# Re: [R] Nested for loop

```Hi Ben,

That's exactly right! Except for each set it's the sample population that
is 400, 800 or 300. I want to take 3 samples, each of 100, where only the
population differs. I can do this separately, but I'm having trouble
putting them all on the same graph.```
```
I'd like to have sample on the x axis (1-300) and estimate on the y axis. I
want to show how population affects the estimates.

Does this make more sense?

Kirsten
On Sun, Aug 6, 2017 at 3:21 PM Ben Tupper <btup...@bigelow.org> wrote:

> Hi Kirsten,
>
>
>
> sampling.  Can you restate the the task?  Below is what I think you are
> asking for, but I have the feeling I may be off the mark.
>
>
>
>
>
> Set A: 400 samples, draw 100 in range of 5 to 15
>
>
>
> Set B: 800 samples, draw 100 in range of 5 to 15
>
>
>
> Set C: 300 samples, draw 100 in range of 5 to 15
>
>
>
> Ben
>
>
>
> > On Aug 5, 2017, at 9:21 AM, Kirsten Morehouse <kmore...@swarthmore.edu>
> wrote:
>
> >
>
> > Hi! Thanks for taking the time to read this.
>
> >
>
> > The code below creates a graph that takes 100 samples that are between 5%
>
> > and 15% of the population (400).
>
> >
>
> > What I'd like to do, however, is add two other sections to the graph. It
>
> > would look something like this:
>
> >
>
> > from 1-100 samples take 100 samples that are between 5% and 15% of the
>
> > population (400). From 101-200 take 100 samples that are between 5% and
> 15%
>
> > of the population (800). From 201-300 take 100 samples that are between
> 5%
>
> > and 15% of the population (300).
>
> >
>
> > I assume this would require a nested for loop. Does anyone have advice as
>
> > to how to do this?
>
> >
>
> > Thanks for your time. Kirsten
>
> >
>
> > ## Mark-Recapture
>
> > ## Estimate popoulation from repeated sampling
>
> >
>
> > ## Population size
>
> > N <- 400
>
> > N
>
> >
>
> > ## Vector labeling each item in the population
>
> > pop <- c(1:N)
>
> > pop
>
> >
>
> > ## Lower and upper bounds of sample size
>
> > lower.bound <- round(x = .05 * N, digits = 0)
>
> > lower.bound ## Smallest possible sample size
>
> >
>
> > upper.bound <- round(x = .15 * N, digits = 0)
>
> > upper.bound ## Largest possible sample size
>
> >
>
> > ## Length of sample size interval
>
> > length.ss.interval <- length(c(lower.bound:upper.bound))
>
> > length.ss.interval ## total possible sample sizes, ranging form
> lower.bound
>
> > to upper.bound
>
> >
>
> > ## Determine a sample size randomly (not a global variable...simply for
>
> > test purposes)
>
> > ## Between lower and upper bounds set previously
>
> > ## Give equal weight to each possible sample size in this interval
>
> > sample(x = c(lower.bound:upper.bound),
>
> >       size = 1,
>
> >       prob = c(rep(1/length.ss.interval, length.ss.interval)))
>
> >
>
> > ## Specify number of samples to take
>
> > n.samples <- 100
>
> >
>
> > ## Initiate empty matrix
>
> > ## 1st column is population (item 1 thorugh item 400)
>
> > ## 2nd through nth column are all rounds of sampling
>
> > dat <- matrix(data = NA,
>
> >              nrow = length(pop),
>
> >              ncol = n.samples + 1)
>
> >
>
> > dat[,1] <- pop
>
> >
>
> > dat
>
> >
>
> > ## Take samples of random sizes
>
> > ## Record results in columns 2 through n
>
> > ## 1 = sampled (marked)
>
> > ## 0 = not sampled (not marked)
>
> > for(i in 2:ncol(dat)) {
>
> >  a.sample <- sample(x = pop,
>
> >                     size = sample(x = c(lower.bound:upper.bound),
>
> >                                   size = 1,
>
> >                                   prob = c(rep(1/length.ss.interval,
>
> > length.ss.interval))),
>
> >                     replace = FALSE)
>
> >  dat[,i] <- dat[,1] %in% a.sample
>
> > }
>
> >
>
> > ## How large was each sample size?
>
> > apply(X = dat, MARGIN = 2, FUN = sum)
