CODEWITHSUNDEEP
rvectorization
TMOTTM
TMOTTM

Reputation: 3381

R: How to provide a vector to the `mean` argument of `rnorm`?

How do I provide a vector to the means argument of rnorm?

around_int1_mean <- seq(1.5, 3.5, 0.1)

I would like to do something like rnorm(n=25, mean=around_int1_mean, sd=0.2), avoiding a for-loop.

I want to get length(around_int1_mean) sets of samples with n=25 with mean (in the first set) of 1.5, then 1.6 and so on until the last set has mean 3.5. So in the end I'd get 21 sets of samples of size 25.

Upvotes: 3

Views: 639

Answers (1)

Zheyuan Li
Zheyuan Li

Reputation: 73325

I want to get length(around_int1_mean) sets of samples with n=25 with mean (in the first set) of 1.5, then 1.6 and so on until the last set has mean 3.5. So in the end I'd get 21 sets of samples of size 25.

You need

rnorm(n = length(around_int1_mean) * 25,
      mean = rep(around_int1_mean, each = 25), sd = 0.2)

The mean and sd argument in rnorm are vectorized. They would first be recycled to have length n. Then, for i = 1, 2, ..., n, the i-th sample is drawn from N(mean[i], sd[i]).

As another example, if you want a single sample for each mean, do:

rnorm(n = length(around_int1_mean), mean = around_int1_mean, sd = 0.2)

As @TMOTTM insists I am wrong and voted down my answer, I must show evidence to defend myself.

around_int1_mean <- seq(1.5, 3.5, by = 0.1)

I would set sd = 0 to eliminate randomness, so random samples will just take mean value with probability of 1. This enables us to prove that rnorm is generating correct set of samples for correct mean.

x <- rnorm(n = length(around_int1_mean) * 25,
           mean = rep(around_int1_mean, each = 25), sd = 0)

Also, I would use a matrix to demonstrate it:

matrix(x, nrow = 25)

#      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
# [1,]  1.5  1.6  1.7  1.8  1.9    2  2.1  2.2  2.3   2.4   2.5   2.6   2.7
# [2,]  1.5  1.6  1.7  1.8  1.9    2  2.1  2.2  2.3   2.4   2.5   2.6   2.7
# [3,]  1.5  1.6  1.7  1.8  1.9    2  2.1  2.2  2.3   2.4   2.5   2.6   2.7
# [4,]  1.5  1.6  1.7  1.8  1.9    2  2.1  2.2  2.3   2.4   2.5   2.6   2.7
# [5,]  1.5  1.6  1.7  1.8  1.9    2  2.1  2.2  2.3   2.4   2.5   2.6   2.7
# [6,]  1.5  1.6  1.7  1.8  1.9    2  2.1  2.2  2.3   2.4   2.5   2.6   2.7
# [7,]  1.5  1.6  1.7  1.8  1.9    2  2.1  2.2  2.3   2.4   2.5   2.6   2.7
# [8,]  1.5  1.6  1.7  1.8  1.9    2  2.1  2.2  2.3   2.4   2.5   2.6   2.7
# [9,]  1.5  1.6  1.7  1.8  1.9    2  2.1  2.2  2.3   2.4   2.5   2.6   2.7
#      [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21]
# [1,]   2.8   2.9     3   3.1   3.2   3.3   3.4   3.5
# [2,]   2.8   2.9     3   3.1   3.2   3.3   3.4   3.5
# [3,]   2.8   2.9     3   3.1   3.2   3.3   3.4   3.5
# [4,]   2.8   2.9     3   3.1   3.2   3.3   3.4   3.5
# [5,]   2.8   2.9     3   3.1   3.2   3.3   3.4   3.5
# [6,]   2.8   2.9     3   3.1   3.2   3.3   3.4   3.5
# [7,]   2.8   2.9     3   3.1   3.2   3.3   3.4   3.5
# [8,]   2.8   2.9     3   3.1   3.2   3.3   3.4   3.5
# [9,]   2.8   2.9     3   3.1   3.2   3.3   3.4   3.5
# [ reached getOption("max.print") -- omitted 16 rows ]

Obviously my answer is correct. Each column has 25 samples with the same mean.

Upvotes: 5

Related Questions