Predict.lm not working for matrix data

Gist demonstrating an R error

This example demonstrates a subtle ggplot2 error that occurs when mapping the `size` aesthetic in a scatter plot while also adding multiple `geom_path` layers for circles. The code uses the `ggplot()` function with `aes(x, y, size = s)` and `geom_point()`, then attempts to overlay concentric circles via `geom_path()` using a helper function `make.circle()`. The error arises only in `make.plot.1()`, which combines both the size mapping and the circle paths, while `make.plot.2()` (no size mapping) and `make.plot.3()` (no circles) run successfully, isolating the conflict between these features.

LLeeMendelowitz

52% match

excel_function_BW

The visualization defines a logistic regression prediction function via `pred()` and renders it as a full-screen D3 SVG canvas. The function weights nine input variables (A2 through O2) with polynomial terms, interactions, and logarithms to compute a probability score. It uses D3.js to set up the scalable vector graphics container, though no data is loaded or drawn. The code itself—a dense Excel-style formula exported to JavaScript—illustrates the model’s coefficients but does not execute or display any actual data points, predictions, or charts.

47% match

tut8

This example shows a machine-learning workflow for predicting Brexit referendum outcomes, comparing predicted versus actual Leave votes in a test dataset. It uses R to fit a final classification model, then applies the `predict` function to generate class predictions on held-out test data. The visualization or summary aspect centers on computing `base_accuracy`, the mean agreement between predicted and observed `Percent.Leave` values. The code relies on base R modeling and prediction APIs, with no plotting libraries, instead focusing on quantitative accuracy assessment rather than graphical output.

46% match

Assignment A

This example evaluates potential predictors of baseball player salaries by computing r-squared values through linear regression. It uses pandas for data management, adding derived features like age, experience, and experience/age, and scipy’s `stats.linregress` to test each variable. The data comes from a baseball CSV file, and for physical attributes it renders scatter plots with the fitted regression line using matplotlib. The code prints r-squared results for both physical and skill-based variables, highlighting experience/age as the strongest predictor.

RRicCheng

45% match

tut13

This chart displays the relative importance of predictor variables in a linear model predicting ozone readings, using a horizontal dot plot with labels. The code loads the `relaimpo` package, fits a linear model with `lm(ozone_reading ~ ., data = Ozone)`, and calculates scaled importance metrics via `calc.relimp(..., type="lmg")`. The result is sorted and rendered with `ggplot2`, using `geom_point`, `geom_text` with a label nudge, `coord_flip`, and `theme_minimal` to show each variable’s contribution on a 0–0.3 scale.

Tut2

This example demonstrates how to train a decision tree classification model using the R `rpart` package. The code defines a model that predicts a target `Column_of_interest` from all other variables in a user-supplied dataframe, using the `method = "class"` setting for categorical outcomes. It also incorporates a parameter for an optimal complexity parameter (`cp`) to control tree pruning, which is passed through `rpart.control`. The snippet does not include any data or visualization, serving instead as a template for building the underlying statistical model.

Matrix Scatterplots in Vega-Lite API 1

This example displays a small matrix of scatterplots comparing horsepower and displacement against fuel efficiency, with points colored by country of origin. Built with the Vega-Lite API, it uses the `repeat` operator to generate plots from a specification, encoding quantitative axes and a nominal color field. The visualization loads automobile data from the UCI Machine Learning Repository via a CSV fetched with d3’s `csv` function, and renders as SVG with custom axis styles and tooltips provided by Vega-Tooltip.

OOleJRask

Many plots. Part 2

This gallery example presents a small-multiple scatter plot matrix exploring eye-tracking data from a WPI MAPLE Lab study of 86 students solving math problems. Each scatter plot shows a different participant’s data, plotting problem order on the x-axis against a selected metric (average fixation duration, no-fixation percentage, or response time) on the y-axis, with points colored by correctness. A dropdown menu switches the y-axis variable, while a legend filters points by hover. The visualization displays the raw data with linear trend lines, built using d3.v7’s scaleLinear and scaleOrdinal APIs, and fetches the dataset from a gist.

AAlena Egorova