#' Green-Book module - Generate area estimates.
#'
#' Generates area estimates by domain (and estimation unit). Calculations are
#' based on Scott et al. 2005 ('the green-book') for mapped forest inventory
#' plots. The non-ratio estimator for estimating area by stratum and domain is
#' used. Plots that are totally nonsampled are excluded from estimation
#' dataset. Next, an adjustment factor is calculated by strata to adjust for
#' nonsampled (nonresponse) conditions that have proportion less than 1. The
#' attribute is the proportion of the plot which is divided by the adjustment
#' factor, and averaged by stratum. Strata means are combined using the strata
#' weights and then expanded to area using the total land area in the
#' population
#'
#' 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 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 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 }
#'
#' For available reference tables: sort(unique(FIESTAutils::ref_codes$VARIABLE)) \cr
#'
#' @param GBpopdat List. Population data objects returned from modGBpop().
#' @param landarea String. The sample area filter for estimates ("ALL",
#' "FOREST", "TIMBERLAND"). If landarea=FOREST, filtered to COND_STATUS_CD =
#' 1; If landarea=TIMBERLAND, filtered to SITECLCD in(1:6) and RESERVCD = 0.
#' @param pcfilter String. A filter for plot or cond attributes (including
#' pltassgn). Must be R logical syntax.
#' @param rowvar String. Name of row domain variable in cond. If only one
#' domain, rowvar = domain variable. If more than one domain, include colvar.
#' If no domain, rowvar = NULL.
#' @param colvar String. Name of column domain variable in cond.
#' @param sumunits Logical. If TRUE, estimation units are summed and returned
#' in one table.
#' @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 ... Parameters for modGBpop() if GBpopdat is NULL.
#' @return A list with estimates with percent sampling error for rowvar (and
#' colvar). If sumunits=TRUE or unitvar=NULL and colvar=NULL, one data frame
#' is returned. Otherwise, a list object is returned with the following
#' information. If savedata=TRUE, all data frames are written to outfolder.
#'
#' \item{est}{ Data frame. Area estimates, in area units (e.g., acres), by
#' rowvar, colvar (and estimation unit). If sumunits=TRUE or one estimation
#' unit and colvar=NULL, or allin1=TRUE, estimates and percent sampling error
#' are in one data frame. }
#' \item{pse}{ Data frame. Percent sampling errors (Confidence level 68%)
#' for estimates by rowvar and colvar (and estimation unit). }
#' \item{titlelst}{ List. 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. Row and column tables are also included
#' in list. }
#' \item{raw}{ List. If rawdata=TRUE, a list including the processing data used
#' for estimation including: number of plots and conditions; stratification
#' information; and 1 to 8 tables with calculated values for table cells and
#' totals (See processing data below). }
#'
#' Raw data
#'
#' \item{plotsampcnt}{ Table. Number of plots by plot status (e.g., 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{unitarea}{ DF. Area by estimation unit. }
#' \item{expcondtab}{ DF. Condition-level area expansion factors. }
#' \item{domdat}{ DF. Final data table used for estimation. }
#'
#' \item{stratdat}{ Data frame. Strata information by estimation unit. }
#' \tabular{lll}{ \tab \bold{Variable} \tab \bold{Description} \cr
#' \tab unitvar \tab estimation unit \cr \tab strvar \tab stratum value \cr
#' \tab strwtvar \tab number of pixels by strata and estimation unit \cr
#' \tab n.strata \tab number of plots in strata (after totally nonsampled
#' plots removed) \cr
#' \tab n.total \tab number of plots for estimation unit \cr
