---
title: "Use Case 06: Thematic mapping of the geography of a CRT"
output:
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  toc: true
vignette: >
  %\VignetteIndexEntry{Use Case 06: Thematic mapping of the geography of a CRT}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

`CRTspat` is intended to facilitate thematic mapping of the geography of a CRT at each stage from
enumeration of the trial population to data analysis. Graphical outputs are generated with `ggplot2()`. In addition there is a function, `CRTwrite()` to export the thematic layers and shapefiles to GIS formats.

The same `plotCRT()` function is used at each stage in the trial (see below), with the functionality available
expanding as more fields are populated in the `CRTsp` object. When applied to output from `CRTanalysis()` `plotCRT()` that analyse the spillover interval, an expanded set of thematic maps are available, including overlay plots showing the spillover zone (i.e. the subset of the study area estimated to have effects of spillover) and thematic maps of spatial predictions.


``` r
# using the same dataset as for Use Case 1.
library(CRTspat)
exampleCRT <- readdata('exampleCRT.txt')
plotCRT(exampleCRT, map = TRUE, fill = 'none', showLocations=TRUE)
```
<p>
    <img src="example6a.r-1.png" alt="" > <br>
    <em>Fig 6.1 Locations </em>
</p>

If the clusters have been established, a map can be drawn showing where they are located.
The clusters can be distinguished by colour or by number. To ensure that the image is not too crowded,
by default the locations are not shown (but they can be shown if required).


``` r
plotCRT(exampleCRT, map = TRUE, fill = 'clusters', showClusterLabels = TRUE, labelsize =3)
```
<p>
    <img src="example6b.r-1.png" alt="" > <br>
    <em>Fig 6.2 Clusters </em>
</p>

Similarly, the map of arms is available if the trial has been randomized. Buffer zones can be plotted on
this map.


``` r
plotCRT(exampleCRT, map = TRUE, fill = 'arms', showLocations=TRUE)
plotCRT(exampleCRT, map = TRUE, fill = 'arms', showBuffer=TRUE,
        showClusterBoundaries = FALSE, buffer_width = 0.5)
```

```
## Buffer includes locations within 500m of the opposing arm
```
<p>
  <img src="example6c.r-1.png" alt="" > <br>
  <em>Fig 6.3 Arms with locations </em>
  </p>
  <p>
  <img src="example6c.r-2.png" alt="" > <br>
  <em>Fig 6.4 Arms with 500m buffer zone shaded </em>
  </p>

  Once data have been collected, `plotCRT()` can be used to generate a bar plot to illustrate
how much of the data are found close to the boundary between the arms.


``` r
plotCRT(exampleCRT, map = FALSE)
```
<p>
  <img src="example6d.r-1.png" alt="" > <br>
  <em>Fig 6.5 Numbers of observations by distance from boundary</em>
</p>

The results of the data analysis can be illustrated with further graphics.
The blue shaded section of Figure 6.8 indicates the spillover zone, corresponding to
those locations that fall within the central 95% of the estimated sigmoid of the
of the effect size by distance from the boundary between the arms.


``` r
analysis <- CRTanalysis(exampleCRT, cfunc = "P", method = "LME4")
```

```
## 
AIC: 1382.59556154402 Spillover scale parameter: 0.21981696975569
```

```
## 
AIC: 1385.16678301441 Spillover scale parameter: 1.89442074481204
```

```
## 
AIC: 1384.56974564433 Spillover scale parameter: 0.0580687950830108
```

```
## 
AIC: 1382.41972768805 Spillover scale parameter: 0.296672334826958
```

```
## 
AIC: 1382.21769016816 Spillover scale parameter: 0.435328418519021
```

```
## 
AIC: 1382.82566400225 Spillover scale parameter: 0.76341943326029
```

```
## 
AIC: 1382.27915707782 Spillover scale parameter: 0.53950586833145
```

```
## 
AIC: 1382.21467421951 Spillover scale parameter: 0.450083930258126
```

```
## 
AIC: 1382.21584616456 Spillover scale parameter: 0.4653395817005
```

```
## 
AIC: 1382.21467421951 Spillover scale parameter: 0.450083930258126
```

``` r
summary(analysis)
```

```
## 
## =====================CLUSTER RANDOMISED TRIAL ANALYSIS =================
## Analysis method:  LME4 
## Link function:  logit 
## Measure of distance or surround: Signed distance to other arm (km)
##  with estimation of scale parameter.  
## Model formula:  effect + (1 | cluster) 
## Error function model for spillover
## Estimates:       Control: 0.417 (95% CL: 0.33 0.509)
##             Intervention: 0.186 (95% CL: 0.136 0.249)
##                 Efficacy: 0.553 (95% CL: 0.331 0.702)
##          Scale parameter: 0.45 (95% CL: 0.45 0.45)
##   Spillover interval(km): 1.749 (95% CL: 1.728 1.762)
## Pr in spillover interval: 0.686 (95% CL: 0.686 0.686)
##    Ipsilateral Spillover: 0.009 (95% CL: 0 0.096)
##  Contralateral Spillover: 0.034 (95% CL: 0 0.122)
## Coefficient of variation: 48.425 (95% CL: 35.969 75.865)
## deviance                :  NA 
## AIC                     :  1382.215 including penalty for the spillover scale parameter
```

``` r
plotCRT(analysis, map = FALSE)
plotCRT(analysis, map = TRUE, fill = 'arms', showBuffer=TRUE, showClusterBoundaries = FALSE)
```

```
## Buffer corresponds to estimated spillover zone
```
  <p>
  <img src="example6e.r-1.png" alt="" > <br>
  <em>Fig 6.6 Plot of estimated spillover function </em>
  </p>
  <p>
  <img src="example6e.r-2.png" alt="" > <br>
  <em>Fig 6.7 Arms with spillover zone shaded </em>
  </p>



## Conclusions

In this example, a large proportion of the data points are close to the boundary between the arms.
The analysis (based on simulated spillover) suggests that there are
effects of spillover far beyond a 500m buffer. However this does not necessarily mean
that the spillover leads to a large bias or loss in power (see [Use Case 7](Usecase7.html)).


