Library ( reshape2 ) library ( tidyverse ) library ( tidymodels ) library ( plotly ) library ( kernlab ) library ( pracma ) #For meshgrid() data ( iris ) mesh_size % select ( Sepal.Width, Sepal.Length ) y % select ( Petal.Width ) model % set_engine ( 'kernlab' ) %>% set_mode ( 'regression' ) %>% fit ( Petal.Width ~ Sepal.Width + Sepal.Length, data = iris ) x_min % predict ( final ) pred % add_markers ( size = 5 ) %>% add_surface ( x = xrange, y = yrange, z = pred, alpha = 0.65, type = 'mesh3d', name = 'pred_surface' ) fig Library ( reshape2 ) library ( tidyverse ) library ( tidymodels ) library ( plotly ) library ( kknn ) data ( tips ) y % set_engine ( 'kknn' ) %>% set_mode ( 'regression' ) %>% fit ( tip ~ total_bill, data = tips ) # Model #2 knn_uni % set_engine ( 'kknn' ) %>% set_mode ( 'regression' ) %>% fit ( tip ~ total_bill, data = tips ) x_range % predict ( xdf ) y_uni % predict ( xdf ) colnames ( y_dist ) % add_trace ( data = tips, x = ~ total_bill, y = ~ tip, type = 'scatter', mode = 'markers', color = ~ sex, alpha = 0.65 ) %>% add_trace ( data = xy, x = ~ total_bill, y = ~ dist, name = 'Weights: Distance', mode = 'lines', alpha = 1 ) %>% add_trace ( data = xy, x = ~ total_bill, y = ~ uni, name = 'Weights: Uniform', mode = 'lines', alpha = 1 ) fig Library ( reshape2 ) library ( tidyverse ) library ( tidymodels ) library ( plotly ) data ( tips ) y % training () tips_test % testing () lm_model % set_engine ( 'lm' ) %>% set_mode ( 'regression' ) %>% fit ( tip ~ total_bill, data = tips_training ) x_range % predict ( xdf ) colnames ( ydf ) % add_trace ( data = tips_test, x = ~ total_bill, y = ~ tip, type = 'scatter', name = 'test', mode = 'markers', alpha = 0.65 ) %>% add_trace ( data = xy, x = ~ total_bill, y = ~ tip, name = 'prediction', mode = 'lines', alpha = 1 ) fig We will be using the Linear Regression, which is a simple model that fits an intercept (the mean tip received by a server), and adds a slope for each feature we use, such as the value of the total bill. In this section, we show you how to apply a simple regression model for predicting tips a server will receive based on various client attributes (such as sex, time of the week, and whether they are a smoker). It is the next-gen version of the popular caret library for R. Tidymodels is a popular Machine Learning (ML) library in R that is compatible with the 'tidyverse' concepts, and offers various tools for creating and training ML algorithms, feature engineering, data cleaning, and evaluating and testing models. We will use tidymodels to split and preprocess our data and train various regression models. We highlight various capabilities of plotly, such as comparative analysis of the same model with different parameters, displaying Latex, and surface plots for 3D data. This page shows how to use Plotly charts for displaying various types of regression models, starting from simple models like Linear Regression and progressively move towards models like Decision Tree and Polynomial Features. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Plotly is a free and open-source graphing library for R. Visualize regression in Tidymodels with Plotly

Plotly line graph r regression