Recovering Marginal Effects and Standard Errors of Interactions Terms Pt. II: Implement and Visualize

March 9, 2012
By

(This article was first published on Frank Davenport's Blog on R, Statistics, and all Things Spatial - R, and kindly contributed to R-bloggers)

In the last post I presented a function for recovering marginal effects of interaction terms. Here we implement the function with simulated data and plot the results using ggplot2.  

 

 

#---Simulate Data and Fit a linear model with an interaction term
y<-rnorm(100,5,1)
x<-rnorm(100,5,1)
d<-data.frame(y=y,x=x,fac=sample(letters[1:3],100,replace=T))
 
mod<-lm(y~x*fac,data=d)
 
#========================================================
 
#---Extract the Main Effects, including the baseline, into a data.frame
dusp<-funinteff(mod,'x') #returns a data.frame of the Estimate and Standard Error, row.names correspond to the variables
 
#----Now Set the data up to visualize in ggplot-----
library(ggplot2)
#------Quick ggplot (move into graph code later)
#quick convenience function to compute significance at .95
funsig<-function(d){
	tstat<-abs(d$b/d$se)
	sig<-ifelse(tstat>=1.96,'yes','no')
	return(sig)
}
 
 
names(dusp)[1:2]<-c('b','se') #change the names to to make typing easier
 
#Add confidence intervals and signficance test
dusp$hi<-dusp$b+1.96*dusp$se
dusp$lo<-dusp$b-1.96*dusp$se
dusp$sig95<-funsig(dusp)
 
dusp$var<-row.names(dusp)
 
 
pd<-dusp
 
p1<-ggplot(data=pd,aes(x=var,y=b,shape=sig95))
p1<-p1+geom_hline(yintercept=0,col='grey')+geom_line()
p1<-p1+geom_pointrange(aes(ymin=lo,ymax=hi)) #+coord_flip() #uncomment coord_flip to switch the axes
p1<-p1+scale_y_continuous(name='Marginal Effect of Interaction Terms')

Created by Pretty R at inside-R.org

 

To leave a comment for the author, please follow the link and comment on their blog: Frank Davenport's Blog on R, Statistics, and all Things Spatial - R.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Tags: ,

Comments are closed.

Sponsors

Mango solutions



RStudio homepage



Zero Inflated Models and Generalized Linear Mixed Models with R

Dommino data lab

Quantide: statistical consulting and training



http://www.eoda.de







ODSC

ODSC

CRC R books series





Six Sigma Online Training





Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)