#' \tab strwt \tab proportion of area (or plots) by strata and estimation
#' unit (strata weight) \cr \tab CONDPROP_UNADJ_SUM \tab summed condition
#' proportion by strata and estimation unit \cr
#' \tab CONDPROP_ADJFAC \tab adjusted condition proportion by strata after
#' nonresponse plots removed \cr
#' \tab AREA \tab total area for estimation unit \cr
#' \tab CONDPROP_ADJFAC \tab average area \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), colvar totals,
#' if not NULL (unit.colvar); and a combination of rowvar and colvar, if colvar
#' is not NULL (unit.grpvar). If sumunits=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 estimate proportion of land \cr
#' \tab nhat.var \tab variance estimate of estimated proportion of land \cr
#' \tab NBRPLT.gt0 \tab Number of non-zero plots used in estimates \cr
#' \tab AREA \tab total area for estimation unit \cr
#' \tab est \tab estimated area of land nhat*areavar \cr
#' \tab est.var \tab variance estimate of estimate acres of land
#' nhat.var*areavar^2 \cr
#' \tab est.se \tab standard error of estimated area of land sqrt(est.var) \cr
#' \tab est.cv \tab coefficient of variation of estimated area of land 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 } }
#'
#' savedata\cr
#' if savedata=TRUE...\cr
#' tables with estimate and percent standard error will be written as *csv
#' files to outfolder. if rawdata=TRUE, the rawdata will be output to the
#' outfolder in a folder named rawdata (if raw_fmt="csv") or a database in
#' the outfolder, if (raw_fmt != "csv").
#'
#' if outfn.pre is not null...\cr
#' a prefix is added to output files if raw_fmt = 'csv', prefix is added to
#' file names in rawdata folder if raw_fmt != 'csv', prefix is added to dsn name
#' @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 condition proportions (CONDPROP_UNADJ) and
#' dividing by the number of plots in the strata/estimation unit.
#'
#' 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.
#'
#' STRATA:\cr Stratification is used to reduce variance in population estimates
#' by partitioning the population into homogenous classes (strata), such as
#' forest and nonforest. For stratified sampling methods, the strata sizes
#' (weights) must be either known or estimated. Remotely-sensed data is often
#' used to generate strata weights with proporation of pixels by strata. If
#' stratification is desired (strata=TRUE), the required data include: stratum
#' assignment for the center location of each plot, stored in either pltassgn
#' or cond; and a look-up table with the area or proportion of the total area
#' of each strata value by estimation unit, making sure the name of the strata
#' (and estimation unit) variable and values match the plot assignment name(s)
#' and value(s).
#'
#' 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.
#'
#' If sumunits=TRUE, estimates are generated by estimation unit, summed
#' together, and returned as one estimate. If rawdata=TRUE, estimates by
#' individual estimation unit are also returned.
#'
#' If sumunits=FALSE, estimates are generated and returned by estimation unit
#' as one data frame. If savedata=TRUE, a separate file is written for each
#' estimation unit.
#'
#' stratcombine:\cr If 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.
#'
#' 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.
#' @author Tracey S. Frescino, Paul L. Patterson, Elizabeth A. Freeman
#' @references Scott, Charles T.; Bechtold, William A.; Reams, Gregory A.;
#' Smith, William D.; Westfall, James A.; Hansen, Mark H.; Moisen, Gretchen G.
#' 2005. Sample-based estimators used by the Forest Inventory and Analysis
#' national information management system. Gen. Tech. Rep. SRS-80. Asheville,
#' NC: U.S. Department of Agriculture, Forest Service, Southern Research
#' Station, p.53-77.
