[Solved] Take one sample per row at a time, compute mean, loop [closed]

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First lets make so data to experiment with

set.seed(100)
example <- matrix(runif(47*30), nrow = 47)
example[sample(length(example), 250)] <- NA

Now we can calculate our means. The apply function samples a random value from each row (!is.na excludes NA values), mean gets the mean, and replicate repeats this 10000 times.

exmeans <- replicate(10000, mean(apply(example, 1, 
                                 function(x) sample(x[!is.na(x)], 1))))

Confidence intervals can be computed two different ways. The first uses the example means as an empirical distribution and calculates the mean from that, the second uses a normal distribution to calculate the probablilities.

confint <- quantile(exmeans, c(0.025, 0.975))
confint <- qnorm(c(0.025, 0.975), mean = mean(exmeans), sd = sd(exmeans))

Next the plots you wanted

hist(exmeans)
abline(v = confint, col = "red")

histogram of quantiles

Finally the p-value information. Once again we can eigther use an empirical distribution or a normal distribution. These provide p-values for the lower tails of the distribution, use 1 - result for upper tails.

newvalue > confint[1] & newvalue < confint[2]
ecdf(exmeans)(newvalue)
pnorm(newvalue, mean = mean(exmeans), sd = sd(exmeans))

solved Take one sample per row at a time, compute mean, loop [closed]