Mud Warrior - 11 months ago 89

R Question

I know my question is linked to stats but I'm looking for a solution in

`R`

I built a generalized linear mixed-effects model (GLMM) using the

`glmer`

`lme4`

`R`

`Mod1 <- glmer(Richness ~ Distance + Depth + Substrate + Beggiatoa +`

Distance*Beggiatoa + (1|Site/transect), family = poisson, data = mydata)

Now I have a full data set collected at different sites and I want to assess how this model performs on the new data set.

Following a question on CV, someone suggested to look for the median absolute deviation (mad) on the new data set. I tried the

`mad`

`stats`

`R`

`Error in x[!is.na(x)] : object of type 'S4' is not subsettable`

In addition: Warning messages:

1: In is.na(x) : is.na() applied to non-(list or vector) of type 'S4'

2: In is.na(x) : is.na() applied to non-(list or vector) of type 'S4'

`mad`

`stats`

`R`

To give you an idea of my data, here's the output from

`dput(head(mydata))`

`structure(list(S = c(0, 1, 2, 3, 3, 2), Site = structure(c(1L,`

1L, 1L, 1L, 1L, 1L), .Label = c("BC", "BH", "GC", "IS", "Ref"

), class = "factor"), Transect = structure(c(4L, 4L, 4L, 4L,

4L, 4L), .Label = c("10GC", "10IS", "10N", "10S", "11IS", "12IS",

"13E", "1GC", "1N", "1W", "2E", "2GC", "2IS", "2N", "2W", "2WA",

"3E", "3GC", "3IS", "3N", "3S", "4E", "4GC", "4IS", "4S", "4W",

"5GC", "5IS", "5S", "6GC", "6IS", "6N", "6S", "6W", "7E", "7GC",

"7IS", "8GC", "8IS", "8W", "9E", "9GC", "9IS", "9N", "RefBC1",

"RefBC10", "RefBC11", "RefBC12", "RefBC2", "RefBC3", "RefBC4",

"RefBC5", "RefBC6", "RefBC7", "RefBC8", "RefBC9", "X1", "X2"), class = "factor"),

Distance = c(2, 20, 40, 80, 120, 160), Depth = c(40L, 40L,

50L, 40L, 40L, 40L), Beggiatoa = c(2, 1, 1, 0, 0, 0)), .Names = c("S",

"Site", "Transect", "Distance", "Depth", "Beggiatoa"), row.names = c(NA,

6L), class = "data.frame")

Answer

For *within-sample* error, the mean absolute deviation computation would just be

```
mad(residuals(fitted_model))
```

... you might want `residuals(fitted_model,type="response")`

, since `residuals`

will give you deviance residuals by default (see `?residuals.merMod`

)

If you want to look at *out-of-sample* error, you could do something like this:

```
pred <- predict(fitted_model,newdata=newdf,type="response",
re.form=~0)
mad(pred,center=newdf$S)
```

(`re.form=~0`

specifies that you want to omit random effects from the prediction, which is your only choice unless you're predicting at sites/transects where you've also got training data)

Source (Stackoverflow)