FIESTA - Model-Assisted Estimators

Model-Assisted (MA) module overview

FIESTA’s Model-Assisted (MA) module calculates population estimates and their sampling errors by taking advantage of available model-assisted survey estimators from the mase R package (McConville, et al. 2018). These estimators can use a variety of auxiliary data to build models and predict over a response variable of interest, while using a bias-correction term so that the bias of the model does not depend on model mis-specification.

Functions in FIESTA used for fitting model-assisted estimators include the modMAarea function for area estimates and modMAtree for tree estimates. The modMApop function is used to get population data needed for model-assisted estimation. Below is a description and table of contents for the sections related to these functions:

FUNCTION DESCRIPTION
modMApop Creates population data for model-assisted estimation.
modMAarea Produces area level estimates through model-assisted estimation.
modMAtree Produces tree level estimates through model-assisted estimation.

Objective of tutorial

The main objective of this tutorial is to demonstrate how to use FIESTA for generating estimates using estimators from mase. The model-assisted estimators can be used with FIA’s standard state-level population data (i.e, Evaluation) from the FIA database (FIADB) and also population data from a custom boundary.

The following examples are for generating estimates and estimated variances using standard FIA Evaluation data from FIA’s National database, with custom Estimation unit and Stratification information. The examples use data from three inventory years of field measurements in the state of Wyoming, from FIADB_1.7.2.00, last updated June 20, 2018, downloaded on June 25, 2018 and stored as internal data objects in FIESTA.

Example data - Wyoming (WY), Inventory Years 2011-2012

View MA Example Data
Data Frame Description
WYplt WY plot-level data
WYcond WY condition-level data
WYtree WY tree-level data
External data Description
WYbighorn_adminbnd.shp Polygon shapefile of WY Bighorn National Forest Administrative boundary*
WYbighorn_districtbnd.shp Polygon shapefile of WY Bighorn National Forest District boundaries**
WYbighorn_forest_nonforest_250m.tif GeoTIFF raster of predicted forest/nonforest (1/0) for stratification***
WYbighorn_dem_250m.img Erdas Imagine raster of elevation change, in meters****

*USDA Forest Service, Automated Lands Program (ALP). 2018. S_USA.AdministrativeForest (http://data.fs.usda.gov/geodata/edw). Description: An area encompassing all the National Forest System lands administered by an administrative unit. The area encompasses private lands, other governmental agency lands, and may contain National Forest System lands within the proclaimed boundaries of another administrative unit. All National Forest System lands fall within one and only one Administrative Forest Area.

**USDA Forest Service, Automated Lands Program (ALP). 2018. S_USA.RangerDistrict (http://data.fs.usda.gov/geodata/edw). Description: A depiction of the boundary that encompasses a Ranger District.

***Based on MODIS-based classified map resampled from 250m to 500m resolution and reclassified from 3 to 2 classes: 1:forest; 2:nonforest. Projected in Albers Conical Equal Area, Datum NAD27 (Ruefenacht et al. 2008). Clipped to extent of WYbighorn_adminbnd.shp.

****USGS National Elevation Dataset (NED), resampled from 30m resolution to 250m. Projected in Albers Conical Equal Area, Datum NAD27 (U.S. Geological Survey 2017). Clipped to boundary of WYbighorn_adminbnd.shp.

Set up

First, you’ll need to load the FIESTA library:

library(FIESTA)

Next, you’ll need to set up an “outfolder”. This is just a file path to a folder where you’d like FIESTA to send your data output. For our purposes in this vignette, we have saved our outfolder file path as the outfolder object in a temporary directory. We also set a few default options preferred for this vignette.

outfolder <- tempdir()

Get data for examples

View Getting Data

Now that we’ve loaded FIESTA and setup our outfolder, we can retrieve the data needed to run the examples. First, we point to some external data and predictor layers stored in FIESTA and derive new predictor layers using the terra package.

# File names for external spatial data
WYbhfn <- system.file("extdata", "sp_data/WYbighorn_adminbnd.shp",
                      package = "FIESTA")
WYbhdistfn <- system.file("extdata", "sp_data/WYbighorn_districtbnd.shp",
                          package = "FIESTA")

## predictor variables
fornffn <- system.file("extdata", "sp_data/WYbighorn_forest_nonforest_250m.tif",
                       package = "FIESTA")
demfn <- system.file("extdata", "sp_data/WYbighorn_dem_250m.img",
                     package = "FIESTA")

# Derive new predictor layers from dem
library(terra)
dem <- rast(demfn)
slpfn <- paste0(outfolder, "/WYbh_slp.img")
slp <- terra::terrain(dem,
                      v = "slope",
                      unit = "degrees",
                      filename = slpfn, 
                      overwrite = TRUE,
                      NAflag = -99999.0)
aspfn <- paste0(outfolder, "/WYbh_asp.img")
asp <- terra::terrain(dem,
                      v = "aspect",
                      unit = "degrees", 
                      filename = aspfn,
                      overwrite = TRUE,
                      NAflag = -99999.0)

Next, we can get our FIA plot data and set up our auxiliary data. We can get our FIA plot data with the spMakeSpatialPoints function from FIESTA. For more information on how to use this function, please see the sp vignette included with FIESTA (link).

WYspplt <- spMakeSpatialPoints(
  xyplt = WYplt,
  xy.uniqueid = "CN",
  xvar = "LON_PUBLIC",
  yvar = "LAT_PUBLIC",
  xy.crs = 4269
)

rastlst.cont <- c(demfn, slpfn, aspfn)
rastlst.cont.name <- c("dem", "slp", "asp")
rastlst.cat <- fornffn
rastlst.cat.name <- "fornf"

Next, we must generate dataset for model-assisted estimation. We can do this with the spGetAuxiliary function from FIESTA. Again, see the sp vignette for further information on this function.

modeldat <- spGetAuxiliary(
  xyplt = WYspplt,
  uniqueid = "CN",
  unit_layer = WYbhfn,
  unitvar = NULL,
  rastlst.cont = rastlst.cont,
  rastlst.cont.name = rastlst.cont.name,
  rastlst.cat = rastlst.cat,
  rastlst.cat.name = rastlst.cat.name,
  rastlst.cont.stat = "mean",
  asptransform = TRUE,
  rast.asp = aspfn,
  keepNA = FALSE,
  showext = FALSE,
  savedata = FALSE)
str(modeldat, max.level = 1)
output 
## List of 12
##  $ unitvar       : chr "ONEUNIT"
##  $ pltassgn      :'data.frame':  56 obs. of  25 variables:
##  $ pltassgnid    : chr "CN"
##  $ unitarea      :'data.frame':  1 obs. of  2 variables:
##  $ areavar       : chr "ACRES_GIS"
##  $ unitzonal     :'data.frame':  1 obs. of  10 variables:
##  $ inputdf       :Classes 'data.table' and 'data.frame': 4 obs. of  7 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##  $ prednames     : chr [1:5] "dem" "slp" "asp_cos" "asp_sin" ...
##  $ zonalnames    : chr [1:7] "dem" "slp" "asp_cos" "asp_sin" ...
##  $ predfac       : chr "fornf"
##  $ npixelvar     : chr "npixels"
##  $ predfac.levels:List of 1

Examples

modMApop

Example 1: Creating our population dataset with modMApop

View Example

We can create our population data for model-assisted estimation. To do so, we use the modMApop function in FIESTA. We must assign our population tables with the popTabs argument (and unique identifiers for these tables with the popTabIDs argument if they are not the default), the plot assignment with the pltassgn argument, and in auxiliary dataset we just created with the auxdat argument. The spGetAuxiliary function has done much of the hard work for us so far, so we can just run a simple call to modMApop:

