(Sorry if this is a repost, I got a bounce reply from the r-help server)
Hi, Im using the biglm() function to create some linear models for a very large data set than lm() cant fit due to memory issues (the problem is with the number of interactions, I can fit the main effects model) I need to determine if the 2-way interactions are necessary or not. Ideally Id like to use anova() to get an anova table and a p-value for the interactions, however it appears that anova is not supported for biglm objects. So my next idea was to compare the main effects model with the 2-way interaction model using a likelihood ratio test. I seem to be able to get the deviance and residual DF from a biglm object, so I think I should be able to calculate the LRT and get my p-value if I assume a chi-squared distribution. I was wondering if anyone sees any problems with this approach (or would be kind enough to confirm it)? Or has any better suggestions, ideas or comments? Thankyou Chris Howden B.Sc. (Hons) GStat. Founding Partner Evidence Based Strategic Development, IP Commercialisation and Innovation, Data Analysis, Modelling and Training (mobile) 0410 689 945 (fax) +612 4782 9023 ch...@trickysolutions.com.au [[alternative HTML version deleted]]
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