In montilab/BS831: Boston University - Genomics Data Mining

This is the code for the plot shown in slide #48 of BS831_class03_ComparativeExperimentLM.Rmd ("Differential Analysis as linear regression (LM)").

knitr::opts_chunk$set(message=FALSE, warning=FALSE)

set.seed(159) # for reproducible results 
nobs <- 10000 # sample size
beta0 <- 5    # mean in class 0
beta1 <- 1.5  # beta0 + this is mean in class 1
X <- sample(0:1,nobs,replace=TRUE)
Y <- rnorm(nobs,mean=beta0 + beta1 * X,sd=1)

## or, equivalently
## Y <- beta0 + beta1 * X + rnorm(nobs,mean=0,sd=1)

par(mar=c(c(5, 4, 4, 5) + 0.1))
boxplot(Y~X,ylab="Y",pch="-",names=paste("X =",0:1),col="antiquewhite")
abline(h=c(beta0=beta0,'beta0+beta1'=beta0+beta1),col="red",lty=3,lwd=2)

## notice the use of 'expression' to display mathematical symbols
axis(side=4,at=c(beta0,beta0+beta1),labels=expression(beta[0],beta[0]+beta[1]),las=1)

We now fit a linear model to the generated data by lm.

LM <- lm(Y ~ X)
print(summary(LM))

As you can see, the estimates are quite close to the generating parameters.

montilab/BS831 documentation built on April 17, 2024, 4:51 p.m.