#' @keywords data
#' @examples
#' \donttest{
#' GBpopdat <- modGBpop(
#' popTabs = list(cond = FIESTA::WYcond,
#' tree = FIESTA::WYtree,
#' seed = FIESTA::WYseed),
#' popTabIDs = list(cond = "PLT_CN"),
#' pltassgn = FIESTA::WYpltassgn,
#' pltassgnid = "CN",
#' pjoinid = "PLT_CN",
#' unitarea = FIESTA::WYunitarea,
#' unitvar = "ESTN_UNIT",
#' strata = TRUE,
#' stratalut = WYstratalut,
#' strata_opts = strata_options(getwt = TRUE)
#' )
#'
#' forest_area <- modGBarea(
#' GBpopdat = GBpopdat,
#' landarea = "FOREST",
#' sumunits = TRUE,
#' )
#' str(forest_area, max.level = 1)
#'
#' forest_area_by_forest_type <- modGBarea(
#' GBpopdat = GBpopdat,
#' landarea = "FOREST",
#' rowvar = "FORTYPCD",
#' sumunits = TRUE
#' )
#' str(forest_area_by_forest_type, max.level = 1)
#' }
#' @export modGBarea
<- (GBpopdat,
landarea = "FOREST",
pcfilter = ,
rowvar = ,
colvar = ,
sumunits = ,
returntitle = ,
savedata = ,
table_opts = ,
title_opts = ,
savedata_opts = ,
){
###################################################################################
## DESCRIPTION:
## Generates acre estimates by domain (and estimation unit)
###################################################################################
## CHECK GUI - IF NO ARGUMENTS SPECIFIED, ASSUME GUI=TRUE
#if (nargs() == 0 && is.null(GBpopdat)) gui <- TRUE
gui <-
## If gui.. set variables to NULL
if (gui) {
landarea=strvar=areavar=sumunits=adj=strata=getwt=cuniqueid=ACI=
puniqueid=savedata=addtitle=returntitle=rawdata=unitvar <-
#if (!row.FIAname) row.FIAname <- NULL
#if (!col.FIAname) col.FIAname <- NULL
}
## INITIALIZE SETTINGS
esttype <- "AREA"
popType <- "CURR"
nonresp <-
substrvar <-
rawdata <-
returnlst <- ()
## Set global variables
ONEUNIT=n.total=n.strata=strwt=TOTAL=rawfolder <-
#estvar <- "CONDPROP_ADJ"
##################################################################
## CHECK PARAMETER NAMES
##################################################################
## Check input parameters
input.params <- ((()))-1]
formallst <- ((()),
(()))
if (!(input.params %in% formallst)) {
miss <- input.params!input.params %in% formallst]
("invalid parameter: ", (miss))
}
## Check parameter lists
pcheck.params(input.params = input.params,
title_opts = title_opts,
table_opts = table_opts,
savedata_opts = savedata_opts)
## Check parameter option lists
optslst <- pcheck.opts(optionlst = (
title_opts = title_opts,
table_opts = table_opts,
savedata_opts = savedata_opts))
title_opts <- optslst$title_opts
table_opts <- optslst$table_opts
savedata_opts <- optslst$savedata_opts
for (i 1:(title_opts)) {
((title_opts)[i]], title_opts[i]])
}
for (i 1:(table_opts)) {
((table_opts)[i]], table_opts[i]])
}
##################################################################
## CHECK PARAMETER INPUTS
##################################################################
list.items <- ("pltcondx", "cuniqueid", "condid",
"unitarea", "unitvar", "stratalut", "strvar",
"plotsampcnt", "condsampcnt")
GBpopdat <- pcheck.object(GBpopdat, "GBpopdat", list.items=list.items)
if ((GBpopdat)) ()
pltidsadj <- GBpopdat$pltidsadj
