 I asked this question on Stack Exchange, but I think it might be too
specialized.  Hopefully someone in the mixed model group can help me out.

I want to be able to bootstrap the variance differences between two data
sets obtained at different times while taking out the error in a random
effect.

I have 2 sets of experimental data, where the data was measured at 2 time
points (initial and final). I also have a set of simulation data. I want to
compare the variance of the simulated date with the variance difference
between the experimental data (final - initial). The idea is to get
confidence intervals from the bootstrap to compare the experimental data
with the simulation.

I am having trouble making the statistic for the bootstrap function in the
boot package for R. So far I have.

varcomp <- function ( formula, data, indices ) {
    d <- data[indices,] #sample for boot
    fit <- lmer(formula, data=d) #linear model
    res.var = (attr (VarCorr(fit), "sc")^2) # variance estimation
    return(res.var)
    }

But this function only returns the variance of a single data set. I want to
be able to input 2 sets of data and have it return the difference between
the two data sets' variance.

When I try something like:

varcomp <- function ( formula, data1, data2, indices ) {
d1 <- data1[indices,] #sample for boot
d2 <- data2[indices,] #sample for boot
fit1 <- lmer(formula, data=d1) #linear model
fit2 <- lmer(formula, data=d2) #linear model
a = (attr (VarCorr(fit1), "sc")^2) #output variance estimation
b = (attr (VarCorr(fit2), "sc")^2) #output variance estimation
drv = a - b #difference between the variance estimations
return(drv)
}

I would then put it into boot such as:

ip1.boot <- boot ( data = ip1, statistic=varcomp, R=100,
formula=CNPC~(1|Cell.line:DNA.extract)+Cell.line)

I can't do it this way because the boot function only allows for one data
set to be inputted.

*Does anyone know how to create the correct statistic function for this?*

An example of the data can also be downloaded
here<http://www.mediafire.com/file/68a3ro1cfneiy2r/data.zip.zip>(2 csv
files zipped 1.22KB.)

My data looks something like the following:

Initial

       Cell.line    Time DNA.extract   Gene      CNPC
1          9 initial           1 atubP1 1778.4589
2          9 initial           1 atubP1 2108.0552
3          9 initial           1 atubP1 2118.6725
4          9 initial           2 atubP1 2018.6593
5          9 initial           2 atubP1 1935.9008
6          9 initial           2 atubP1 1749.9158
7          9 initial           3 atubP1 1524.7475
8          9 initial           3 atubP1 1532.9781
9          9 initial           3 atubP1 1693.3098
10        17 initial           1 atubP1 1076.4720
11        17 initial           1 atubP1 1101.3315
12        17 initial           1 atubP1 1185.3606
13        17 initial           2 atubP1 1131.1118
14        17 initial           2 atubP1  892.7087
15        17 initial           2 atubP1 1028.5465
16        17 initial           3 atubP1  887.9972
17        17 initial           3 atubP1  732.9646
18        17 initial           3 atubP1  680.6724

Final

   Cell.line  Time DNA.extract   Gene      CNPC
1          9 final           1 atubP1 1262.2378
2          9 final           1 atubP1 1261.9858
3          9 final           1 atubP1 1390.6873
4          9 final           2 atubP1 1539.7180
5          9 final           2 atubP1 1510.5405
6          9 final           2 atubP1 1443.1767
7          9 final           3 atubP1 1456.2050
8          9 final           3 atubP1 1578.6396
9          9 final           3 atubP1 1656.1822
10        17 final           1 atubP1 1462.5179
11        17 final           1 atubP1 1580.9956
12        17 final           1 atubP1 1255.9020
13        17 final           2 atubP1  886.7579
14        17 final           2 atubP1  581.8116
15        17 final           2 atubP1  722.0526
16        17 final           3 atubP1 4168.7895
17        17 final           3 atubP1 3266.2105
18        17 final           3 atubP1 4219.5645

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