But with three lines for the three habitat types and grass length at the
bottom




On Thu, Oct 10, 2013 at 5:52 PM, Rebecca Stirnemann
<rstirnemann@gmail.com>wrote:

> Thanks Jim for helping,
>
> Your sample data actually looks like my dataset. The one I put up looks
> strange for some reason so please ignore that.
> I have three landusenumb variables 1 2 and 3. is rep (1,2,3) correct?
>
> When I run the following code I am getting:
>
> > mod1 <- glmer(frat ~ flandusenumb + ground.cover_lo + (1|fsite) ,family
> = binomial, data= mao1)
> >
> > #Calculate predicted values
> > newdata1 <- data.frame(ground.cover_lo = c(25,50,100), flandusenumb =
> rep(1,2,3))
> > pred34 <- predict(mod1,newdata=newdata1,type="response")
>
> Error in UseMethod("predict") :
>   no applicable method for 'predict' applied to an object of class "mer"
>
> Can you see what I am doing wrong?
> What I am aiming for is a graph which looks like this.
>
> Thanks
> Rebecca
>
>
>
>
>
>
> On Thu, Oct 10, 2013 at 5:33 PM, Jim Lemon <jim@bitwrit.com.au> wrote:
>
>> On 10/10/2013 08:35 AM, Rebecca Stirnemann wrote:
>>
>>> Dear R wizards,
>>>
>>> Though I hate to do it after weeks of my code not working I need some
>>> help
>>> since I cant find an example which seems to work.
>>> I am trying to create a graph which show the probability of predation of
>>> a
>>> nest on one side (either 1 to 0) or (0% to 100%) on one side
>>> and grass height at the bottom. I want to then add my predicted lines
>>> from
>>> my glmr onto the graph for three habitat types.
>>>
>>> I would like to repeat this procedure 3 times for three different grass
>>> heights 25- 50- 100 to see the effect size.
>>>
>>> My data:
>>>     landusenumb landuse sitename rat ground.cover_lo  1  plantation
>>> far.leftroad_LHS 0 60  1 plantation far.leftroad_LHS 1 70  1 plantation
>>> far.leftroad_LHS 1 10  1 plantation far.leftroad_LHS 1 30  1 plantation
>>> far.leftroad_LHS 1 50  1 plantation far.leftroad_LHS 0 20  1 plantation
>>> far.leftroad_LHS 0 70  1 plantation far.leftroad_LHS 0 100  1 plantation
>>> far.leftroad_LHS 0 90
>>>
>>> #Graph
>>>
>>>
>>> #Fit model
>>>
>>> mod1<- glmer(frat ~ flandusenumb + ground.cover_lo + (1|fsite) ,family =
>>> binomial, data= mao1)
>>>
>>>
>>> #Calculate predicted values
>>>
>>> newdata1<- data.frame(ground.cover_lo = seq(0,10,length=100),
>>> flandusenumb
>>> = rep(1,2,3))
>>>
>>> pred34<- predict(mod1,newdata=newdata1,**type="response")
>>>
>>>
>>>
>>> #Plot model predicted curves
>>>
>>> plot(c(0,100),c(0,1),type="n",**xlab="grasscover",ylab="**Probability of
>>> predation")
>>>
>>> lines(newdata1$frat,pred34,**lwd=3,col="blue")
>>>
>>>
>>>  Hi Rebecca,
>> First, your sample data are a bit mangled, and should look like this:
>>
>> mao1
>>
>> landusenumb landuse    sitename rat ground.cover_lo
>> 1           plantation far.leftroad_LHS   0      60
>> 1           plantation far.leftroad_LHS   1      70
>> 1           plantation far.leftroad_LHS   1      10
>> 1           plantation far.leftroad_LHS   1      30
>> 1           plantation far.leftroad_LHS   1      50
>> 1           plantation far.leftroad_LHS   0      20
>> 1           plantation far.leftroad_LHS   0      70
>> 1           plantation far.leftroad_LHS   0     100
>> 1           plantation far.leftroad_LHS   0      90
>>
>> If you want the predicted values with ground cover as above, then:
>>
>> ground.cover_lo = c(25,50,100)
>>
>> The variable names in the first model don't match those in the data
>> frame, but I assume these were typos. What does "pred34" look like? This
>> will tell you what function you should be using to plot it.
>>
>> Jim
>>
>
>
>
> --
> www.samoanbirds.com
>



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