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minor touch ups.
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nspyrison committed Dec 6, 2021
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14 changes: 7 additions & 7 deletions index.html

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10 changes: 5 additions & 5 deletions index.rmd
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Expand Up @@ -155,8 +155,8 @@ knitr::include_graphics("./figures/munzner_datatypes.PNG")

# Example data -- Palmer penguins

- Penguins near Palmer Station, Antarctica
- 330 Observations
- penguins near Palmer Station, Antarctica
- 330 observations
- X variables: 4 physical measurements
- species of penguin mapped to color & shape

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```

<br>
Using linear combinations of variables we can find bases (orientation) that separate clusters the most (OLD)
Using linear combinations of variables we can find bases (orientation) that separate clusters the most (oLDA)
<br>

--
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<br><br>
1) Scale each variable to [0, 1] or by standard deviations away from the mean<br>
2) Some people 'Whiten' or 'sphere' transform the covariance matrix to an identity matrix; should be justified<br>
2) Some people 'whiten' or 'sphere' transform the covariance matrix to an identity matrix; should be justified<br>
3) If $p$ is sizable, say more than 10 or, may do an initial round of dimension reduction to get to a realistic view-space<br>
- Typically with PCA by eyeballing an elbow in a scree plot
- "We approximate 90% of the variation of our 20 variable in the first 5 principal components"
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```{r, results='markup'}
kableExtra::kbl(data.frame(
Package = c("{tourr}", "{spinifex}", "{ferrn}"),
Description = c("Tour paths, geodesic interpolation, display in *interactive* base R", "Manual tours, basis function, display with {ggplot2} to {plotly}/{gganimate} animations", "Diagnostic plots for projection pursuit (guided tour), tracing basis-paths"),
Description = c("Tour paths, geodesic interpolation, display in *interactive* base R", "Manual tours, basis function, display with ggplot2 to plotly/gganimate animations", "Diagnostic plots for projection pursuit (guided tour), tracing basis-paths"),
Authors = c("Wickham et al., 2011", "Spyrison & Cook, 2020", "Zhang et al., 2021")
))
```
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