search for: mod4

...ariable is gender. At the end of the selection I have found one model with only one explanatory variable (cohort, factor variable with 10 levels). I want to test the significance of the variable "cohort" that, I believe, is the same as the significance of this selected model: > anova(mod4,update(mod4,~.-cohort),test="Chisq") Analysis of Deviance Table Model 1: site ~ cohort Model 2: site ~ 1 Resid. Df Resid. Dev Df Deviance P(>|Chi|) 1 993 1283.7 2 1002 1368.2 -9 -84.554 2.002e-14 *** --- Signif. codes: 0 ?***? 0.001...

...type of smoothing basis (bs), the same type of smoother ( e.g te) and the same dimension of the basis (k)? Example: Option 1: a) mod1 <- gam (bm ~ t, data = data) b) mod2 <- gam (bm ~ te (t, k = 5, bs = ?cr?), data = data) c) mod3 <- gam (bm ~ te (t_year, k = 5, bs = ?cc?), data = data) d) mod4 <- gam (bm ~ te (t_year, temp_W, k = 5, bs = c(?cc?,?cr?)), data = data) e) mod5 <- gam (bm ~ te (t_year, temp_W, temp_sept, k = 5, bs = c(?cc?,?cr?,?cr?)), data = data). Here the limitation for k = 5, is due to mod5, I don?t use s () because in mod4 and mod5 te () is used and finally, I alwa...

...rossed.: mod0 <- lmer(dT_purs ~ T + Z + (1|subject), data = x) mod1 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject), data = x) mod2 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject) + (1+ Z| subject), data = x) mod3 <- lmer(dT_purs ~ T * Z + (1 +tempo| subject) + (1+ Z| subject), data = x) mod4 <- lmer(dT_purs ~ T * Z + (1| subject), data = x) anova(mod0, mod1,mod2, mod3, mod4) Data: x Models: mod0: dT_purs ~ T + Z + (1 | subject) mod4: dT_purs ~ T * Z + (1 | subject ) mod1: dT_purs ~ T + Z + (1 + T| subject) mod2: dT_purs ~ T + Z + (1 + T| subject ) + (1 + Z | subject) m...

Hi all, I run CentOS5.1 with all updates, and like to use metacity's keybindings (gconf-editor->/apps/metacity/global_keybindings and keybinding_commands) to set shortcut keys for xterm, nautilus, and the like, so <Mod4>x runs xterm and <Mod4>e runs nautilus, rather how Windows-XP keyboard shortcuts work. I always log on as 'neek', the only user other than 'root' that I use for user shells on this laptop. I've used a default Gnome desktop since fresh install about 4 months ago. I...

..., NA X4 <- xi2,LY42, NA X5 <- xi2,LY52, NA X6 <- xi2,LY62, NA X7 <- xi3,LY73, NA X8 <- xi3,LY83, NA X9 <- xi3,LY93, NA xi1 <-> xi1,NA,1 xi2 <-> xi2,NA,1 xi3 <-> xi3,NA,1 xi1 <-> xi2 ,PH12,NA xi1 <-> xi3 ,PH13,NA xi2 <-> xi3 ,PH23,NA mod4<-specify.model(); X1 <-> X1,TD11,NA X2 <-> X2,TD22,NA X3 <-> X3,TD33,NA X4 <-> X4,TD44,NA X5 <-> X5,TD55,NA X6 <-> X6,TD66,NA X7 <-> X7,TD77,NA X8 <-> X8,TD88,NA X9 <-> X9,TD99,NA X10 <-> X10,TDaa,NA X11 <-> X11,TDbb,NA X...

...t;-1)+z ,random=~x|id, data=data.frame(x,y,z,id)) summary(mod3) #new id newframe2<-data.frame( #fictious id id="fictious", x, z) #predict yy3<-predict(mod3,level=0, newdata=newframe2) lines(x[order(x)],yy3[order(x)],col="green",lwd=2) # ADD INTERACTION z:x mod4<-lme(y~x+x*(x>-1)+ z+ z:x+ z:x*(x>-1) ,random=~x|id, data=data.frame(x,y,z,id)) #predict yy4<-predict(mod4,level=0, newdata=newframe2) lines(x[order(x)],yy4[order(x)],col="violet",lwd=2) #something bizarre...

