I'm stuck with a - i think very basic - problem, but I'm not getting any further for weeks now, thats why I finally decided to ask. I have some data which represent damage done by game in fields.

The damage was maped. If there where many small damages they were maped as one big one and the overall intensity was estimated (5 classes). I took a grid of 1x1m and for every cell I calculated the distance class (1 = 1-10m, 2=10-10m) from different structural parameters (forest, wheat, maiz, roads etc.).

Question 1 (not the most important one): According to the damage intensity I copied the rows to finally get a 1/5m² resolution.

Code:

```
Intensity 1 (0-20% damage) → *5
Intensity 2 (21-40% damage) → *4
Intensity 3 (41-60% damage) → *3
Intensity 4 (61-80% damage) → *2
Intensity 5 (81-100% damage) → *1
```

Code:

```
| Intensity | dist_forest | dist_maiz | dist_roads |
|1 | 50| 20| 70|
|2 | 40| 10| 90|
|5 | 20| 20| 40|
```

Code:

```
| Intensity | dist_forest | dist_maiz | dist_roads |
|1 | 50| 20| 70|
|1 | 50| 20| 70|
|1 | 50| 20| 70|
|1 | 50| 20| 70|
|1 | 50| 20| 70|
|2 | 40| 10| 90|
|2 | 40| 10| 90|
|2 | 40| 10| 90|
|2 | 40| 10| 90|
|5 | 20| 20| 40|
```

Is that a valid method, or do I somehow rack the statistical output.

Question 2:

My procedure above no leads to data like this:

Code:

```
| damage | dist_forest | dist_maiz | dist_roads |
|0 | 30| 20| 70|
|0 | 20| 10| 60|
|0 | 60| 10| 80|
|0 | 40| 70| 10|
|0 | 20| 60| 50|
|1 | 10| 10| 50|
|1 | 05| 20| 30|
|1 | 20| 30| 20|
|1 | 30| 20| 90|
|1 | 40| 10| 10|
```

Now I would like to know if any of the parameters has a significant influence if damage occures or not. Therefor I would use a binary logistic regression, in R like this:

Code:

`glm(damage ~ dist_forest + dist_maiz, dist_roads, family=binomial(logit), data=data)`

Btw.: my sample size (number of recorded damages) is in real not very big, only the 1x1m resolution makes the dataset big.

Thanks for your help!

Stame