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Num | X | Y | Age | VitD | Status | |
---|---|---|---|---|---|---|
1 | 248 | 767 | 37 | 107.59 | A | |
2 | 552 | 830 | 60 | 118.45 | A | |
3 | 870 | 524 | 84 | 65.69 | A | |
4 | 777 | 511 | 77 | 63.45 | A | |
5 | 301 | 471 | 41 | 56.55 | A | |
6 | 420 | 464 | 50 | 55.34 | A | |
7 | 486 | 513 | 55 | 63.79 | A | |
8 | 579 | 502 | 62 | 61.90 | A | |
9 | 632 | 484 | 66 | 58.79 | A |
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set.seed(1) | |
# Number of datasets to generate. | |
iter <- 1000 | |
# Generate random sample sizes. | |
# Initially I only put the Nmin parameter there to avoid silly sizes like 0 or 1, | |
# but then I found that something interesting happens. | |
# Try setting Nmean to 10 instead of 50, so that all sample sizes are 20, and watch | |
# what happens to the correlation between d and its SE. |
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