Pvalue.norm.sim | R Documentation |
Simulate and plot p-values from a normal or binomial based test under various conditions. When all the assumptions are true, the p-values should follow an approximate uniform distribution. These functions show that along with how violating the assumptions changes the distribution of the p-values.
Pvalue.norm.sim(n = 50, mu = 0, mu0 = 0, sigma = 1, sigma0 = sigma,
test= c("z", "t"), alternative = c("two.sided", "less", "greater", "<>",
"!=", "<", ">"), alpha = 0.05, B = 10000)
Pvalue.binom.sim(n=100, p=0.5, p0=0.5, test=c('exact','approx'),
alternative=c('two.sided', 'less', 'greater',
'<>','!=','<','>'),
alpha=0.05, B=1000)
run.Pvalue.norm.sim()
run.Pvalue.binom.sim()
n |
Sample Size for each simulated dataset |
mu |
Simulation mean for samples |
mu0 |
Hypothesized mean for tests |
sigma |
Simulation SD for samples |
sigma0 |
Hypothesized SD for tests, if blank or missing, use the sample SD in the tests |
p |
Simulation proportion for samples |
p0 |
Hypothesized proportion for tests |
test |
Which test to use, "z" or "t" tests for normal, "exact" (binomial) or "approx" (normal approximation) for binomial |
alternative |
Direction for alternative hypothesis |
alpha |
alpha level for test (optional) |
B |
Number of simulated datasets |
These functions generate B
samples from either a normal or
binomial distribution, then compute the P-values for the test of
significance on each sample and plot the P-values.
The run.Pvalue.norm.sim
and run.Pvalue.binom.sim
functions are GUI wrappers for the other 2 functions allowing you to
change the parameters and click on "refresh" to run a new set of
simulations.
Using NA
for sigma0
will result in the sample standard
deviations being used (leave blank in the GUI).
When the simulation conditions and the hypothesized values match, the distributions of the p-values will be approximately uniform. Changing the parameter of interest will show the idea of power. Changing the other parameters can show the effects of assumptions not being met.
The P-values are invisibly returned.
Note: the 2-sided p-values for the binomial may not match the results from binom.test and prop.test. The method used here is an approximation for speed.
Greg Snow, 538280@gmail.com
Murdock, D, Tsai, Y, and Adcock, J (2008) _P-Values are Random Variables_. The American Statistician. (62) 242-245.
t.test
, z.test
,
binom.test
, prop.test
, tkexamp
if(interactive()) {
run.Pvalue.norm.sim()
run.Pvalue.binom.sim()
}
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