search for: factorb

I wonder if someone could explain the behavior of the anova() and lm() functions in the following situation: I have a standard 3x2 factorial design, factorA has 3 levels, factorB has 2 levels, they are fully crossed. I have a dependent variable DV. Of course I can do the following to get the usual anova table: > anova(lm(DV~factorA+factorB+factorA:factorB)) Analysis of Variance Table Response: DV Df Sum Sq Mean Sq F value Pr(>F) factorA...

...ponse<-c(32,54,32,65,34,65,65,45,54,23,43,23,76,87,65,45) factorA<-c("A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B") factorB<-c("a","a","a","a","b","b","b","b","a","a","a","a","b","b","b","b") data<-data.frame(response,factorA,factorB) bargraph.CI(x.factor =...

...is as a separate column in my data. So, it looks like I have factor A + factor B + factor C:factor B, but I don't want terms for the base level of factor B for that factor C:factor B interaction. An example of the data I'm trying to fit a model to could be as follows: Record FactorA FactorB Weight Response 1 1 1 1 0.73 2 1 2 0.5 0 3 1 3 1 1.00 4 2 1 0.33 2.77 5 2 2 0.4 0 6 2 3 5 0 (I've given a sample here, as my data h...

...ject.org Dear Rsociety, I'd like to kingly ask to anyone is willing to answer me how to implement a NON NESTED random effects structure in lme() In particular I've tried the following translation from lmer to lme, as suggested from some web example mod1<-lmer(y~x*z+(x*z|factorA1/factorB)+(x*z|factorA2/factorB)) # y,x,z continuous mod2<-lme(y~x*z, random= pdBlocked(list(pdIdent(~1|factorA1/factorB ),pdIdent(~1|factorA2/factorB)))) In detail check how I've tried to state in mod1 that Iwant to evaluate randomness in the interaction x*z (i.e intercept, slope, interaction) g...

How to generate a new factor variable by two other factor variables? For example, if I have two factor variables, factorA and factorB, factorA factorB 0 0 0 0 1 0 0 1 1 1 Is there a simple way to generate a new 4-levels factor variable as factorC factorA factorB 0 0 0 0 0 0 1 1 0 2 0 1 3 1 1 -- Shuguang...

Hello list, I wish to perform a randomization test on the F-statistics of a 2 way ANOVA but have not been able to find out how to do so - is there a package / function that can perform this that I am unaware of? FactorA has 6 levels (0,1,2,3,4,5) whereas FactorB has 3 (1,2,3). A sample: Resp. FactorA FactorB 2 0 2 3 1 2 1 2 2 0 3 2 0 4 2 0 5 2 4 0 1 6 1 1 1 2 1 0 3 1 1 4 1 0 5 1 2 0 2 3 1 2 1 2 2 2 3 2 1 4 2 0 5 2 3 0 1 3 1 1 1 2 1 0 3 1 0 4 1 7 1 3 2 2 3 0 3 3 1 4 3 0 5 3 1 0 3 Also, is the F-statistic an appropriate test-statistic for the randomization...

Hi, As a follow up to Patch D23646 <https://reviews.llvm.org/D23646>, I'm trying to figure out if there should be an alignment check and what the correct approach is. Some background: For stores, the pass turns: %i.vec = shuffle <8 x i32> %v0, <8 x i32> %v1, <0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11> store <12 x i32> %i.vec, <12 x i32>* %ptr

...ind examples for the strips using which.given and which.panel. But this does not work for the panels... Can Anybody give a hint? Thanks Joris I work with R 1.9.1 under Linux panel.txt = function(sometxt,x,y,...){ grid.text(sometxt,x,y) } xyplot(data = Pdata, P ~ DAS | FactorA, groups = FactorB, type ="s", col = c("red","blue"), panel = function(x,y,...){ panel.abline(h = 100, lty = 5, lwd =0.5, col = "darkgrey") panel.txt(mytext, 0.2, 0.8) } -- ====================================================================== Jo...

...[i+2] * Factor; // B > > This pattern is easily vectorised on most platforms, since loads, muls > and stores are the exact same operation. which can be combined. > > for (i..N) > out[i] = in[i] * FactorR; // R > out[i+1] = in[i+1] * FactorG; // G > out[i+2] = in[i+2] * FactorB; // B > > This still can be vectorised easily, since the Factor vector can be > easily constructed. > > for (i..N) > out[i] = in[i] + FactorR; // R > out[i+1] = in[i+1] - FactorG; // G > out[i+2] = in[i+2] * FactorB; // B > > Now it gets complicated, because the...

Hello every body, I am trying to do a factorial anova analysis following this model: model<-anova(lm(responsevariable~factorA*factorB)) model<-anova(lm(luz$dosel~luz$estado*luz$Bosque)) Df Sum Sq Mean Sq F value Pr(>F) estado 1 6931.1 6931.1 41.6455 7.974e-06 *** Bosque 1 36.6 36.6 0.2197 0.6456 estado:Bosque 1 36.6 36.6 0.2197 0.6456 Residuals 16 2662.9 166.4 Strange is that...

Hello everyone, I would like to perform a 2-way manova test, but I'm having some issues. I implemented like this Y<-cbind(Resp1,Resp2,Resp3,....,Respn) model<-manova(Y "tilda" FactorA*FactorB) summary.aov(model) 1. I don't know at what level I have to do the Type I error correction. Is it on p-values returned by "summary.aov(model)? Or is it when I compare each subgroup with another subgroup? Or is it for summary(model)? 2. I have a significant interaction on summary(model)...

...r C has 5 levels (C1,C2,C3,C4,C5). The experiment has only partial coverage, that is not every A is tested with every B and every C. However, I was careful in the experimental design to ensure that every A and every B was tested against at least one C. Here is my experimental data: FactorA FactorB FactorC Hit Miss A1 B1 C1 17 83 A1 B1 C2 17 83 A1 B1 C3 18 82 A1 B1 C4 NA NA A1 B1...

Suppose you have 4 related vectors: a.id<-c(1:25, 1:25, 1:25) a.vals <- c(101:175) # same length as a.id (the values for those IDs) a.id.levels <- c(1:25) a.id.ratings <- rep(letters[1:5], times=5) # same length as a.id.levels What I would like to do is specify a rating from a.ratings (e.g. "e"), get the vector of corresponding IDs from a.id.levels (via

...ject.org Dear Rsociety, I'd like to kingly ask to anyone is willing to answer me how to implement a NON NESTED random effects structure in lme() In particular I've tried the following translation from lmer to lme, as suggested from some web example mod1<-lmer(y~x*z+(x*z|factorA1/factorB)+(x*z|factorA2/factorB)) # y,x,z continuous mod2<-lme(y~x*z, random= pdBlocked(list(pdIdent(~1|factorA1/factorB ),pdIdent(~1|factorA2/factorB)))) In detail check how I've tried to state in mod1 that Iwant to evaluate randomness in the interaction x*z (i.e intercept, slope, interaction) g...