Dear helpeRs,

I'm estimating a series of linear models (using lm) in which in every  
new model variables are added. I want to test to what degree the new  
variables can explain the effects of the variables already present in  
the models. In order to do that, I simply observe wether these  
effects decrease in strength and / or lose their significance.

My question is: does any of you know a package / function in R that  
can test whether these changes in effects between models are  
significant? I figure these effects follow a T-distribution and I  
know the std. devs., so it must be easy to do manually. But I would  
like not to invent the wheel, when the function is already present.

Below is an example of what I mean. In model2, the variable z is  
added, which is hypothesized to partly explain the effect of x.  
Indeed, the effect of x decreases in model2, compared to model1. What  
I want to find out, is if this decrease is statistically significant.

Many thanks,

Rense


x <- c(1,1,1,1,1,2,2,2,2,2,3,4,4,4,5)
z <- c(2,2,2,2,2,2,2,2,3,3,3,3,4,4,5)
y <- c(1,2,2,2,3,3,3,3,4,4,4,5,5,5,5)

model1 <- lm(y~x)
model2 <- lm(y~x+z)



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