I need to see if different fits of different data sets are significantly different, preferrably in R.

I have sample series A, B and C. These are different in length and all contain variables n, m, o which in turn depend on variables x, y, z...

Fitting these different series to assess how they behave as we change x, y, z is easy enough, but how can I check if a fit assessing series A variable n is significantly different from one concerning series B or C, variable n?

Or to put it differently:

fitA=lm(Formula=n ~ x + y + z,subset=Group=="A",data=mydata)
fitB=lm(Formula=n ~ x + y + z,subset=Group=="B",data=mydata)
fitC=lm(Formula=n ~ x + y + z,subset=Group=="C",data=mydata)

What can I use where I squeezed in sometest? Not necessarily using lm as the fitting method, but for the current data set it appears suitable, so we can assume it is for this question.

  • $\begingroup$ You refer to these as "series", are they time series data or cross-sectional? Is there any reason you can't fit a model that includes all 3? $\endgroup$ Jan 12, 2016 at 18:32
  • $\begingroup$ Cross sectional. I don't think fitting all three series into one function will help as I need to see if there are differences between the series. I need to see how samples in series A behave in response to changed x, y, z as compared to how samples in series B or C behaves in response to the same changes. The x, y, z variables are likely independent. My naïve assumption (I am no statistician. At all.) was to fit them using MLR or somesuch and compare the fits. $\endgroup$
    – The V
    Jan 12, 2016 at 20:21

1 Answer 1


If you're interested in comparing $R^2$ for different groups you can extract the $R^2$ using:

R_Sq_A <- summary(fitA)$r.squared
    R_Sq_B <- summary(fitB)$r.squared

These can then be compared using:

paired.r(sqrt(R_Sq_A), sqrt(R_Sq_B), NULL, 
         dim(mydata[which(Group=="A"),][1], dim(mydata[which(Group=="B"),][1])

You can replace the dim(mydata[which(Group=="A"),][1] with the size of Group A since you know what that is.

  • $\begingroup$ The OP's question doesn't seem to be about $R^2$, but if the coefficients associated with the variables change. $\endgroup$ Jan 12, 2016 at 20:37
  • $\begingroup$ It seems to be about how the collective change of 'x, y, and z' affects various outcome variables. That can be interpreted at R^2...maybe I'm misreading though. If it's about the different effect of, say, x on Outcome1 for the three different groups, the entire sample should be used with a categorical variable indicating group membership say G - this varaible should be included in the model with an interaction term G*x... $\endgroup$ Jan 12, 2016 at 20:51
  • $\begingroup$ This shows how to test of 'different slopes' between groups (in SPSS, but it's easily extended to R): stats.stackexchange.com/questions/141325/… $\endgroup$ Jan 12, 2016 at 20:53
  • $\begingroup$ R squared is not a definitive solution to my question, is it? I confess, it has been years since my last statistics course, so I freely admit to being fairly ignorant on this, but I recall that R2 has some pitfalls. I would expect a method for comparing different fits of the three situations would be more robust. $\endgroup$
    – The V
    Jan 13, 2016 at 13:52
  • $\begingroup$ It's not a definitive solution, no - just a quick and common one and one where 'significance' can be easily tested for - which was in your question; this is what my answer provides. Since you're fitting three different models, it looks like you're interested in how well x,y,z explain Outcome1 in Group 1 vs Group 2, say. R^2 will give you this. The 'pitfalls' of R^2 that you're remembering might have to do with comparing nested models: models with more independent variables will have a higher R^2...so adjusted R^2 is used, for instance. But that's not what the question said was of interest... $\endgroup$ Jan 13, 2016 at 17:22

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