Generates area estimates by estimation unit. Estimates are calculated from McConville et al. (2018)'s mase R package.
Usage
modMAarea(
MApopdat,
MAmethod,
FIA = TRUE,
prednames = NULL,
modelselect = FALSE,
landarea = "FOREST",
pcfilter = NULL,
rowvar = NULL,
colvar = NULL,
bootstrap = FALSE,
returntitle = FALSE,
savedata = FALSE,
table_opts = NULL,
title_opts = NULL,
savedata_opts = NULL,
modelselect_bydomain = FALSE,
...
)Arguments
- MApopdat
List. Population data objects returned from modMApop().
- MAmethod
String. mase (i.e., model-assisted) method to use ('greg', 'gregEN', 'ratio').
- FIA
Logical. If TRUE, the finite population term is removed from estimator to match FIA estimates.
- prednames
String vector. Name(s) of predictor variables to include in model.
- modelselect
Logical. If TRUE, an elastic net regression model is fit to the entire plot level data, and the variables selected in that model are used for the proceeding estimation.
- landarea
String. The sample area filter for estimates ('ALL', 'FOREST', 'TIMBERLAND'). If landarea=FOREST, filtered to COND_STATUS_CD = 1; If landarea=TIMBERLAND, filtered to SITECLCD in(1:6) and RESERVCD = 0.
- pcfilter
String. A filter for plot or cond attributes (including pltassgn). Must be R logical syntax.
- rowvar
String. Name of the row domain variable in cond or tree. If only one domain, rowvar = domain variable. If more than one domain, include colvar. If no domain, rowvar = NULL.
- colvar
String. Name of the column domain variable in cond or tree.
- bootstrap
Logical. If TRUE, returns bootstrap variance estimates, otherwise uses Horvitz-Thompson estimator under simple random sampling without replacement.
- returntitle
Logical. If TRUE, returns title(s) of the estimation table(s).
- savedata
Logical. If TRUE, saves table(s) to outfolder.
- table_opts
List. See help(table_options()) for a list of options.
- title_opts
List. See help(title_options()) for a list of options.
- savedata_opts
List. See help(savedata_options()) for a list of options. Only used when savedata = TRUE.
- modelselect_bydomain
Logical. If TRUE, modelselection will occur at the domain level as specified by rowvar and/or colvar and not at the level of the entire sample.
- ...
Parameters for modMApop() if MApopdat is NULL.
Value
If FIA=TRUE or unitvar=NULL and colvar=NULL, one data frame is returned with tree estimates and percent sample errors. Otherwise, a list is returned with tree estimates in one data frame (est) and percent sample errors in another data frame (est.pse). If rawdata=TRUE, another list is returned including raw data used in the estimation process. If addtitle=TRUE and returntitle=TRUE, the title for est/pse is returned. If savedata=TRUE, all data frames are written to outfolder.
- est
Data frame. Tree estimates by rowvar, colvar (and estimation unit). If FIA=TRUE or one estimation unit and colvar=NULL, estimates and percent sampling error are in one data frame.
- pse
Data frame. Percent sampling errors for estimates by rowvar and colvar (and estimation unit).
- titlelst
List with 1 or 2 string vectors. If returntitle=TRUE a list with table title(s). The list contains one title if est and pse are in the same table and two titles if est and pse are in separate tables.
- raw
List of data frames. If rawdata=TRUE, a list including: number of plots by plot status, if in dataset (plotsampcnt); number of conditions by condition status (condsampcnt); data used for post-stratification (stratdat); and 1-8 tables with calculated variables used for processing estimates and percent sampling error for table cell values and totals (See processing data below).
Raw data
- plotsampcnt
Table. Number of plots by plot status (ex. sampled forest on plot, sampled nonforest, nonsampled).
- condsampcnt
DF. Number of conditions by condition status (forest land, nonforest land, noncensus water, census water, nonsampled).