pltcondx <- GBpopdat$pltcondx
cuniqueid <- GBpopdat$cuniqueid
condid <- GBpopdat$condid
ACI <- GBpopdat$ACI
pltassgnx <- GBpopdat$pltassgnx
unitarea <- GBpopdat$unitarea
areavar <- GBpopdat$areavar
areaunits <- GBpopdat$areaunits
unitvar <- GBpopdat$unitvar
unitvars <- GBpopdat$unitvars
strata <- GBpopdat$strata
stratalut <- GBpopdat$stratalut
strvar <- GBpopdat$strvar
expcondtab <- GBpopdat$expcondtab
plotsampcnt <- GBpopdat$plotsampcnt
condsampcnt <- GBpopdat$condsampcnt
states <- GBpopdat$states
invyrs <- GBpopdat$invyrs
stratcombinelut <- GBpopdat$stratcombinelut
strwtvar <- GBpopdat$strwtvar
adj <- GBpopdat$adj
strunitvars <- (unitvar, strvar)
strata <- GBpopdat$strata
popdatindb <- GBpopdat$popdatindb
pop_fmt <- GBpopdat$pop_fmt
pop_dsn <- GBpopdat$pop_dsn
pop_schema <- GBpopdat$pop_schema
popconn <- GBpopdat$popconn
dbqueries <- GBpopdat$dbqueries
dbqueriesWITH <- GBpopdat$dbqueriesWITH
areawt <- GBpopdat$areawt
areawt2 <- GBpopdat$areawt2
adjcase <- GBpopdat$adjcase
pltidsid <- GBpopdat$pjoinid
pltassgnid <- GBpopdat$pltassgnid
pltflds <- GBpopdat$pltflds
condflds <- GBpopdat$condflds
if (popdatindb) {
if ((popconn) || !DBI::dbIsValid(popconn)) {
if (!(pop_dsn)) {
if (pop_fmt == "sqlite") {
popconn <- (pop_dsn, dbconnopen = )
}
} {
("invalid database connection")
}
}
}
########################################
## Check area units
########################################
unitchk <- pcheck.areaunits(unitarea=unitarea, areavar=areavar,
areaunits=areaunits, metric=metric)
unitarea <- unitchk$unitarea
areavar <- unitchk$areavar
areaunits <- unitchk$outunits
if ((key(unitarea))) {
setkeyv(unitarea, unitvar)
}
###################################################################################
## Check parameter inputs and plot/condition filters
###################################################################################
estdat <-
check.estdata(esttype = esttype,
popType = popType,
popdatindb = popdatindb,
popconn = popconn, pop_schema = pop_schema,
pltcondx = pltcondx,
pltflds = pltflds,
condflds = condflds,
dbqueriesWITH = dbqueriesWITH,
dbqueries = dbqueries,
totals = totals,
pop_fmt = pop_fmt, pop_dsn = pop_dsn,
sumunits = sumunits,
landarea = landarea,
ACI = ACI,
pcfilter = pcfilter,
allin1 = allin1, divideby = divideby,
estround = estround, pseround = pseround,
returntitle = returntitle,
rawonly = rawonly,
savedata = savedata,
savedata_opts = savedata_opts,
gui = gui)
if ((estdat)) ()
esttype <- estdat$esttype
sumunits <- estdat$sumunits
totals <- estdat$totals
landarea <- estdat$landarea
allin1 <- estdat$allin1
divideby <- estdat$divideby
estround <- estdat$estround
pseround <- estdat$pseround
addtitle <- estdat$addtitle
returntitle <- estdat$returntitle
rawonly <- estdat$rawonly
savedata <- estdat$savedata
outfolder <- estdat$outfolder
overwrite_layer <- estdat$overwrite_layer
outfn.pre <- estdat$outfn.pre
outfn.date <- estdat$outfn.date
append_layer = estdat$append_layer
rawfolder <- estdat$rawfolder
raw_fmt <- estdat$raw_fmt
raw_dsn <- estdat$raw_dsn
pcwhereqry <- estdat$where.qry
SCHEMA. <- estdat$SCHEMA.
