On Mon, Apr 17, 2006 at 02:29:16PM -0600, Tobah Gass wrote: > Hello, helpeRs, > > I recently used a linear mixed effects model followed by ANOVA to > assess the relationship between a categorical predictor variable with 2 > levels (and random effects) and a numeric response variable. As I was > concerned about the lack of a power analysis prior to data collection, > it was suggested that I use an equivalence test to complement the > conventional hypothesis test. Using "equiv.boot" in > package "equivalence", I get an "NA" response for the test of the > intercept ($rs.b0 and $Test.b0) and the following warnings after the > last line of output: > > Warning messages: > 1: argument is not numeric or logical: returning NA in: mean.default(x, > na.rm = TRUE) > 2: argument is not numeric or logical: returning NA in: mean.default(x, > na.rm = TRUE) > > "equiv.p" produced the following: > > Error in lm.fit(x, y, offset = offset, singular.ok = > singular.ok, ...) : > 0 (non-NA) cases > In addition: Warning messages: > 1: argument is not numeric or logical: returning NA in: mean.default(x, > na.rm = TRUE) > 2: "-" not meaningful for factors in: Ops.factor(x, mean(x, na.rm = > TRUE)) > > I received the same warnings and errors using the simple simulation: > > low <- rnorm(50, 100, 25) > high <- rnorm(50, 300, 75) > ages = c(low,high) > levels = 1:100 > levels[1:50] = "L" > levels[51:100] = "H" > test = data.frame(ages, levels) > class(test$levels) > equiv.boot(test$levels, test$ages) > equiv.p(test$levels, test$ages)
You are being caught out by infelicitious documentation. To do an equivalence test using the equiv.boot function as I intended it, you need model predictions and independent observations (i.e. observations that are not those with which the model was fit). Report your model predictions in the "x" argument, and the observations in the "y" argument. equiv.boot(x=predictions, y=observations) The documentation is written from the point of view of the application of a particular test, essentially trying to use predictions as an explanatory variable for observations as a response variable. The wording is unclear. I will make fixing that a priority. > Can the equivalence package, or parts thereof, handle categorical > predictor variables of 2 or more levels? Yes, used as above. > Can the package, or the package plus another function, be used to > test the similarity of the predictions produced by two or more > factor levels from nested or otherwise correlated data? Not as it stands, but it can probably be modified. This is not a high priority for me. > Which of the equivalence functions accomodates unbalanced designs? > Using functions such as tost.data to compare observations associated > with the 2 predictors in my data is complicated both by the > unbalanced design and the lack of accomodation of the nested design. As far as I can tell, all of them accommodate unbalanced designs, but not nested designs. I don't see any reason that an unbalanced design wouldn't work, but I haven't thought about it deeply. > Note that I am running R 2.0.1 which predates the version in > which "equivalence" was developed. You should make sure that you upgrade before posting questions to the list, as it may well be that the problem exists only with early versions. It does not, in this case. > Thank you in advance. > > Toby Andrew -- Andrew Robinson Department of Mathematics and Statistics Tel: +61-3-8344-9763 University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599 Email: [EMAIL PROTECTED] http://www.ms.unimelb.edu.au ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
