Thanks for all help so far! And I seems as you are correct Peter (and Jean too). And I have now investigated and found how it is connected with the standard errors:
If use the following code (taking from Jeans example code), where we have one manova and two individual models (continue read comments in code): # ------- code starts -------- # mydata <- data.frame(y1=rnorm(50), y2=rnorm(50), x1=rnorm(50), x2=rnorm(50), x3=rnorm(50)) myfit <- manova(cbind(y1, y2) ~ x1 + x2 + x3, data=mydata) myfit1 <- lm(y1 ~ x1 + x2 + x3, data=mydata) myfit2 <- lm(y2 ~ x1 + x2 + x3, data=mydata) # And then gets the standard error for each of the three models: stderr <- predict.lm(myfit, type="response", se.fit=TRUE)[[2]] stderr1 <- predict.lm(myfit1, type="response", se.fit=TRUE)[[2]] stderr2 <- predict.lm(myfit2, type="response", se.fit=TRUE)[[2]] # Will we get that stderr = sqrt(stderr1^2+stderr2^2) print(cbind(stderr,sqrt(stderr1^2 + stderr2^2))) # ------- code ends -------- # So, the reason why the output only gave one standard error when I was writing: “But if I type: predict.lm(myfit, type="response", se.fit=TRUE) I get the predicted values and standard deviation, but only for y1 (and nothing from y2...). “ was because the output gave the combined error for both y1 and y2 by using the following formula: sqrt(stderr1^2 + stderr2^2). So it wasn't standard error for just y1 as I thought. You can test the provided code above and see that relationship is as described. So then my problem is solved as I can get the individual standard error for y1 and y2. Even if I don’t understand how you could/should use the combined standard error you get from the manova model (myfit)… //BF Mattias Siljestam Uppsala University -- View this message in context: http://r.789695.n4.nabble.com/Standard-deviation-from-MANOVA-tp4641322p4641622.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
