I'm trying to perform correlation analysis with R's linear model
lm()
I'm wondering what is the reasonable minimum sample for it? Is there any rule for determining that?
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4This question belongs here: http://stats.stackexchange.com/ – sashkello Apr 19 '13 at 03:04
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1I think if you have two data columns or vectors with minimum two data points then the lm function should work. – Jd Baba Apr 19 '13 at 03:06
1 Answers
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As a rule of thumb, 20, 30, 1000, samples As a rule of thumb, you should be wary of rules of thumb. Excluding perhaps that "less is more, except of course for sample size" (Cohen & Cohen, 1983: 169-171).
You could ask your question on https://stats.stackexchange.com/ but they're probably going to give you answers that might not be the round number that you're looking for. For example:
- Is the number 20 magic?
- Is there a reference that suggest using 30 as a large enough sample size?
- Rules of thumb for minimum sample size for multiple regression
- What is a reasonable sample size for correlation analysis for both overall and sub-group analyses?
- 30 Samples. Standard, Suggestion, or Superstition?
- etc.
You'll get more useful responses if you edit your question here to include a reproducible example that resembles your actual use-case and then ask for help coding calculations of relevant measures of error. You might explore the pwr package before you edit your question (see here for examples: http://www.statmethods.net/stats/power.html).
Do a bit of googling to find the names of error measures you think will be useful to you. You might start with these:
Lenth, R. V. (2001), Some Practical Guidelines for Effective Sample Size Determination, The American Statistician, 55, 187-193.
Wheeler, R. E. (1974), 'Portable Power', Technometrics, 16, 193–201.
Cohen, J. & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.).(Hillsdale, NJ: Erlbaum)
