# Regression Diagnostic Plots using R and Plotly

December 25, 2015
By

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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

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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