....2)on a windows 7 machine. DATASET mtcars full.model<-lm(mpg~cyl+disp+hp+drat, data=mtcars) CODE stepFO<-function(model) { m<-data.frame(model.frame(model)) num.of.var<-length(colnames(m)) mod1<-lm(m[,1]~m[,2]) mod2<-lm(m[,1]~m[,2]+m[,3]) mod3<-lm(m[,1]~m[,2]+m[,3]+m[,4]) mod4<-lm(m[,1]~m[,2]+m[,3]+m[,4]+m[,5]) #Comment 1--I don't know how to automated this process(above) of adding #...additional variables. Probably a loop is needed but I don't understand #...how to apply it here. Maybe update.model [1:num.ofvar]? a1<-anova(mod1) a2<-anova(mod2) a3&l...

...es, I suppose that’s right, they are not the same fixed effects. But why does REML give them such different Log likelihoods? And what should I do if I want to compare a larger set of models. For example the following, admittedly overparameterised model, can be fitted (slowly) by either method >mod4<-lme(wthole~poly(nplants,2),data=d3,random=~poly(nplants,2)|field/ subplot,method="ML") But this doesn’t work by either method… > mod4<-lme(wthole~nplants+I(nplants^2),data=d3,random=~nplants+I(nplants^ 2)|field/subplot,method="ML") Error in solve.default(pdMatrix...

...crossed.: mod0 <- lmer(dT_purs ~ T + Z + (1|subject), data = x) mod1 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject), data = x) mod2 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject) + (1+ Z| subject), data = x) mod3 <- lmer(dT_purs ~ T * Z + (1 +tempo| subject) + (1+ Z| subject), data = x) mod4 <- lmer(dT_purs ~ T * Z + (1| subject), data = x) anova(mod0, mod1,mod2, mod3, mod4) Data: x Models: mod0: dT_purs ~ T + Z + (1 | subject) mod4: dT_purs ~ T * Z + (1 | subject ) mod1: dT_purs ~ T + Z + (1 + T| subject) mod2: dT_purs ~ T + Z + (1 + T| subject ) + (1 + Z | subject) mod3...

...rs to be used together in an instruction, should i try to change it in the register allocation pass or should i try it somewhere else?? example. lets say we have to add 2 registers addu rx ,ry ,rz there is a limitation that says that the two regs that will be added they can not have the same mod4 so we can add r1 , r2 but cannot add r1,r5. thanks Stavropoulos Nikos -- View this message in context: http://llvm.1065342.n5.nabble.com/Register-allocation-limitations-tp62967.html Sent from the LLVM - Dev mailing list archive at Nabble.com.

...m(250), group=rep(letters[1:10],25) ) mod1 <- lme(resp~cov1, a, ~cov1|group, method="ML") mod2 <- stepAIC(mod1, scope=list(upper=~(cov1+cov2)^2, lower=~cov1) ) # it doesn't happen for normal linear models: mod3 <- lm(resp~cov1, data=a) mod4 <- stepAIC(mod3, scope=list(upper=~(cov1+cov2)^2, lower=~cov1) ) Thanks, Robert

...recisely the wrong moment! All I am trying to do is to add superscript to two of the labels for which i tried using the expression function. It partly works, but it prints only the first replacement label inside the plotting region and forgets the rest...what am I missing? Thank you xyplot(resid(mod4)~factor(distance),aspect=1.0,cex=1.0,xlab="Distance",ylab="Residuals",data=meanAG, span=1, panel=function(x,y,span){ panel.grid(h=0, v=-1) panel.xyplot(x,y,cex=1.0,points="jitter") panel.loess(x,y, span) panel.axis(side="bottom",at=TRUE, labels=c(expression(B...