MApopdat <- modMApop(popTabs = list(tree = WYtree, 
                                    cond = WYcond, 
                                    plt = WYplt),
                     auxdat = modeldat)

Note that the modMApop function returns a list with lots of information and data for us to use. For a quick look at what this list includes we can use the str function:

str(MApopdat, max.level = 1)
output 
## List of 40
##  $ module       : chr "MA"
##  $ popType      : chr "VOL"
##  $ pltidsadj    :Classes 'data.table' and 'data.frame':  56 obs. of  5 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##   ..- attr(*, "sorted")= Named chr "CN"
##   .. ..- attr(*, "names")= chr "CN"
##  $ pltcondx     :Classes 'data.table' and 'data.frame':  67 obs. of  37 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##   ..- attr(*, "sorted")= chr [1:2] "PLT_CN" "CONDID"
##  $ pltcondflds  : chr [1:36] "PLT_CN" "CONDID" "COND_NONSAMPLE_REASN_CD" "CONDPROP_UNADJ" ...
##  $ pjoinid      : Named chr "CN"
##   ..- attr(*, "names")= chr "CN"
##  $ cuniqueid    : chr "PLT_CN"
##  $ pltassgnid   : Named chr "CN"
##   ..- attr(*, "names")= chr "CN"
##  $ condid       : chr "CONDID"
##  $ ACI          : logi FALSE
##  $ areawt       : chr "CONDPROP_UNADJ"
##  $ areawt2      : NULL
##  $ adjcase      : Named chr "ADJ_FACTOR_COND"
##   ..- attr(*, "names")= chr "COND"
##  $ dbqueries    :List of 4
##  $ dbqueriesWITH:List of 4
##  $ pltassgnx    :Classes 'data.table' and 'data.frame':  56 obs. of  8 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##   ..- attr(*, "sorted")= Named chr "CN"
##   .. ..- attr(*, "names")= chr "CN"
##  $ unitlut      :Classes 'data.table' and 'data.frame':  1 obs. of  9 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##  $ unitarea     :Classes 'data.table' and 'data.frame':  1 obs. of  2 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##   ..- attr(*, "sorted")= chr "ONEUNIT"
##  $ npixels      :Classes 'data.table' and 'data.frame':  1 obs. of  2 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##  $ npixelvar    : chr "npixels"
##  $ estvar.area  : chr "CONDPROP_ADJ"
##  $ areavar      : chr "ACRES_GIS"
##  $ areaunits    : chr "acres"
##  $ unitvar      : chr "ONEUNIT"
##  $ unitvars     : chr "ONEUNIT"
##  $ plotsampcnt  :'data.frame':   2 obs. of  3 variables:
##  $ condsampcnt  :'data.frame':   4 obs. of  3 variables:
##  $ states       : chr "Wyoming"
##  $ invyrs       : int [1:3] 2011 2012 2013
##  $ adj          : chr "plot"
##  $ P2POINTCNT   :'data.frame':   0 obs. of  0 variables
##  $ plotunitcnt  :'data.frame':   1 obs. of  4 variables:
##  $ treex        :Classes 'data.table' and 'data.frame':  1706 obs. of  19 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##   ..- attr(*, "sorted")= chr [1:4] "PLT_CN" "CONDID" "SUBP" "TREE"
##  $ tuniqueid    : chr "PLT_CN"
##  $ adjtree      : logi FALSE
##  $ adjfactors   :Classes 'data.table' and 'data.frame':  56 obs. of  5 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##   ..- attr(*, "sorted")= Named chr "CN"
##   .. ..- attr(*, "names")= chr "CN"
##  $ adjvarlst    : Named chr [1:4] "ADJ_FACTOR_COND" "ADJ_FACTOR_SUBP" "ADJ_FACTOR_MACR" "ADJ_FACTOR_MICR"
##   ..- attr(*, "names")= chr [1:4] "COND" "SUBP" "MACR" "MICR"
##  $ prednames    : chr [1:5] "dem" "slp" "asp_cos" "asp_sin" ...
##  $ predfac      : chr "fornf"
##  $ popdatindb   : logi FALSE

Now that we’ve created our population dataset, we can move on to estimation.

modMAarea

Example 2: Area of forest land, Wyoming, 2011-2013

View Example

In this example, we look at estimating the area of forest land in Wyoming from 2011 to 2013 with the generalized regression estimator (MAmethod = "greg"). FIESTA returns raw data for area of forest land, Wyoming, 2011-2013 (sum estimation units).

area1 <- modMAarea(
  MApopdat = MApopdat, # pop - population calculations for WY, post-stratification
  MAmethod = "greg", # est - model-assisted method
  landarea = "FOREST" # est - forest land filter
  )

We can look at the structure of this output with str and the estimates below. Note that again FIESTA outputs a list.

str(area1, max.level = 2)
output 
## List of 5
##  $ est    :'data.frame': 1 obs. of  3 variables:
##   ..$ ONEUNIT               : chr "1"
##   ..$ Estimate              : num 1036942
##   ..$ Percent Sampling Error: num 3.76
##  $ raw    :List of 10
##   ..$ unit_totest  :'data.frame':    1 obs. of  18 variables:
##   ..$ domdat       :'data.frame':    40 obs. of  4 variables:
##   ..$ plotweights  :Classes 'data.table' and 'data.frame':   37 obs. of  5 variables:
##   .. ..- attr(*, ".internal.selfref")=<externalptr> 
##   ..$ module       : chr "MA"
##   ..$ esttype      : chr "AREA"
##   ..$ MAmethod     : chr "greg"
##   ..$ predselectlst:List of 1
##   ..$ rowvar       : chr "TOTAL"
##   ..$ colvar       : chr "NONE"
##   ..$ areaunits    : chr "acres"
##  $ statecd: int 56
##  $ states : chr "Wyoming"
##  $ invyr  : int [1:3] 2011 2012 2013
area1$est
output 
##   ONEUNIT Estimate Percent Sampling Error
## 1       1  1036942                   3.76

Example 3: Area of forest land, Wyoming, 2011-2013, using the Elastic Net for variable selection

View Example

Here, we fit the same model as the above example, but rather than using "greg" are our model-assisted method, we can use "gregEN" where the EN stands for “elastic net”. The elastic net performs variable selection for us, grabbing predictors it finds to be most useful in the model.

area2 <- modMAarea(
  MApopdat = MApopdat, # pop - population calculations for WY, post-stratification
  MAmethod = "gregEN", # est - model-assisted method
  landarea = "FOREST", # est - forest land filter
  )

We can again see that the structure of the list is very similar to that in the above example:

str(area2, max.level = 2)
output 
## List of 5
##  $ est    :'data.frame': 1 obs. of  3 variables:
##   ..$ ONEUNIT               : chr "1"
##   ..$ Estimate              : num 1012195
##   ..$ Percent Sampling Error: num 4.05
##  $ raw    :List of 10
##   ..$ unit_totest  :'data.frame':    1 obs. of  18 variables:
##   ..$ domdat       :'data.frame':    40 obs. of  4 variables:
##   ..$ plotweights  :List of 1
##   ..$ module       : chr "MA"
##   ..$ esttype      : chr "AREA"
##   ..$ MAmethod     : chr "gregEN"
##   ..$ predselectlst:List of 1
##   ..$ rowvar       : chr "TOTAL"
##   ..$ colvar       : chr "NONE"
##   ..$ areaunits    : chr "acres"
##  $ statecd: int 56
##  $ states : chr "Wyoming"
##  $ invyr  : int [1:3] 2011 2012 2013

However now the raw list has an item call predselectlst. We can look at this item now:

area2$raw$predselectlst$totest
output 
##    ONEUNIT TOTAL   dem   slp asp_cos asp_sin fornf2
##     <fctr> <int> <num> <num>   <num>   <num>  <num>
## 1:       1     1     0     0       0       0      0

Notably, we can see that dem, slp, asp_cos, and asp_sin were removed from the model.

Example 4: Area by forest type on forest land, Wyoming, 2011-2013

View Example

In this example, we look at adding rows to the output and include returntitle=TRUE to return title information.

area3 <- modMAarea(
    MApopdat = MApopdat,         # pop - population calculations for WY, post-stratification
    MAmethod = "greg",           # est - model-assisted method
    landarea = "FOREST",         # est - forest land filter
    rowvar = "FORTYPCD",         # est - row domain
    returntitle = TRUE           # out - return title information
    )

Again, we can look at the contents of the output list. The output now includes titlelst, a list of associated titles.

str(area3, max.level = 1)
output 
## List of 6
##  $ est     :'data.frame':    8 obs. of  3 variables:
##  $ titlelst:List of 10
##  $ raw     :List of 11
##  $ statecd : int 56
##  $ states  : chr "Wyoming"
##  $ invyr   : int [1:3] 2011 2012 2013

And the estimates:

## Estimate and percent sampling error of estimate
area3$est
output 
##   Forest type Estimate Percent Sampling Error
## 1         201  44286.4                   85.2
## 2         265  89101.3                  52.03
## 3         266  97017.3                   42.4
## 4         268 112697.6                  34.19
## 5         281 618425.6                  13.59
## 6         901  38648.7                  75.39
## 7         999    36765                  64.92
## 8       Total  1036942                   3.76

Along with raw data and titles:

## Raw data (list object) for estimate
raw3 <- area3$raw      # extract raw data list object from output
names(raw3)
output 
##  [1] "unit_totest"   "unit_rowest"   "domdat"        "plotweights"  
##  [5] "module"        "esttype"       "MAmethod"      "predselectlst"
##  [9] "rowvar"        "colvar"        "areaunits"
head(raw3$unit_totest) # estimates by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT      nhat    nhat.var NBRPLT NBRPLT.gt0 ACRES_GIS AREAUSED     est
## 1       1 0.9321562 0.001228216     37         37   1112412  1112412 1036942
##      est.var  est.se     est.cv      pse CI99left CI99right CI95left CI95right
## 1 1519869489 38985.5 0.03759661 3.759661   936522   1137362 960531.9   1113352
##   CI68left CI68right
## 1 998172.6   1075711
head(raw3$unit_rowest) # estimates by row, by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT Forest type       nhat     nhat.var NBRPLT NBRPLT.gt0 ACRES_GIS
## 1       1         201 0.03981113 0.0011505987     37          2   1112412
## 2       1         265 0.08009738 0.0017367503     37          4   1112412
## 3       1         266 0.08721348 0.0013673266     37          5   1112412
## 4       1         268 0.10130923 0.0012000844     37          3   1112412
## 5       1         281 0.55593198 0.0057107487     37         23   1112412
## 6       1         901 0.03474316 0.0006861348     37          1   1112412
##   AREAUSED       est    est.var   est.se    est.cv      pse  CI99left CI99right
## 1  1112412  44286.39 1423821285 37733.56 0.8520351 85.20351      0.00  141481.6
## 2  1112412  89101.31 2149161132 46359.05 0.5202959 52.02959      0.00  208514.3
## 3  1112412  97017.35 1692013618 41134.09 0.4239870 42.39870      0.00  202971.7
## 4  1112412 112697.64 1485057928 38536.45 0.3419455 34.19455  13434.33  211961.0
## 5  1112412 618425.60 7066830141 84064.44 0.1359330 13.59330 401889.95  834961.3
## 6  1112412  38648.72  849065242 29138.72 0.7539376 75.39376      0.00  113705.1
##    CI95left CI95right   CI68left CI68right
## 1      0.00 118242.80   6761.956  81810.82
## 2      0.00 179963.37  42999.193 135203.43
## 3  16396.01 177638.69  56111.224 137923.47
## 4  37167.59 188227.70  74374.767 151020.52
## 5 453662.33 783188.88 534827.056 702024.15
## 6      0.00  95759.57   9671.489  67625.96
## Titles (list object) for estimate
titlelst3 <- area3$titlelst
names(titlelst3)
output 
##  [1] "title.est"     "title.pse"     "title.unitvar" "title.ref"    
##  [5] "outfn.estpse"  "outfn.rawdat"  "outfn.param"   "title.rowvar" 
##  [9] "title.row"     "title.unit"
titlelst3
output 
## $title.est
## [1] "Area, in acres, on forest land by forest type"
## 
## $title.pse
## [1] "Percent sampling error of area, in acres, on forest land by forest type"
## 
## $title.unitvar
## [1] "ONEUNIT"
## 
## $title.ref
## [1] "Wyoming, 2011-2013"
## 
## $outfn.estpse
## [1] "area_FORTYPCD_forestland"
## 
## $outfn.rawdat
## [1] "area_FORTYPCD_forestland_rawdata"
## 
## $outfn.param
## [1] "area_FORTYPCD_forestland_parameters"
## 
## $title.rowvar
## [1] "Forest type"
## 
## $title.row
## [1] "Area, in acres, on forest land by forest type; Wyoming, 2011-2013"
## 
## $title.unit
## [1] "acres"

Example 5: Area by forest type and stand-size class on forest land, Wyoming, 2011-2013

View Example

In this example, we look at adding rows and columns to output, including FIA names. We also output estimates and percent standard error in the same cell with the allin1 argument in table_options and save data to an outfolder with the outfolder argument in savedata_options.

## Area of forest land by forest type and stand-size class, Wyoming, 2011-2013
area4 <- modMAarea(
    MApopdat = MApopdat,         # pop - population calculations for WY, post-stratification
    MAmethod = "greg",           # est - model-assisted method
    landarea = "FOREST",         # est - forest land filter
    rowvar = "FORTYPCD",         # est - row domain
    colvar = "STDSZCD",          # est - column domain
    savedata = TRUE,             # out - save data to outfolder
    returntitle = TRUE,          # out - return title information
    table_opts = list(
      row.FIAname = TRUE,          # table - row domain names
      col.FIAname = TRUE,          # table - column domain names
      allin1 = TRUE                # table - return output with est(pse)
      ),
    savedata_opts = list(
      outfolder = outfolder,       # save - outfolder for saving data
      outfn.pre = "WY"             # save - prefix for output files
      )
    )

area4$est
output 
##                        Forest type      Large diameter     Medium diameter
## 1                      Douglas-fir  28,381.5 (  95.01)        -- (     --)
## 2                 Engelmann spruce  97,387.6 (  40.28)  -8,286.3 (-339.39)
## 3 Engelmann spruce / subalpine fir  42,290.6 (  91.23)  28,627.2 (  48.64)
## 4                    Subalpine fir  36,053.1 (  81.54)        -- (     --)
## 5                   Lodgepole pine 221,069.2 (  28.62) 289,086.8 (  20.06)
## 6                            Aspen        -- (     --)        -- (     --)
## 7                       Nonstocked        -- (     --)        -- (     --)
## 8                            Total 425,182.1 (  15.23) 309,427.6 (  19.74)
##        Small diameter         Nonstocked                 Total
## 1  15,904.9 ( 179.36)       -- (     --)    44,286.4 (  85.20)
## 2        -- (     --)       -- (     --)    89,101.3 (  52.03)
## 3  26,099.5 (  65.28)       -- (     --)    97,017.3 (  42.40)
## 4  76,644.6 (  33.27)       -- (     --)   112,697.6 (  34.19)
## 5 108,269.6 (  35.83)       -- (     --)   618,425.6 (  13.59)
## 6  38,648.7 (  75.39)       -- (     --)    38,648.7 (  75.39)
## 7        -- (     --) 36,765.0 (  64.92)    36,765.0 (  64.92)
## 8 265,567.3 (  20.58) 36,765.0 (  64.92) 1,036,942.0 (   3.76)

