# 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  8 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),
                         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 26
    ##  $ module     : chr "MA"
    ##  $ popType    : chr "VOL"
    ##  $ condx      :Classes 'data.table' and 'data.frame':    66 obs. of  17 variables:
    ##   ..- attr(*, "sorted")= chr [1:2] "PLT_CN" "CONDID"
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##  $ pltcondx   :Classes 'data.table' and 'data.frame':    66 obs. of  30 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= chr "DSTRBCD1"
    ##  $ cuniqueid  : chr "PLT_CN"
    ##  $ condid     : chr "CONDID"
    ##  $ ACI.filter : chr "COND_STATUS_CD == 1"
    ##  $ unitarea   :Classes 'data.table' and 'data.frame':    1 obs. of  2 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= chr "ONEUNIT"
    ##  $ areavar    : chr "ACRES_GIS"
    ##  $ areaunits  : chr "acres"
    ##  $ unitvar    : chr "ONEUNIT"
    ##  $ unitvars   : chr "ONEUNIT"
    ##  $ unitlut    :Classes 'data.table' and 'data.frame':    1 obs. of  7 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= chr "ONEUNIT"
    ##  $ plotsampcnt:'data.frame': 2 obs. of  3 variables:
    ##  $ condsampcnt:'data.frame': 4 obs. of  3 variables:
    ##  $ npixels    :Classes 'data.table' and 'data.frame':    1 obs. of  2 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##  $ npixelvar  : chr "npixels"
    ##  $ states     : chr "Wyoming"
    ##  $ invyrs     :List of 1
    ##  $ estvar.area: chr "CONDPROP_ADJ"
    ##  $ adj        : chr "plot"
    ##  $ treex      :Classes 'data.table' and 'data.frame':    1691 obs. of  21 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= chr [1:2] "PLT_CN" "CONDID"
    ##  $ tuniqueid  : chr "PLT_CN"
    ##  $ adjtree    : logi FALSE
    ##  $ prednames  : chr [1:5] "dem" "slp" "asp_cos" "asp_sin" ...
    ##  $ predfac    : chr "fornf"

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 summed to the population unit (`sumunit = TRUE`) 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 4
    ##  $ est    :Classes 'data.table' and 'data.frame':    1 obs. of  3 variables:
    ##   ..$ ONEUNIT               : chr "1"
    ##   ..$ Estimate              : num 652879
    ##   ..$ Percent Sampling Error: num 9.5
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= chr "ONEUNIT"
    ##  $ raw    :List of 9
    ##   ..$ unit_totest  :'data.frame':    1 obs. of  18 variables:
    ##   ..$ domdat       :'data.frame':    66 obs. of  20 variables:
    ##   ..$ 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
    ##  $ invyr  : int [1:3] 2011 2012 2013

    area1$est

output

    ## Key: <ONEUNIT>
    ##    ONEUNIT Estimate Percent Sampling Error
    ##     <char>    <num>                  <num>
    ## 1:       1 652879.1                    9.5

#### 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 4
    ##  $ est    :Classes 'data.table' and 'data.frame':    1 obs. of  3 variables:
    ##   ..$ ONEUNIT               : chr "1"
    ##   ..$ Estimate              : num 667090
    ##   ..$ Percent Sampling Error: num 10.2
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= chr "ONEUNIT"
    ##  $ raw    :List of 9
    ##   ..$ unit_totest  :'data.frame':    1 obs. of  18 variables:
    ##   ..$ domdat       :'data.frame':    66 obs. of  20 variables:
    ##   ..$ 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
    ##  $ 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> <num>        <num> <num>   <num>   <num>  <num>
    ## 1:       1     1 -0.000256447     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 5
    ##  $ est     :'data.frame':    8 obs. of  3 variables:
    ##  $ titlelst:List of 9
    ##  $ raw     :List of 10
    ##  $ statecd : int 56
    ##  $ 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    Total                  Total