- stratdat
Data frame. Strata information by estimation unit.
| Variable | Description | |
| ESTUNIT | estimation unit | |
| STRATA | strata | |
| ACRES | area by strata for estimation unit | |
| n.strata | number of plots in strata (and estimation unit) | |
| n.total | number of plots for estimation unit | |
| TOTACRES | total area for estimation unit | |
| strwt | proportion of area (or number of plots) by strata (strata weight) | |
| expfac.strata | expansion factor (in area unit (e.g., acres) by strata (areavar/n.strata) |
- processing data
Data frames. Separate data frames containing calculated variables used in estimation process. The number of processing tables depends on the input parameters. The tables include: total by estimation unit (unit.totest); rowvar totals (unit.rowest), and if colvar is not NULL, colvar totals, (unit.colvar); and a combination of rowvar and colvar (unit.grpvar). If FIA=TRUE, the raw data for the summed estimation units are also included (totest, rowest, colest, grpest, respectively). These tables do not included estimate proportions (nhat and nhat.var).
The data frames include the following information:
Variable Description nhat estimated proportion of trees nhat.var estimated variance of estimated proportion of trees ACRES total area for estimation unit est estimated area of trees nhat*ACRES est.var estimated variance of estimated area of trees nhat.var*areavar^2 est.se standard error of estimated area of trees sqrt(est.var) est.cv coefficient of variation of estimated area of trees est.se/est pse percent sampling error of estimate est.cv*100 CI99left left tail of 99 percent confidence interval for estimated area CI99right right tail of 99 percent confidence interval for estimated area CI95left left tail of 95 percent confidence interval for estimated area CI95right right tail of 95 percent confidence interval for estimated area CI67left left tail of 67 percent confidence interval for estimated area CI67right right tail of 67 percent confidence interval for estimated area
Table(s) are also written to outfolder.
Details
If variables are NULL, then it will prompt user to input variables.
Necessary variables:
| Data | Variable | Description | |
| tree | tuniqueid | Unique identifier for each plot, to link to pltstrat (ex. PLT_CN). | |
| CONDID | Unique identifier of each condition on plot, to link to cond. Set CONDID=1, if only 1 condition per plot. | ||
| cond | cuniqueid | Unique identifier for each plot, to link to pltstrat (ex. PLT_CN). | |
| CONDID | Unique identifier of each condition on plot. Set CONDID=1, if only 1 condition per plot. | ||
| CONDPROP_UNADJ | Unadjusted proportion of condition on each plot. Set CONDPROP_UNADJ=1, if only 1 condition per plot. | ||
| COND_STATUS_CD | Status of each forested condition on plot (i.e. accessible forest, nonforest, water, etc.) | ||
| NF_COND_STATUS_CD | If ACI=TRUE. Status of each nonforest condition on plot (i.e. accessible nonforest, nonsampled nonforest) | ||
| SITECLCD | If landarea=TIMBERLAND. Measure of site productivity. | ||
| RESERVCD | If landarea=TIMBERLAND. Reserved status. | ||
| pltstrat | puniqueid | Unique identifier for each plot, to link to cond (ex. CN). | |
| STATECD | Identifies state each plot is located in. | ||
| INVYR | Identifies inventory year of each plot. | ||
| PLOT_STATUS_CD | Status of each plot (i.e. sampled, nonsampled). If not included, all plots are assumed as sampled. |
Reference names are available for the following variables:
ADFORCD,
AGENTCD, CCLCD, DECAYCD, DSTRBCD, KINDCD, OWNCD, OWNGRPCD, FORTYPCD,
FLDTYPCD, FORTYPCDCALC, TYPGRPCD, FORINDCD, RESERVCD, LANDCLCD, STDSZCD,
FLDSZCD, PHYSCLCD, MIST_CL_CD, PLOT_STATUS_CD, STATECD, TREECLCD, TRTCD,
SPCD, SPGRPCD
Note
ADJUSTMENT FACTOR:
The adjustment factor is necessary to account for
nonsampled conditions. It is calculated for each estimation unit by strata.
by summing the unadjusted proportions of the subplot, microplot, and
macroplot (i.e. *PROP_UNADJ) and dividing by the number of plots in the
strata/estimation unit).
An adjustment factor is determined for each tree based on the size of the plot it was measured on. This is identified using TPA_UNADJ as follows:
| PLOT SIZE | TPA_UNADJ | |
| SUBPLOT | 6.018046 | |
| MICROPLOT | 74.965282 | |
| MACROPLOT | 0.999188 |
If ACI=FALSE, only nonsampled forest conditions are accounted for in the
adjustment factor.
