JP Monteagudo

Lissajous Curves   Lissajous curves are created by plotting two oscillations on perpendicular axes  and ​​​. These oscillations, represented by sinusoidal functions, intersect to create different patterns. When the ratio of these frequencies is equal to — the oscillations are equally phased— the curve is a straight line.

Beautiful movement—the music and beauty of Lissajous(Liss–uh–joo) curves

Visualization &amp; Pearson’s correlation coefficient   The 30DayChartChallenge is a data visualization community that hosts daily challenges for April. Today’s challenge involves dinosaurs. I used the Datasaurus package to create an animated visualization to demonstrate the importance of graphing data, and the effects of outliers on statistical properties.

Animating the Datasaurus dataset as part of the 30 Day Chart Challenge

TL; DR:   Don’t be an absolutist– use relative paths. Use the here package instead of setwd() or getwd() to increase reproducibility and avoid wasting your and other people’s time.

Don’t be an absolutist. Use the here package for reproducible workflows

monthly changes in her circle orb
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Tip  If you’re wondering where I’m getting my titles and headings, they come from Shakespeare’s sonnets.  The moon has always fascinated me. Eversince I was a child, I was entranced by it; her constant pursuit everywhere I went and her sneaky tactics during the day. This year, before the solar eclipse, I decided to do a little digging and replicate one algorithm used to determine the phases of the moon.

swear not by the moon, the fickle moon

not so quickly! Recently, I become intrigued by game theory algorithms, not because I absolutely love playing games, but because it offers a quantifiable, systematic way to understand human relationships. I find the certainty of science and systematization reassuring to some degree so when I learned about this game called “the prisoner’ dilemma”, I thought it would fun to experiment and figure out the answer to this classic scenario.

the iterated prisoners’ dilemma

Quantile Beta Regression   The quantile beta regression model discussed in the 2021 paper by Running Code   When you click the
<strong>
 Render
</strong>
button a document will be generated that includes both content and the output of embedded code.

A parametric quantile beta regression for modeling case fatality rates of COVID-19. Building models using the betareg and RBE3 packages

RQ: What exactly is the CLT and what’s the deal with n  30?   CLT - mathematical definition and simple terms definition  How we use CLT  How to know when empirical distribution converges to normal distribution? How did we come up with n $ $ 30?  Cohen (1991) Taught that at least 30 observations were needed to use critical-ratio approach used in t-tables when comparing groups.

“{Can I have the CLT, please– a straightforward explanation of the Central Limit Theorem and the magic (n )}”

JP's blog

::: {#quantile-beta-regression .section .level2}
## Quantile Beta Regression {.anchored anchor-id="quantile-beta-regression"}

The quantile beta regression model discussed in the 2021 paper by
:::

::: {#running-code .section .level2}
## Running Code {.anchored anchor-id="running-code"}

When you click the **Render** button a document will be generated that
includes both content and the output of embedded code. You can embed
code like this:

::: {.cell layout-align="center"}
::: {#cb1 .sourceCode style="background: #f1f3f5;"}
``` {.downlit .sourceCode .r .code-with-copy}
1 + 1
## [1] 2
```
:::
:::

You can add options to executable code like this

::: {.cell layout-align="center"}
    ## [1] 4
:::

The `echo: false` option disables the printing of code (only output is
displayed).
:::

::: {#quarto-appendix .default}
::: {#quarto-citation .section .quarto-appendix-contents}
## Citation {#citation .anchored .quarto-appendix-heading}

<div>

::: {.quarto-appendix-secondary-label}
BibTeX citation:
:::

``` {.sourceCode .code-with-copy .quarto-appendix-bibtex}
@online{monteagudo2024,
  author = {Monteagudo, JP},
  title = {A Parametric Quantile Beta Regression for Modeling Case
    Fatality Rates of {COVID-19.} {Building} Models Using the Betareg
    and {RBE3} Packages},
  date = {2024-02-26},
  url = {https://www.jpmonteagudo.com/blog/2024/02/betareg},
  doi = {10.59350/pe5s8-e0f47},
  langid = {en}
}
```

::: {.quarto-appendix-secondary-label}
For attribution, please cite this work as:
:::

::: {#ref-monteagudo2024 .csl-entry .quarto-appendix-citeas}
Monteagudo, JP. 2024. "A Parametric Quantile Beta Regression for
Modeling Case Fatality Rates of COVID-19. Building Models Using the
Betareg and RBE3 Packages." February 26, 2024.
<https://doi.org/10.59350/pe5s8-e0f47>.
:::

</div>
:::
:::
