similar to: Nonlinear regression question&In-Reply-To=6rya22mljx.fsf@franz.stat.wisc.edu

Hello, I have a problem with selecting the right type of sums of squares for an ANCOVA for my specific experimental data and hypotheses. I do have a basic understanding of the differences between Type-I, II, and III SSs, have read about the principle of marginality, and read Venable's "Exegeses on Linear Models" (http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf). I am pretty new to

Dear all,   Does some body have idea on how to extract Type III sum of Square from "lm" or "aov" function in R ? I could not figure out it.   If this is minor and irrelevant to post in this mail, I am sorry for that. Thanks.     Sincerely, Ram Kumar Basnet Wageningen University, Netherlands   [[alternative HTML version deleted]]

Hello all, I've become confused by the output produced by a call to anova(model1,model2). First a brief background. My model used to predict final tree height is summarised here: Df Sum Sq Mean Sq F value Pr(>F) Treatment 2 748.35 374.17 21.3096 7.123e-06 *** HeightInitial 1 0.31 0.31 0.0178 0.89519

I am using R to make two-way ANOVA on a number of variables using g <- aov(var ~ fact1*fact2) where var is a matrix containing the variables. However the outcome seem to be dependent on the order of fact1 and fact2 (i.e. fact2*fact1) gives a slightly (factor of 1.5) different result. Any ideas why this is? Thanks for any help Anders

I am running a two-way anova with Type III sums of squares and would like to be able to understand what the different SS mean when I use different contrasts, e.g. treatment contrasts vs helmert contrasts. I have read John Fox's "An R and S-Plus Companion to Applied Regression" approach -p. 140- suggesting that treatment contrasts do not usually result in meaningful results with Type

Dear R users I recently migrated from Statistica/SigmaPlot (Windows) to R (Linux), so please excuse if this may sound 'basic'. When running a nonlinear regression (V = Vmax * conc / (Ks + conc), i.e. Michaelis-Menten) on SigmaPlot, I get the output listed below: >>>Begin SigmaPlot Output<<< R = 0.94860969 Rsqr = 0.89986035 Adj Rsqr = 0.89458984 Standard Error of

Hello. Please excuse this off-topic request, but I know that the question has been debated in summary form on this list a number of times. I would find a paper that lays out the advantages and disadvantages of using different types of SS in the context of unbalanced data in ANOVA, regression and ANCOVA, especially including the use of different types of contrasts and the meaning of the

Dear useRs, I need to compute zero of polynomial function fitted by lm. For example if I fit cubic equation by fit=lm(y~x+I(x^2)+i(x^3)) I can do it simply by polyroot(fit$coefficients). But, if I fit polynomial of higher order and optimize it by stepAIC, I get of course some coefficients removed. Then, if i have model y ~ I(x^2) + I(x^4) i cannot call polyroot in such way, because there is

Dear helpeRs, I'm estimating a series of linear models (using lm) in which in every new model variables are added. I want to test to what degree the new variables can explain the effects of the variables already present in the models. In order to do that, I simply observe wether these effects decrease in strength and / or lose their significance. My question is: does any of you know

Dear R Gurus: I vaguely remember reading that if interaction was present in a factorial design, then the main effect results were suspect. However, I was reading a text which now uses the tests for main effects even if interaction is present. Which is correct, please? Thanks, Edna Bell

Hello R-users, I am trying to obtain Type III SS for an ANOVA with subsampling. My design is slightly unbalanced with either 3 or 4 subsamples per replicate. The basic aov model would be: fit <- aov(y~x+Error(subsample)) But this gives Type I SS and not Type III. But, using the drop() option: drop1(fit, test="F") I get an error message: "Error in

I am trying to use R lm() with quantitative and qualitative predictors, but am getting different results than those that I get in SAS. In the R ANOVA table documentation I see that "Type-II tests corresponds to the tests produced by SAS for analysis-of-variance models, where all of the predictors are factors, but not more generally (i.e., when there are quantitative predictors)." Is

Dear all I have found that the two "equivalent" commands do not produce the same results. 1. (I wrote this command by hand, this is what I would do usually) >summary(aov(eduyrs ~ cntry * edf, data=ESS1)) Df Sum Sq Mean Sq F value Pr(>F) cntry 1 257 256.65 21.2251 4.243e-06 *** edf 4 11010 2752.42 227.6296 <

Dear all, It appears that MASS::polr() and Design::lrm() return the same point estimates but different st.errs when fitting proportional odds models, grade<-c(4,4,2,4,3,2,3,1,3,3,2,2,3,3,2,4,2,4,5,2,1,4,1,2,5,3,4,2,2,1) score<-c(525,533,545,582,581,576,572,609,559,543,576,525,574,582,574,471,595, 557,557,584,599,517,649,584,463,591,488,563,553,549) library(MASS) library(Design)

Hi, When doing a two-ways anova in R and comparing my same results with an SPSS output, I noticed that R calculated type I Sum of Squares. Is it possible to use Type III Sum of Squares? Thanks, S?bastien Plante Institut des Sciences de la Mer de Rimouski (ISMER) -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

(This time with the r-help in the recipients...) Be careful when mixing lme4 and lmerTest together -- lmerTest extends and changes the behavior of various lme4 functions. >From the help page for lme4-anova (?lme4::anova.merMod) > ?anova?: returns the sequential decomposition of the contributions > of fixed-effects terms or, for multiple arguments, model >

I think a good try is update the new server to latest dovecot version. I migrate about 1.5M mailboxes from dovecot 1.0.X to Dovecot 2.0.X and no problems (and we have a lot of users with the "leave copy on server" option checked). http://wiki2.dovecot.org/Migration On Thu, Apr 28, 2011 at 9:41 AM, Pete Conkin <pete at reach.net> wrote: > ----- Original Message ----- From:

Please reread my point #1: the tests of the (individual) coefficients in the model summary are not the same as the ANOVA tests. There is a certain correspondence between the two (i.e. between the coding of your categorical variables and the type of sum of squares; and for a model with a single predictor, F=t^2), but they are not the same in general. The t-test in the model coefficients is simply

Dear all, I have a model with simple terms, quadratic effects, and interactions. I am wondering what to do when a variable is involved in a significant interaction and in a non-significant quadratic effect. Here is an example d = data.frame(a=runif(20), b=runif(20)) d$y = d$a + d$b^2 So I create both an simple effect of a and a quadratic effect of b. m = lm(y ~ a + b + I(a^2) + I(b^2) +

Hi I am performing an analysis of variance with two factors, each with two levels. I have differing numbers of observations in each of the four combinations, but all four combinations *are* present (2 of the factor combinations have 3 observations, 1 has 4 and 1 has 5) I have used both anova(aov(...)) and anova(lm(...)) in R and it gave the same result - as expected. I then plugged this into