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Last active Sep 7, 2019
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Exploring rnorm
# regarding:
# set the RNG seed so the results are reporducible
# Generate 100 target means beteen 0 and 100 from the uniform distribution
generated_means <- runif(100, 0, 100)
# Generate 100 standard deviation values also from uniform distribution
generated_sdev <- runif(100, 1, 20)
# generate number of samples to create for each set
generated_sample_size <- runif(100, 1000, 30000)
# Generate 10000 samples from each target mean and target standard deviation
samples_vary_size <- mapply(FUN=rnorm, generated_sample_size, generated_means, 10, SIMPLIFY=FALSE)
samples_vary_sdev <- mapply(FUN=rnorm, 10000, generated_means, generated_sdev, SIMPLIFY=FALSE)
# Calculate the means of each sample
vary_size_means <- sapply(samples_vary_size, mean)
vary_sdev_means <- sapply(samples_vary_sdev, mean)
# Calculate differences betwen target_means
size_differences <- abs(generated_means - vary_size_means)
sdev_differences <- abs(generated_means - vary_sdev_means)
# plot differences between means as function of what was varied
plot(generated_sdev, sdev_differences, ylab="differences between generated and sampled mean")
plot(generated_sample_size, size_differences, ylab="differences between generated and sampled mean")
# return to usual operation
# t-test for difference of means for samples and generated means
vary_size_results <- mapply(FUN=t.test, x=samples_vary_size, mu=generated_means, SIMPLIFY = F)
vary_size_p_values <- sapply(vary_size_results, FUN=getElement, name="p.value")
vary_sdev_results <- mapply(FUN=t.test, x=samples_vary_sdev, mu=generated_means, SIMPLIFY = F)
vary_sdev_p_values <- sapply(vary_sdev_results, FUN=getElement, name="p.value")
# approximatedly 5 of 100 expected positives just by chance
sum(vary_sdev_p_values < 0.05)
sum(vary_size_p_values < 0.05)
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