### import the boot package
#insert code here to import boot package



### step 1 - import the data
#insert code here to import data


### step 2 - analyze the data
### We'll use a new function for this that fits non-linear
### equations. It's very similar to lm, but requires a parametric structure
### and starting values, as I've specified below

parInit=list(a=1,b=1) #this is a list of starting values
CoyoteResults=nls(CoyoteKill~a*PreyDens/(b+PreyDens),data=datum,start=parInit)
LynxResults=nls(LynxKill~a*PreyDens/(b+PreyDens),data=datum,start=parInit)

### a is the asymptotic value of the predator kill rate
### b is the prey density at which half the maximum prey kill rate is achieved
### nls is a function that fits non-linear least squares models to data

summary(CoyoteResults)
summary(LynxResults)

### calculate the estimated difference in the asymptotes
CoyoteA=summary(CoyoteResults)$parameters[1] #extracts the value of a from the coyote regression
LynxA=summary(LynxResults)$parameters[1] #extracts the value of a from the lynx regression
Difference=LynxA-CoyoteA #calculates the difference in asymptotes
Difference ### reports the difference in asymptotes

### step 3 and 4 - write a function to resample and reanalyze the data
#insert code to complete "bootFunc" below. 
#Note that you can basically copy and past step 2 into bootFunc
#although it will requires some modification to work correctly

bootFunc=function(bootData,repeats){

}

### Step 5 - repeat function 1000 times
### create a bootstrapped object that runs the function 1000 times
#insert code to run bootstrap analysis

set.seed(123) #Be sure you run this code before you run the bootstrap



### Step 6 - calculate the confidence interval on the data
#insert code here to calculate confidence intervals



### Step 7 - Extract sorted bootstrapped estimates of difference in asymptotes
# insert code here to extract and sort bootstraped estimates of difference in asymptotes




### Step 8 - Plot the results
# insert code here to plot bootstrapped estimates of the difference in asymptotes