarn.filter=estvar.filter, addtitle=addtitle,
returntitle=returntitle, rawdata=rawdata,
states=states, invyrs=invyrs, landarea=landarea, pcfilter=pcfilter,
allin1=allin1, divideby=divideby, outfn.pre=outfn.pre)
title.unitvar <- alltitlelst$title.unitvar
title.est <- alltitlelst$title.est
title.pse <- alltitlelst$title.pse
title.estpse <- alltitlelst$title.estpse
title.ref <- alltitlelst$title.ref
outfn.estpse <- alltitlelst$outfn.estpse
outfn.param <- alltitlelst$outfn.param
if (rawdata) {
outfn.rawdat <- alltitlelst$outfn.rawdat
outfn.rawdat <- paste0(outfn.rawdat, "_modMA_mase", "_", MAmethod)
}
## Append name of package and method to outfile name
outfn.estpse <- paste0(outfn.estpse, "_modMA_mase", "_", MAmethod)
#################################################################################
## GENERATE ESTIMATES
#################################################################################
unit_totest=unit_rowest=unit_colest=unit_grpest=rowunit=totunit <- NULL
addtotal <- ifelse(((rowvar == "TOTAL" || length(unique(tdomdat[[rowvar]])) > 1) ||
(!is.null(tdomvarlst) && length(tdomvarlst) > 1)), TRUE, FALSE)
response <- estvar.name
estunits <- sort(unique(tdomdat[[unitvar]]))
masemethod <- ifelse(MAmethod == "PS", "postStrat",
ifelse(MAmethod == "greg", "greg",
ifelse(MAmethod == "gregEN", "gregElasticNet",
ifelse(MAmethod == "ratio", "ratioEstimator", "horvitzThompson"))))
message("generating estimates using mase::", masemethod, " function...\n")
predselect.overall <- NULL
if (MAmethod == "greg" && modelselect == T) {
# want to do variable selection on plot level data...
pltlvl <- tdomdat[ , lapply(.SD, sum, na.rm = TRUE),
by=c(unitvar, cuniqueid, "TOTAL", strvar, prednames),
.SDcols=response]
y <- pltlvl[[response]]
xsample <- pltlvl[ , prednames, with = F, drop = F]
# need to go means -> totals -> summed totals
xpop <- unitlut[ , c(unitvar, prednames), with = F, drop = F]
xpop_npix <- merge(xpop, npixels, by = unitvar, all.x = TRUE)
# multiply unitvar level population means by corresponding npixel values to get population level totals
xpop_npix[ ,2:ncol(xpop)] <- lapply(xpop_npix[ ,2:ncol(xpop)], function(x) xpop_npix[["npixels"]] * x)
# sum those values
xpop_totals <- colSums(xpop_npix[ ,2:ncol(xpop)])
# format xpop for mase input
xpop_totals <- data.frame(as.list(xpop_totals))
N <- sum(npixels[["npixels"]])
# since we want to do modelselection + get the coefficients, just use MAest.greg
coefs_select <- MAest.greg(y = y,
N = N,
x_sample = setDF(xsample),
x_pop = xpop_totals,
modelselect = TRUE)
predselect.overall <- coefs_select$predselect
prednames <- names(predselect.overall[ ,(!is.na(predselect.overall))[1,], with = F])
message(paste0("Predictors ", "[", paste0(prednames, collapse = ", "), "]", " were chosen in model selection.\n"))
}
if (!MAmethod %in% c("HT", "PS")) {
message("using the following predictors...", toString(prednames))
}
getweights <- ifelse(MAmethod %in% c("greg", "PS", "HT"), TRUE, FALSE)
getweights <- FALSE
if(bootstrap) {
if(MAmethod %in% c('greg', 'gregEN', 'ratio')) {
var_method <- "bootstrapSRS"
}
} else {
if(MAmethod %in% c('greg', 'gregEN', 'ratio')) {
var_method <- "LinHTSRS"
}
}
# if (addtotal) {
## Get total estimate and merge area
tdomdattot <- tdomdat[, lapply(.SD, sum, na.rm=TRUE),
by=c(unitvar, cuniqueid, "TOTAL", strvar, prednames), .SDcols=response]
unit_totestlst <- lapply(estunits, MAest.unit,
dat=tdomdattot, cuniqueid=cuniqueid,
unitlut=unitlut, unitvar=unitvar, esttype=esttype,
MAmethod=MAmethod, strvar=strvar, prednames=prednames,
domain="TOTAL", response=response, npixels=npixels,
FIA=FIA, modelselect=modelselect_bydomain, getweights=getweights,
var_method=var_method)
unit_totest <- do.call(rbind, sapply(unit_totestlst, '[', "unitest"))
unit_weights <- do.call(rbind, sapply(unit_totestlst, '[', "weights"))
unit_weights$areaweights <- unit_weights$weights * sum(unitarea[[areavar]])
if (MAmethod %in% c("greg", "gregEN")) {
predselectlst$totest <- do.call(rbind, sapply(unit_totestlst, '[', "predselect"))
}
tabs <- check.matchclass(unitarea, unit_totest, unitvar)
unitarea <- tabs$tab1
unit_totest <- tabs$tab2
setkeyv(unit_totest, unitvar)
unit_totest <- unit_totest[unitarea, nomatch=0]
if (totals) {
unit_totest <- getpse(unit_totest, areavar=areavar, esttype=esttype)
} else {
unit_totest <- getpse(unit_totest, esttype=esttype)
}
# }
## Get row, column, cell estimate and merge area if row or column in cond table
if (rowvar != "TOTAL") {
tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE),
by=c(unitvar, cuniqueid, rowvar, strvar, prednames), .SDcols=response]