pltcondflds <- estdat$pltcondflds
pltcondxadjWITHqry <- estdat$pltcondxadjWITHqry
pltcondxWITHqry <- estdat$pltcondxWITHqry
###################################################################################
### Check row and column data
###################################################################################
rowcolinfo <-
check.rowcol(esttype = esttype,
popType = popType,
popdatindb = popdatindb,
popconn = popconn, SCHEMA. = SCHEMA.,
pltcondx = pltcondx,
pltcondflds = pltcondflds,
withqry = pltcondxWITHqry,
cuniqueid = cuniqueid, condid = condid,
rowvar = rowvar, colvar = colvar,
row.FIAname = row.FIAname, col.FIAname = col.FIAname,
row.orderby = row.orderby, col.orderby = col.orderby,
row.add0 = row.add0, col.add0 = col.add0,
row.classify = row.classify, col.classify = col.classify,
title.rowvar = title.rowvar, title.colvar = title.colvar,
rowlut = rowlut, collut = collut,
rowgrp = rowgrp, rowgrpnm = rowgrpnm,
rowgrpord = rowgrpord, title.rowgrp = )
uniquerow <- rowcolinfo$uniquerow
uniquecol <- rowcolinfo$uniquecol
bydomainlst <- rowcolinfo$domainlst
rowvar <- rowcolinfo$rowvar
colvar <- rowcolinfo$colvar
rowvarnm <- rowcolinfo$rowvarnm
colvarnm <- rowcolinfo$colvarnm
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
grpvar <- rowcolinfo$grpvar
classifyrow <- rowcolinfo$classifyrow
classifycol <- rowcolinfo$classifycol
#rm(rowcolinfo)
## Generate a uniquecol for estimation units
if (!sumunits && colvar == "NONE") {
uniquecol <- data.table(unitarea[unitvar]])
setnames(uniquecol, unitvar)
uniquecol[unitvar]] <- (uniquecol[unitvar]])
}
###################################################################################
### Get condition-level domain data
###################################################################################
conddat <-
check.cond(areawt = areawt,
areawt2 = areawt2,
adj = adj,
adjcase = adjcase,
cuniqueid = cuniqueid,
condid = condid,
rowvar = rowvar,
colvar = colvar,
pcdomainlst = ((bydomainlst, "TOTAL")),
popdatindb = popdatindb,
popconn = popconn,
pltcondx = pltcondx,
pltidsadj = pltidsadj,
pltidsid = pltidsid,
pltcondxadjWITHqry = pltcondxadjWITHqry,
pcwhereqry = pcwhereqry,
classifyrow = classifyrow,
classifycol = classifycol)
if ((conddat)) ()
cdomdat <- conddat$cdomdat
cdomdatqry <- conddat$cdomdatqry
estnm <- conddat$estnm
rowvar <- conddat$rowvar
colvar <- conddat$colvar
grpvar <- conddat$grpvar
###################################################################################
### Get titles for output tables
###################################################################################
alltitlelst <-
check.titles(dat = cdomdat, esttype = esttype,
sumunits = sumunits,
title.main = title.main,
title.ref = title.ref,
title.rowvar = title.rowvar,
title.rowgrp = title.rowgrp,
title.colvar = title.colvar,
title.unitvar = title.unitvar,
title.filter = title.filter,
title.unitsn = areaunits,
unitvar = unitvar,
rowvar = rowvar, colvar=colvar,
addtitle = addtitle,
returntitle = returntitle,
rawdata = rawdata,
states = states, invyrs = invyrs,
landarea = landarea, pcfilter = pcfilter,
allin1 = allin1, divideby = divideby,
outfn.pre = outfn.pre)
title.unitvar <- alltitlelst$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$outfn.rawdat
}
###################################################################################
## GENERATE ESTIMATES
###################################################################################
estdat <-
