Simple random sample
It only takes a minute to sign up. I vaguely recall from grad school that the following is a valid approach to do a weighted sampling without replacement:. Thank you everyone for your comments and responses! Regarding code, here's an example that faithfully reproduces what I'm trying to do although I'm actually calling R from Python with RPy, but it seems the runtime characteristics are unchanged.
First, define the following function, which assumes a sufficiently long sequence of with-replacement samples as input:. It simply does a binary search for the number of non-unique samples needed to get enough unique samples.
Now, in the interpreter:. As you can see, even for just elements the difference is dramatic. If this equivalence isn't perfectly clear, there's a straightforward mathematical demonstration. It forms a geometric series which is elementary to put into closed form the first equality. The second equality is a trivial algebraic reduction. For this to be worth doing instead of using the sample function in R the vector you're sampling from needs to be about 1e7 or greater in size and the sample has to be relatively small.
If the sample you want is much bigger or the one you're sampling from is smaller sample will be faster. But once a tipping point is achieved a method like Juan describes will be much faster. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered.
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I want all members of each group to be unique. I've tried using various permutation methods from vegan, picante, EcoSimR, but they don't do quite what I want, or seem to struggle with the large amount of data.
I wondered if there was just some way of using the sample function that I can't figure out? Any help or alternative suggestions would be much appreciated As noted by nico you probably just need to use the unique function.
A very simple sampling program is below which ensures that there won't be duplication across the groups which isn't totally sensible, because you could just create one big sample instead To do what eipi10 mentioned and get a weighted distribution, you just need to get the frequency of the distribution first. A way of doing this:. Learn more.
Selecting Random Samples in R: Sample() Function
Sample without replacement, or duplicates, in R Ask Question. Asked 5 years, 2 months ago. Active 5 years, 2 months ago. Viewed 10k times. Alex A.
Do you need the samples to be unique across groups i. I sample records, then I sample more records, and so on - but for all the groups across all the samples a given record appears only once?
The unique function springs to mind Even though you want each value to appear only once, do you want the probability of being sampled to be proportional to the number of times it appears in your original data?
If so, you can create a vector of just the unique values, but use the prob argument of the sample function to set sampling probabilities that are proportional to the number of times each value appears in your original list. No, I'm happy for samples to duplicate between groups, just not within a group. Ideally, I want allvalues to be defined to a group at the same time groups of So, each group will have unique samples, but samples can be repeated between groups.If x has length 1, is numeric in the sense of is.
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. Non-integer positive numerical values of n or x will be truncated to the next smallest integer, which has to be no larger than.
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 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. Argument n can be larger than the largest integer of type integerup 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. For sample a vector of length size with elements drawn from either x or from the integers 1:x.
For sample. Becker, R. RNGkind sample. Created by DataCamp. Random Samples and Permutations sample takes a sample of the specified size from the elements of x using either with or without replacement.
Community examples Alettadieben yahoo. Alettadieben yahoo. Post a new example: Submit your example. API documentation. Put your R skills to the test Start Now.By Andrie de Vries, Joris Meys. Statisticians often have to take samples of data and then calculate statistics. Taking a sample is easy with R because a sample is really nothing more than a subset of data.
To do so, you make use of samplewhich takes a vector as input; then you tell it how many samples to draw from that list. Say you wanted to simulate rolls of a die, and you want to get ten results.
How to Take Samples from Data in R
Because the outcome of a single roll of a die is a number between one and six, your code looks like this:. You tell sample to return ten values, each in the range This is the correct behavior in most cases, but sometimes you may want to get repeatable results every time you run the function. Usually, this will occur only when you develop and test your code, or if you want to be certain that someone else can test your code and get the same values you did.
If you provide a seed value, the random-number sequence will be reset to a known state. A pseudo-random sequence is a set of numbers that, for all practical purposes, seem to be random but were generated by an algorithm. When you set a starting seed for a pseudo-random process, R always returns the same pseudo-random sequence.
You can read the Help for? RNG to get more detail. In R, you use the set. The argument to set. If you draw another sample, without setting a seed, you get a different set of results, as you would expect:.
Now, to demonstrate that set. But this time, set the seed once more:. You get exactly the same results as the first time you used set. You can use sample to take samples from the data frame iris. With over 20 years of experience, he provides consulting and training services in the use of R. How to Take Samples from Data in R.Many statistical and business analysis projects will require you to select a sample from a list of values.
