CODEWITHSUNDEEP
rsamplemean
YesSure
YesSure

Reputation: 195

Seeking mean and SD from multiple sample in R

I want to generate 25 normal samples from an normal distribution. I'm looking to do this in a intelligent manner where I don't have all these samples as separate entities.

This is the code I have so far for that portion

data <- replicate(25, rnorm(100))

So far this is what as it generates 25 samples of 100. When extracting the mean and sd for data, obviously the values are for the entire data set.

So my question is how do I disaggregate this and determine mean and sd for each of the 25 samples?

Upvotes: 3

Views: 2936

Answers (2)

Jilber Urbina
Jilber Urbina

Reputation: 61154

A nice alternative to apply(x, 2, mean) is colMeans(x), but there's not such alternative for apply(x, 2, sd) :( But you can also get both the mean and the standard deviation in just one shot using apply function, let's do it:

set.seed(42)
x <- replicate(25, rnorm(100))

Stats <- t(apply(x, 2, function(x) c(Mean=mean(x), Sd=sd(x))))
Stats
              Mean        Sd
 [1,]  0.032514816 1.0413570
 [2,] -0.087483707 0.9041735
 [3,] -0.010368172 1.0170123
 [4,]  0.032936464 0.8761978
 [5,] -0.117830506 1.0199916
 [6,]  0.002363510 1.0633145
 [7,] -0.086747228 0.9755756
 [8,] -0.169291497 0.8830939
 [9,]  0.061457015 1.0377577
[10,]  0.084205039 1.1804565
[11,] -0.129164759 1.0080920
[12,]  0.039991367 0.9814254
[13,]  0.078980115 0.9719501
[14,] -0.148572682 0.9125126
[15,] -0.048566771 0.9562642
[16,]  0.006789862 1.0347380
[17,]  0.274102604 1.0212837
[18,] -0.113169899 0.9988576
[19,]  0.151418057 0.9830082
[20,] -0.164987838 0.9348188
[21,] -0.035644377 1.0214245
[22,] -0.041569005 1.0159495
[23,]  0.051384229 1.0944096
[24,]  0.073521001 0.9084400
[25,]  0.021893835 0.9438906

Upvotes: 3

Andrie
Andrie

Reputation: 179428

Use apply to sweep out the summaries.

set.seed(42)
x <- replicate(25, rnorm(100))

Since your data is a column-wise matrix, you need to apply your function to the second dimension. 

apply(x, 2, mean)
 [1]  0.032514816 -0.087483707 -0.010368172  0.032936464
 [5] -0.117830506  0.002363510 -0.086747228 -0.169291497
 [9]  0.061457015  0.084205039 -0.129164759  0.039991367
[13]  0.078980115 -0.148572682 -0.048566771  0.006789862
[17]  0.274102604 -0.113169899  0.151418057 -0.164987838
[21] -0.035644377 -0.041569005  0.051384229  0.073521001
[25]  0.021893835


apply(x, 2, sd)
 [1] 1.0413570 0.9041735 1.0170123 0.8761978 1.0199916
 [6] 1.0633145 0.9755756 0.8830939 1.0377577 1.1804565
[11] 1.0080920 0.9814254 0.9719501 0.9125126 0.9562642
[16] 1.0347380 1.0212837 0.9988576 0.9830082 0.9348188
[21] 1.0214245 1.0159495 1.0944096 0.9084400 0.9438906

Upvotes: 0

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