In Seren1127/quantiles: Calculating Scores for Quantile-Quantile Plot

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 4
)

library(quantiles)

It is not hard to see Normality assumption in many Statistical context. One of the ways to check the normality is Quantile-Quantile plot, shortly Q-Q plot.

To draw the plot, we need Normal Score, theoretical quantiles of the data, and Standard Score, sample quantiles.

Function normal_scores helps us to calculate these.

For example, in normally distributed case

set.seed(1)
dat_norm<-rnorm(30)
with(normal_scores(dat_norm),
     plot(x = normal_score, y = standard_score))

On the other hand, in non-normally distributed case

set.seed(1)
dat_exp<-rexp(30,2)
with(normal_scores(dat_exp),
     plot(x = normal_score, y = standard_score))

Seren1127/quantiles documentation built on Dec. 31, 2020, 4:31 p.m.