We can again look at the output list, estimates, raw data, and titles:

## Look at output list
names(area4)
output 
## [1] "est"      "pse"      "titlelst" "raw"      "statecd"  "states"   "invyr"
## Estimate and percent sampling error of estimate
head(area4$est)
output 
##                        Forest type      Large diameter     Medium diameter
## 1                      Douglas-fir  28,381.5 (  95.01)        -- (     --)
## 2                 Engelmann spruce  97,387.6 (  40.28)  -8,286.3 (-339.39)
## 3 Engelmann spruce / subalpine fir  42,290.6 (  91.23)  28,627.2 (  48.64)
## 4                    Subalpine fir  36,053.1 (  81.54)        -- (     --)
## 5                   Lodgepole pine 221,069.2 (  28.62) 289,086.8 (  20.06)
## 6                            Aspen        -- (     --)        -- (     --)
##        Small diameter   Nonstocked               Total
## 1  15,904.9 ( 179.36) -- (     --)  44,286.4 (  85.20)
## 2        -- (     --) -- (     --)  89,101.3 (  52.03)
## 3  26,099.5 (  65.28) -- (     --)  97,017.3 (  42.40)
## 4  76,644.6 (  33.27) -- (     --) 112,697.6 (  34.19)
## 5 108,269.6 (  35.83) -- (     --) 618,425.6 (  13.59)
## 6  38,648.7 (  75.39) -- (     --)  38,648.7 (  75.39)
## Raw data (list object) for estimate
raw4 <- area4$raw      # extract raw data list object from output
names(raw4)
output 
##  [1] "unit_totest"   "unit_rowest"   "unit_colest"   "unit_grpest"  
##  [5] "domdat"        "plotweights"   "module"        "esttype"      
##  [9] "MAmethod"      "predselectlst" "rowvar"        "colvar"       
## [13] "areaunits"
head(raw4$unit_totest) # estimates by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT      nhat    nhat.var NBRPLT NBRPLT.gt0 ACRES_GIS AREAUSED     est
## 1       1 0.9321562 0.001228216     37         37   1112412  1112412 1036942
##      est.var  est.se     est.cv      pse CI99left CI99right CI95left CI95right
## 1 1519869489 38985.5 0.03759661 3.759661   936522   1137362 960531.9   1113352
##   CI68left CI68right
## 1 998172.6   1075711
head(raw4$unit_rowest) # estimates by row, by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT FORTYPCD       nhat     nhat.var NBRPLT NBRPLT.gt0
## 1       1      201 0.03981113 0.0011505987     37          2
## 2       1      265 0.08009738 0.0017367503     37          4
## 3       1      266 0.08721348 0.0013673266     37          5
## 4       1      268 0.10130923 0.0012000844     37          3
## 5       1      281 0.55593198 0.0057107487     37         23
## 6       1      901 0.03474316 0.0006861348     37          1
##                        Forest type ACRES_GIS AREAUSED       est    est.var
## 1                      Douglas-fir   1112412  1112412  44286.39 1423821285
## 2                 Engelmann spruce   1112412  1112412  89101.31 2149161132
## 3 Engelmann spruce / subalpine fir   1112412  1112412  97017.35 1692013618
## 4                    Subalpine fir   1112412  1112412 112697.64 1485057928
## 5                   Lodgepole pine   1112412  1112412 618425.60 7066830141
## 6                            Aspen   1112412  1112412  38648.72  849065242
##     est.se    est.cv      pse  CI99left CI99right  CI95left CI95right
## 1 37733.56 0.8520351 85.20351      0.00  141481.6      0.00 118242.80
## 2 46359.05 0.5202959 52.02959      0.00  208514.3      0.00 179963.37
## 3 41134.09 0.4239870 42.39870      0.00  202971.7  16396.01 177638.69
## 4 38536.45 0.3419455 34.19455  13434.33  211961.0  37167.59 188227.70
## 5 84064.44 0.1359330 13.59330 401889.95  834961.3 453662.33 783188.88
## 6 29138.72 0.7539376 75.39376      0.00  113705.1      0.00  95759.57
##     CI68left CI68right
## 1   6761.956  81810.82
## 2  42999.193 135203.43
## 3  56111.224 137923.47
## 4  74374.767 151020.52
## 5 534827.056 702024.15
## 6   9671.489  67625.96
head(raw4$unit_colest) # estimates by column, by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT STDSZCD       nhat     nhat.var NBRPLT NBRPLT.gt0 Stand-size class
## 1       1       1 0.38221627 0.0033901272     37         18   Large diameter
## 2       1       2 0.27815912 0.0030155927     37         14  Medium diameter
## 3       1       3 0.23873097 0.0024143561     37          6   Small diameter
## 4       1       5 0.03304982 0.0004603932     37          1       Nonstocked
##   ACRES_GIS AREAUSED       est    est.var   est.se    est.cv      pse CI99left
## 1   1112412  1112412 425182.10 4195150958 64769.99 0.1523347 15.23347 258345.7
## 2   1112412  1112412 309427.64 3731679124 61087.47 0.1974209 19.74209 152076.7
## 3   1112412  1112412 265567.28 2987672110 54659.60 0.2058220 20.58220 124773.5
## 4   1112412  1112412  36765.03  569718693 23868.78 0.6492251 64.92251      0.0
##   CI99right CI95left CI95right  CI68left CI68right
## 1 592018.52 298235.3 552128.93 360771.07 489593.12
## 2 466778.53 189698.4 429156.88 248678.72 370176.55
## 3 406361.09 158436.4 372698.13 211210.61 319923.96
## 4  98246.94      0.0  83546.98  13028.53  60501.53
head(raw4$unit_grpest) # estimates by row and column, by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT grpvar         nhat     nhat.var NBRPLT NBRPLT.gt0 FORTYPCD STDSZCD
## 1       1  201#1  0.025513469 0.0005875578     37          1      201       1
## 2       1  201#3  0.014297657 0.0006576539     37          1      201       3
## 3       1  265#1  0.087546327 0.0012436962     37          3      265       1
## 4       1  265#2 -0.007448949 0.0006391340     37          1      265       2
## 5       1  266#1  0.038017056 0.0012029502     37          3      266       1
## 6       1  266#2  0.025734305 0.0001566975     37          1      266       2
##   Stand-size class                      Forest type ACRES_GIS AREAUSED
## 1   Large diameter                      Douglas-fir   1112412  1112412
## 2   Small diameter                      Douglas-fir   1112412  1112412
## 3   Large diameter                 Engelmann spruce   1112412  1112412
## 4  Medium diameter                 Engelmann spruce   1112412  1112412
## 5   Large diameter Engelmann spruce / subalpine fir   1112412  1112412
## 6  Medium diameter Engelmann spruce / subalpine fir   1112412  1112412
##         est    est.var   est.se     est.cv        pse CI99left CI99right
## 1 28381.499  727079950 26964.42  0.9500703   95.00703        0  97837.24
## 2 15904.890  813821229 28527.55  1.7936340  179.36340        0  89386.99
## 3 97387.615 1539025822 39230.42  0.4028276   40.28276        0 198438.48
## 4 -8286.303  790903534 28123.01 -3.3939149 -339.39149        0  64153.76
## 5 42290.643 1488604221 38582.43  0.9123161   91.23161        0 141672.41
## 6 28627.159  193907140 13925.05  0.4864281   48.64281        0  64495.72
##    CI95left CI95right  CI68left CI68right
## 1     0.000  81230.79  1566.518  55196.48
## 2     0.000  71817.86     0.000  44274.34
## 3 20497.406 174277.82 58374.615 136400.62
## 4     0.000  46833.78     0.000  19680.84
## 5     0.000 117910.82  3922.037  80659.25
## 6  1334.554  55919.76 14779.279  42475.04
## Titles (list object) for estimate
titlelst4 <- area4$titlelst
names(titlelst4)
output 
##  [1] "title.estpse"  "title.unitvar" "title.ref"     "outfn.estpse" 
##  [5] "outfn.rawdat"  "outfn.param"   "title.rowvar"  "title.row"    
##  [9] "title.colvar"  "title.col"     "title.unit"
titlelst4
output 
## $title.estpse
## [1] "Area, in acres (percent sampling error), by forest type and stand-size class on forest land"
## 
## $title.unitvar
## [1] "ONEUNIT"
## 
## $title.ref
## [1] "Wyoming, 2011-2013"
## 
## $outfn.estpse
## [1] "WY_area_FORTYPCD_STDSZCD_forestland"
## 
## $outfn.rawdat
## [1] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata"
## 
## $outfn.param
## [1] "WY_area_FORTYPCD_STDSZCD_forestland_parameters"
## 
## $title.rowvar
## [1] "Forest type"
## 
## $title.row
## [1] "Area, in acres (percent sampling error), by forest type on forest land; Wyoming, 2011-2013"
## 
## $title.colvar
## [1] "Stand-size class"
## 
## $title.col
## [1] "Area, in acres (percent sampling error), by stand-size class on forest land; Wyoming, 2011-2013"
## 
## $title.unit
## [1] "acres"
## List output files in outfolder
list.files(outfolder, pattern = "WY_area")
output 
## [1] "WY_area_FORTYPCD_STDSZCD_forestland.csv"                
## [2] "WY_area_FORTYPCD_STDSZCD_forestland_modMA_mase_greg.csv"
list.files(paste0(outfolder, "/rawdata"), pattern = "WY_area")
output 
##  [1] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_colest.csv"                     
##  [2] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_domdat.csv"                     
##  [3] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_grpest.csv"                     
##  [4] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_domdat.csv"     
##  [5] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_plotweights.csv"
##  [6] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_colest.csv"
##  [7] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_grpest.csv"
##  [8] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_rowest.csv"
##  [9] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_totest.csv"
## [10] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_rowest.csv"                     
## [11] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_totest.csv"                     
## [12] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_colest.csv"                
## [13] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_grpest.csv"                
## [14] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_rowest.csv"                
## [15] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_totest.csv"

