The new sample size calculator for R (already)

aka “Maybe I shouldn’t post so quickly”

Just hours ago, I posted my first set of functions for R to determine the sample size for a known population. Then, I had to update that post to reflect my newfound knowledge, and now, I thought I would update again, so that the best functions I came up with would all be in one place.

There are two functions, sample.size.table() and sample.size() which you can easily load in R by typing source("http://news.mrdwab.com/samplesize") at the command prompt.

Here’s some more information about each.

sample.size.table()

The new code looks like this:

^?View Code RSPLUS

> sample.size.table = function(c.lev, margin=.5, c.interval=.05, population) {
+      z.val = qnorm(.5+c.lev/200)
+      ss = (z.val^2 * margin * (1-margin))/(c.interval^2)
+      p.ss = ss/(1 + ((ss-1)/population))
+      c.level = c(paste(c.lev, "%", sep = ""))
+      results = data.frame(c.level, round(p.ss, digits = 0))
+      names(results) = c("Confidence Level", "Sample Size")
+      METHOD = c("Suggested sample sizes at different confidence levels")
+      moe = paste((c.interval*100), "%", sep="")
+      resp.dist = paste((margin*100),"%", sep="")
+      pre = structure(list(Population=population, "Margin of error" = moe,
+  		    "Response distribution" = resp.dist, method = METHOD),
+ 		    class = "power.htest")
+      print(pre)
+      print(results)
+ }

What’s good about it or when do you use it? Now, you can specify the confidence levels you want to experiment with. See the following example:

^?View Code RSPLUS

> sample.size.table(c.lev = c(90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 99.9,
+ 			    99.99, 99.999, 99.9999), population = 100)
 
     Suggested sample sizes at different confidence levels
 
           Population = 100
      Margin of error = 5%
Response distribution = 50%
 
   Confidence Level Sample Size
1               90%          73
2               91%          74
3               92%          76
4               93%          77
5               94%          78
6               95%          80
7               96%          81
8               97%          83
9               98%          85
10              99%          87
11            99.9%          92
12           99.99%          94
13          99.999%          95
14         99.9999%          96

Unlike the original function I had written, this one allows you to enter any values your heart desires (logical values would be from 1 to 99.99-something) and even lets you enter an array of numbers like we did in the example above. Pretty spiffy, right?

sample.size()

If you don’t care for a whole array of results, you might be more interested in simply using the sample.size() function, which looks like this:

^?View Code RSPLUS

> sample.size = function(c.lev, margin=.5, c.interval=.05, population) {
+     z.val = qnorm(.5+c.lev/200)
+     ss = (z.val^2 * margin * (1-margin))/c.interval^2
+     p.ss = round((ss/(1 + ((ss-1)/population))), digits=0)
+     METHOD = paste("Recommended sample size for a population of ", population,
+ 		   " at a ", c.lev, "% confidence level", sep = "")
+     structure(list(Population = population, "Confidence level" = c.lev,
+ 	      "Margin of error" = c.interval, "Response distribution" = margin,
+ 	      "Recommended sample size" = p.ss, method = METHOD),
+ 	      class = "power.htest")
+ }

It’s pretty easy to use. Here’s an example:

^?View Code RSPLUS

> sample.size(c.lev = 97, population = 300)
 
     Recommended sample size for a population of 300 at a 97% confidence level
 
             Population = 300
       Confidence level = 97
        Margin of error = 0.05
  Response distribution = 0.5
Recommended sample size = 183

Now, I guess we need to see if this post was made prematurely too!

The new sample size calculator for R (already)

sample.size.table()

sample.size()

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