Conditional Least Squared (CLS) Method for the Model SINAR(1,1).

sinar

The goal of sinar is to implement the Conditional Least Square method for the Spatial non-negative Integer-valued Autoregressive (SINAR(1,1)).

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("gilberto-sassi/sinar")

Example: simulated case

library(sinar)

## Simulated data matrix from SINAR(1,1) with Poison(5) innovation
matrix_simulated <- sinar_pois(15, 15, 0.2, 0.2, 0.4, 5)

## Conditional Least Square (CLS) estimates
cls(matrix_simulated)
#>       a10       a01       a11        mu 
#> 0.1605389 0.2860054 0.4277413 3.1261927

## Covariance matrix of CLS estimates
emp_cov(matrix_simulated)
#>               a10           a01          a11          mu
#> a10  0.0044018403  0.0001991086 -0.001362643 -0.08051497
#> a01  0.0001991086  0.0032884060 -0.000882474 -0.06218858
#> a11 -0.0013626431 -0.0008824740  0.004125110 -0.04507648
#> mu  -0.0805149667 -0.0621885767 -0.045076478  4.67716808

Example: real dataset (nematodoes)

library(sinar)

## Nematodes counting datasets
data("nematodes")

## Conditional Least Square (CLS) estimates
cls(nematodes)
#>        a10        a01        a11         mu 
#> 0.20664577 0.33147378 0.04523086 2.14476453

## Covariance matrix of CLS estimates
emp_cov(nematodes)
#>               a10           a01          a11           mu
#> a10  0.0111169222 -0.0009999304 -0.003310576 -0.017278481
#> a01 -0.0009999304  0.0082946407 -0.001503724 -0.009838536
#> a11 -0.0033105760 -0.0015037242  0.004507501  0.004049939
#> mu  -0.0172784806 -0.0098385364  0.004049939  0.268045835

Example: real dataset (carabidae)

library(sinar)

## Carabidae counting dataset
data("carabidae")

## Conditional Least Square (CLS) estimates
cls(carabidae)
#>        a10        a01        a11         mu 
#> 0.14595392 0.12725313 0.08798513 9.10361759

## Covariance matrix of CLS estimates
emp_cov(carabidae)
#>              a10          a01          a11          mu
#> a10  0.014484776 -0.003141815 -0.005525906 -0.06795645
#> a01 -0.003141815  0.014365625 -0.001265544 -0.11558802
#> a11 -0.005525906 -0.001265544  0.023795735 -0.25417404
#> mu  -0.067956449 -0.115588024 -0.254174036  7.22525572