modMAtree

We will set our estimate variable and filter now. We set estvar to "VOLCFNET" for net cubic foot volume, and filter with estvar.filter set to "STATUSCD == 1" so we only consider live trees in our estimation.

estvar <- "VOLCFNET"
live_trees <- "STATUSCD == 1"

Example 6: Net cubic-foot volume of live trees, Wyoming, 2011-2013

View Example

We now will generate estimates by estimation unit (i.e., ESTN_UNIT) and sum to population (i.e., WY) with modMAtree.

## Return raw data and titles
## Total net cubic-foot volume of live trees (at least 5 inches diameter), Wyoming, 2011-2013 
tree1 <- modMAtree(
    MApopdat = MApopdat,         # pop - population calculations
    MAmethod = "greg",           # est - model-assisted method
    landarea = "FOREST",         # est - forest land filter
    estvar = estvar,             # est - net cubic-foot volume
    estvar.filter = live_trees,  # est - live trees only
    returntitle = TRUE           # out - return title information
    )

names(tree1)
output 
## [1] "est"      "titlelst" "raw"      "statecd"  "states"   "invyr"
tree1$raw$unit_totest
output 
##   ONEUNIT     nhat nhat.var NBRPLT NBRPLT.gt0 ACRES_GIS AREAUSED        est
## 1       1 1440.829  27864.9     37         34   1112412  1112412 1602795606
##        est.var    est.se    est.cv      pse   CI99left  CI99right   CI95left
## 1 3.448173e+16 185692566 0.1158554 11.58554 1124483254 2081107957 1238844865
##    CI95right   CI68left  CI68right
## 1 1966746346 1418132170 1787459041

Example 7: Net cubic-foot volume of live trees, Wyoming, 2011-2013, using the Elastic Net for variable selection

View Example

Here, we fit the same model as the above example, but rather than using "greg" are our model-assisted method, we can use "gregEN" where the EN stands for “elastic net”. The elastic net performs variable selection for us, grabbing predictors it finds to be most useful in the model.

## Return raw data and titles
## Total net cubic-foot volume of live trees (at least 5 inches diameter), Wyoming, 2011-2013 
tree2 <- modMAtree(
    MApopdat = MApopdat,         # pop - population calculations
    MAmethod = "gregEN",         # est - model-assisted method
    landarea = "FOREST",         # est - forest land filter
    estvar = estvar,             # est - net cubic-foot volume
    estvar.filter = live_trees,  # est - live trees only
    returntitle = TRUE           # out - return title information
    )

We can again see that the structure of the list is very similar to that in the above example:

str(tree2, max.level = 2)
output 
## List of 6
##  $ est     :'data.frame':    1 obs. of  3 variables:
##   ..$ ONEUNIT               : chr "1"
##   ..$ Estimate              : num 1.61e+09
##   ..$ Percent Sampling Error: num 11.6
##  $ titlelst:List of 10
##   ..$ title.estpse : chr "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet, and perce"| __truncated__
##   ..$ title.yvar   : chr "Net volume, in cubic feet"
##   ..$ title.estvar : chr "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia)"
##   ..$ title.unitvar: chr "ONEUNIT"
##   ..$ title.ref    : chr "Wyoming, 2011-2013"
##   ..$ outfn.estpse : chr "tree_VOLCFNET_forestland"
##   ..$ outfn.rawdat : chr "tree_VOLCFNET_forestland_rawdata"
##   ..$ outfn.param  : chr "tree_VOLCFNET_forestland_parameters"
##   ..$ title.tot    : chr "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet, on forest"| __truncated__
##   ..$ title.unit   : chr "cubic feet"
##  $ raw     :List of 13
##   ..$ unit_totest  :'data.frame':    1 obs. of  18 variables:
##   ..$ domdat       :'data.frame':    40 obs. of  5 variables:
##   ..$ plotweights  :List of 1
##   ..$ estvar       : chr "VOLCFNET"
##   ..$ estvar.filter: chr "STATUSCD == 1"
##   ..$ module       : chr "MA"
##   ..$ esttype      : chr "TREE"
##   ..$ MAmethod     : chr "gregEN"
##   ..$ predselectlst:List of 1
##   ..$ rowvar       : chr "TOTAL"
##   ..$ colvar       : chr "NONE"
##   ..$ areaunits    : chr "acres"
##   ..$ estunits     : chr "cubic feet"
##  $ statecd : int 56
##  $ states  : chr "Wyoming"
##  $ invyr   : int [1:3] 2011 2012 2013

However now the raw list has an item call predselectlst. We can look at this item now:

tree2$raw$predselectlst
output 
## $totest
##    ONEUNIT TOTAL      dem     slp  asp_cos   asp_sin    fornf2
##     <fctr> <int>    <num>   <num>    <num>     <num>     <num>
## 1:       1     1 413.2831 -478.77 -142.546 -616.3711 -756.6359

Notably, we can see that [INSERT CORRECT PREDS] dem, slp, asp_cos, and asp_sin were removed from the model.

