Salmo salar - 3 months ago 16

R Question

I am attempting to run a mixed effect model (using R) on some data but struggling with one of the fixed effects, I think primarily due to it a factor?!

sample data:

`data4<-structure(list(code = structure(1:10, .Label = c("10888", "10889",`

"10890", "10891", "10892", "10893", "10894", "10896", "10897",

"10898", "10899", "10900", "10901", "10902", "10903", "10904",

"10905", "10906", "10907", "10908", "10909", "10910", "10914",

"10916", "10917", "10919", "10920", "10922", "10923", "10924",

"10925", "10927"), class = "factor"), speed = c(0.0296315046039244,

0.0366986630049636, 0.0294297725505692, 0.048316183511095, 0.0294275666501456,

0.199924957584131, 0.0798850288176711, 0.0445886457047146, 0.0285993712316451,

0.0715158276875623), meanflow = c(0.657410742496051, 0.608271363339857,

0.663241108786611, 0.538259450171821, 0.666299529534762, 0.507156583629893,

0.762448863636364, 37.6559178370787, 50.8557196935557, 31.6601587837838

), length = c(136, 157, 132, 140, 135, 134, 144, 149, 139, 165

), river = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L

), .Label = c("c", "f"), class = "factor")), .Names = c("code",

"speed", "meanflow", "length", "river"), row.names = c(2L, 4L,

6L, 8L, 10L, 12L, 14L, 16L, 18L, 20L), class = "data.frame")

my model is as such:

`model1<-lmer(speed ~ river + length +(1|meanflow)+(1|code), data4)`

and when run returns error message:

`Error in checkNlevels(reTrms$flist, n = n, control) :`

number of levels of each grouping factor must be < number of observations

having trawled the internet I have found one response:

http://stats.stackexchange.com/questions/57912/random-effects-must-be-less-than-the-number-of-observations-error-in-lmer-pac

but for the life of me do not understand the responses to the question!

a point in the right direction will be more than welcome

Answer

You have two problems here:

It looks like you have one observation for every value of

`code`

. That means that you can't estimate both a residual variance (which is built in to`lmer`

, and linear mixed models more generally) and an among-`code`

variance -- both of these parameters will be trying to estimate the same variance component, and any combination of`var(residual)`

and`var(code)`

that adds up to the same value will represent an equally good fit to the data.You also have one observation for every value of

`meanflow`

; this is because`meanflow`

is a continuous variable, which is not usually something you want to use as a*grouping variable*in the model. I'm not sure what you're trying to capture with this term.

You can actually fit these models if you insist by using `lmerControl`

to bypass the checks, but you won't necessarily get a sensible result!

```
model2 <- lmer(speed ~ river + length +(1|meanflow)+(1|code), data4,
control=lmerControl(check.nobs.vs.nlev = "ignore",
check.nobs.vs.rankZ = "ignore",
check.nobs.vs.nRE="ignore"))
```

Here the variance has been divided approximately in equal thirds:

```
VarCorr(model2)
## Groups Name Std.Dev.
## meanflow (Intercept) 0.035354
## code (Intercept) 0.032898
## Residual 0.033590
```

If we use only one (still inappropriate) random effect,

```
model0 <- lmer(speed ~ river + length +(1|meanflow), data4,
control=lmerControl(check.nobs.vs.nlev = "ignore",
check.nobs.vs.rankZ = "ignore",
check.nobs.vs.nRE="ignore"))
```

Now the variance is divided exactly in halves:

```
VarCorr(model0)
## Groups Name Std.Dev.
## meanflow (Intercept) 0.041596
## Residual 0.041596
```