getGBestimates(esttype = esttype,
domdatn = cdomdat,
uniqueid = pltassgnid, condid = condid,
estvarn.name = estnm,
rowvar = rowvar, colvar = colvar,
grpvar = grpvar,
pltassgnx = pltassgnx,
unitarea = unitarea,
unitvar = unitvar,
areavar = areavar,
stratalut = stratalut,
strvar = strvar,
strwtvar = strwtvar,
totals = totals,
sumunits = sumunits,
uniquerow = uniquerow,
uniquecol = uniquecol,
row.orderby = row.orderby,
col.orderby = col.orderby,
row.add0 = row.add0,
col.add0 = col.add0,
row.NAname = row.NAname,
col.NAname = col.NAname)
if ((estdat)) ()
unit_totest <- estdat$unit_totest
unit_rowest <- estdat$unit_rowest
unit_colest <- estdat$unit_colest
unit_grpest <- estdat$unit_grpest
rowunit <- estdat$rowunit
totunit <- estdat$totunit
unitvar <- estdat$unitvar
###################################################################################
## GENERATE OUTPUT TABLES
###################################################################################
("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 = rowvarnm, colvar = colvarnm,
uniquerow = uniquerow, uniquecol = uniquecol,
rowgrp = rowgrp, rowgrpnm = rowgrpnm,
rowunit = rowunit, totunit = totunit,
allin1 = allin1,
savedata = savedata, addtitle = addtitle,
title.ref = title.ref,
title.rowvar = title.rowvar, title.colvar = title.colvar,
title.rowgrp = title.rowgrp,
title.unitvar = title.unitvar,
title.estpse = title.estpse,
title.est = title.est, title.pse = title.pse,
rawdata = rawdata, rawonly = rawonly,
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,
raw.keep0 = raw.keep0)
est2return <- tabs$tabest
pse2return <- tabs$tabpse
if (!(est2return)) {
returnlst$est <- setDF(est2return)
}
if (!(pse2return)) {
returnlst$pse <- setDF(pse2return)
}
if (returntitle) {
returnlst$titlelst <- alltitlelst
}
if (rawdata) {
## Add total number of plots in population to unit_totest and totest (if sumunits=TRUE)
UNITStot <- ((unit_totest[unitvar]]))
NBRPLTtot <- stratalut[stratalut[unitvar]] %in% UNITStot, (NBRPLT = (n.strata, na.rm=)),
=unitvars]
if ("unit_totest" %in% (tabs$rawdat)) {
tabs$rawdat$unit_totest <- (tabs$rawdat$unit_totest, NBRPLTtot, =unitvars)
}
if (sumunits && "totest" %in% (tabs$rawdat)) {
tabs$rawdat$totest <- (tabs$rawdat$totest, NBRPLT = (NBRPLTtot$NBRPLT))
}
rawdat <- tabs$rawdat
rawdat$domdat <- setDF(cdomdat)
rawdat$domdatqry <- cdomdatqry
if (savedata) {
if (!(title.estpse)) {
title.raw <- (title.estpse, title.ref)
} {
title.raw <- (title.est, title.ref, sep="; ")
}
for (i 1:(rawdat)) {
tabnm <- (rawdat[i])
rawtab <- rawdat[i]]
outfn.rawtab <- (outfn.rawdat, "_", tabnm)
if (tabnm %in% ("plotsampcnt", "condsampcnt", "stratcombinelut")) {
write2csv(rawtab, outfolder=rawfolder, outfilenm=outfn.rawtab,
outfn.date=outfn.date, overwrite=overwrite_layer)
} if ((rawtab)) {
if (raw_fmt != "csv") {
out_layer <- tabnm
} {
out_layer <- outfn.rawtab
}
(rawtab,
savedata_opts = (outfolder = rawfolder,
out_fmt = raw_fmt,
out_dsn = raw_dsn,
out_layer = out_layer,
overwrite_layer = overwrite_layer,
append_layer = append_layer,
add_layer = ))
}
}
}
rawdat$module <- "GB"
rawdat$esttype <- esttype
rawdat$popType <- popType
rawdat$GBmethod <- (strata, "PS", "HT")
if (!(rowvar)) rawdat$rowvar <- rowvar
if (!(colvar)) rawdat$colvar <- colvar
rawdat$areaunits <- areaunits
returnlst$ <- rawdat
}
returnlst$statecd <- (pcheck.states(states, statereturn = "VALUE"))
returnlst$states <- states
returnlst$invyr <- (((invyrs)))
(returnlst)
}