If ACI=TRUE, the nonsampled nonforest conditions are
removed as well and accounted for in adjustment factor. This is if you are
interested in estimates for all lands or nonforest lands in the
All-Condition-Inventory.
stratcombine:
If MAmethod='PS', and stratcombine=TRUE, and less than 2
plots in any one estimation unit, all estimation units with 10 or less plots
are combined. The current method for combining is to group the estimation
unit with less than 10 plots with the estimation unit following in
consecutive order (numeric or alphabetical), restrained by survey unit
(UNITCD) if included in dataset, and continuing until the number of plots
equals 10. If there are no estimation units following in order, it is
combined with the estimation unit previous in order.
autoxreduce:
If MAmethod='GREG', and autoxreduce=TRUE, and there is an
error because of multicolinearity, a variable reduction method is applied to
remove correlated variables. The method used is based on the
variance-inflation factor (vif) from a linear model. The vif estimates how
much the variance of each x variable is inflated due to mulitcolinearity in
the model.
rowlut/collut:
There are several objectives for including rowlut/collut
look-up tables: 1) to include descriptive names that match row/column codes
in the input table; 2) to use number codes that match row/column names in
the input table for ordering rows; 3) to add rows and/or columns with 0
values for consistency. No duplicate names are allowed.
Include 2 columns in the table:
1-the merging variable with same name as
the variable in the input merge table;
2-the ordering or descriptive
variable.
If the ordering variable is the rowvar/colvar in the input
table and the descriptive variable is in rowlut/collut, set
row.orderby/col.orderby equal to rowvar/colvar. If the descriptive variable
is the rowvar/colvar in the input table, and the ordering code variable is
in rowlut/collut, set row.orderby/col.orderby equal to the variable name of
the code variable in rowlut/collut.
UNITS:
The following variables are converted from pounds (from FIA
database) to short tons by multiplying the variable by 0.0005. DRYBIO_AG,
DRYBIO_BG, DRYBIO_WDLD_SPP, DRYBIO_SAPLING, DRYBIO_STUMP, DRYBIO_TOP,
DRYBIO_BOLE, DRYBIOT, DRYBIOM, DRYBIOTB, JBIOTOT, CARBON_BG, CARBON_AG
MORTALITY:
For Interior-West FIA, mortality estimates are mainly based on
whether a tree has died within the last 5 years of when the plot was
measured. If a plot was remeasured, mortality includes trees that were alive
the previous visit but were dead in the next visit. If a tree was standing
the previous visit, but was not standing in the next visit, no diameter was
collected (DIA = NA) but the tree is defined as mortality.
Common tree filters:
| FILTER | DESCRIPTION | |
| "STATUSCD == 1" | Live trees | |
| "STATUSCD == 2" | Dead trees | |
| "TPAMORT_UNADJ > 0" | Mortality trees | |
| "STATUSCD == 2 & DIA >= 5.0" | Dead trees >= 5.0 inches diameter | |
| "STATUSCD == 2 & AGENTCD == 30" | Dead trees from fire |
References
Kelly McConville, Becky Tang, George Zhu, Shirley Cheung, and Sida Li (2018). mase: Model-Assisted Survey Estimation. R package version 0.1.2 https://cran.r-project.org/package=mase
Examples
# \donttest{
# Set up population dataset (see ?modMApop() for more information)
MApopdat <- modMApop(popTabs = list(tree = FIESTA::WYtree,
cond = FIESTA::WYcond),
pltassgn = FIESTA::WYpltassgn,
pltassgnid = "CN",
unitarea = FIESTA::WYunitarea,
unitvar = "ESTN_UNIT",
unitzonal = FIESTA::WYunitzonal,
prednames = c("dem", "tcc", "tpi", "tnt"),
predfac = "tnt")
#> variables are missing from dataset: STATECD, UNITCD, COUNTYCD, PLOT, PLOT_STATUS_CD, PLOT_NONSAMPLE_REASN_CD, INTENSITY, SUBCYCLE, MACRO_BREAKPOINT_DIA
#> SURVEY table does not exist in database... assuming annual inventory plots
#> INVYR not in input data... no invyrs returned...
#> SUBCYCLE not in data... assuming all SUBCYCLE <> 99
#> removing nonsampled forest plots...
#> returning data needed for estimation...
#> there are 14 nonsampled conditions
# Use GREG estimator to estimate area of forest land in our population
mod1 <- modMAarea(MApopdat = MApopdat,
MAmethod = "greg",
landarea = "FOREST")
#> generating estimates using mase::greg function...