unit_rowestlst <- lapply(estunits, MAest.unit,
dat=tdomdatsum, cuniqueid=cuniqueid,
unitlut=unitlut, unitvar=unitvar, esttype=esttype,
MAmethod=MAmethod, strvar=strvar, prednames=prednames,
domain=rowvar, response=response, npixels=npixels,
FIA=FIA, modelselect=modelselect_bydomain, getweights=getweights,
var_method=var_method)
unit_rowest <- do.call(rbind, sapply(unit_rowestlst, '[', "unitest"))
if (MAmethod %in% c("greg", "gregEN")) {
predselectlst$rowest <- do.call(rbind, sapply(unit_totestlst, '[', "predselect"))
}
if (colvar != "NONE") {
tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE),
by=c(unitvar, cuniqueid, colvar, strvar, prednames), .SDcols=response]
unit_colestlst <- lapply(estunits, MAest.unit,
dat=tdomdatsum, cuniqueid=cuniqueid,
unitlut=unitlut, unitvar=unitvar, esttype=esttype,
MAmethod=MAmethod, strvar=strvar, prednames=prednames,
domain=colvar, response=response, npixels=npixels,
FIA=FIA, modelselect=modelselect_bydomain, var_method=var_method)
unit_colest <- do.call(rbind, sapply(unit_colestlst, '[', "unitest"))
if (MAmethod %in% c("greg", "gregEN")) {
predselectlst$colest <- do.call(rbind, sapply(unit_colestlst, '[', "predselect"))
}
tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE),
by=c(unitvar, cuniqueid, grpvar, strvar, prednames), .SDcols=response]
tdomdatsum[, grpvar := do.call(paste, c(.SD, sep="#")), .SDcols=grpvar]
unit_grpestlst <- lapply(estunits, MAest.unit,
dat=tdomdatsum, cuniqueid=cuniqueid,
unitlut=unitlut, unitvar=unitvar, esttype=esttype,
MAmethod=MAmethod, strvar=strvar, prednames=prednames,
domain="grpvar", response=response, npixels=npixels,
FIA=FIA, modelselect=modelselect_bydomain, var_method=var_method)
unit_grpest <- do.call(rbind, sapply(unit_grpestlst, '[', "unitest"))
if (MAmethod %in% c("greg", "gregEN")) {
predselectlst$grpest <- do.call(rbind, sapply(unit_grpestlst, '[', "predselect"))
}
if (any(unit_grpest$grpvar == "NA#NA")) {
unit_grpest <- unit_grpest[unit_grpest$grpvar != "NA#NA", ]
}
unit_grpest[, c(rowvar, colvar) := tstrsplit(grpvar, "#", fixed=TRUE)]
}
}
###############################################################################
## Check add0 and Add area
###############################################################################
if (!sumunits && nrow(unitarea) > 1) col.add0 <- TRUE
if (!is.null(unit_rowest)) {
unit_rowest <- add0unit(x=unit_rowest, xvar=rowvar, uniquex=uniquerow,
unitvar=unitvar, xvar.add0=row.add0)
tabs <- check.matchclass(unitarea, unit_rowest, unitvar)
unitarea <- tabs$tab1
unit_rowest <- tabs$tab2
if (!is.null(row.orderby) && row.orderby != "NONE") {
setorderv(unit_rowest, c(row.orderby))
}
setkeyv(unit_rowest, unitvar)
unit_rowest <- unit_rowest[unitarea, nomatch=0]
if (totals) {
unit_rowest <- getpse(unit_rowest, areavar=areavar, esttype=esttype)
} else {
unit_rowest <- getpse(unit_rowest, esttype=esttype)
}
setkeyv(unit_rowest, c(unitvar, rowvar))
}
if (!is.null(unit_colest)) {
unit_colest <- add0unit(x=unit_colest, xvar=colvar, uniquex=uniquecol,
unitvar=unitvar, xvar.add0=col.add0)
tabs <- check.matchclass(unitarea, unit_colest, unitvar)
unitarea <- tabs$tab1
unit_colest <- tabs$tab2
if (!is.null(col.orderby) && col.orderby != "NONE") {
setorderv(unit_colest, c(col.orderby))
}
setkeyv(unit_colest, unitvar)
unit_colest <- unit_colest[unitarea, nomatch=0]
if (totals) {
unit_colest <- getpse(unit_colest, areavar=areavar, esttype=esttype)
} else {
unit_colest <- getpse(unit_colest, esttype=esttype)
}
setkeyv(unit_colest, c(unitvar, colvar))
}
if (!is.null(unit_grpest)) {
unit_grpest <- add0unit(x=unit_grpest, xvar=rowvar, uniquex=uniquerow,
unitvar=unitvar, xvar.add0=row.add0, xvar2=colvar, uniquex2=uniquecol,
xvar2.add0=col.add0)
tabs <- check.matchclass(unitarea, unit_grpest, unitvar)
unitarea <- tabs$tab1
unit_grpest <- tabs$tab2
if (!is.null(row.orderby) && row.orderby != "NONE") {
if (!is.null(col.orderby) && col.orderby != "NONE") {
setorderv(unit_grpest, c(row.orderby, col.orderby))
} else {
setorderv(unit_grpest, c(row.orderby))
}
} else if (!is.null(col.orderby) && col.orderby != "NONE") {
setorderv(unit_grpest, c(col.orderby))
}
setkeyv(unit_grpest, unitvar)
unit_grpest <- unit_grpest[unitarea, nomatch=0]
if (totals) {
unit_grpest <- getpse(unit_grpest, areavar=areavar, esttype=esttype)
} else {
unit_grpest <- getpse(unit_grpest, esttype=esttype)
}
setkeyv(unit_grpest, c(unitvar, rowvar, colvar))
}
###############################################################################
## GENERATE OUTPUT TABLES
###############################################################################
message("getting output...")
