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 <j...@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
>
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