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"        "module"
    ##  [5] "esttype"       "MAmethod"      "predselectlst" "rowvar"
    ##  [9] "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 0.5869038 0.003106112     56         37   1112412  1112412 652879.1
    ##      est.var   est.se    est.cv     pse CI99left CI99right CI95left CI95right
    ## 1 3843693079 61997.52 0.0949602 9.49602   493184  812574.1 531366.2    774392
    ##   CI68left CI68right
    ## 1 591225.1    714533

    raw3$totest            # estimates for population (i.e., WY)

output

    ## NULL

    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

    head(raw3$rowest)      # estimates by row for population (i.e., WY)

output

    ## NULL

    ## Titles (list object) for estimate
    titlelst3 <- area3$titlelst
    names(titlelst3)

output

    ## [1] "title.estpse"  "title.unitvar" "title.ref"     "outfn.estpse"
    ## [5] "outfn.rawdat"  "outfn.param"   "title.rowvar"  "title.row"
    ## [9] "title.unitsn"

    titlelst3

output

    ## $title.estpse
    ## [1] "Area, in acres, and percent sampling error 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.unitsn
    ## [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
    ##                              <char>              <char>              <char>
    ## 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
    ##                 <char>             <char>              <char>
    ## 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) 652,879.1 (   9.50)

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"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(area4$est)

output

    ##                         Forest type      Large diameter     Medium diameter
    ##                              <char>              <char>              <char>
    ## 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
    ##                 <char>       <char>              <char>
    ## 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"        "module"        "esttype"       "MAmethod"
    ##  [9] "predselectlst" "rowvar"        "colvar"        "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 0.5869038 0.003106112     56         37   1112412  1112412 652879.1
    ##      est.var   est.se    est.cv     pse CI99left CI99right CI95left CI95right
    ## 1 3843693079 61997.52 0.0949602 9.49602   493184  812574.1 531366.2    774392
    ##   CI68left CI68right
    ## 1 591225.1    714533

    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 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$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 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$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  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

    head(raw4$grpest)      # estimates by row and column for population (i.e., WY)

output

    ## NULL

    ## 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.unitsn"

    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.unitsn
    ## [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_unit_colest.csv"
    ##  [6] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_grpest.csv"
    ##  [7] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_rowest.csv"
    ##  [8] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_totest.csv"
    ##  [9] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_rowest.csv"
    ## [10] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_totest.csv"
    ## [11] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_colest.csv"
    ## [12] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_grpest.csv"
    ## [13] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_rowest.csv"
    ## [14] "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"  "invyr"

    tree1$raw$unit_totest

output

    ##   ONEUNIT     nhat nhat.var NBRPLT NBRPLT.gt0 ACRES_GIS AREAUSED        est
    ## 1       1 1029.962 22849.33     56         34   1112412  1112412 1145742756
    ##        est.var    est.se    est.cv      pse  CI99left  CI99right  CI95left
    ## 1 2.827516e+16 168152198 0.1467626 14.67626 712611397 1578874115 816170504
    ##    CI95right  CI68left  CI68right
    ## 1 1475315008 978522477 1312963035

#### 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 5
    ##  $ est     :Classes 'data.table' and 'data.frame':   1 obs. of  3 variables:
    ##   ..$ ONEUNIT               : chr "1"
    ##   ..$ Estimate              : num 1.17e+09
    ##   ..$ Percent Sampling Error: num 14.5
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= chr "ONEUNIT"
    ##  $ 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.unitsn : chr "cubic feet"
    ##  $ raw     :List of 13
    ##   ..$ unit_totest  :'data.frame':    1 obs. of  18 variables:
    ##   ..$ domdat       :'data.frame':    66 obs. of  20 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     : Factor w/ 1 level "1": 1
    ##  $ statecd : int 56
    ##  $ 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> <num>     <num>     <num>   <num>     <num>     <num>
    ## 1:       1     1 0.5491194 -61.97322       0 -524.6906 -1162.521

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"  "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          NA           --                     --
    ## 9       Total        Total                  Total

    ## 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 1029.962 22849.33     56         34   1112412  1112412 1145742756
    ##        est.var    est.se    est.cv      pse  CI99left  CI99right  CI95left
    ## 1 2.827516e+16 168152198 0.1467626 14.67626 712611397 1578874115 816170504
    ##    CI95right  CI68left  CI68right
    ## 1 1475315008 978522477 1312963035

    head(raw3$totest)        # estimates for population (i.e., WY)

output

    ## NULL

    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

    head(raw3$rowest)        # estimates by row for population (i.e., WY)

output

    ## NULL

    ## Titles (list object) for estimate
    titlelst3 <- tree3$titlelst
    names(titlelst3)

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.unitsn"

    titlelst3

output

    ## $title.estpse
    ## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet, and percent sampling error 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.unitsn
    ## [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"  "invyr"

