search for: linfct

Hello everybody, problem solved, there was a typo. I wrote Type instead of Material Best ----Messaggio originale---- Da: angelo.arcadi@virgilio.it Data: 22-giu-2012 11.05 A: <r-help@r-project.org> Ogg: Error with glht function: Error in mcp2matrix(model, linfct = linfct) : Variable(s) 'Type' have been specified in 'linfct' but cannot be found in 'model'! Dear list members, I get the following error when using the glht function to perform a post hoc analysis for an ANOVA with repeated measures: require(nlme) lme_H2H_musician...

Dear list members, I get the following error when using the glht function to perform a post hoc analysis for an ANOVA with repeated measures: require(nlme) lme_H2H_musicians = lme(H2H ~ Emotion*Material, data=musicians, random = ~1|Subject) require(multcomp) summary(glht(lme_H2H_musicians, linfct=mcp(Type = "Tukey")), test = adjusted(type = "bonferroni")) Error in mcp2matrix(model, linfct = linfct) : Variable(s) 'Type' have been specified in 'linfct' but cannot be found in 'model'! I don´t understand why I get this error, but at the same ti...

...Data,Attribute=="ChromaL") tapply(data$value, list(Product=data$Product), mean, na.rm=TRUE) model <- lme(value ~ Product+Time+Arm+Product*Arm+Product*Time+Product*Arm*Time, random = ~1 | Subj,data =data) summary(model) x<-anova(model) x library(multcomp) su<-summary(glht(model,linfct=mcp(Product="Tukey"))) ##length(su) ##su[1:(length(su)-4)] x11() plot(summary(glht(model,linfct=mcp(Product="Tukey"))),cex.axis=0.6) ##For Each Product Comparison across All Time Points. data<-MyData data<-subset(data,Product=="a") tapply(data$value, list(Time=...

...odel? Thanks Sibylle --> works fine ME$Diversity=factor(ME$Diversity) H08_lme<-lme(log(Height2005_mean)~Diversity, data=ME, random=~1|Plot/ SubPlot, na.action=na.omit, subset=ME$Species=="Pse_men", method="ML") summary(H08_lme) anova(H08_lme) g_H08_lme<-glht(H08_lme, linfct=mcp(Diversity="Tukey")) print(summary(g_H08_lme)) --> using lme with weights I changed the order of factor() and introduced as.factor in the model H08_lme<-lme(log(Height2008_mean)~as.factor(Diversity), data=ME, random=~1|Plot/SubPlot, weights=varPower(form=~Diversity), na.a...

...rv(stop, status > 0) ~ treatment, data = bladder1) coef exp(coef) se(coef) z p treatmentpyridoxine -0.063 0.939 0.161 -0.391 0.70 treatmentthiotepa -0.159 0.853 0.168 -0.947 0.34 Likelihood ratio test=0.91 on 2 df, p=0.635 n= 294 > glht(fit,linfct=mcp(treatment='Tukey')) General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Linear Hypotheses: Estimate pyridoxine - placebo == 0 -0.06303 thiotepa - placebo == 0 -0.15885 thiotepa - pyridoxine == 0 -0.09582 However, once I a...

...The lm statement runs fine, but for the glht command I get the following error. I noted that someone else asked the same question a while back but received no reply. I am hoping someone might know what is happening. anovaf2<-lm(achiev ~ major, data=data_mcp) > pairwise<- glht(anovaf2,linfct = mcp(major = "tukey")) Error in mcp2matrix(model, linfct = linfct) : Factor(s) major have been specified in 'linfct' but cannot be found in 'model'! Any help would be much appreciated. Rob. -- Robert A. Cribbie, PhD Department of Psychology York University...

...ppropriate pairwise comparison analysis and I cannot get the code to work with my data. I am *unclear how to code it to work with the interaction*; however, even when I attempt to use it only for a single factor, it does not work (see below). x<-aov(glm(Cucs_m~as.factor(BA),data=cuc)) glht(x, linfct=mcp(BA="Tukey")) .................................... Error in mcp2matrix(model, linfct = linfct) : Variable(s) ?BA? have been specified in ?linfct? but cannot be found in ?model?! Can anyone off suggestions on potential problems with my approach and/or script issues? Thank you very...

...ulation were re-measured each week) > anova(model.3) numDF denDF F-value p-value (Intercept) 1 592 649.7753 <.0001 Week 5 592 302.9706 <.0001 Pop 1 222 70.7268 <.0001 Week:Pop 5 592 36.8576 <.0001 > summary(glht(model.3, linfct=mcp(Wk = "Tukey"))) Error in mcp2matrix(model, linfct = linfct) : Variable(s) ?Wk? of class ?integer? is/are not contained as a factor in ?model?. > is.factor(Wk) [1] FALSE > Week<-factor(Wk) > is.factor(Week) [1] TRUE > model.3<-lme(fixed=Totnum~Week*Pop, random=~1|n...

...'number' levels #B similar factor with 'letter' levels dat<-data.frame(y=1:4,A=factor(c(1,1,2,2)), B=factor(c('e','e','f','f')) ) motA<-lm(y~A,data=dat) motB<-lm(y~B,data=dat) library(multcomp) #does not work glht(motA,linfct=mcp(A=c("2 - 1 = 0 "))) #the error message is # Error in coefs(ex[[3]]) : # cannot interpret expression '1' as linear function #works glht(motB,linfct=mcp(B=c("f - e = 0"))) regards Ulrich Halekoh Aarhus University e-mail: Ulrich.Halekoh at agrsci.dk I use R.2.1...