estnm <- "est"
tabs <- est.outtabs(esttype=esttype, sumunits=sumunits, areavar=areavar,
unitvar=unitvar, unitvars=unitvars, unit_totest=unit_totest,
unit_rowest=unit_rowest, unit_colest=unit_colest, unit_grpest=unit_grpest,
rowvar=rowvar, colvar=colvar, uniquerow=uniquerow, uniquecol=uniquecol,
rowgrp=rowgrp, rowgrpnm=rowgrpnm, rowunit=rowunit, totunit=totunit,
allin1=allin1, savedata=savedata, addtitle=addtitle,
title.ref=title.ref, title.colvar=title.colvar,
title.rowvar=title.rowvar, title.rowgrp=title.rowgrp,
title.unitvar=title.unitvar, title.estpse=title.estpse,
title.est=title.est, title.pse=title.pse, rawdata=rawdata,
outfn.estpse=outfn.estpse, outfolder=outfolder, outfn.date=outfn.date,
overwrite=overwrite_layer, estnm=estnm,
estround=estround, pseround=pseround, divideby=divideby,
returntitle=returntitle, estnull=estnull, psenull=psenull)
est2return <- tabs$tabest
pse2return <- tabs$tabpse
if (!is.null(est2return)) {
returnlst$est <- est2return
}
if (!is.null(pse2return)) {
returnlst$pse <- pse2return
}
if (returntitle) {
returnlst$titlelst <- alltitlelst
}
if (rawdata) {
rawdat <- tabs$rawdat
rawdat$domdat <- setDF(tdomdat)
#rawdat$expcondtab <- unit_weights
rawdat$plotweights <- unit_weights
rawdat$estvar <- estvar.name
rawdat$estvar.filter <- estvar.filter
if (savedata) {
if (!is.null(title.estpse)) {
title.raw <- paste(title.estpse, title.ref)
} else {
title.raw <- title.est
}
for (i in 1:length(rawdat)) {
tabnm <- names(rawdat[i])
if (!tabnm %in% c(estvar, prednames)) {
rawtab <- rawdat[[i]]
outfn.rawtab <- paste0(outfn.rawdat, "_", tabnm)
if (tabnm %in% c("plotsampcnt", "condsampcnt", "stratcombinelut")) {
write2csv(rawtab, outfolder=rawfolder, outfilenm=outfn.rawtab,
outfn.date=outfn.date, overwrite=overwrite_layer)
} else if (is.data.frame(rawtab)) {
if (raw_fmt != "csv") {
out_layer <- tabnm
} else {
out_layer <- outfn.rawtab
}
datExportData(rawtab,
savedata_opts=list(outfolder=rawfolder,
out_fmt=raw_fmt,
out_dsn=raw_dsn,
out_layer=out_layer,
overwrite_layer=overwrite_layer,
append_layer=append_layer,
add_layer=TRUE))
}
}
}
}
rawdat$module <- "MA"
rawdat$esttype <- "TREE"
rawdat$MAmethod <- MAmethod
rawdat$predselect.overall <- predselect.overall
rawdat$predselectlst <- predselectlst
rawdat$estvar <- estvar
rawdat$estvar.filter <- estvar.filter
if (!is.null(rowvar)) rawdat$rowvar <- rowvar
if (!is.null(colvar)) rawdat$colvar <- colvar
rawdat$areaunits <- areaunits
rawdat$estunits <- estunits
returnlst$raw <- rawdat
}
if ("STATECD" %in% names(pltcondf)) {
returnlst$statecd <- sort(unique(pltcondf$STATECD))
}
if ("INVYR" %in% names(pltcondf)) {
returnlst$invyr <- sort(unique(pltcondf$INVYR))
}
return(returnlst)
}
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