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seed
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seed

Number, positive integer or null

range

  • min: Number, positive integer not null
  • max: Number, positive integer not null

number

Number, positive integer not null

replace

Boolean, true is duplicate, false is non duplicate

ratio

Decimal, a vector less than 1 or null

Documentation

Description

This website implements randomization based on R language, in the blockrand(v1.5) package of R language. This feature creates random assignments for clinical trials (or any experiment that only accepts one subject at a time). Randomization is completed within the block to maintain a close to equal balance between treatments throughout the entire trial process.

Seed

The so-called random number is actually a "pseudo-random number", which is obtained by iterating forward from a set of starting values (called seeds) using a certain recursive calculation method. So starting from the same seed, the sequence of random numbers obtained is the same.

Usage


  sample(x, size, replace = FALSE, prob = NULL)
  
  sample.int(n, size = n, replace = FALSE, prob = NULL,
           useHash = (!replace && is.null(prob) && size <= 2="" n="" &&=""> 1e7))
      

Arguments

【x】either a vector of one or more elements from which to choose, or a positive integer. See ‘Details.’

【n】a positive number, the number of items to choose from. See ‘Details.’

【size】a non-negative integer giving the number of items to choose.

【replace】should sampling be with replacement?

【prob】a vector of probability weights for obtaining the elements of the vector being sampled.

【useHash】logical indicating if the hash-version of the algorithm should be used. Can only be used for replace = FALSE, prob = NULL, and size <= n/2, and really should be used for large n, as useHash=FALSE will use memory proportional to n.

Value

For sample a vector of length size with elements drawn from either x or from the integers 1:x.

For sample.int, an integer vector of length size with elements from 1:n, or a double vector if n >= 2^31

Details

If x has length 1, is numeric (in the sense of is.numeric) and x >= 1, sampling via sample takes place from 1:x. Note that this convenience feature may lead to undesired behaviour when x is of varying length in calls such as sample(x). See the examples.

Otherwise x can be any R object for which length and subsetting by integers make sense: S3 or S4 methods for these operations will be dispatched as appropriate.

For sample the default for size is the number of items inferred from the first argument, so that sample(x) generates a random permutation of the elements of x (or 1:x).

It is allowed to ask for size = 0 samples with n = 0 or a length-zero x, but otherwise n > 0 or positive length(x) is required.

Non-integer positive numerical values of n or x will be truncated to the next smallest integer, which has to be no larger than .Machine$integer.max.

The optional prob argument can be used to give a vector of weights for obtaining the elements of the vector being sampled. They need not sum to one, but they should be non-negative and not all zero. If replace is true, Walker's alias method (Ripley, 1987) is used when there are more than 200 reasonably probable values: this gives results incompatible with those from R < 2.2.0.

If replace is false, these probabilities are applied sequentially, that is the probability of choosing the next item is proportional to the weights amongst the remaining items. The number of nonzero weights must be at least size in this case.

sample.int is a bare interface in which both n and size must be supplied as integers.

Argument n can be larger than the largest integer of type integer, up to the largest representable integer in type double. Only uniform sampling is supported. Two random numbers are used to ensure uniform sampling of large integers.

References

1. Random Number Seed

2. Sample integer values

3. Random Samples and Permutations

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Ripley, B. D. (1987) Stochastic Simulation. Wiley.

See Also

RNGkind(sample.kind = ..) about random number generation, notably the change of sample() results with R version 3.6.0. CRAN package sampling for other methods of weighted sampling without replacement.

Examples


  # NOT RUN {
  x <- 1:12
  # a random permutation
  sample(x)
  # bootstrap resampling -- only if length(x) > 1 !
  sample(x, replace = TRUE)
  
  # 100 Bernoulli trials
  sample(c(0,1), 100, replace = TRUE)
  
  ## More careful bootstrapping --  Consider this when using sample()
  ## programmatically (i.e., in your function or simulation)!
  
  # sample()'s surprise -- example
  x <- 1:10
      sample(x[x >  8]) # length 2
      sample(x[x >  9]) # oops -- length 10!
      sample(x[x > 10]) # length 0
  
  ## safer version:
  resample <- function(x, ...) x[sample.int(length(x), ...)]
  resample(x[x >  8]) # length 2
  resample(x[x >  9]) # length 1
  resample(x[x > 10]) # length 0
  
  ## R 3.x.y only
  sample.int(1e10, 12, replace = TRUE)
  sample.int(1e10, 12) # not that there is much chance of duplicates
  # }
  

在线随机数生成器

  • 1. 可以随机生成唯一的或者重复的的随机数,根据您设定的随机数的数目,最小值和最大值,生成抽奖或者需要随机数字。

随机数

  • 1. 物理性随机数:比如掷钱币、骰子、转轮、使用电子元件的噪音、核裂变等等。是真正的随机数。叫做物理性随机数发生器,它们的缺点是技术要求比较高。
  • 2. 伪随机数:数列是“似乎”随机的数,实际上它们是通过一个固定的、可以重复的计算方法产生的。它们不真正地随机,因为它们实际上是可以计算出来的,但是它们具有类似于随机数的统计特征。