Example 8: Net cubic-foot volume of live trees by forest type, Wyoming, 2011-2013

View Example

This example adds rows to the output for net cubic-foot volume of live trees (at least 5 inches diameter) by forest type, Wyoming, 2011-2013. We also choose to return titles with returntitle = TRUE.

tree3 <- modMAtree(
    MApopdat = MApopdat,         # pop - population calculations
    MAmethod = "greg",           # est - model-assisted method
    landarea = "FOREST",         # est - forest land filter
    estvar = "VOLCFNET",               # est - net cubic-foot volume
    estvar.filter = "STATUSCD == 1",   # est - live trees only
    rowvar = "FORTYPCD",         # est - row domain 
    returntitle = TRUE           # out - return title information
    )

Again, we investigate the output of the returned list:

## Look at output list
names(tree3)
output 
## [1] "est"      "titlelst" "raw"      "statecd"  "states"   "invyr"
## Estimate and percent sampling error of estimate
tree3$est
output 
##   Forest type     Estimate Percent Sampling Error
## 1         201   17386950.9                  82.45
## 2         265  308069606.4                  55.88
## 3         266  124752652.1                  87.24
## 4         268   56756979.9                  82.14
## 5         281 1095829416.3                  19.18
## 6         901           --                     --
## 7         999           --                     --
## 8       Total 1602795605.6                  11.59
## Raw data (list object) for estimate
raw3 <- tree3$raw      # extract raw data list object from output
names(raw3)
output 
##  [1] "unit_totest"   "unit_rowest"   "domdat"        "plotweights"  
##  [5] "estvar"        "estvar.filter" "module"        "esttype"      
##  [9] "MAmethod"      "predselectlst" "rowvar"        "colvar"       
## [13] "areaunits"     "estunits"
head(raw3$unit_totest)   # estimates by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT     nhat nhat.var NBRPLT NBRPLT.gt0 ACRES_GIS AREAUSED        est
## 1       1 1440.829  27864.9     37         34   1112412  1112412 1602795606
##        est.var    est.se    est.cv      pse   CI99left  CI99right   CI95left
## 1 3.448173e+16 185692566 0.1158554 11.58554 1124483254 2081107957 1238844865
##    CI95right   CI68left  CI68right
## 1 1966746346 1418132170 1787459041
head(raw3$unit_rowest)   # estimates by row, by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT Forest type      nhat   nhat.var NBRPLT NBRPLT.gt0 ACRES_GIS AREAUSED
## 1       1         201  15.62995   166.0663     37          2   1112412  1112412
## 2       1         265 276.93832 23952.0386     37          4   1112412  1112412
## 3       1         266 112.14605  9572.0563     37          5   1112412  1112412
## 4       1         268  51.02153  1756.4496     37          2   1112412  1112412
## 5       1         281 985.09282 35687.1045     37         23   1112412  1112412
## 6       1         901   0.00000     0.0000     37          0   1112412  1112412
##          est      est.var    est.se    est.cv      pse  CI99left  CI99right
## 1   17386951 2.055007e+14  14335294 0.8244858 82.44858         0   54312222
## 2  308069606 2.963972e+16 172161899 0.5588409 55.88409         0  751529271
## 3  124752652 1.184505e+16 108834961 0.8724060 87.24060         0  405092933
## 4   56756980 2.173538e+15  46621222 0.8214183 82.14183         0  176845290
## 5 1095829416 4.416141e+16 210146160 0.1917690 19.17690 554528778 1637130054
## 6          0 0.000000e+00         0       NaN      NaN         0          0
##    CI95left  CI95right  CI68left  CI68right
## 1         0   45483612   3131104   31642797
## 2         0  645500728 136861849  479277364
## 3         0  338065256  16520867  232984437
## 4         0  148132896  10394138  103119822
## 5 683950510 1507708322 886847910 1304810922
## 6         0          0         0          0
## Titles (list object) for estimate
titlelst3 <- tree3$titlelst
names(titlelst3)
output 
##  [1] "title.est"     "title.pse"     "title.yvar"    "title.estvar" 
##  [5] "title.unitvar" "title.ref"     "outfn.estpse"  "outfn.rawdat" 
##  [9] "outfn.param"   "title.rowvar"  "title.row"     "title.unit"
titlelst3
output 
## $title.est
## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet, on forest land by forest type"
## 
## $title.pse
## [1] "Percent sampling error of net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet, on forest land by forest type"
## 
## $title.yvar
## [1] "Net volume, in cubic feet"
## 
## $title.estvar
## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia)"
## 
## $title.unitvar
## [1] "ONEUNIT"
## 
## $title.ref
## [1] "Wyoming, 2011-2013"
## 
## $outfn.estpse
## [1] "tree_VOLCFNET_FORTYPCD_forestland"
## 
## $outfn.rawdat
## [1] "tree_VOLCFNET_FORTYPCD_forestland_rawdata"
## 
## $outfn.param
## [1] "tree_VOLCFNET_FORTYPCD_forestland_parameters"
## 
## $title.rowvar
## [1] "Forest type"
## 
## $title.row
## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet, on forest land by forest type; Wyoming, 2011-2013"
## 
## $title.unit
## [1] "cubic feet"

We can also create a simple barplot from the output:

## Create barplot
datBarplot(
      raw3$unit_rowest, 
      xvar = titlelst3$title.rowvar, 
      yvar = "est"
      )
plot

And a fancier barplot:

## Create fancier barplot
datBarplot(
      raw3$unit_rowest, 
      xvar = titlelst3$title.rowvar, 
      yvar = "est",
      errbars = TRUE, 
      sevar = "est.se", 
      main = FIESTAutils::wraptitle(titlelst3$title.row, 75),
      ylabel = titlelst3$title.yvar, 
      divideby = "million"
      )
plot

Example 9: Net cubic-foot volume of live trees by forest type and stand-size class, Wyoming, 2011-2013

View Example

This examples adds rows and columns to the output, including FIA names, for net cubic-foot volume of live trees (at least 5 inches diameter) by forest type and stand-size class, Wyoming, 2011-2013. We also use the *_options functions to return output with estimates (est) and percent standard error (pse) in same cell - est(pse) with allin1 = TRUE and save data to an outfolder with savedata = TRUE and outfolder = outfolder.

tree4 <- modMAtree(
    MApopdat = MApopdat,         # pop - population calculations
    MAmethod = "greg",           # est - model-assisted method
    landarea = "FOREST",         # est - forest land filter
    estvar = "VOLCFNET",               # est - net cubic-foot volume
    estvar.filter = "STATUSCD  == 1",   # est - live trees only
    rowvar = "FORTYPCD",         # est - row domain
    colvar = "STDSZCD",          # est - column domain
    returntitle = TRUE,          # out - return title information
    savedata = TRUE,             # out - save data to outfolder
    table_opts = table_options(
      row.FIAname = TRUE,          # est - row domain names
      col.FIAname = TRUE,          # est - column domain names
      allin1 = TRUE                # out - return output with est(pse)
    ),
    savedata_opts = savedata_options(
      outfolder = outfolder,       # out - outfolder for saving data
      outfn.pre = "WY"             # out - prefix for output files
      )
    )