This is particularly true for simulation requests. To select a sample, r has the sample function. This function can be used for combinatoric problems and statistical simulation. Tempers flare a bit when you talk about random samples in certain audiences.
This article is going to focus on the essence of using sample to select values from a list. We are also going to briefly discuss more advanced options for sampling and random number generation. R has a convenient function for handling sample selection; sample. This function addresses the common cases:. The default setting for this function is it will randomly sort the values on a list. These are returned to the user in random order.
Sample code is below:. But what if a value can be selected multiple times? This is known as sampling with replacement. Replace can be T true or F false. The default case assumes no replacement. Code example looks like:. We can add the size parameter to return only a few values. The following code will pick three values.
As a practical use case, we can use this to figure out who will pick up the bar tab for a R meetup. The prior examples assume we are selecting values at random from a list. But R sample also allows us to adjust the probability of each item being selected. We do this with the prob argument.The main benefit of the simple random sample is that each member of the population has an equal chance of being chosen for the study. This means that it guarantees that the sample chosen is representative of the population and that the sample is selected in an unbiased way.
There are multiple ways of creating a simple random sample. These include the lottery method, using a random number table, using a computer, and sampling with or without replacement. The lottery method of creating a simple random sample is exactly what it sounds like. A researcher randomly picks numbers, with each number corresponding to a subject or item, in order to create the sample. To create a sample this way, the researcher must ensure that the numbers are well mixed before selecting the sample population.
One of the most convenient ways of creating a simple random sample is to use a random number table. These are commonly found at the back of textbooks on the topics of statistics or research methods. Most random number tables will have as many as 10, random numbers.
These will be composed of integers between zero and nine and arranged in groups of five. These tables are carefully created to ensure that each number is equally probable, so using it is a way to produce a random sample required for valid research outcomes. In practice, the lottery method of selecting a random sample can be quite burdensome if done by hand.
Typically, the population being studied is large and choosing a random sample by hand would be very time-consuming. Instead, there are several computer programs that can assign numbers and select n random numbers quickly and easily.
Many can be found online for free. Sampling with replacement is a method of random sampling in which members or items of the population can be chosen more than once for inclusion in the sample. All of those pieces of paper are put into a bowl and mixed up. The researcher picks a name from the bowl, records the information to include that person in the sample, then puts the name back in the bowl, mixes up the names, and selects another piece of paper.
The person that was just sampled has the same chance of being selected again. This is known as sampling with replacement. Sampling without replacement is a method of random sampling in which members or items of the population can only be selected one time for inclusion in the sample. This time, however, we record the information to include that person in the sample and then set that piece of paper aside rather than putting it back into the bowl.
Here, each element of the population can only be selected one time. Share Flipboard Email.
By Ashley Crossman. Updated January 29, To create a simple random sample using a random number table just follow these steps. Number each member of the population 1 to N.
Determine the population size and sample size. Select a starting point on the random number table. The best way to do this is to close your eyes and point randomly onto the page. Whichever number your finger is touching is the number you start with. Choose a direction in which to read up to down, left to right, or right to left. Select the first n numbers however many numbers are in your sample whose last X digits are between 0 and N.
For instance, if N is a 3 digit number, then X would be 3. Put another way, if your population contained people, you would use numbers from the table whose last 3 digits were between 0 and If the number on the table wasyou would not use it because the last 3 digits is greater than You would skip this number and move to the next one.
If the number isyou would use it and you would select the person in the population who is assigned the number Sometimes you may be analyzing a very large data file and want to work with just a simple random sample of the data file. Other times you may want to draw a simple random sample with replacement from a small data file.Sampling Without Replacement for Size n=2 By Sir Tanveer
Either way, SAS proc surveyselect is one way to do it, and it is fairly straightforward. In a simple random sample without replacement each observation in the data set has an equal chance of being selected, once selected it can not be chosen again.
The following code creates a simple random sample of size 10 from the data set hsb Here the method option on the proc surveyselect statement specifies the method to be SRS simple random sampling.
The sampsize is a required option here specifying the size of the random sample. This number has to be smaller than the size of the original data set, since the sampling is done without replacement. You can also specify the seed so a precise replicate can be reproduced later using the same seed.
The id statement is used to specify the variables to be included in the sample. In a random sample with replacement, each observation in the data set has an equal chance to be selected and can be selected over and over again. The following code creates a random sample with replacement of size We will only include variables idreadwritemathscience and socst in the sample data set.
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