# Regression Diagnostic Plots using R and Plotly

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Plotly is a platform for making, editing, and sharing customizable and interactive graphs. Embedding Plotly graphs in a R-Markdown document is very easy. Here, we will genarate a R-Markdown document with embedded Plotly charts to visualize regression diagnostic plots similar to the ones generated by using `plot()`

on a fitted `lm()`

object.

### R-Studio

First step is to install R-Studio. R-Studio makes it very easy to write R-Markdown documents.

### Install Plotly

Run the following command(s) in the console

# Not run # install.packages("plotly") # install.packages("MASS") library(plotly)

### Start a new R-Markdown document

For an introduction to R-Markdown visit http://rmarkdown.rstudio.com/

### Function to generate plots

The following code snippet creates a function that accepts a fitted `lm()`

object and returns plotly charts. Paste the following code snippet(s) as chunks in the R-Markdown document as shown.

RegressionPlots <- function(fit){ # Extract fitted values from lm() object Fitted.Values <- fitted(fit) # Extract residuals from lm() object Residuals <- resid(fit) # Extract standardized residuals from lm() object Standardized.Residuals <- MASS::stdres(fit) # Extract fitted values for lm() object Theoretical.Quantiles <- qqnorm(Residuals, plot.it = F)$x # Square root of abs(residuals) Root.Residuals <- sqrt(abs(Standardized.Residuals)) # Calculate Leverage Leverage <- lm.influence(fit)$hat # Create data frame # Will be used as input to plot_ly regMat <- data.frame(Fitted.Values, Residuals, Standardized.Residuals, Theoretical.Quantiles, Root.Residuals, Leverage) # Plot using Plotly # Fitted vs Residuals # For scatter plot smoother LOESS1 <- loess.smooth(Fitted.Values, Residuals) plt1 <- regMat %>% plot_ly(x = Fitted.Values, y = Residuals, type = "scatter", mode = "markers", hoverinfo = "x+y", name = "Data", marker = list(size = 10, opacity = 0.5), showlegend = F) %>% add_trace(x = LOESS1$x, y = LOESS1$y, type = "scatter", mode = "line", name = "Smooth", line = list(width = 2)) %>% layout(title = "Residuals vs Fitted Values", plot_bgcolor = "#e6e6e6", width = 1000) # QQ Pot plt2 <- regMat %>% plot_ly(x = Theoretical.Quantiles, y = Standardized.Residuals, type = "scatter", mode = "markers", hoverinfo = "x+y", name = "Data", marker = list(size = 10, opacity = 0.5), showlegend = F) %>% add_trace(x = Theoretical.Quantiles, y = Theoretical.Quantiles, type = "scatter", mode = "line", name = "", line = list(width = 2)) %>% layout(title = "Q-Q Plot", plot_bgcolor = "#e6e6e6") # Scale Location # For scatter plot smoother LOESS2 <- loess.smooth(Fitted.Values, Root.Residuals) plt3 <- regMat %>% plot_ly(x = Fitted.Values, y = Root.Residuals, type = "scatter", mode = "markers", hoverinfo = "x+y", name = "Data", marker = list(size = 10, opacity = 0.5), showlegend = F) %>% add_trace(x = LOESS2$x, y = LOESS2$y, type = "scatter", mode = "line", name = "Smooth", line = list(width = 2)) %>% layout(title = "Scale Location", plot_bgcolor = "#e6e6e6", width = 1000) # Residuals vs Leverage # For scatter plot smoother LOESS3 <- loess.smooth(Leverage, Residuals) plt4 <- regMat %>% plot_ly(x = Leverage, y = Residuals, type = "scatter", mode = "markers", hoverinfo = "x+y", name = "Data", marker = list(size = 10, opacity = 0.5), showlegend = F) %>% add_trace(x = LOESS3$x, y = LOESS3$y, type = "scatter", mode = "line", name = "Smooth", line = list(width = 2)) %>% layout(title = "Leverage vs Residuals", plot_bgcolor = "#e6e6e6") plt = list(plt1, plt2, plt3, plt4) return(plt) }

### Create regression model

Let’s create a sample regression model using the `mtcars`

dataset.

fit = lm(mpg ~ cyl + wt + disp, data = mtcars)

### Call function

We can simply pass the fitted lm object to the above function.

plt = RegressionPlots(fit)

### Plot

Note that the function returns a list of four plots. Unlike `par(mfrow = c())`

we’ll use `<div>`

tags to arrange the four plots in a matrix like layout of 2 rows and 2 columns. Use the `fig.width`

and `fig.height`

chunk options to set appropriate plot width and height. Simply use double square brackets `[[]]`

to refer to each figure and print the same. Or you could use `print()`

. See image below.

The output should look like this:

### Further Resources

This post hopefully helps you get started with Ploty and using it in R-Markdown documents. Refer to Plotly’s Figure Reference for more details.

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