similar to: simple main effect.

Dear R user, I am new with split-plot designs and I have problems with multiple comparisons. This data correspond to an split-plot experiment with two replications (bloque).(Hoshmand, 2006 pp 138). Briefly, the whole-plot factor is Nitrogen concentration ("nitrogeno") and the subplot factor is the variety of corn ("hibrido"). The aim is to determine if major differences

HI, I am trying to perform ANOVA with 2 factors: material (3), temperature(3). The interaction is significant. I tried something like, ( summary(avt, split=list("temp:mat"=list("15"=1, "70"=2, "125"=3))) is not correct). Thanks, av <- aov(time ~ mat*temp, data=dados) avt <- aov(time ~ temp/mat) summary(avt,

Dear R Help - I am analyzing data from an ecological experiment and am having problems with the ANOVA functions I've tried thus far. The experiment consists of a blocked/split-plot design, with plant biomass as the response. The following is an overview of the treatments applied (nitrogen addition, phosphorus addition, and seeded/not seeded) and at what level (block, main-plot, and sub-plot):

dear all, I want to perform a posthoc test for my ANCOVA: a1<-aov(seeds~treatment*length) With summary(glht(a1, linfct = mcp(treatment = "Tukey"))) R tells me: "covariate interactions found -- please choose appropriate contrast" How do I build these contrasts? Ideally, I would like to have the posthoc test for the ANCOVA including a block-effect

Dear list, i have a quick and (hopefully) straightforward question regarding the plot-function after running aov. if i plot an equation like this: plot(dataSubjects~factorA, data=mydata) R gives me the boxplots for this particular factor A. my model, however contains several factors. is there a straightforward way to plot barplots for a specific factor with the constraint that those values

I have difficulties in interpreting high order interaction terms in high-way ANOVA. According to Introductory Statistics with R by Peter Dalgaard (Section 12.5), "The exact definition of the interaction terms and the interpretation of their associated regression coefficients can be elusive. Some peculiar things happen if an interaction term is present but one or more of the main effects are

Dear all, how can I perform a post hoc analysis for ANOVA with repeated measures (in presence of a balanced design)? I am not able to find a good example over internet in R...is there among you someone so kind to give me an hint with a R example please? For example, the aov result of my analysis says that there is a statistical difference between stimuli (there are 7 different stimuli). ...I

Hi, I would like to compare to regression models - each model has a different dependent variable. The first model uses a number that represents the learning curve for reward. The second model uses a number that represents the learning curve from punishment stimuli. The first model is significant and the second isn't. I want to compare those two models and show that they are significantly

Hello, I am very new to R but would like to use the software to analyse the attached data. The experiment followed a split-split plot design There were two blocks and the whole plot is CO2 with two levels. The sub-plot is soil temperature with three levels and the sub-sub plot is soil moisture content with three levels (low, intermediate and high-similar for soil temperature). I had 7 plants per

Hi, I created a simple data frame with one factor and one numerical variable. The factor was actually a vector of names of techniques to trimm reaction time data. I want to create a plot that shows the value of F test for every trimming method. So the data frame has its trim factor (who has those labels

Hi everyone, I am fairly new to R, and I am aware that others have had this problem before, but I have failed to solve the problem from previous replies I found in the archives. As this is such a standard procedure in psychological science, there must be an elegant solution to this...I think. I would much appreciate a solution that even I could understand... ;-) Now, I want to calculate a

Hello everyone, So I ran an anova with aov and then I want to run post-hoc comparisons but keep receiving this message : > no applicable method for "TukeyHSD" Here is my code: > d<-read.table("d.txt") > d > Obs subj Hand Gaze RT > 1 1 s1 1 1 401.4 > 2 2 s2 1 1 363.3...... > summary(ano <-

Hi everyone, I'm trying to do a 2-way repeated measures ANOVA with data that looks like this: subject block rep day light response 1 1 1 one L1 5.5 2 1 2 one L1 4.5 3 1 1 one L2 4 4 1 2 one L2 5.1 5 2 1 one L1 5.3 6 2 2 one L1

Hi I'd really like to get a bar plot showing the means of my anova data. I have looked everywhere and can only seem to find instructions for 2 way anova's. I basically want to look at the mean condition of my subjects spilt by age, sex and year (as a factor rather than a continuous variable, hence Anova and not Ancova). and want to show it firstly as a bar graph with standard error. I

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)

Hi, I just started with R and I found that there are many options to rearrange the data to do mixed models. I want to use the reshape function. I have 2 between subject variables and one within. I was able to change the data structure but still - the result of the aov functions are calculating everything as a within subject. the table looks like this: SerialNo breed treatment distance_1

Is there a standardized way in R to perform post-hoc comparisons for main adn interaction effects of between- and within-subject factors? For instance, I have a data set of performance of adults of different age groups (20-30, 60-70,70-80) performing a WM task (n-back, with n=1,2,3,4) in two different conditions (while sitting or walking). The corresponding ANOVA produces the following output

Hello, I want to know if there are some functions or packages to solve differential and integral equation using R. Thanks. Shao chunxuan. [[alternative HTML version deleted]]

I'm interested in plotting a y with an x factor as the combination of 2 factors and colour with respect to a third, which the code below does with interaction.plot(). However, this is because I redefine the x to be 1 factor. Is there a way of getting it to plot without redefining it, and ideally to not join up the lines BETWEEN levels a and b, but just join those between after and before for

I am trying to plot an interaction in a multilevel model. Here is some sample data. In the following example, it is longitudinal (i.e., repeated measures), so the outcome, score (at each of the three time points), is nested within the individual. I am interested in the interaction between gender and happiness predicting score. id <- c(1,1,1,2,2,2,3,3,3) age <-