`saeczi`

is an R package that implements a small area
estimator that uses a two-stage modeling approach for zero-inflated
response variables. In particular, we are working with variables that
follow a semi-continuous distribution with a mixture of zeroes and
positive continuously distributed values. An example can be seen
below.

`saeczi`

first fits a linear mixed model to the non-zero
portion of the response and then a generalized linear mixed model with
binomial response to classify the probability of zero for a given data
point. In estimation these models are each applied to new data points
and combined to compute a final prediction.

The package can also generate MSE estimates using a parametric bootstrap approach described in Chandra and Sud (2012) either in parallel or sequentially.

Install the latest CRAN release with:

`install.packages("saeczi")`

You can also install the developmental version of `saeczi`

from GitHub with:

```
# install.packages("pak")
::pkg_install("harvard-ufds/saeczi") pak
```

We’ll use the internal package data to show an example of how to use
`saeczi`

. The two data sets contained within the package
contain example forestry data collected by the Forestry Inventory and
Analysis (FIA) research program.

`saeczi::samp`

: Example FIA plot-level sample data for each county in Oregon.`saeczi::pop`

: Example FIA pixel level population auxiliary data for each county in Oregon.

The main response variable included in `samp`

is above
ground live biomass and our small areas in this case are the counties in
Oregon. To keep things simple we will use tree canopy cover (tcc16) and
elevation (elev) as our predictors in both of the models. We can use
`saeczi`

to get estimates for the mean biomass in each county
as well as the corresponding bootstrapped (B = 500) MSE estimate as
follows.

```
library(saeczi)
data(pop)
data(samp)
<- saeczi(samp_dat = samp,
result pop_dat = pop,
lin_formula = DRYBIO_AG_TPA_live_ADJ ~ tcc16 + elev,
log_formula = DRYBIO_AG_TPA_live_ADJ ~ tcc16,
domain_level = "COUNTYFIPS",
mse_est = TRUE,
B = 1000L)
```

The function returns the following objects:

Name | Description |
---|---|

`call` |
The original function call |

`res` |
A data.frame containing the estimates |

`lin_mod` |
The linear model object of class
`merMod` used to compute the estimates |

`log_mod` |
The logistic model object of class
`merMod` used to compute the estimates |

As there are 36 total counties in Oregon, we will just look at the first few rows of the results:

```
$res |> head()
result#> COUNTYFIPS mse est
#> 1 41001 38.30647 14.57288
#> 2 41003 122.90662 103.33016
#> 3 41005 1069.30963 86.08616
#> 4 41007 4691.01214 78.79615
#> 5 41009 356.53805 73.98920
#> 6 41011 273.34697 90.44174
```

`saeczi`

supports parallelization through the
`future`

package to speed up the bootstrapping process, but
requires a small amount of additional work on the part of the user. It
is not enough just to specify `parallel = TRUE`

in the
function signature as a `future::plan`

must also be
specified.

Below is an example that uses multisession’ future resolution with 6 threads:

```
::plan("multisession", workers = 6)
future<- saeczi(samp_dat = samp,
result_par pop_dat = pop,
lin_formula = DRYBIO_AG_TPA_live_ADJ ~ tcc16 + elev,
log_formula = DRYBIO_AG_TPA_live_ADJ ~ tcc16,
domain_level = "COUNTYFIPS",
mse_est = TRUE,
parallel = TRUE,
B = 1000L)
```