Thank you Michael for your suggestions. I will try them out and try to understand their interpretations.
Regards Virendra Sent from Outlook Mobile<https://aka.ms/blhgte> On Mon, Feb 22, 2016 at 5:39 AM -0800, "Michael Friendly" <frien...@yorku.ca<mailto:frien...@yorku.ca>> wrote: Hi Vivendra A few suggestions: * You will get more interpretable tests by using Type II (partial) tests of terms in your model via library(car) Manova(MRI_model) as opposed to the Type I (sequential) tests available from manova() * You will be able to understand the results better by making heplots via library(helplots) heplot(MRI_model) but you will have to read the associated vignettes to learn how to interpret them. * You can test for equality of covariance matrices in the various groups using heplots::boxM(), new in the development version on R-Forge install.packages("heplots", repos="http://R-Forge.R-project.org";) library(helplots) res <- boxM(MRI_model, group=group) res plot(res) * You can visually assess the correlations in the groups using car::scatterplot(..., ellipse=TRUE, groups=) hope this is helpful, -Michael On 2/20/2016 12:53 PM, Virendra Mishra wrote: > Hi R-users, > > I have a fairly simple question to ask but I havent yet got an answer to > the question. I will describe my experiment, analysis and what have I done > and what is the question in the following paragraphs and I would appreciate > if anyone could point me to use right statistical tools to answer my > question. > > Experiment: > I have 2 groups and both groups undergo 2 set of evaluations, one with MRI > scanner and the other in the lab to test for their behavior. Both these > evaluations are known to have statistically significant relationship with > age and gender. > > Statistical question of interest: > Whether there is: > 1) statistically significant difference between the 2 groups on each > evaluation ? > 2) Whether there is any relationship between and within the 2 groups > between each evaluation > > Model: > > I model the problem as following: > MRI_measure = Intercept + Slope1 * Age + Slope 2 * Gender + Slope3 * Group > [Age is continuous and gender , Group are factors/categorical] > > Lab_measure = Intercept + Slope1 * Age + Slope 2 * Gender + Slope3 * Group > [Age is continuous and gender , Group are factors/categorical] > > In order to obtain the solution in R: > MRI_model<-lm(cbind(MRI_measure, Lab_measure) ~ age+gender+group, > data=data) > > Result of R: > manova(MRI_model) suggests that yes indeed all the slopes are significantly > different than 0 suggesting a relationship between my measures. > > Question: > 1) In order to test whether the difference in the MRI_measure is > statistically significant different between the 2 groups, I use > MRI_model$fitted.values for each dependent measure and do a statistical > test (either t-test or Wilcox) and claim that the difference is > significant. > In the paper I write, multivariate multiple linear regression was performed > for the groups while controlling for age and gender. The regressed out > MRI_measure was statistically compared to see if the difference is > different. > > I am assuming that the predicted/fitted.values in model are the regressed > out variables. Can I show this and use this result? Is this right > > If no, what is the correct way to statistically compare whether my 2 groups > differ in their MRI measure and lab measure when controlled for age and > gender. Any R library, literature, possibly a script will be greatly > appreciated. > > 2) I also want to see if there is any relationship between MRI_measure and > Lab_measure within the group after they are controlled for age and gender. > What is the correct way to do this in R? > > Further, I also want to see if there is any significantly different > association between the 2 groups for my set of dependent variables. I am > thinking this can be done: I first find the correlation between 2 dependent > variable in each group and test if this correlation is statistically > different between the 2 groups? Is this logic right? And if it is, how do I > compare the correlation? If not, what is the right way to do this? Any R > library, literature, possibly a script will be greatly appreciated. > > I do appreciate any reply. > > Thanks > > Regards > > Virendra > > [[alternative HTML version deleted]] > ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
