CODEWITHSUNDEEP
rplotstatisticsdistribution
MacroChair
MacroChair

Reputation: 77

Creating distribution curves with specific moments

Is there a way to create a distribution curve given the 1st through 4th moments (mean, variance or standard deviation, skewness, and kurtosis)? Here is a small table of the descriptive statistics. The fifth variable has stronger positive skew and larger kurtosis than the rest, and leads me to believe a non-normal distribution may need to be used.

dput(summarystats_factors)
structure(list(ERVALUEY = c(1.21178722715092, 8.4400515531338, 
0.226004674926861, 3.89328347004421), ERVOLY = c(0.590757887612924, 
7.48697754999463, 0.295973723450469, 3.31326615805655), ERQUALY = c(1.59367031426668, 
4.57371901763411, 0.601172123904339, 3.89080479205755), ERMOMTY = c(3.09719686678745, 
7.01446175391253, 0.260638252621096, 3.28326189430607), ERSIZEY = c(1.69935727981412, 
6.1917295410928, 1.24021163316834, 6.23493767854042), Moment = structure(c("Mean", 
"Standard Deviation", "Skewness", "Kurtosis"), .Dim = c(4L, 1L
))), row.names = c(NA, -4L), class = "data.frame")

Summary Stats Table

Upvotes: 4

Views: 624

Answers (2)

jay.sf
jay.sf

Reputation: 73252

We could use curve with PearsonDS::dpearson. Note, that the moments= argument expects exactly the order mean, variance, skewness, kurtosis, so that the rows of the data must be ordered correspondingly (as is the case in your example data).

FUN <- function(d, xlim, ylim, lab=colnames(d), main='Theoretical Distributions') {
  s <- seq(d)
  lapply(s, \(i) {
    curve(PearsonDS::dpearson(x, moments=d[, i]), col=i + 1, xlim=xlim, ylim=ylim, 
          add=ifelse(i == 1, FALSE, TRUE), ylab='y', main=main)
  })
  legend('topright', col=s + 1, lty=1, legend=lab, cex=.8, bty='n')
}

FUN(dat[-6], xlim=c(-2, 10), ylim=c(-.01, .2))

enter image description here

Data:

dat <- structure(list(ERVALUEY = c(1.21178722715092, 8.4400515531338, 
0.226004674926861, 3.89328347004421), ERVOLY = c(0.590757887612924, 
7.48697754999463, 0.295973723450469, 3.31326615805655), ERQUALY = c(1.59367031426668, 
4.57371901763411, 0.601172123904339, 3.89080479205755), ERMOMTY = c(3.09719686678745, 
7.01446175391253, 0.260638252621096, 3.28326189430607), ERSIZEY = c(1.69935727981412, 
6.1917295410928, 1.24021163316834, 6.23493767854042), Moment = structure(c("Mean", 
"Standard Deviation", "Skewness", "Kurtosis"), .Dim = c(4L, 1L
))), row.names = c(NA, -4L), class = "data.frame")

Upvotes: 3

Jim
Jim

Reputation: 191

Use the PearsonDS package, the pearson0 family creates "normal" distributions matching specified moments, but other options are available.

Upvotes: 1

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