My loop knowledge is very minimal but I currently have a loop written, which takes values from three vectors (small.dens, med.dens, and large.dens) and each vector has 17 values. I have the loop setup to randomly select 2 values, then 3, then 4... all the way up to 17. Using these values, it calculates the mean and standard error (using the plotrix package). It then places these calculated means and standard errors into new vectors (small.density, small.stanerr, medium.density, medium.stanerr, large.density, and large.stanerr). Next, separately from the loop, I combine these vectors into a dataframe.
library(plotrix)
small.density = rep(NA,16)
small.stanerr = rep(NA,16)
medium.density = rep(NA,16)
medium.stanerr = rep(NA,16)
large.density = rep(NA,16)
large.stanerr = rep(NA,16)
for(i in 2:17){
xx=sample(small.dens,i,replace=TRUE)
small.density[[i]] = mean(xx)
small.stanerr[[i]] = std.error(xx)
yy = sample(med.dens, i, replace=TRUE)
medium.density[[i]] = mean(yy)
medium.stanerr[[i]] = std.error(yy)
zz = sample(large.dens, i, replace=TRUE)
large.density[[i]] = mean(zz)
large.stanerr[[i]] = std.error(zz)
}
I then want to run this loop 100 times, ultimately taking the mean, if that makes sense. For example, I would like it to select 2,3,4...17 values 100 times, taking the mean and standard error each time, and then taking the mean of all 100 times. Does this make sense? Would I make another for loop, turning this into a nested loop? How would I go about doing this? Thanks!
There are other ways to achieve what you want, but if you do not want to change your code, then just wrap it in something like this
res <- do.call(rbind, lapply(1:100, function(x) {
within(data.frame(
n = x,
size = 2:17,
small.density = rep(NA,16),
small.stanerr = rep(NA,16),
medium.density = rep(NA,16),
medium.stanerr = rep(NA,16),
large.density = rep(NA,16),
large.stanerr = rep(NA,16)
), {
for(i in 2:17){
xx = sample(small.dens,i,replace=TRUE)
small.density[[i - 1L]] = mean(xx)
small.stanerr[[i - 1L]] = std.error(xx)
yy = sample(med.dens, i, replace=TRUE)
medium.density[[i - 1L]] = mean(yy)
medium.stanerr[[i - 1L]] = std.error(yy)
zz = sample(large.dens, i, replace=TRUE)
large.density[[i - 1L]] = mean(zz)
large.stanerr[[i - 1L]] = std.error(zz)
}
rm(xx, yy, zz, i)
})
}))
res looks like this
> head(res, 20)
n size small.density small.stanerr medium.density medium.stanerr large.density large.stanerr
1 1 2 -0.04716195 0.35754422 13.1014925 4.374055 -42.089591 30.87786
2 1 3 -0.15893367 0.34557922 -0.2680632 6.206081 52.984076 36.85058
3 1 4 0.10013995 0.62374467 -0.1944930 5.784211 -112.684774 30.50707
4 1 5 0.40654132 0.40815013 1.6096970 5.026714 45.810098 46.58469
5 1 6 0.13310242 0.32104512 -6.9989844 4.232091 -22.312165 48.14705
6 1 7 0.21283027 0.53633472 -5.0702365 3.829677 -43.266482 41.74286
7 1 8 0.13870439 0.27161346 4.1629469 3.214053 -9.045643 48.49930
8 1 9 0.06495734 0.36738163 3.9742069 3.540913 -43.954345 38.23816
9 1 10 -0.01882762 0.37570468 -3.1764203 3.740403 -43.156792 38.47531
10 1 11 -0.02115580 0.26239465 -2.2026077 2.702412 7.343837 30.58314
11 1 12 0.09967753 0.27360125 3.9603382 3.214921 -13.461632 29.39910
12 1 13 0.53121414 0.27561862 4.3593802 1.872685 -38.572491 25.37029
13 1 14 0.21547909 0.36345292 -0.3377787 2.732968 17.305232 26.08317
14 1 15 0.33957964 0.23029520 0.4832063 2.886160 8.145410 18.23901
15 1 16 0.26871985 0.26846012 -6.7634873 3.436742 -4.011269 20.33814
16 1 17 0.24927792 0.20534048 -0.7481315 1.899348 9.993280 24.49623
17 2 2 -1.10840346 0.07123407 -3.4317644 6.966096 -30.384945 121.00972
18 2 3 1.73947551 0.35986535 -2.1415966 5.628115 -57.857871 10.47413
19 2 4 0.40033834 0.41963615 -4.2156733 1.206414 27.891021 13.84453
20 2 5 -0.08704736 0.52872770 0.3137693 2.974888 -3.100414 57.89126
If you want to calculate the mean of the 100 simulated values for each size, then just
aggregate(. ~ size, res[-1L], mean)
which gives you
size small.density small.stanerr medium.density medium.stanerr large.density large.stanerr
1 2 0.02872578 0.6341294 1.0938287 5.518797 3.141204 53.20675
2 3 0.16985732 0.5388110 -0.1627867 5.185643 -6.660756 49.83607
3 4 0.20543404 0.4815581 0.1385016 4.519419 -8.093673 46.64984
4 5 0.13019280 0.4546794 0.1299331 4.166335 -10.300542 41.40444
5 6 0.10675158 0.4307113 0.2191516 4.033863 -12.068151 38.95312
6 7 0.19326831 0.3834507 0.8784275 3.513812 -6.920378 36.17856
7 8 0.09020638 0.3580780 0.4388388 3.443349 -5.335405 30.49615
8 9 0.13956838 0.3558005 0.3740251 3.313501 -15.290834 31.64833
9 10 0.18368962 0.3397191 0.4600761 3.051425 -5.505220 29.46165
10 11 0.20653866 0.3116104 0.9913534 2.804659 -8.809398 28.79097
11 12 0.14653661 0.2988422 0.3337274 2.624418 -5.128882 26.78074
12 13 0.12255652 0.2864998 0.2085829 2.719396 -11.548064 27.08497
13 14 0.13102809 0.2830709 0.6448798 2.586491 -4.676053 25.21800
14 15 0.14536840 0.2749606 0.3415879 2.522826 -11.968496 24.44427
15 16 0.14871831 0.2571571 0.2218365 2.463486 -10.335511 23.64304
16 17 0.13664397 0.2461108 0.3387764 2.348594 -9.969407 22.84736