#> using the following predictors...dem, tcc, tpi, tnt2
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> getting output...
str(mod1)
#> List of 4
#> $ est :'data.frame': 23 obs. of 3 variables:
#> ..$ ESTN_UNIT : chr [1:23] "1" "11" "13" "15" ...
#> ..$ Estimate : num [1:23] 2608734 1605443 5543547 1428579 894498 ...
#> ..$ Percent Sampling Error: num [1:23] 3.2 3.78 2 0 15.48 ...
#> $ raw :List of 10
#> ..$ unit_totest :'data.frame': 23 obs. of 17 variables:
#> .. ..$ ESTN_UNIT : chr [1:23] "1" "11" "13" "15" ...
#> .. ..$ nhat : num [1:23] 0.946 0.874 0.935 1 0.697 ...
#> .. ..$ nhat.var : num [1:23] 9.15e-04 1.09e-03 3.50e-04 1.11e-31 1.16e-02 ...
#> .. ..$ NBRPLT : int [1:23] 24 26 53 2 8 22 2 38 7 2 ...
#> .. ..$ NBRPLT.gt0: int [1:23] 24 26 53 2 8 22 2 38 7 2 ...
#> .. ..$ AREAUSED : num [1:23] 2757613 1837124 5930088 1428579 1283969 ...
#> .. ..$ est : num [1:23] 2608734 1605443 5543547 1428579 894498 ...
#> .. ..$ est.var : num [1:23] 6.96e+09 3.69e+09 1.23e+10 2.26e-19 1.92e+10 ...
#> .. ..$ est.se : num [1:23] 8.34e+04 6.07e+04 1.11e+05 4.76e-10 1.38e+05 ...
#> .. ..$ est.cv : num [1:23] 3.20e-02 3.78e-02 2.00e-02 3.33e-16 1.55e-01 ...
#> .. ..$ pse : num [1:23] 3.20 3.78 2.00 3.33e-14 1.55e+01 ...
#> .. ..$ CI99left : num [1:23] 2393899 1449001 5257580 1428579 537834 ...
#> .. ..$ CI99right : num [1:23] 2823568 1761886 5829514 1428579 1251162 ...
#> .. ..$ CI95left : num [1:23] 2445265 1486406 5325953 1428579 623111 ...
#> .. ..$ CI95right : num [1:23] 2772203 1724481 5761141 1428579 1165886 ...
#> .. ..$ CI68left : num [1:23] 2525792 1545045 5433143 1428579 756800 ...
#> .. ..$ CI68right : num [1:23] 2691676 1665841 5653951 1428579 1032197 ...
#> ..$ domdat :'data.frame': 590 obs. of 4 variables:
#> .. ..$ PLT_CN : chr [1:590] "40404729010690" "40404730010690" "40404737010690" "40404738010690" ...
#> .. ..$ CONDID : int [1:590] 1 1 2 1 1 1 2 1 1 1 ...
#> .. ..$ TOTAL : int [1:590] 1 1 1 1 1 1 1 1 1 1 ...
#> .. ..$ ESTIMATED_VALUE: num [1:590] 1 1 0.25 1 1 ...
#> ..$ plotweights :Classes ‘data.table’ and 'data.frame': 556 obs. of 5 variables:
#> .. ..$ ESTN_UNIT : Factor w/ 23 levels "1","3","5","7",..: 1 1 1 1 1 1 1 1 1 1 ...
#> .. ..$ TOTAL : int [1:556] 1 1 1 1 1 1 1 1 1 1 ...
#> .. ..$ CN : chr [1:556] "40404729010690" "40404730010690" "40404737010690" "40404738010690" ...
#> .. ..$ weights : num [1:556] 0.0854 0.0135 0.0375 0.0259 0.0997 ...
#> .. ..$ areaweights: num [1:556] 5347694 844154 2347478 1618639 6240620 ...
#> .. ..- attr(*, ".internal.selfref")=<externalptr>
#> ..$ module : chr "MA"
#> ..$ esttype : chr "AREA"
#> ..$ MAmethod : chr "greg"
#> ..$ predselectlst:List of 1
#> .. ..$ totest:Classes ‘data.table’ and 'data.frame': 23 obs. of 6 variables:
#> .. .. ..$ ESTN_UNIT: Factor w/ 23 levels "1","3","5","7",..: 1 2 3 4 5 6 7 8 9 10 ...