Again, we investigate the output of the returned list:

## Look at output list from modGBarea()
names(tree4)
output 
## [1] "est"      "pse"      "titlelst" "raw"      "statecd"  "states"   "invyr"
## Estimate and percent sampling error of estimate
tree4$est
output 
##                        Forest type            Large diameter
## 1                      Douglas-fir    14,477,768.8 (  95.01)
## 2                 Engelmann spruce   337,559,236.7 (  44.35)
## 3 Engelmann spruce / subalpine fir    88,261,500.4 ( 124.07)
## 4                    Subalpine fir    56,756,979.9 (  82.14)
## 5                   Lodgepole pine   590,014,944.8 (  31.92)
## 6                            Aspen              -- (     --)
## 7                       Nonstocked              -- (     --)
## 8                            Total 1,087,070,430.7 (  17.29)
##           Medium diameter         Small diameter   Nonstocked
## 1            -- (     --)  2,909,182.1 ( 179.36) -- (     --)
## 2 -29,489,630.3 (-339.39)           -- (     --) -- (     --)
## 3  28,528,421.9 (  48.64)  7,962,729.8 (  65.28) -- (     --)
## 4            -- (     --)           -- (     --) -- (     --)
## 5 487,026,140.7 (  22.47) 18,788,330.8 (  34.12) -- (     --)
## 6            -- (     --)           -- (     --) -- (     --)
## 7            -- (     --)           -- (     --) -- (     --)
## 8 486,064,932.3 (  28.02) 29,660,242.6 (  29.35) -- (     --)
##                       Total
## 1    17,386,950.9 (  82.45)
## 2   308,069,606.4 (  55.88)
## 3   124,752,652.1 (  87.24)
## 4    56,756,979.9 (  82.14)
## 5 1,095,829,416.3 (  19.18)
## 6              -- (     --)
## 7              -- (     --)
## 8 1,602,795,605.6 (  11.59)
## Raw data (list object) for estimate
raw4 <- tree4$raw      # extract raw data list object from output
names(raw4)
output 
##  [1] "unit_totest"   "unit_rowest"   "unit_colest"   "unit_grpest"  
##  [5] "domdat"        "plotweights"   "estvar"        "estvar.filter"
##  [9] "module"        "esttype"       "MAmethod"      "predselectlst"
## [13] "rowvar"        "colvar"        "areaunits"     "estunits"
head(raw4$unit_totest)   # estimates by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT     nhat nhat.var NBRPLT NBRPLT.gt0 ACRES_GIS AREAUSED        est
## 1       1 1440.829  27864.9     37         34   1112412  1112412 1602795606
##        est.var    est.se    est.cv      pse   CI99left  CI99right   CI95left
## 1 3.448173e+16 185692566 0.1158554 11.58554 1124483254 2081107957 1238844865
##    CI95right   CI68left  CI68right
## 1 1966746346 1418132170 1787459041
head(raw4$totest)        # estimates for population (i.e., WY)
output 
## NULL
head(raw4$unit_rowest)   # estimates by row, by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT FORTYPCD      nhat   nhat.var NBRPLT NBRPLT.gt0
## 1       1      201  15.62995   166.0663     37          2
## 2       1      265 276.93832 23952.0386     37          4
## 3       1      266 112.14605  9572.0563     37          5
## 4       1      268  51.02153  1756.4496     37          2
## 5       1      281 985.09282 35687.1045     37         23
## 6       1      901   0.00000     0.0000     37          0
##                        Forest type ACRES_GIS AREAUSED        est      est.var
## 1                      Douglas-fir   1112412  1112412   17386951 2.055007e+14
## 2                 Engelmann spruce   1112412  1112412  308069606 2.963972e+16
## 3 Engelmann spruce / subalpine fir   1112412  1112412  124752652 1.184505e+16
## 4                    Subalpine fir   1112412  1112412   56756980 2.173538e+15
## 5                   Lodgepole pine   1112412  1112412 1095829416 4.416141e+16
## 6                            Aspen   1112412  1112412          0 0.000000e+00
##      est.se    est.cv      pse  CI99left  CI99right  CI95left  CI95right
## 1  14335294 0.8244858 82.44858         0   54312222         0   45483612
## 2 172161899 0.5588409 55.88409         0  751529271         0  645500728
## 3 108834961 0.8724060 87.24060         0  405092933         0  338065256
## 4  46621222 0.8214183 82.14183         0  176845290         0  148132896
## 5 210146160 0.1917690 19.17690 554528778 1637130054 683950510 1507708322
## 6         0       NaN      NaN         0          0         0          0
##    CI68left  CI68right
## 1   3131104   31642797
## 2 136861849  479277364
## 3  16520867  232984437
## 4  10394138  103119822
## 5 886847910 1304810922
## 6         0          0
head(raw4$rowest)        # estimates by row for population (i.e., WY)
output 
## NULL
head(raw4$unit_colest)   # estimates by column, by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT STDSZCD      nhat    nhat.var NBRPLT NBRPLT.gt0 Stand-size class
## 1       1       1 977.21895 28542.07717     37         18   Large diameter
## 2       1       2 436.94672 14991.78126     37         14  Medium diameter
## 3       1       3  26.66299    61.25535     37          4   Small diameter
## 4       1       5   0.00000     0.00000     37          0       Nonstocked
##   ACRES_GIS AREAUSED        est      est.var    est.se    est.cv      pse
## 1   1112412  1112412 1087070431 3.531971e+16 187935399 0.1728824 17.28824
## 2   1112412  1112412  486064932 1.855175e+16 136204803 0.2802194 28.02194
## 3   1112412  1112412   29660243 7.580112e+13   8706384 0.2935372 29.35372
## 4   1112412  1112412          0 0.000000e+00         0       NaN      NaN
##    CI99left  CI99right  CI95left  CI95right  CI68left  CI68right
## 1 602980922 1571159939 718723817 1455417045 900176591 1273964270
## 2 135224610  836905254 219108425  753021440 350614993  621514872
## 3   7234084   52086401  12596044   46724441  21002111   38318375
## 4         0          0         0          0         0          0
head(raw4$colest)        # estimates by column for population (i.e., WY)
output 
## NULL
head(raw4$unit_grpest)   # estimates by row and column, by estimation unit (i.e., ESTN_UNIT)
output 
##   ONEUNIT grpvar       nhat   nhat.var NBRPLT NBRPLT.gt0 FORTYPCD STDSZCD
## 1       1  201#1  13.014750   152.8914     37          1      201       1
## 2       1  201#3   2.615201    22.0028     37          1      201       3
## 3       1  265#1 303.447940 18110.8690     37          3      265       1
## 4       1  265#2 -26.509621  8094.8527     37          1      265       2
## 5       1  266#1  79.342431  9690.2120     37          3      266       1
## 6       1  266#2  25.645546   155.6185     37          1      266       2
##   Stand-size class                      Forest type ACRES_GIS AREAUSED
## 1   Large diameter                      Douglas-fir   1112412  1112412
## 2   Small diameter                      Douglas-fir   1112412  1112412
## 3   Large diameter                 Engelmann spruce   1112412  1112412
## 4  Medium diameter                 Engelmann spruce   1112412  1112412
## 5   Large diameter Engelmann spruce / subalpine fir   1112412  1112412
## 6  Medium diameter Engelmann spruce / subalpine fir   1112412  1112412
##         est      est.var    est.se     est.cv        pse CI99left CI99right
## 1  14477769 1.891972e+14  13754899  0.9500703   95.00703        0  49908040
## 2   2909182 2.722761e+13   5218008  1.7936340  179.36340        0  16349880
## 3 337559237 2.241150e+16 149704704  0.4434917   44.34917        0 723173001
## 4 -29489630 1.001707e+16 100085296 -3.3939149 -339.39149        0 228313008
## 5  88261500 1.199126e+16 109504620  1.2406839  124.06839        0 370326709
## 6  28528422 1.925718e+14  13877026  0.4864281   48.64281        0  64273272
##   CI95left CI95right    CI68left CI68right
## 1        0  41436875    799101.3  28156436
## 2        0  13136290         0.0   8098271
## 3 44143408 630975066 188684213.4 486434260
## 4        0 166673945         0.0  70040981
## 5        0 302886611         0.0 197159233
## 6  1329951  55726893  14728304.3  42328540
head(raw4$grpest)        # estimates by row and column for population (i.e., WY)
output 
## NULL
## Titles (list object) for estimate
titlelst4 <- tree4$titlelst
names(titlelst4)
output 
##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.unitvar"
##  [5] "title.ref"     "outfn.estpse"  "outfn.rawdat"  "outfn.param"  
##  [9] "title.rowvar"  "title.row"     "title.colvar"  "title.col"    
## [13] "title.unit"
titlelst4
output 
## $title.estpse
## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet (percent sampling error), by forest type and stand-size class on forest land"
## 
## $title.yvar
## [1] "Net volume, in cubic feet"
## 
## $title.estvar
## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia)"
## 
## $title.unitvar
## [1] "ONEUNIT"
## 
## $title.ref
## [1] "Wyoming, 2011-2013"
## 
## $outfn.estpse
## [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland"
## 
## $outfn.rawdat
## [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata"
## 
## $outfn.param
## [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_parameters"
## 
## $title.rowvar
## [1] "Forest type"
## 
## $title.row
## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet (percent sampling error), by forest type on forest land; Wyoming, 2011-2013"
## 
## $title.colvar
## [1] "Stand-size class"
## 
## $title.col
## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet (percent sampling error), by stand-size class on forest land; Wyoming, 2011-2013"
## 
## $title.unit
## [1] "cubic feet"
## List output files in outfolder
list.files(outfolder, pattern = "WY_tree")
output 
## [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_modMA_mase_greg.csv"
## [2] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland.csv"
list.files(paste0(outfolder, "/rawdata"), pattern = "WY_tree")
output 
##  [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_domdat.csv"     
##  [2] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_plotweights.csv"
##  [3] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_colest.csv"
##  [4] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_grpest.csv"
##  [5] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_rowest.csv"
##  [6] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_totest.csv"
##  [7] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_colest.csv"        
##  [8] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_domdat.csv"        
##  [9] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_grpest.csv"        
## [10] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_rowest.csv"        
## [11] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_totest.csv"        
## [12] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_colest.csv"   
## [13] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_grpest.csv"   
## [14] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_rowest.csv"   
## [15] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_totest.csv"