>
> > ## 1st element is irrelevant
>
> > ## 2nd element through nth element: sample size for each of the 100
> samples
>
> >
>
> > ## At this point, all computations can be done using dat
>
> >
>
> > ## Create Schnabel dataframe using dat
>
> > ## Google the Schnabel formula
>
> >
>
> > schnabel.comp <- data.frame(sample = 1:n.samples,
>
> >                            n.sampled = apply(X = dat, MARGIN = 2, FUN =
>
> > sum)[2:length(apply(X = dat, MARGIN = 2, FUN = sum))]
>
> > )
>
> >
>
> > ## First column: which sample, 1-100
>
> > ## Second column: number selected in that sample
>
> >
>
> >
>
> > ## How many items were previously sampled?
>
> > ## For 1st sample, it's 0
>
> > ## For 2nd sample, code is different than for remaning samples
>
> >
>
> > n.prev.sampled <- c(0, rep(NA, n.samples-1))
>
> > n.prev.sampled
>
> >
>
> > n.prev.sampled[2] <- sum(ifelse(test = dat[,3] == 1 & dat[,2] == 1,
>
> >                                yes = 1,
>
> >                                no = 0))
>
> >
>
> > n.prev.sampled
>
> >
>
> > for(i in 4:ncol(dat)) {
>
> >  n.prev.sampled[i-1] <- sum(ifelse(test = dat[,i] == 1 &
>
> > rowSums(dat[,2:(i-1)]) > 0,
>
> >                                    yes = 1,
>
> >                                    no = 0))
>
> > }
>
> >
>
> > schnabel.comp\$n.prev.sampled <- n.prev.sampled
>
> >
>
> > ## n.newly.sampled: in each sample, how many items were newly sampled?
>
> > ## i.e., never seen before?
>
> > schnabel.comp\$n.newly.sampled <- with(schnabel.comp,
>
> >                                      n.sampled - n.prev.sampled)
>
> >
>
> > ## cum.sampled: how many total items have you seen?
>
> > schnabel.comp\$cum.sampled <- c(0,
>
> > cumsum(schnabel.comp\$n.newly.sampled)[2:n.samples-1])
>
> >
>
> > ## numerator of schnabel formula
>
> > schnabel.comp\$numerator <- with(schnabel.comp,
>
> >                                n.sampled * cum.sampled)
>
> >
>
> > ## denominator of schnable formula is n.prev.sampled
>
> >
>
> > ## pop.estimate -- after each sample (starting with 2nd -- need at least
>
> > two samples)
>
> > schnabel.comp\$pop.estimate <- NA
>
> >
>
> > for(i in 1:length(schnabel.comp\$pop.estimate)) {
>
> >  schnabel.comp\$pop.estimate[i] <- sum(schnabel.comp\$numerator[1:i]) /
>
> > sum(schnabel.comp\$n.prev.sampled[1:i])
>
> > }
>
> >
>
> >
>
> > ## Plot population estimate after each sample
>
> > if (!require("ggplot2")) {install.packages("ggplot2");
> require("ggplot2")}
>
> > if (!require("scales")) {install.packages("scales"); require("scales")}
>
> >
>
> >
>
> > small.sample.dat <- schnabel.comp
>
> >
>
> > small.sample <- ggplot(data = small.sample.dat,
>
> >                       mapping = aes(x = sample, y = pop.estimate)) +
>
> >  geom_point(size = 2) +
>
> >  geom_line() +
>
> >  geom_hline(yintercept = N, col = "red", lwd = 1) +
>
> >  coord_cartesian(xlim = c(0:100), ylim = c(300:500)) +
>
> >  scale_x_continuous(breaks = pretty_breaks(11)) +
>
> >  scale_y_continuous(breaks = pretty_breaks(11)) +
>
> >  labs(x = "\nSample", y = "Population estimate\n",
>
> >       title = "Sample sizes are between 5% and 15%\nof the population") +
>
> >  theme_bw(base_size = 12) +
>
> >  theme(aspect.ratio = 1)
>
> >
>
> > small.sample
>
> >
>
> >       [[alternative HTML version deleted]]
>
> >
>
> > ______________________________________________
>
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>
> > https://stat.ethz.ch/mailman/listinfo/r-help
>
> http://www.R-project.org/posting-guide.html
>
> > and provide commented, minimal, self-contained, reproducible code.
>
>
>
> Ben Tupper
>
> Bigelow Laboratory for Ocean Sciences
>
> 60 Bigelow Drive, P.O. Box 380
>
> East Boothbay, Maine 04544
>
> http://www.bigelow.org
>
>
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