Gday, This is a repost since I only had one direct reply and I remain mystified- This may be stupidity on my part but it may not be so simple.
In brief, my problem is I'm not sure how to extract parameter values/effect sizes from a nonlinear regression model with a significant interaction term. My data sets are dose response curves (force and dose) for muscle that also have two treatments applied Treatment A (A- or A+) and Treatment B (B-/B+). A single muscle was used for each experiment - a full dose response curve and one treatment from the matrix A*B (A-/B-, A+/B-, A-/B+ and A+,B+). There are 8 replicates for each combination of treatments We fit a dose response curve to each experiment with parameters upper, ed50 and slope; we expect treatment A to change upper and ed50. We want to know if treatment B blocks the effect of treatment A and if so to what degree. This is similar to the Ludbrook example in Venables and Ripley, however they only had one treatment and I have two. my approach The dataframe is structured like this: expt treatA treatB dose force. 1 - - 0.1 20 1 - - 0.2 40 ... 4 + + 0.1 20 4 + I used a groupedData object: mydata=groupedData(force ~ dose | expt) I used an nlme obect to model the data as follows (pseudocode): myfit.nlme <- nlme(force ~ ss_tpl(dose, upper, ed50,slope), fixed=list(ed50~factor(treatA)*factor(treatB))) The function ss_tpl is a properly debugged and fully functional selfstarting three parameter logistic function that I wrote- no problem here. In my analysis I also included fixed terms for the other fit parameters; upper and slope, but my main problem is with the ed50 so that's all I've included here. Running an anova on the resulting object (anova(myfit.nlme) I found the A -/B- (control) to be significantly different from zero, treatment A was significantly different, treatment B had no significant effect and there was a significant interaction between treatment A and treatment B. The interaction term is likely to be real. The treatments are on sequential steps in a pathway and treatment B may be blocking the effect of treatment A, i.e. treatment B alone has no effect because it blocks a pathway that is not active, treatment A reduces force via this pathway and treament B therefore blocks the effect of treatment A when used together. From what I understand, please correct me if I'm wrong, the parameter estimates from summary(model.nlme) are not correct for main effects if a significant interaction is present. For example in my data treatment B alone has no signifcant effect in the anova but the interaction term A:B is significant. I believe The summary estimate for B is the estimate across all levels of A. What I want to do is pull out the estimate for B when A is not present. I suppose I can do it manually from the list of coefficients from nls or fit a oneway model with treatment levels A, B, AB. But I was kind of hoping there was some extractor function. The reason I need this is that the co-authors want to include a table of parameter values with std errs or confidence intervals ala: Treat upper ed50 slope A-/B- x x x <- shows value for comparison to control studies A+/B- x x x <-Shows A is working0 A-/B+ x x x <- Shows B has no effect alone A+/B + x x x <-shows B blocks A (not necessarily total) So back to my question,How do I extract estimates of the parameters from my model object for a specific combination of factors including the interaction term. i.e. what is the ed50 (and std err) for A-/B-, A+/B-, A-/B+, A+/B+ ? I think this is a fair question and one that many biomedical scientists would need. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html