#> .. .. ..$ TOTAL : int [1:23] 1 1 1 1 1 1 1 1 1 1 ...
#> .. .. ..$ dem : num [1:23] 0.0338 0.1518 -0.2106 0.1384 -0.0321 ...
#> .. .. ..$ tcc : num [1:23] 0.0773 -0.0118 -0.1142 -0.0436 0.0942 ...
#> .. .. ..$ tpi : num [1:23] -0.0391 0.05248 -0.0127 -0.00545 0.02948 ...
#> .. .. ..$ tnt2 : num [1:23] 0.0863 -0.0581 -0.1842 -0.1591 0.0331 ...
#> .. .. ..- attr(*, ".internal.selfref")=<externalptr>
#> ..$ rowvar : chr "TOTAL"
#> ..$ colvar : chr "NONE"
#> ..$ areaunits : chr "acres"
#> $ statecd: int 56
#> $ states : chr "Wyoming"
# Use GREG estimator to estimate area of forest land by forest type and
# stand-size class
mod2 <- modMAarea(MApopdat = MApopdat,
MAmethod = "greg",
landarea = "FOREST",
rowvar = "FORTYPCD",
colvar = "STDSZCD")
#> generating estimates using mase::greg function...
#> using the following predictors...dem, tcc, tpi, tnt2
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> model resulted in negatives... indicating model instability
#> getting output...
str(mod2)
#> List of 5
#> $ est :'data.frame': 460 obs. of 8 variables:
#> ..$ ESTN_UNIT : chr [1:460] "1" "1" "1" "1" ...
#> ..$ Forest type: chr [1:460] "182" "184" "185" "201" ...
#> ..$ 1 : chr [1:460] "220976.1" "--" "--" "37185.9" ...
#> ..$ 2 : chr [1:460] "--" "--" "--" "--" ...
#> ..$ 3 : chr [1:460] "254987.7" "--" "--" "--" ...
#> ..$ 5 : chr [1:460] "--" "--" "--" "--" ...
#> ..$ NA : chr [1:460] "--" "--" "--" "--" ...
#> ..$ Total : chr [1:460] "475963.8" "--" "--" "37185.9" ...
#> $ pse :'data.frame': 460 obs. of 8 variables:
#> ..$ ESTN_UNIT : chr [1:460] "1" "1" "1" "1" ...
#> ..$ Forest type: chr [1:460] "182" "184" "185" "201" ...
#> ..$ 1 : chr [1:460] "49.03" "--" "--" "259.58" ...
#> ..$ 2 : chr [1:460] "--" "--" "--" "--" ...
#> ..$ 3 : chr [1:460] "43.02" "--" "--" "--" ...
#> ..$ 5 : chr [1:460] "--" "--" "--" "--" ...
#> ..$ NA : chr [1:460] "--" "--" "--" "--" ...
#> ..$ Total : chr [1:460] "30.06" "--" "--" "259.58" ...
#> $ raw :List of 13
#> ..$ unit_totest :'data.frame': 23 obs. of 17 variables:
#> .. ..$ ESTN_UNIT : chr [1:23] "1" "11" "13" "15" ...
#> .. ..$ nhat : num [1:23] 0.946 0.874 0.935 1 0.697 ...
#> .. ..$ nhat.var : num [1:23] 9.15e-04 1.09e-03 3.50e-04 1.11e-31 1.16e-02 ...
#> .. ..$ NBRPLT : int [1:23] 24 26 53 2 8 22 2 38 7 2 ...
#> .. ..$ NBRPLT.gt0: int [1:23] 24 26 53 2 8 22 2 38 7 2 ...
#> .. ..$ AREAUSED : num [1:23] 2757613 1837124 5930088 1428579 1283969 ...
#> .. ..$ est : num [1:23] 2608734 1605443 5543547 1428579 894498 ...
#> .. ..$ est.var : num [1:23] 6.96e+09 3.69e+09 1.23e+10 2.26e-19 1.92e+10 ...
#> .. ..$ est.se : num [1:23] 8.34e+04 6.07e+04 1.11e+05 4.76e-10 1.38e+05 ...
#> .. ..$ est.cv : num [1:23] 3.20e-02 3.78e-02 2.00e-02 3.33e-16 1.55e-01 ...
