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How to implement stratified sampling Stratified in R language? in order to solve this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

1. Observation data set

Head (iris)

Selecting the first six data in the dataset, we can see that the iris dataset has a total of five fields.

Dim (iris)

The iris dataset has 150 pieces of data and 5 fields

Summary (iris)

Observing the contents of each variable, we can see that the first four variables (Sepal.Length Sepal.Width Petal.Length Petal.Width) are quantitative variables, while the last variable (Species) is a qualitative variable. We will use the last variable as the basis for stratified sampling.

Library (sampling)

Load stratified sampling package sampling

N=round (3/5*nrow (iris) / 3)

Calculate the number of samples for each category. Here we take 3 prime 5 samples from each "Species" for sampling.

Sub_train=strata (iris,stratanames= ("Species"), size=rep (nMagol 3), method= "srswor") head (sub_train)

The stratanames parameter is the variable on which the sampling is based, and the size parameter is the sampling number of each category. Here we use the n calculated in the previous step as the sampling number, method is the sampling method, and we choose srswor.

Data_train= iris [sub _ train$ID_unit,] data_test= iris [- sub_train$ID_unit,]

The sampling results are defined as training set (data_train) and test set (data_test) respectively.

Dim (data_train); dim (data_test)

Observe the number of fields and data in the training set and test set. In line with our sampling expectations.

Head (data_train); head (data_test)

Observe the first few pieces of data from the training set and the test set.

Data_train;data_test

Look at the overall sampling results, where the amount of data is too large to be given.

Write.csv (data_train, "C:/Users/cnrozh/Desktop/iris_data_train.csv") write.csv (data_test, "C:/Users/cnrozh/Desktop/iris_data_test.csv")

Save dataset

This is the answer to the question about how to achieve stratified sampling Stratified in R language. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel to learn more about it.

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