Example 10: Number of live trees by species, Wyoming, 2011-2013

View Example

We can use tree domain in estimation output rows:

## Number of live trees (at least 1 inch diameter) by species
tree5 <- modMAtree(
    MApopdat = MApopdat,         # pop - population calculations
    MAmethod = "greg",           # est - model-assisted method
    landarea = "FOREST",         # est - forest land filter
    estvar = "TPA_UNADJ",               # est - number of trees per acre 
    estvar.filter = "STATUSCD == 1",    # est - live trees only
    rowvar = "SPCD",             # est - row domain
    returntitle = TRUE,          # out - return title information
    table_opts = table_options(    
      row.FIAname = TRUE,          # est - row domain names
      allin1 = FALSE               # out - return output with est and pse
      )
    )

We can also look at the output list and estimates again:

## Look at output list
names(tree5)
output 
## [1] "est"      "titlelst" "raw"      "statecd"  "states"   "invyr"
## Estimate and percent sampling error of estimate
tree5$est
output 
##                  Species    Estimate Percent Sampling Error
## 1          subalpine fir 103475208.2                  23.27
## 2 Rocky Mountain juniper          --                     --
## 3       Engelmann spruce  97646186.9                  20.67
## 4         lodgepole pine 252258761.7                  17.89
## 5            limber pine   3646725.4                 164.03
## 6            Douglas-fir  18648490.5                 144.02
## 7          quaking aspen    13740873                  89.54
## 8                  Total 489416245.7                  11.17

Example 11: Number of live trees (plus seedlings) by species, Wyoming, 2011-2013

View Example

We can also add seedlings.

Note: seedling data are only available for number of trees (estvar = TPA_UNADJ).

Note: must include seedling data in population data calculations.

MApopdat_seed <- modMApop(popTabs = list(tree = WYtree,
                                         cond = WYcond,
                                         seed = WYseed),
                     pltassgn = WYpltassgn,
                     auxdat = modeldat)
## Number of live trees by species, including seedlings
tree6 <- modMAtree(
    MApopdat = MApopdat_seed,         # pop - population calculations
    MAmethod = "greg",           # est - model-assisted method
    estseed = "add",             # est - add seedling data
    landarea = "FOREST",         # est - forest land filter
    estvar = "TPA_UNADJ",               # est - number of trees per acre 
    estvar.filter = "STATUSCD == 1",    # est - live trees only
    rowvar = "SPCD",             # est - row domain
    returntitle = TRUE,          # out - return title information
    table_opts = table_options(
      row.FIAname = TRUE,          # est - row domain names
      allin1 = FALSE)              # out - return output with est and pse
    )

And again we can look at our outputs and compare estimates:

## Look at output list
names(tree6)
output 
## [1] "est"      "titlelst" "raw"      "statecd"  "states"
## Estimate and percent sampling error of estimate
tree6$est
output 
##                  Species     Estimate Percent Sampling Error
## 1          subalpine fir  808695574.3                  13.26
## 2 Rocky Mountain juniper           --                     --
## 3       Engelmann spruce    225509499                  21.86
## 4         lodgepole pine  326287489.6                  17.92
## 5            limber pine   33484206.5                 157.79
## 6            Douglas-fir   39217783.1                 149.26
## 7          quaking aspen  155557143.7                  61.68
## 8                  Total 1588751696.1                  11.74
## Compare estimates with and without seedlings
head(tree5$est)
output 
##                  Species    Estimate Percent Sampling Error
## 1          subalpine fir 103475208.2                  23.27
## 2 Rocky Mountain juniper          --                     --
## 3       Engelmann spruce  97646186.9                  20.67
## 4         lodgepole pine 252258761.7                  17.89
## 5            limber pine   3646725.4                 164.03
## 6            Douglas-fir  18648490.5                 144.02
head(tree6$est)
output 
##                  Species    Estimate Percent Sampling Error
## 1          subalpine fir 808695574.3                  13.26
## 2 Rocky Mountain juniper          --                     --
## 3       Engelmann spruce   225509499                  21.86
## 4         lodgepole pine 326287489.6                  17.92
## 5            limber pine  33484206.5                 157.79
## 6            Douglas-fir  39217783.1                 149.26