#> .. ..$ pse : num [1:23] 3.20 3.78 2.00 3.33e-14 1.55e+01 ...
#> .. ..$ CI99left : num [1:23] 2393899 1449001 5257580 1428579 537834 ...
#> .. ..$ CI99right : num [1:23] 2823568 1761886 5829514 1428579 1251162 ...
#> .. ..$ CI95left : num [1:23] 2445265 1486406 5325953 1428579 623111 ...
#> .. ..$ CI95right : num [1:23] 2772203 1724481 5761141 1428579 1165886 ...
#> .. ..$ CI68left : num [1:23] 2525792 1545045 5433143 1428579 756800 ...
#> .. ..$ CI68right : num [1:23] 2691676 1665841 5653951 1428579 1032197 ...
#> ..$ unit_rowest :'data.frame': 135 obs. of 18 variables:
#> .. ..$ ESTN_UNIT : chr [1:135] "1" "1" "1" "1" ...
#> .. ..$ Forest type: Factor w/ 19 levels "182","184","185",..: 1 4 5 7 8 10 11 17 18 5 ...
#> .. ..$ nhat : num [1:135] 0.1726 0.0135 0.2302 -0.0139 0.0147 ...
#> .. ..$ nhat.var : num [1:135] 0.00269 0.00123 0.00309 0.00337 0.00162 ...
#> .. ..$ NBRPLT : int [1:135] 24 24 24 24 24 24 24 24 24 26 ...
#> .. ..$ NBRPLT.gt0 : int [1:135] 2 1 4 4 1 4 2 4 2 18 ...
#> .. ..$ AREAUSED : num [1:135] 2757613 2757613 2757613 2757613 2757613 ...
#> .. ..$ est : num [1:135] 475964 37186 634707 -38226 40580 ...
#> .. ..$ est.var : num [1:135] 2.05e+10 9.32e+09 2.35e+10 2.56e+10 1.23e+10 ...
#> .. ..$ est.se : num [1:135] 143066 96527 153302 160065 110859 ...
#> .. ..$ est.cv : num [1:135] 0.301 2.596 0.242 -4.187 2.732 ...
#> .. ..$ pse : num [1:135] 30.1 259.6 24.2 -418.7 273.2 ...
#> .. ..$ CI99left : num [1:135] 107450 0 239828 0 0 ...
#> .. ..$ CI99right : num [1:135] 844477 285822 1029586 374074 326134 ...
#> .. ..$ CI95left : num [1:135] 195560 0 334241 0 0 ...
#> .. ..$ CI95right : num [1:135] 756368 226375 935173 275496 257860 ...
#> .. ..$ CI68left : num [1:135] 333691 0 482255 0 0 ...
#> .. ..$ CI68right : num [1:135] 618237 133178 787159 120952 150825 ...
#> ..$ unit_colest :'data.frame': 73 obs. of 18 variables:
#> .. ..$ ESTN_UNIT : chr [1:73] "1" "1" "1" "1" ...
#> .. ..$ Stand-size class: Factor w/ 5 levels "1","2","3","5",..: 1 2 3 4 1 2 3 4 1 2 ...
#> .. ..$ nhat : num [1:73] 0.40336 -0.00388 0.4542 0.09232 0.62726 ...
#> .. ..$ nhat.var : num [1:73] 0.0083 0.00354 0.00554 0.00168 0.007 ...
#> .. ..$ NBRPLT : int [1:73] 24 24 24 24 26 26 26 26 53 53 ...
#> .. ..$ NBRPLT.gt0 : int [1:73] 9 6 7 2 18 4 4 1 23 16 ...
#> .. ..$ AREAUSED : num [1:73] 2757613 2757613 2757613 2757613 1837124 ...
#> .. ..$ est : num [1:73] 1112321 -10692 1252510 254595 1152362 ...
#> .. ..$ est.var : num [1:73] 6.31e+10 2.69e+10 4.21e+10 1.28e+10 2.36e+10 ...
#> .. ..$ est.se : num [1:73] 251214 164035 205290 113005 153718 ...
#> .. ..$ est.cv : num [1:73] 0.226 -15.342 0.164 0.444 0.133 ...
#> .. ..$ pse : num [1:73] 22.6 -1534.2 16.4 44.4 13.3 ...
#> .. ..$ CI99left : num [1:73] 465235 0 723719 0 756410 ...