Dear R help, I am trying to work out if I am justified in log-transforming data and specifying Gamma in the same glm. Does it have to be one or the other? I have attached an R script and the datafile to show what I mean. Also, I cannot find a mixed-model that allows Gamma errors (so I cannot find a way of including random effects). What should I do? Many thanks, Pete --------------

Dear List-members, I found a strange behaviour in the lqs function. Suppose I have the following data: y <- c(7.6, 7.7, 4.3, 5.9, 5.0, 6.5, 8.3, 8.2, 13.2, 12.6, 10.4, 10.8, 13.1, 12.3, 10.4, 10.5, 7.7, 9.5, 12.0, 12.6, 13.6, 14.1, 13.5, 11.5, 12.0, 13.0, 14.1, 15.1) x1 <- c(8.2, 7.6,, 4.6, 4.3, 5.9, 5.0, 6.5, 8.3, 10.1, 13.2, 12.6, 10.4, 10.8, 13.1, 13.3, 10.4, 10.5, 7.7, 10.0, 12.0,

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

...ould i try to change it in the register allocation pass > or should i try it somewhere else?? > > example. > > lets say we have to add 2 registers > > addu rx ,ry ,rz > > there is a limitation that says that the two regs that will be added they > can not have the same mod4 > so we can add r1 , r2 but cannot add r1,r5. > > > thanks > Stavropoulos Nikos > > > > > -- > View this message in context: > http://llvm.1065342.n5.nabble.com/Register-allocation-limitations-tp62967.html > Sent from the LLVM - Dev mailing list archive at Na...

...tly what does the ":" do. Can some one give me an explenation of the models below so I can replicate the results in other program. #common intercept mod2<-lm(TS~log(BodyWt)+log(BodyWt):factor(D),data=sleep2) #paralell lines mod3<-lm(TS~factor(D)+log(BodyWt)-1,data=sleep2) #general mod4<-lm(TS~factor(D)+log(BodyWt):factor(D)-1,data=sleep2) TS is the dependent variable, log(bodyWt) is a covariate and D is either 1 or 2, depending in the case. Thanks -- View this message in context: http://www.nabble.com/lm-function-tp21816422p21816422.html Sent from the R help mailing list ar...

...e en blanc sur fond blanc sauf apr?s validation o? l? il apparait miraculeusement (on a le m?me probl?me quand il s'agit d'utiliser le cahier de texte pour saisir les exercices et devoir donn?s). J'ai tent? une installation de Wine 1.2.6, j'ai essay? d'inverser les couleurs (par Mod4 + N) mais rien n'y fait. Y a-t-il d'autres utilisateurs de PRONOTE-Client ici ? ont-ils le m?me probl?me ? Ma configuration : Ubuntu 10.10 amd64 (j'avais le m?me probl?me avec Ubuntu 10.04) Cordialement MIchel

...;size; i+=4) > + nv_wo32(bios, i, nv_rd32(bios, 0x300000 + i)); > + } > + > + /* check the PCI record header again, now that we can make > + * un-aligned accesses Why bother with the two separate mechanisms? In practice, is PCIR ever at a non-mod4 location? (Should be easy to check our bios collection...) > + */ > + if (bios->data[pcir + 0] != 'P' || > + bios->data[pcir + 1] != 'C' || > + bios->data[pcir + 2] != 'I' || > + bios->data[pcir + 3] != &...

...(rawData[[1]]); cuts = len*c(0.01, 0.03, 0.08, 0.18, 0.38, 0.69, 1); cuts = as.integer(cuts); mod1 = lm(rawData[[2]][1:cuts[1]]~rawData[[1]][1:cuts[1]]); mod2 = lm(rawData[[2]][cuts[1]:cuts[2]]~rawData[[1]][cuts[1]:cuts[2]]); mod3 = lm(rawData[[2]][cuts[2]:cuts[3]]~rawData[[1]][cuts[2]:cuts[3]]); mod4 = lm(rawData[[2]][cuts[3]:cuts[4]]~rawData[[1]][cuts[3]:cuts[4]]); mod5 = lm(rawData[[2]][cuts[4]:cuts[5]]~rawData[[1]][cuts[4]:cuts[5]]); mod6 = lm(rawData[[2]][cuts[5]:cuts[6]]~rawData[[1]][cuts[5]:cuts[6]]); mod7 = lm(rawData[[2]][cuts[6]:cuts[7]]~rawData[[1]][cuts[6]:cuts[7]]); plot(rawData[[1...