    ## Estimate and percent sampling error of estimate
    tree4$est

output

    ##                         Forest type            Large diameter
    ##                              <char>                    <char>
    ## 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
    ##                     <char>                 <char>       <char>
    ## 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
    ##                       <char>
    ## 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: 1,145,742,756.1 (  14.68)

    ## 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 1029.962 22849.33     56         34   1112412  1112412 1145742756
    ##        est.var    est.se    est.cv      pse  CI99left  CI99right  CI95left
    ## 1 2.827516e+16 168152198 0.1467626 14.67626 712611397 1578874115 816170504
    ##    CI95right  CI68left  CI68right
    ## 1 1475315008 978522477 1312963035

    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.unitsn"

    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.unitsn
    ## [1] "cubic feet"

    ## List output files in outfolder
    list.files(outfolder, pattern = "WY_tree")

output

    ## [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland.csv"
    ## [2] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_modMA_mase_greg.csv"

    list.files(paste0(outfolder, "/rawdata"), pattern = "WY_tree")

output

    ##  [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_colest.csv"
    ##  [2] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_domdat.csv"
    ##  [3] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_grpest.csv"
    ##  [4] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_domdat.csv"
    ##  [5] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_colest.csv"
    ##  [6] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_grpest.csv"
    ##  [7] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_rowest.csv"
    ##  [8] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_totest.csv"
    ##  [9] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_rowest.csv"
    ## [10] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_totest.csv"
    ## [11] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_unit_colest.csv"
    ## [12] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_unit_grpest.csv"
    ## [13] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_unit_rowest.csv"
    ## [14] "WY_tree_VOLCFNET_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"  "invyr"

    ## Estimate and percent sampling error of estimate
    tree5$est

output

    ##                  Species    Estimate Percent Sampling Error
    ## 1          subalpine fir 117036939.1                  20.78
    ## 2 Rocky Mountain juniper          --                     --
    ## 3       Engelmann spruce 123334760.1                  15.94
    ## 4         lodgepole pine 260668686.6                  18.14
    ## 5            limber pine   4996348.9                 125.15
    ## 6            Douglas-fir  25445385.3                 109.24
    ## 7          quaking aspen  15095940.7                   86.1
    ## 8                  Total       Total                  Total

#### 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"  "invyr"

    ## Estimate and percent sampling error of estimate
    tree6$est

output

    ##                  Species    Estimate Percent Sampling Error
    ## 1          subalpine fir 795266354.7                  13.82
    ## 2 Rocky Mountain juniper          --                     --
    ## 3       Engelmann spruce 226522023.3                  22.37
    ## 4         lodgepole pine 333518419.3                  17.97
    ## 5            limber pine  45851027.5                  117.1
    ## 6            Douglas-fir  56082522.7                 105.42
    ## 7          quaking aspen 153434173.9                  64.29
    ## 8                  Total       Total                  Total

    ## Compare estimates with and without seedlings
    head(tree5$est)

output

    ##                  Species    Estimate Percent Sampling Error
    ## 1          subalpine fir 117036939.1                  20.78
    ## 2 Rocky Mountain juniper          --                     --
    ## 3       Engelmann spruce 123334760.1                  15.94
    ## 4         lodgepole pine 260668686.6                  18.14
    ## 5            limber pine   4996348.9                 125.15
    ## 6            Douglas-fir  25445385.3                 109.24

    head(tree6$est)

output

    ##                  Species    Estimate Percent Sampling Error
    ## 1          subalpine fir 795266354.7                  13.82
    ## 2 Rocky Mountain juniper          --                     --
    ## 3       Engelmann spruce 226522023.3                  22.37
    ## 4         lodgepole pine 333518419.3                  17.97
    ## 5            limber pine  45851027.5                  117.1
    ## 6            Douglas-fir  56082522.7                 105.42