#> .. ..$ CI99right : num [1:73] 1759406 411833 1781301 545676 1548314 ...
#> .. ..$ CI95left : num [1:73] 619950 0 850149 33109 851079 ...
#> .. ..$ CI95right : num [1:73] 1604691 310810 1654870 476080 1453644 ...
#> .. ..$ CI68left : num [1:73] 862499 0 1048358 142216 999495 ...
#> .. ..$ CI68right : num [1:73] 1362143 152434 1456662 366973 1305228 ...
#> ..$ unit_grpest :'data.frame': 217 obs. of 20 variables:
#> .. ..$ ESTN_UNIT : chr [1:217] "1" "1" "1" "1" ...
#> .. ..$ grpvar : chr [1:217] "182#1" "182#3" "201#1" "221#1" ...
#> .. ..$ nhat : num [1:217] 0.08013 0.09247 0.01348 0.23017 -0.00585 ...
#> .. ..$ nhat.var : num [1:217] 0.00154 0.00158 0.00123 0.00309 0.00192 ...
#> .. ..$ NBRPLT : int [1:217] 24 24 24 24 24 24 24 24 24 24 ...
#> .. ..$ NBRPLT.gt0 : int [1:217] 1 1 1 4 2 2 1 3 1 1 ...
#> .. ..$ Forest type : Factor w/ 19 levels "182","184","185",..: 1 1 4 5 7 7 8 10 10 11 ...
#> .. ..$ Stand-size class: Factor w/ 5 levels "1","2","3","5",..: 1 3 1 1 1 2 2 2 3 1 ...
#> .. ..$ AREAUSED : num [1:217] 2757613 2757613 2757613 2757613 2757613 ...
#> .. ..$ est : num [1:217] 220976 254988 37186 634707 -16120 ...
#> .. ..$ est.var : num [1:217] 1.17e+10 1.20e+10 9.32e+09 2.35e+10 1.46e+10 ...
#> .. ..$ est.se : num [1:217] 108343 109685 96527 153302 120925 ...
#> .. ..$ est.cv : num [1:217] 0.49 0.43 2.596 0.242 -7.502 ...
#> .. ..$ pse : num [1:217] 49 43 259.6 24.2 -750.2 ...
#> .. ..$ CI99left : num [1:217] 0 0 0 239828 0 ...
#> .. ..$ CI99right : num [1:217] 500049 537517 285822 1029586 295363 ...
#> .. ..$ CI95left : num [1:217] 8628 40010 0 334241 0 ...
#> .. ..$ CI95right : num [1:217] 433324 469966 226375 935173 220889 ...
#> .. ..$ CI68left : num [1:217] 113233 145911 0 482255 0 ...
#> .. ..$ CI68right : num [1:217] 328719 364064 133178 787159 104135 ...
#> ..$ domdat :'data.frame': 590 obs. of 6 variables:
#> .. ..$ PLT_CN : chr [1:590] "40404729010690" "40404730010690" "40404737010690" "40404738010690" ...
#> .. ..$ CONDID : int [1:590] 1 1 2 1 1 1 2 1 1 1 ...
#> .. ..$ FORTYPCD : int [1:590] 366 201 999 366 901 221 221 221 221 999 ...
#> .. ..$ STDSZCD : int [1:590] 1 1 5 3 3 1 1 1 1 5 ...
#> .. ..$ TOTAL : int [1:590] 1 1 1 1 1 1 1 1 1 1 ...
#> .. ..$ ESTIMATED_VALUE: num [1:590] 1 1 0.25 1 1 ...
#> ..$ plotweights :Classes ‘data.table’ and 'data.frame': 556 obs. of 5 variables:
#> .. ..$ ESTN_UNIT : Factor w/ 23 levels "1","3","5","7",..: 1 1 1 1 1 1 1 1 1 1 ...
#> .. ..$ TOTAL : int [1:556] 1 1 1 1 1 1 1 1 1 1 ...
#> .. ..$ CN : chr [1:556] "40404729010690" "40404730010690" "40404737010690" "40404738010690" ...
#> .. ..$ weights : num [1:556] 0.0854 0.0135 0.0375 0.0259 0.0997 ...
#> .. ..$ areaweights: num [1:556] 5347694 844154 2347478 1618639 6240620 ...
