我试图从现有的样本数据中生成更大的数据样本。例如iris是N = 150，我想将其重新缩放4500（1500每个物种）。https://seslezak.github.io/IrisData/上的帖子中描述了一个示例。我不想，resample或者bootstrap我对使用例如生成新值感兴趣，rnorm这是到目前为止我尝试过的。
muSepal.Length = mean(iris$Sepal.Length)
sdSepal.Length = sd(iris$Sepal.Length)
muSepal.Width= mean(iris$Sepal.Width)
sdSepal.Width = sd(iris$Sepal.Width)
N = 5000
simulated_data = data.frame(Sepal.Length = rnorm(N, muSepal.Length,sdSepal.Length),Sepal.Width =rnorm(N,muSepal.Width,sdSepal.Width))
在这里，我从样本分布中提取了值，但是我在努力了解如何有效地构建整个“新”数据集？
正如@db在几个小时前指出的那样，您面临选择采样现有数据或假定它适合于诸如理论分布的选择rnorm。很显然，您要模拟的文章的作者选择了后者。新数据集的摘要清楚地显示了一些setosa Petal.Width不在原始值iris和NAs中的值，因为在一个较大的样本中，对于测量而言，我们必然会低于0。
这是一组快速而肮脏的代码，您应该能够对自己的数据进行处理。
set.seed(2020)
library(dplyr)
testing <- iris %>%
group_by(Species) %>%
summarise_at(vars(Sepal.Length:Petal.Width), list(mean = mean,
sd = sd)) %>%
rowwise() %>%
group_by(Species) %>%
summarise(Sepal.Length = rnorm(1500,
mean = Sepal.Length_mean,
sd = Sepal.Length_sd),
Sepal.Width = rnorm(1500,
mean = Sepal.Width_mean,
sd = Sepal.Width_sd),
Petal.Length = rnorm(1500,
mean = Petal.Length_mean,
sd = Petal.Length_sd),
Petal.Width = rnorm(1500,
mean = Petal.Width_mean,
sd = Petal.Width_sd)) %>%
ungroup %>% # so we stop being rowwise
filter_at(vars(Sepal.Length:Petal.Width), ~ . > .1) # to eliminate ridiculously small or negative values
summary(testing)
#> Species Sepal.Length Sepal.Width Petal.Length
#> setosa :1368 Min. :3.784 Min. :1.719 Min. :0.8857
#> versicolor:1500 1st Qu.:5.168 1st Qu.:2.746 1st Qu.:1.6116
#> virginica :1500 Median :5.834 Median :3.014 Median :4.2998
#> Mean :5.855 Mean :3.047 Mean :3.8148
#> 3rd Qu.:6.443 3rd Qu.:3.322 3rd Qu.:5.2312
#> Max. :8.304 Max. :4.547 Max. :7.5825
#> Petal.Width
#> Min. :0.1001
#> 1st Qu.:0.3373
#> Median :1.3439
#> Mean :1.2332
#> 3rd Qu.:1.8460
#> Max. :3.0523
比我能流利的人可以通过ivot_longer或一个避免4个重复调用的自定义函数来做更好的工作rnorm。您需要寻找不合理的值，并证明为什么rnorm它非常适合您的数据。
添加一个更复杂的解决方案来解决MASS::mvrnormRemi在他的回答中提到的相关性。对不起，懒得思考更好的代码，只是在这里强行重复。
library(dplyr)
# Get the covariance matrix by species
sigma.setosa <- iris %>%
filter(Species == "setosa") %>%
select(-Species) %>%
cov
sigma.versicolor <- iris %>%
filter(Species == "versicolor") %>%
select(-Species) %>%
cov
sigma.virginica <- iris %>%
filter(Species == "virginica") %>%
select(-Species) %>%
cov
# generate samples based on those covariance matricies
set.seed(2020)
setosa.rows <- MASS::mvrnorm(n = 1500,
c(mean(iris$Sepal.Length), mean(iris$Sepal.Width), mean(iris$Petal.Length), mean(iris$Petal.Width)),
sigma.setosa,
empirical = TRUE)
versicolor.rows <- MASS::mvrnorm(n = 1500,
c(mean(iris$Sepal.Length), mean(iris$Sepal.Width), mean(iris$Petal.Length), mean(iris$Petal.Width)),
sigma.versicolor,
empirical = TRUE)
virginica.rows <- MASS::mvrnorm(n = 1500,
c(mean(iris$Sepal.Length), mean(iris$Sepal.Width), mean(iris$Petal.Length), mean(iris$Petal.Width)),
sigma.virginica,
empirical = TRUE)
# convert to dataframes
setosa.df <- data.frame(setosa.rows, Species = "setosa")
versicolor.df <- data.frame(setosa.rows, Species = "versicolor")
virginica.df <- data.frame(setosa.rows, Species = "virginica")
# bind them return species to a factor
newiris <- rbind(setosa.df, versicolor.df, virginica.df)
newiris$Species <- factor(newiris$Species)
summary(newiris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Min. :4.669 Min. :1.759 Min. :3.183 Min. :0.820
#> 1st Qu.:5.598 1st Qu.:2.805 1st Qu.:3.637 1st Qu.:1.130
#> Median :5.848 Median :3.064 Median :3.761 Median :1.199
#> Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
#> 3rd Qu.:6.083 3rd Qu.:3.306 3rd Qu.:3.878 3rd Qu.:1.267
#> Max. :6.969 Max. :4.288 Max. :4.342 Max. :1.578
#> Species
#> setosa :1500
#> versicolor:1500
#> virginica :1500
#>
#>
#>
summary(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
#> 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
#> Median :5.800 Median :3.000 Median :4.350 Median :1.300
#> Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
#> 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
#> Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
#> Species
#> setosa :50
#> versicolor:50
#> virginica :50
#>
#>
#>
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