diff --git a/index.html b/index.html index 3a0e3be..e8ddbed 100644 --- a/index.html +++ b/index.html @@ -79,8 +79,8 @@ # 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 @@ -236,7 +236,7 @@ <img src="./figures/linear_proj_wide.png" width="100%" style="display: block; margin: auto;" /> <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> -- @@ -252,7 +252,7 @@ <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" @@ -371,7 +371,7 @@ </tr> <tr> <td style="text-align:left;"> {spinifex} </td> - <td style="text-align:left;"> Manual tours, basis function, display with {ggplot2} to {plotly}/{gganimate} animations </td> + <td style="text-align:left;"> Manual tours, basis function, display with ggplot2 to plotly/gganimate animations </td> <td style="text-align:left;"> Spyrison &amp; Cook, 2020 </td> </tr> <tr> @@ -457,8 +457,8 @@ # Global View -
- +
+ - Select a primary and comparison point, typically misclassified and neighboring correctly classified - Use the SHAP values of the primary point as the basis, perform a 1D radial (manual) tour to interrogate the models explanation diff --git a/index.rmd b/index.rmd index 54d1b43..9fcb408 100644 --- a/index.rmd +++ b/index.rmd @@ -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 @@ -285,7 +285,7 @@ knitr::include_graphics( ```
-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)
-- @@ -301,7 +301,7 @@ __Caveat:__

1) Scale each variable to [0, 1] or by standard deviations away from the mean
-2) Some people 'Whiten' or 'sphere' transform the covariance matrix to an identity matrix; should be justified
+2) Some people 'whiten' or 'sphere' transform the covariance matrix to an identity matrix; should be justified
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
- 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" @@ -432,7 +432,7 @@ manual_tour() %>% ```{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") )) ```