similar to: Warning messages in lmer function (package lme4)

Hi, I am trying to run an analysis with the package maanova and I am not getting success. I suppose that I am wrong on set up the formula, so the issue 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

Dear professors and collegues I need to perform a analysis of dates from a nested experimental design. From "Bioestatical Analysis" of Zar "Bimetry of Sokal" & Rohlf "Design and Analysis of Experiments" of Montgomery I have: Sum (mean(x)_i - mean(x)_T)2 / (a-1) -> var(epsilon) + n sigma2_B + n b (sum alfa_i)2 / (a-1) Sum (mean(x)_ij - mean(x)_i)2 /

Hi, I run a maanova analysis and found this message error: Error in ma.svd(X, 0, 0) : 0 extent dimensions I did a google search and found this: \item ma.svd: function to compute the sigular-value decomposition of a rectangular matrix by using LAPACK routines DEGSVD AND ZGESVD. \item fdr: function to calculate the adjusted P values for FDR control. I did a search for LAPACK and

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

Hi all, I'm working with a dataset from 10 treatments, each treatment with 30 subjects, each subject measured 5 times. The plot of the dataset suggests that a 3-parameter logistic could be a reasonable function to describe the data. When I try to fit the model using gnls I got the message 'Step halving factor reduced below minimum in NLS step'. I´m using as the initial values of the

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

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

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

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,

Hi!! Can anyone help me, i have problems to converge the following data with 5 nonlinears models that 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

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)

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

Hi R-users I'm trying to do multivariate analysis of variance of a experiment with 3 treatments, 2 variables and 5 replicates. The procedure adopted in SAS is as follow, but I'm having difficulty in to implement the contrasts for comparison of all treatments in R. I have already read manuals and other materials about manova in R, but nothing about specific contrasts were found in them,

Buenas, En R, como en la mayoría del software estadístico, no se utiliza ningún nivel de confianza sino que lo que se calcula es el p-valor asociado al contraste. De forma que cuanto 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,

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 ? ?

El 24/01/16 a las 16:29, Fernando Sánchez Lasheras escribió: > Hola José, > > Efectivamente le mensaje me lo enviaste a mí, pero yo al contestar lo mandé > a la lista. > > Los grupos son tratamientos distintos. Es decir, se estudia a una serie de > personas a las que se les aplican tres tratamientos distintos (A, B y C) > durante el mismo tiempo y vemos el número de eventos

Hi all, I have a little problem. I make 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 <-

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=T) Y obtengo este error: Error en FUN(X[[1L]], ...) : 2 arguments

Hi, I make a upgrade to R 2.1.0 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:

Hi, I build a graph taking into account the times: 1, 2,4,6,8,10,12,15,18,21,24,28,32 and 48. Be that the scale of the X axis does not look right. It seems equidistant. (graph attached) What changes have I to do in the following commands so that the scale be correct? interaction.plot(Tempo, Trat, Valor, ylim=c(0, 2), las=1, lty=c(1,2,3,4), lwd=3, bty='l',