projective output layouts and reduced interactive surfaces in R
**Features**:
* **Upload Dataset:** You can upload your own dataset in CSV format using the "Upload Dataset" button. The application will automatically validate the file and handle potential errors.
* **Example Datasets:** `polarisR` comes with two pre-loaded datasets: `four_clusters` and `pdfsense`. You can select any of these to explore the application's features without needing your own data.
```r
# Access the datasets directly
data(four_clusters, package = "polarisR")
data(pdfsense, package = "polarisR")
# View dataset information
?four_clusters
?pdfsense
```
**Dataset Descriptions:**
* **four_clusters**: A synthetic dataset with four distinct clusters, perfect for testing clustering visualization
* **pdfsense**: A high-energy physics dataset representing parton distribution function fits
**Additional Features:**
* **Column Selection:** After loading a dataset, you can choose which columns to include in the NLDR analysis. By default, all columns are selected. You can manually select or deselect columns and apply the changes.
* **Data Preview:** A table displays the first few rows of your dataset, allowing you to inspect the data and ensure it has been loaded correctly.
* **Dataset Information:** This section provides a summary of your dataset, including the number of rows, columns, and the types of columns (numeric or categorical).
* **NLDR Datasets:** As you run different NLDR analyses, the results will be stored and listed here. You can easily switch between different results to compare them.
## Non-linear dimension reduction (NLDR) Tab
The **Non-linear dimension reduction (NLDR)** tab is where the main NLDR analysis happens. You can choose between t-SNE and UMAP, configure their parameters, and visualize the results.
**Features**:
* **Choose Method:** Select either `t-SNE` or `UMAP` as your NLDR method.
* **t-SNE Parameters:**
* **Perplexity:** Adjust the perplexity value, which influences the number of nearest neighbors for each point.
* **Max Iterations:** Set the maximum number of iterations for the t-SNE algorithm.
* **Auto-adjust perplexity:** Let the application automatically choose a suitable perplexity value based on your data. The formula used is: `perplexity = max(5, min(30, floor(n_samples / 3) - 1))`, which ensures perplexity is between 5 and 30, and scales with dataset size to maintain effective neighborhood structure.
* **UMAP Parameters:**
* **Number of Neighbors:** Control the size of the local neighborhood UMAP will use.
* **Min. Distance:** Set the minimum distance between embedded points.
* **Color Options:** Choose a column from your dataset to color the points in the visualization. This is useful for identifying clusters or patterns.
* **Reproducibility Options:** Set a random seed to ensure that your NLDR results are reproducible.
* **Run Visualization:** Click this button to start the NLDR computation. The progress will be displayed, and the resulting visualization will be shown on the right.
* **Visualization Information:** This panel displays the parameters used for the current visualization, making it easy to track your experiments.
## Dynamic Tour Tab
The **Dynamic Tour** tab offers an interactive way to explore the high-dimensional space of your data. It provides a dynamic projection of the data, which can be viewed as a scatter plot, sage plot, or slice plot.
**Features**:
* **Select Tour Display:** Choose from three types of dynamic tours:
* **Scatter:** A standard scatter plot of the projected data.
* **Sage:** A scatter plot display that adjusts for the projected volume, defined in [Laa et al. (2021)](https://doi.org/10.1080/10618600.2021.1963264).
* **Slice:** A scatter plot display that highlights points close to the projection plane, defined in [Laa et al. (2020)](https://doi.org/10.1080/10618600.2020.1777140).
* **Tour Options:**
* **Show Axes:** Toggle the visibility of the axes in the tour plot.
* **Show Wireframe:** Toggle the visibility of wireframe edges in the tour plot for enhanced structural visualization.
* **Point Opacity (Alpha):** Adjust the transparency of the points.
* **Gamma (for Sage):** Control the effective dimensionality parameter for the sage plot.
* **Slice Relative Volume (for Slice):** Adjust the thickness of the slice.
* **Enable Linked Brushing:** When enabled, you can select points in the NLDR plot, and the corresponding points will be highlighted in the dynamic tour plot, and vice-versa. This is a powerful feature for exploring the relationship between the low-dimensional embedding and the original high-dimensional data.
## Diagnosing Tab
The **Diagnosing** tab provides tools to assess the selected NLDR layout. It uses the [`quollr` package] (https://github.com/JayaniLakshika/quollr) to perform a quantitative analysis of the NLDR layout and helps you to find the optimal binwidth for the model fitting.
**Features**:
* **Binwidth Optimization:** This feature automatically tests a range of bin widths for the `quollr` analysis and finds the optimal configuration based on the Root Mean Square Error (RMSE).
* **Run Quollr Analysis:** After optimizing the binwidth, you can run the full `quollr` analysis to get a detailed assessment of your embedding.
* **Analysis Results:** The results of the analysis are presented in several tabs:
* **RMSE vs Binwidth:** An interactive plot showing the RMSE for different binwidth values.
* **Model Summary:** A table with the detailed results of the binwidth optimization.
* **Model Error:** A visualization shows the NLDR layout, with points colored according to the high-dimensional model error.
* **Configuration & Summary:** This section displays the optimal binwidth configuration and a summary of the `quollr` model fit.
## 2-D Layout Comparison Tab
The **2-D Layout Comparison** tab allows you to compare the results of different NLDR layouts. You can compare different methods (t-SNE vs. UMAP) or the same method with different hyper-parameters.
**Features**:
* **Choose Comparison Type:**
* **NLDR Settings Comparison:** Compare the RMSE of different NLDR configurations. This is useful for finding the best method and hyper-parameters for your data.
* **Side-by-Side Visualization:** Display two NLDR plots next to each other for a direct visual comparison.
* **Enable Linked Brushing:** In the side-by-side visualization mode, you can enable linked brushing to select points in one plot and see the corresponding points highlighted in the other.
* **Dataset Selection:** Select the NLDR results you want to compare from the list of stored results.
* **Run Comparison Plot:** Generate the comparison plot based on your selection.
* **Best Configuration Summary:** When comparing NLDR settings, this section will show you the best configuration found based on the RMSE.