Assumption Checking – Part I

July 12, 2014
By

(This article was first published on Bearded Analytics » R, and kindly contributed to R-bloggers)

Often when working, we are under deadlines to produce results in a reasonable timeframe. Sometimes an analyst may not check his assumptions if he is under a tight deadline. A simple example to illustrate this would be a one sample t-test. You might need to test your sample to see if the mean is different from a specific number. One assumption of a t-test that is often overlooked, is that the sample needs be drawn randomly from the population and the population is suppose to follow a Gaussian distribution. When is the last time in the workplace that you heard of someone performing a normality test before running a t-test? It is considered an extra step that is not usually taken. It should really not be considered a burden and can easily be accomplished with a wrapper function in R.

mytest <- function(x, value=0) {
xx <- as.character(substitute(x))
if(!is.numeric(x)) stop(sprintf('%s is not numeric', xx))
if(shapiro.test(x)$p.value>.10){
print(t.test(x, mu=value))
}else{
print(wilcox.test(x, mu=value))
}}

We can combine that with another function to produce a density plot.

myplot <- function(x,color="blue"){
xx <- as.character(substitute(x))
if(!is.numeric(x)) stop(sprintf('%s is not numeric', xx))
title <- paste("Density Plot","n","Dataset = ",deparse(substitute(x)))
mydens <- density(x)
plot(mydens,main=title,las=1)
polygon(mydens,col=color)
}

Now, let’s see how our functions work.  If we generate some random values from a Gaussian distribution, we would expect it to “normally” pass a normality test and a t-test to be performed. However, if we had data that was generated from another distribution that is not ‘normal’, than typically we would expect to see the results from the Wilcox test.

set.seed(123)
n <- 1000
normal <- rnorm(n,0,1)
chisq <- rchisq(n,df=5)

mytest(normal)
myplot(normal)

#Test for difference from 5 for chi-square data
mytest(chisq,value=5)
myplot(chisq ,color="orange")

Density Plots

Results from ‘mytest(normal)’:

One Sample t-test
data: x
t = 0.5143, df = 999, p-value = 0.6072
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.04541145 0.07766719
sample estimates:
mean of x
0.01612787

Results from ‘mytest(chisq,value=5)’:

Wilcoxon signed rank test with continuity correction

data: x
V = 214385, p-value = 8.644e-05
alternative hypothesis: true location is not equal to 5

Conclusion
The benefit of working ahead can be seen. Once you have these functions written you can add them to your personal R package that you host on github. Then you will be able to use them whenever you have an internet connection and the whole R community has the chance to benefit. Also, it is easy to combine these two functions into one.

 

#Combine the functions
PlotAndTest <- function(x){
mytest(x)
myplot(x)
}

 


To leave a comment for the author, please follow the link and comment on their blog: Bearded Analytics » 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...

Comments are closed.

Sponsors

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)