search for: sstype

Hello, It seems that, unlike Splus, the anova.lm function of R does not take the argument ssType=3 into account. How can one get an anova table with the adjusted sum of squares in R? Thanks in advance, Patrick Giraudoux University of Franche-Comté Department of Environmental Biology EA3184 af. INRA F-25030 Besançon Cedex tel.: +33 381 665 745 fax.: +33 381 665 797 http://lbe.univ-fcomte.f...

...ated measures anova's might know: aov( RT ~ color * shape * MyGroup + Error( Subjects/( color*shape) ) In spss you would have something like this (of course with the data organized slightly differently : GLM x1 x2 x3 x4 BY MyGroup /WSFACTOR = color 2 Polynomial shape 2 Polynomial /METHOD = SSTYPE(3) /CRITERIA = ALPHA(.05) /WSDESIGN = color shape color*shape /DESIGN = VAR00001 . Ok, the question is. If the group sizes are different (say 10 people in one group and 12 people in the other group) R is going to give the wrong answer. Of course that is not R's fault. BUT MY QUESTION IS:...

...r.test (WK1FREQ,WK2FREQ) I have also tried: (by (GROUP) cor.test (WK1FREQ,WK2FREQ)) (2) I also wanted to run the following analyses, written in SPSS syntax. I have searched the R archives and gather I may need to use lm instead of glm, but got lost. UNIANOVA avfreq BY sex WITH blik /METHOD = SSTYPE(3) /INTERCEPT = INCLUDE /EMMEANS = TABLES(OVERALL) /EMMEANS = TABLES(sex) /PRINT = DESCRIPTIVE ETASQ OPOWER HOMOGENEITY /CRITERIA = ALPHA(.05) /DESIGN = blik sex blik*sex . GLM avmth3fq avfreq BY SEX /WSFACTOR = timex 2 Polynomial /METHOD = SSTYPE (3) /PRINT = DESCRIPTIVE ETASQ TEST...

I'm an SPSS user trying to make the transition to R. Can someone help me translate the following SPSS code into R?: GLM Total_tp1 Total_tp2 WITH Age Sex   /WSFACTOR=Time 2 Repeated   /METHOD=SSTYPE(3)   /CRITERIA=ALPHA(.05)   /WSDESIGN= Time   /DESIGN= Age Sex Age*Sex. Also. can anyone recommend any resources to help SPSS users learn to things in R? Thanks, -kristi [[alternative HTML version deleted]]

When you use the 'general linear model' analysis in SPSS, the first result is a table with all terms with F-tests and significance values for all IV's. It uses http://joyx.joensuu.fi/~ek/anova/sstypes.txt SS Type III , which has the advantage that the order in which the variables are added to the model does not matter, and therefore it is relatively objective. I would like to reproduce this output in R. However, when using anova(glm.object, test="F"), the F test shows "Terms add...

...ckage. library(lme4) fit=lmer(y ~ time+dye+trt+trt:time + (1|rep), data=dataset, na.action='na.omit') anova(fit) The anova gives sequential F-tests and sequential SS. My question is: how I can get partial F-tests and partial SS? For lm (not lmer) anova(lm(y~x+z)) we can use anova(fit, ssType=3) but it is not work for lmer. Natalia.

Hello everybody, This is my firth question in this mailing list. The question is about the how to deal with a model with both random and fixed factors. I know to do the analysis using SPSS. The command line wit be as follows: UNIANOVA alpha.ug.mg BY Line Year Rep /RANDOM=Year Rep /METHOD=SSTYPE(4) /INTERCEPT=EXCLUDE /PRINT=HOMOGENEITY /CRITERIA=ALPHA(0.05) /DESIGN= Year Rep*Year Line Line*Year Could anyone give me a translation of above SPSS command line to R? Thanks very much -- Fernando Piston Piston fpiston@gmail.com [[alternative HTML version deleted]]

Greetings. I'm trying to replicate an analysis I did a few years ago, then in SPSS, using the SPSS GLM command: GLM n_diffpt WITH age_i inc_i join_i work_i educ_i give_i cs_i eff_i age_a inc_a join_a work_a educ_a give_a cs_a eff_a /METHOD = SSTYPE(3) /INTERCEPT = INCLUDE /PRINT = PARAMETER ETASQ RSSCP GEF /CRITERIA = ALPHA(.05) /DESIGN = age_i*age_a inc_i*inc_a join_i*join_a work_i*work_a educ_i*educ_a give_i*give_a cs_i*cs_a eff_i*eff_a age_i inc_i join_i work_i educ_i give_i cs_i eff_i age_a inc_...

