---
title: "Information Consistency-Based Measures for Spatial Association"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{icm}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---



## 1. The principle of the information consistency-based measures

$$
I_{N}\left(d,s\right) = \frac{I \left(d,s\right)}{I \left(d\right)} =
\frac{I \left(d\right) - I \left(d \mid s\right)}{I \left(d\right)} =
1 - \frac{\sum_{s_i \in S}\sum_{x \in V_d} p\left(s_i,x\right) \log p\left(x \mid s_i\right)}{\sum_{x \in V_d} p\left(x\right) \log p\left(x\right)}
$$

where $p\left(x\right)$ is the probability of observing $x$ in $U$, $p\left(s_i,x\right)$ is the probability of observing $s_i$ and $x$ in $U$, and $p\left(x \mid s_i\right)$ is the probability of observing $x$ given that the stratum is $s_i$.

## 2. Example

```r
install.packages("itmsa", dep = TRUE)
install.packages("gdverse", dep = TRUE)
```


``` r
library(itmsa)
```


``` r
ntds = gdverse::NTDs
ntds$incidence = sdsfun::discretize_vector(ntds$incidence, 5)
itm(incidence ~ watershed + elevation + soiltype,
    data = ntds, method = "icm")
## # A tibble: 3 × 3
##   Variable     Iv    Pv
##   <chr>     <dbl> <dbl>
## 1 watershed 0.445     0
## 2 elevation 0.390     0
## 3 soiltype  0.210     0
```