Dear *, I would like to obtain for each factor of my anova model the "response variable vs factor" plot with means and 95% Tukey HSD intervals. I would appreciate any information on how to do that. Cheers -------------------------------------------------------------------- Max MANFRIN Tel.: +32 (0)2 650 3168 IRIDIA - CoDE, CP 194/6

...A linear model run on the updated dataframe works but notes that it coerces "SitePos" from character to factor e.g. Model.G = lmer(log10(SrCa) ~ SitePos + (1 | Eel), data = Data1) . The next step of a multiple comparison test on the output of the linear model: Model.G.mct = glht(Model.G, linfct = mcp(SitePos = "Tukey")) fails because the mct does not recognize "SitePos" as a factor and gives error message: "Error in mcp2matrix (model, linfct = linfct): Variable(s) 'SitePos' of class 'character' is/are not contained as a factor in 'model'....

...*** edadysexo:log(lcc) 2 0.063 0.0317 1.6021 0.2025 Residuals 509 10.085 0.0198 Then, I tried to do a post-hoc using glht from multcomp package, but, because the interaction it's not siginificant i did it just for the factor > summary(glht(ancova, linfct=mcp(edadysexo="Tukey"))) Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: aov(formula = log(peso) ~ edadysexo * log(lcc)) Linear Hypotheses: Estimate Std. Error t value Pr(>|t|) M - H == 0 -1.8218 1.1...

...omp? I am running R 2.13.0 under Windows 7. The following linear model and mct produces a global mct of 15 paired contrasts of the combined (Site, Position) factor SitePos of which only 9 are of interest. Model.G = lmer(log10(SrCa) ~ SitePos + (1 | Eel), data = Data1) Model.G.mct = glht(Model.G, linfct = mcp(SitePos = "Tukey"))summary (Model.G.mct) The following code creates the desired reduced set of contrasts but I have been unable to apply it correctly to the mct. contr = rbind("CR core - MH core" = c(1,0,0,-1,0,0),"CR core - CR edge" = c(1,0,-1,0,0,0), "C...

Dear R-user I used lme to fit a linear mixed model inlcuding weights=varPower(). Additionally I wanted to use glht to calculate Tukey-Kramer multiple comparision. error: > glht(modelF, linfct=mcp(Species="Tukey")) Error in glht.matrix(model = list(modelStruct = list(reStruct = list(SubPlot = -0.305856275920955, : ?ncol(linfct)? is not equal to ?length(coef(model))? > glht(modelF, linfct=mcp(Diversity="Tukey")) Error in mcp2matrix(model, linfct = linfct) :...

...t;- lme(soi ~ solution + iobs, random = ~1 | iobs/solution, data = stack) > anova(Lmes.mod) numDF denDF F-value p-value (Intercept) 1 2383 2894.8342 <.0001 solution 1 2383 0.0003 0.9870 iobs 1 147 0.0126 0.9109 > summary(glht(Lmes.mod, linfct=mcp(solution="Tukey"))) Error in mcp2matrix(model, linfct = linfct) : Variable(s) ''solution'' of class ''numeric'' is/are not contained as a factor in ''model''. > I don''t understand the error since "solution"...

...dist 1 0.51291 0.51291 92.6908 5.703e-12 *** sampling:dist 3 0.05112 0.01704 3.0792 0.03822 * Residuals 40 0.22134 0.00553 Because there exist significantly different regression slopes, I did a post hoc test with glht() to find out between which samplings: >summary(glht(mod, linfct=mcp(sampling="Tukey"))) The results seem to say that there are no significantly different slopes for any of the pair-wise comparisons of factor levels: Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: aov(formula = h ~ sampling *...

...$semio2)<-c("A","B","C","D") semiochemical$CO22<-factor(semiochemical$CO2) levels(semiochemical$CO22)<-c("1","2") model1<-glm.nb(total ~ semio + CO2 + location + temp + mean.wind.speed) model.glht.Semio <- glht(model1, linfct=mcp(semio="Tukey")) model.glht.CO2 <- glht(model1, linfct=mcp(CO2="Tukey")) [[alternative HTML version deleted]]

Hi all: There's a question about glht function. My data:data_ori,which inclue CD4, GROUP,time. f_GROUP<-factor(data_ori$GROUP) f_GROUP is a factor of 3 levels(0,1,2,3) result <- lme(sqrt(CD4) ~ f_GROUP*time ,random = ~time|ID,data=data_ori) glht(result, linfct = mcp(f_GROUP="Tukey") ) Error in `[.data.frame`(mf, nhypo[checknm]) : undefined columns selected I can't find out the reason for Error. Any help from you are welcome. Many thanks! [[alternative HTML version deleted]]

...row=5, ncol=9) f <- gl(3,3,9, label=c("C", "Tl", "T2")) aof <- function(x) { m <- data.frame(f, x); aov(x ~ f, m) } amod <- apply(dat,1,aof) Now, how can I use 'glht' for the above amod. I know that I cannot use simply glht(amod, linfct = mcp(f = "Dunnett")). Also, if I want to use Dunnett for comparing C vs (T1 and T2), how can I specify this in the glht function. Thanks in advance. Regards, Ezhil

...g mixed effects model, which works fine: > > LM<-lme(EFN~Leafage,L1,~1|Plant) > Now all I want to do is a post-hoc analysis (multiple comparisons) for the fixed factor EFN. I tried the following code. According to the documentation this should work: > > Post <- glht(LM, linfct = mcp(Leafage = "Tukey")) > However, I get this error message and don't know what to do: Error in mcp2matrix(model, linfct = linfct) : Factor(s) Leafage have been specified in ?linfct? but cannot be found in ?model?! The factor is specified, right? So what is the problem?...