Hello, I'd like to propose an extension to the function summary.aov. In Splus (2000, I don't know about other versions), summary.aov allows a parameter ssType to be set to 1 or 3 (defaults to 1) to choose the type of Sums of Squares. I know I can get Type III SS in R with drop1(model), but including the functionality into summary.aov would, in my opinion, - yield a more usable table (for example, the residual Df are missing in the drop1 table) - put...

...VARIABLE. # # GLM RT.1.1.1 RT.1.1.2 RT.1.1.3 RT.1.2.1 RT.1.2.2 RT.1.2.3 RT.1.3.1 RT.1.3.2 RT.1.3.3 RT.2.1.1 # RT.2.1.2 RT.2.1.3 RT.2.2.1 RT.2.2.2 RT.2.2.3 RT.2.3.1 RT.2.3.2 RT.2.3.3 # /WSFACTOR=TaskSwitch 2 Repeated CongruenceNow 3 Repeated CongruenceBefore 3 Repeated # /METHOD=SSTYPE(3) # /PRINT=DESCRIPTIVE OPOWER TEST(SSCP) # /CRITERIA=ALPHA(.05) # /WSDESIGN=TaskSwitch CongruenceNow CongruenceBefore TaskSwitch*CongruenceNow # TaskSwitch*CongruenceBefore CongruenceNow*CongruenceBefore # TaskSwitch*CongruenceNow*CongruenceBefore. ###### END: SPSS code #...

...vels, meaning different nrs of B per A, different nrs of C for B, different nrs of values per C In SPSS this seems to be quite simple with a small writing in the syntax (see below). But how do I get the same results in R with lme? SPSS syntax: UNIANOVA var BY A B C /RANDOM = B C /METHOD = SSTYPE(3) /INTERCEPT = INCLUDE /CRITERIA = ALPHA(.05) /DESIGN = A B(A) C(B(A))). 2. the same model but than for count data (like 15 out of 30, 23 out of 60) instead of the continous variable(I know the basics of glm in R) Thanks a lot for your help Jelmer

...a model without the Treatment effect and use that model to examine the significane of the random effects. > But the above models will not give me any information of the importance of the random factors. If I do the same in SPSS: > > glm > DeathDay by Cup Clutch Treatment > /method=sstype(1) > /random Cup Clutch > /design Cup(Clutch). > > ...I get an ANOVA table that I (and referees) can understand. It tells me about where the variation originates from; in one experiment only Clutch is significant, in another there is also a (surprising but explainable) effect of Cup. A...

...peated measures anova's might know: aov( RT ~ color * shape * MyGroup + Error( Subjects/( color*shape) ) In spss you would have something like this (of course with the data organized slightly differently : GLM x1 x2 x3 x4 BY MyGroup /WSFACTOR = color 2 Polynomial shape 2 Polynomial /METHOD = SSTYPE(3) /CRITERIA = ALPHA(.05) /WSDESIGN = color shape color*shape /DESIGN = VAR00001 . Ok, the question is. If the group sizes are different (say 10 people in one group and 12 people in the other group) R is going to give the wrong answer. Of course that is not R's fault. === message truncated =...

I have a two part question one about statistical theory and the other about implementations in R. Thank you for all help in advance. (1) Am I correct in understanding that Heteroscedasticity is a problem for Generalized Additive Models as it is for standard linear models? I am asking particularly about the GAMs as implemented in the mgcv package. Based upon my online search it seems that some