From b451d90c3745bb4779933898e4d07832a1438209 Mon Sep 17 00:00:00 2001 From: Manny Gimond Date: Sat, 30 Dec 2023 12:51:28 -0500 Subject: [PATCH] fixed a few typos --- docs/articles/qq.html | 8 ++++---- vignettes/qq.Rmd | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/articles/qq.html b/docs/articles/qq.html index 367471a..d580594 100755 --- a/docs/articles/qq.html +++ b/docs/articles/qq.html @@ -77,7 +77,7 @@

Manuel Gimond

-

2023-12-28

+

2023-12-30

Source: vignettes/qq.Rmd
qq.Rmd
@@ -411,7 +411,7 @@

Perfectly alligned points are rareeda_qq(x, y)

Note how the points not only meander about the x=y line, -but some of the points tail off near the end of the distributions.

+but some of the points tail off near the end of the distribution.

@@ -481,7 +481,7 @@

The Tukey mean-difference plotA working example

Many datasets will have distributions that differ not only additively -and/or Multiplicatively, but also by their general shape. This may +and/or multiplicatively, but also by their general shape. This may create complex point patterns in your QQ plot. Such cases can be indicative of different processes at play for different ranges of values. We’ll explore such a case using wat95 and @@ -549,7 +549,7 @@

A working example)

The proposed offsets seem to do a good job in characterizing the -differences in temperatures .

+differences in temperatures.

The characterization of the differences in normal temperatures between the old and new set of normals can be formalized as follows:

\[ diff --git a/vignettes/qq.Rmd b/vignettes/qq.Rmd index 2044393..bc47a47 100755 --- a/vignettes/qq.Rmd +++ b/vignettes/qq.Rmd @@ -261,7 +261,7 @@ y <- rnorm(100) eda_qq(x, y) ``` -Note how the points not only meander about the `x=y` line, but some of the points tail off near the end of the distributions. +Note how the points not only meander about the `x=y` line, but some of the points tail off near the end of the distribution. # Power transformation @@ -308,7 +308,7 @@ par(OP) # A working example -Many datasets will have distributions that differ not only additively and/or Multiplicatively, but also by their general shape. This may create complex point patterns in your QQ plot. Such cases can be indicative of different *processes* at play for different ranges of values. We'll explore such a case using `wat95` and `wat05` dataframes available in this package. The data represent derived normal temperatures for the 1981-2010 period (`wat95`) and the 1991-2020 period (`wat05`) for the city of Waterville, Maine (USA). We will subset the data to the daily *average* normals, `avg`: +Many datasets will have distributions that differ not only additively and/or multiplicatively, but also by their general shape. This may create complex point patterns in your QQ plot. Such cases can be indicative of different *processes* at play for different ranges of values. We'll explore such a case using `wat95` and `wat05` dataframes available in this package. The data represent derived normal temperatures for the 1981-2010 period (`wat95`) and the 1991-2020 period (`wat05`) for the city of Waterville, Maine (USA). We will subset the data to the daily *average* normals, `avg`: ```{r} old <- wat95$avg # legacy temperature normals @@ -378,7 +378,7 @@ par(OP) ``` -The proposed offsets seem to do a good job in characterizing the differences in temperatures . +The proposed offsets seem to do a good job in characterizing the differences in temperatures. The characterization of the differences in normal temperatures between the old and new set of normals can be formalized as follows: