search for: trat

...(mean(x)_ij - mean(x)_i)2 / (ba-a) -> var(epsilon) + n sigma2_B Sum (x_ijl - mean(x)_ij)2 / (abn-ab) -> var(epsilon) Dates for the execution of a example: #> Mesures <- as.data.frame(matrix(rnorm(45*1, mean=10, sd=1), ncol=1)) #> colnames(Mesures) <- "VR" #> Mesures$trat <- as.factor(rep(1:5,rep(9,5))) #> Mesures$patient <- as.factor(rep(1:15,rep(3,15))) The Analysis of Variance could be: #> AnovaModel.1 <-aov(VR ~ trat + trat/patient, data=Mesures) #> summary(AnovaModel.1) Df Sum Sq Mean Sq F value Pr(>F) trat 4 1.58...

Hi, I'm building Tukey's table using qtukey function. It happens that I can't get the values of Tukey's one degree of freedom and also wanted to eliminate the first column. The program is: Trat <- c(1:30) # number of treatments gl <- c(1:30, 40, 60, 120) # degree freedom tukval <- matrix(0, nr=length(gl), nc=length(Trat)) for(i in 1:length(gl)) for(j in 1:length(Trat)) tukval[i,j] <- qtukey(.95, Trat[j], gl[i]) rownames(tukval) <- gl colname...

...ssue may not be related to R, properly. I have two varieties of plants (V1 and V2). A group of each ones were treated and another was not treated. After treatment, in three different time RNA was collected from treated and from not treated plants for both varieties. So, I have: Var: 2 (varieties) Trat: 2 (treatment) Time: 3 Sample: 3 (biological replicate) Probes: 3575 Spots: 2 for each probe I used the following code: > library(vsn) > todos <- read.matrix("todos_v1_e_v2_back_c.txt",sep="\t") > todos.norm <- vsn2(todos) > write.table(exprs(todos.norm),&quo...

...lues for FDR control. I did a search for LAPACK and not found a package. Could you help me on how I could solve this problem? I am try to do this: library(maanova) read the data file and design ##### Make the full model based on the design anova.full.mix <- fitmaanova(data, formula=~ Var+ind+Trat+Time+ Var:ind+Var:Trat+Var:Time+ ind:Trat+ind:Time+ Trat:Time+ Var:ind:Trat+ Var:ind:Time+ Sample,random=~Sample) If I remove the Var:ind:Trat and Var:ind:Time from formula the script runs very well. Did is not possible to do that interactions (Var:ind:Trat and Var:ind:Time)? What you suggest me...

...ters those obtained from the nls fit. Is a problem of the initial estimates of the parameters that I get the error or could be something else? The code for the nls fit was: options(contrasts=c("contr.helmert","contr.poly")) VA1.lis<-nlsList(DRAM~SSlogis(MED,phi1,phi2,phi3)|TRAT, data=VA1,na.action=na.omit) The code for the gnls fit was (using a 'difference parameterization' like SAS): options(contrasts=c("contr.SAS","contr.poly")) VA1.gnls<-gnls(DRAM~SSlogis(MED,phi1,phi2,phi3), data=VA1,params=list(phi1~TRAT,phi2~TRAT,phi3~TRAT), start=...

Hi, I used the commands below to make Hartley's table, but some values are NA. require(SuppDists) trat = seq(2, 15, 1) gl = seq(2, 40, 1) har = matrix(0, nr=length(gl), nc=length(trat)) for(i in 1:length(gl)) for(j in 1:length(trat)) har[i,j] <- qmaxFratio(.95, df=gl[i], k=trat[j]) rownames(har) <- gl colnames(har) <- trat head(har) The output (head): 2 3...

I'm new using R im trying to do a tukey test, but when i see the results the p value results in NA im guessing its because i have missing values but im not sure how to fix it AnovaModel.2 <- aov(area ~ trat, data=apilados) > summary(AnovaModel.2) Df Sum Sq Mean Sq F value Pr(>F) trat 4 11847 2961.76 9.9905 1.500e-06 *** Residuals 76 22531 296.46 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ...

Hi, I did the analysis of variance of a split-plot and the effect of treatment was significant. I would like compare treatment means using Tukey. I can't extract the mean square to apply HSD.test to use in agricolae package. anava = aov(ganhos ~ Blocos + Trat*Supl + Error(Blocos/Trat)) names(anava) summary(anava) require(agricolae) HSD.test(ganhos, Trat, df, MSerror, alpha = 0.05) Thanks -------------------------------------- Silvano Cesar da Costa Departamento de Estat?stica Universidade Estadual de Londrina Fone: 3371-4346

Hi, I try to plot bars with different colours in a barchart graphic. My idea is make that all X-Levels from trat var with different colour (grey scale). I search for a solution but dont find any. Any help? Thanks dados <- structure(list(Medias = c(0.994169096209855, 0.99416342412449, 0.974683544303797, 0.954430379746835, 0.669047619047619, 0.999999998475569, 0.994163424124514, 0.99240506329114, 0.92911...

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Hi, How I could improve this graphic? http://www.divshare.com/download/10754700-f81 I would like to write groups labels in each panel and override the labels from object. I am try this code: xyplot(percentagem.mortos~tempo|trat, data=bio.ens, type="a", ? ? ? auto.key=list(points=FALSE, lines=TRUE, columns=3), ? ? ? ylim=c(0,100),scales = list(x = list(at = c(48, 72, 96), labels ? ? ? = c("48", "72", "96")), cex=2.0), ? ? ? ylab=list(expression("Percentagem de mortos"),cex=...

...at i evaluated. Firtly, i send my data (totalsinatipicos) that i just try to fit with the nonlinear models. Next, i have the following script where i called the data as totalsinatipicos. I made selfstarting each nonlinear model. ###Library library(NRAIA) ###Data d<-totalsinatipicos f=unique(d$TRAT) dia=unique(d$DDT) REG=4:15 ###Models monomolecular=function(x,a,b,c){return(a*(1-exp(-b*(x-c))))} gompertz=function(x,a,b,c){return(a*exp(-exp(-b*(x-c))))} logistico=function(x,a,b,c){return(a/(1+exp(-b*(x-c))))} gauss=function(x,a,b,c){return(a*exp((-((x-c)*(x-c))/(2*(b*b)))))} richards=function(...

...ake an weibull survival analysis using the survival package. It,s OK, them I have the functions. I plot this funcions with curve(). I want to make a plot with the real survival points (proportion of alive x time) and them add the curves to points. I have the time to dead, the censor data and my trataments. To analysis the model is: model1 <- survreg(Surv(time,censor)~trat) Exist any function in R to plot the real point (proportion of alive in time)? Thanks Ronaldo -- That's odd. That's very odd. Wouldn't you say that's very odd? -- | //|\\ [**************************...

...690920 87.8173 4 20 0.0001 Roy's Greatest Root 34.75791615 208.5475 2 12 0.0001 ### R X1 = c(4.63,4.38,4.94,4.96,4.48,6.03,5.96,6.16,6.33,6.08,4.71,4.81,4.49,4.43, 4.56) X2 = c(0.95,0.89,1.01,1.23,0.94,1.08,1.05,1.08,1.19,1.08,0.96,0.93,0.87,0.82, 0.91) Trat = as.factor(c(rep("TESTE",5),rep("TURFE",5), rep("TURNA",5))) Y = cbind(X1,X2) fit = manova(Y ~ Trat) summary.aov(fit) summary(fit, test= "Wilks") ANOVA: Response X1 : Df Sum Sq Mean Sq F value Pr(>F) Trat 2 7.2476 3.6238 94....

Dear R-users: I am using the package MAANOVA to analyze microarray data and have encountered problems when trying to get interactions. I am a newbie in both, R and maanova, and I do not have good knowledge in statistical methods. I have four effects: Effects Levels Var 2 Ind 2 Trat 2 Time 4 Sample 3 <-- biological replicate Spot 4 <-- technical replicate I had fitted a model to got all interactions with Samples in random effect (mixed). But, I got an error when I include the Var:ind:Trat and Var:ind:Time in the fitted model. May be I didn't had degr...

...uot; So I checked them: Warnings 1 to 11 said: 1: optim returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH in: "LMEoptimize<-"(`*tmp*`, value = structure(list(maxIter = 50, ... and Warning 12 said: 12: IRLS iterations for glmm did not converge in: lmer(Enfermo ~ Bloque + Trat * Var * dia + (1 | IDfruto), data = desinf, ... There was no error message in the call of lmer function, so: What do these warnings mean? Do these errors occur at earlier iterations but finally the model converges? Can the model I got be trust? Thanks, Gabriela. PD: I'm using: Wi...

...and in some analysis I give an error: anova(model,test="F") Analysis of Deviance Table Model: binomial, link: logit Response: landing/total Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev F Pr(>F) NULL 16 105.079 trat 1 93.149 15 11.930 93.15 Warning message: NaNs produced in: pf(q, df1, df2, lower.tail, log.p) In old version it work. Inte Ronaldo -- You look tired. -- |> // | \\ [***********************************] | ( ?? ?? ) [Ronaldo Reis J??nior ] |>...

...anto más cerca de 0 esté el p-valor "menos credibilidad le damos a la hipótesis nula". Dicho mejor, debemos rechazar la hipótesis nula si el p-valor está por debajo de nuestro nivel de confianza. Por ejemplo, la primera línea del ejemplo: Df Sum Sq Mean Sq F value Pr(>F) TRAT 6 0.0028 0.00046 0.777 0.590 El p-valor es el número que aparece en la última columna, es decir, para este contraste tenemos un p-valor de 0.590, esto es mayor que cualquiera de los niveles de confianza que se utilizan habitualmente y, por tanto, la hipótesis nula nunca se va a recha...

Hi All, I would like to ask a question on fixed and random effecti in lme. I am fiddlying around Mick Crawley dataset "rats" : http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368:

Hola Compañeros, Estoy tratando de "contar" utilizando la función tapply y length y obtengo un error que no entiendo. Agradecería su ayuda. Las líneas son las siguientes: UNO <- subset (datos, CRIH2008 == "VERDADERO") dim (UNO) Abund2008 <- tapply (UNO$Cons, list(UNO$Site, UNO$Trat), length, na.rm=...