 nievergeltlab of Simulations
7/11/2017 - 6:28 PM

## Demonstrate how intercept terms will adjust out bias in estimation from sampling method, in the context of methylation age analysis

Demonstrate how intercept terms will adjust out bias in estimation from sampling method, in the context of methylation age analysis

``````#Sample size
N=10000
#Simulate a population sample, ages ranging from 20-40
age1 <- runif(N,20,40)
#Where trauma follows an exponential distribution (most people have little trauma)
trauma1 <- rexp(N,rate=.5)

#Model methylation age as real age + random noise (3 years deviation on average) + Acceleration due to trauma (.1 years for every unit increase in trauma)
age1_dnam <- age1 + rnorm(N,0,3) + .5*trauma1

#Model methylation age as a function of age, get the residuals
age1_resids <- lm(age1_dnam ~ age1 )\$residual
summary(lm(age1_dnam ~ age1 ))\$coefficients[2,1] #Beta should be close to 1

#Association the residuals to trauma
summary(lm(age1_resids ~ trauma1))

##Traumatized sample

#Take a sample of data with the same ages
age2 <-  age1

#But who have been sampled on the upper range of the trauma distribution (Median or above)
trauma2 <- runif(N,median(trauma1),max(trauma1))

#Model acceleration in the same way
age2_dnam <- age2 + rnorm(N,0,3) + .5*trauma2

#Model methylation age as a function of age, get the residuals
age2_resids <- lm(age2_dnam ~ age2 )\$residual
summary(lm(age2_dnam ~ age2 ))\$coefficients[2,1] #Beta should be close to 1

#Association the residuals to trauma
summary(lm(age2_resids ~ trauma2))

pdf('age1plots.pdf',7,7)
plot(age1,age1_dnam)
plot(age2,age2_dnam)

dev.off()
``````