#> .. ..- attr(*, ".internal.selfref")=<externalptr>
#> ..$ module : chr "MA"
#> ..$ esttype : chr "AREA"
#> ..$ MAmethod : chr "greg"
#> ..$ predselectlst:List of 4
#> .. ..$ totest:Classes ‘data.table’ and 'data.frame': 23 obs. of 6 variables:
#> .. .. ..$ ESTN_UNIT: Factor w/ 23 levels "1","3","5","7",..: 1 2 3 4 5 6 7 8 9 10 ...
#> .. .. ..$ TOTAL : int [1:23] 1 1 1 1 1 1 1 1 1 1 ...
#> .. .. ..$ dem : num [1:23] 0.0338 0.1518 -0.2106 0.1384 -0.0321 ...
#> .. .. ..$ tcc : num [1:23] 0.0773 -0.0118 -0.1142 -0.0436 0.0942 ...
#> .. .. ..$ tpi : num [1:23] -0.0391 0.05248 -0.0127 -0.00545 0.02948 ...
#> .. .. ..$ tnt2 : num [1:23] 0.0863 -0.0581 -0.1842 -0.1591 0.0331 ...
#> .. .. ..- attr(*, ".internal.selfref")=<externalptr>
#> .. ..$ rowest:Classes ‘data.table’ and 'data.frame': 135 obs. of 6 variables:
#> .. .. ..$ ESTN_UNIT: Factor w/ 23 levels "1","3","5","7",..: 1 1 1 1 1 1 1 1 1 2 ...
#> .. .. ..$ FORTYPCD : int [1:135] 366 201 999 901 221 182 266 281 268 281 ...
#> .. .. ..$ dem : num [1:135] -0.00365 -0.14641 -0.03097 0.30822 -0.36685 ...
#> .. .. ..$ tcc : num [1:135] -0.0486 0.0828 -0.0306 -0.2399 0.0269 ...
#> .. .. ..$ tpi : num [1:135] -0.00774 -0.03501 0.01288 -0.07509 0.06412 ...
#> .. .. ..$ tnt2 : num [1:135] -0.02737 -0.01041 -0.00545 0.01363 -0.04324 ...
#> .. .. ..- attr(*, ".internal.selfref")=<externalptr>
#> .. ..$ colest:Classes ‘data.table’ and 'data.frame': 73 obs. of 6 variables:
#> .. .. ..$ ESTN_UNIT: Factor w/ 23 levels "1","3","5","7",..: 1 1 1 1 2 2 2 2 3 3 ...
#> .. .. ..$ STDSZCD : int [1:73] 1 5 3 2 1 3 2 5 1 3 ...
#> .. .. ..$ dem : num [1:73] -0.2611 -0.031 0.2434 0.0825 0.1098 ...
#> .. .. ..$ tcc : num [1:73] 0.1045 -0.0306 -0.2964 0.2998 0.1725 ...
#> .. .. ..$ tpi : num [1:73] 0.0622 0.0129 -0.0826 -0.0317 0.173 ...
#> .. .. ..$ tnt2 : num [1:73] 0.04795 -0.00545 -0.06109 0.10492 0.03418 ...
#> .. .. ..- attr(*, ".internal.selfref")=<externalptr>
#> .. ..$ grpest:Classes ‘data.table’ and 'data.frame': 217 obs. of 6 variables:
#> .. .. ..$ ESTN_UNIT: Factor w/ 23 levels "1","3","5","7",..: 1 1 1 1 1 1 1 1 1 1 ...
#> .. .. ..$ grpvar : chr [1:217] "366#1" "201#1" "999#5" "366#3" ...
#> .. .. ..$ dem : num [1:217] 0.0567 -0.1464 -0.031 -0.0603 0.3082 ...
#> .. .. ..$ tcc : num [1:217] -0.0167 0.0828 -0.0306 -0.0319 -0.2399 ...
#> .. .. ..$ tpi : num [1:217] -0.008384 -0.035014 0.012878 0.000639 -0.075086 ...
#> .. .. ..$ tnt2 : num [1:217] 0.04418 -0.01041 -0.00545 -0.07155 0.01363 ...
#> .. .. ..- attr(*, ".internal.selfref")=<externalptr>
#> ..$ rowvar : chr "FORTYPCD"
#> ..$ colvar : chr "STDSZCD"
#> ..$ areaunits : chr "acres"
#> $ statecd: int 56
#> $ states : chr "Wyoming"
# }