similar to: Interpretation of high order interaction terms.

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 <-

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,

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]]

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'm a bit new to R and I would like to know how can I compare simple main effects when using the aov function. I'm doing a mixed model ANOVA with two between subjects variables and one within. When I get an interaction of two of the variables I don't know how to check for simple main effect of that interaction (A at B1 and A at B2 for example). The aov function is very simple but

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

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

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

I think it is not a bug. It is a general property of interactions. This property is best observed if all variables are factors (qualitative). For example, you have three variables (factors). You ask for as many interactions as possible, except an interaction term between two particular variables. When this interaction is not a constant, it is different for different values of the remaining

Dear all, Consider the model below: > x <- lm(mpg ~ cyl * disp * hp * drat, mtcars) > summary(x) Call: lm(formula = mpg ~ cyl * disp * hp * drat, data = mtcars) Residuals: Min 1Q Median 3Q Max -3.5725 -0.6603 0.0108 1.1017 2.6956 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.070e+03 3.856e+02 2.776 0.01350 * cyl

Hi, I recently ran into an inconsistency in the way model.matrix.default handles factor encoding for higher level interactions with categorical variables when the full hierarchy of effects is not present. Depending on which lower level interactions are specified, the factor encoding changes for a higher level interaction. Consider the following minimal reproducible example: -------------- >

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 Arie, Thank you for your further research into the issue. Regarding Stata: On the other hand, JMP gives model matrices that use the main effects contrasts in computing the higher order interactions, without the dummy variable encoding. I verified this both by analyzing the linear model given in my first example and noting that JMP has one more degree of freedom than R for the same model, as

Hi Arie, The book out of which this behavior is based does not use factor (in this section) to refer to categorical factor. I will again point to this sentence, from page 40, in the same section and referring to the behavior under question, that shows F_j is not limited to categorical factors: "Numeric variables appear in the computations as themselves, uncoded. Therefore, the rule does not

Hi Everybody, A new package called ``Bayesian Prediction with High-order Interactions'' is available from CRAN. The description of this package is as follows" "This R package is used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discreate

Hi Everybody, A new package called ``Bayesian Prediction with High-order Interactions'' is available from CRAN. The description of this package is as follows" "This R package is used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discreate

Hi Arie, I understand what you're saying. The following excerpt out of the book shows that F_j does not refer exclusively to categorical factors: "...the rule does not do anything special for them, and it remains valid, in a trivial sense, whenever any of the F_j is numeric rather than categorical." Since F_j refers to both categorical and numeric variables, the behavior of

Hello, I have a 2 factor linear model, in which the only terms I am interested in estimating and testing are the interaction terms. I want to control for the main effects but have no interest in estimating or testing them. However, I would like an estimate of the interaction effects for every level of the interactions, whereas what I get is one fewer estimate than this, with the first level

I?ve used logistic regression to create models to assess the effect of 3 variables on the presence or absence of a species, including the interaction terms between variables and model averaging using MuMI: model.avg The top models (delta<4) include several models with interaction terms and some models without; model weights are quite low for all models (<0.25). My problem is that the models

Dear R users, How do I drop multiplication terms from a formula using update? e.g. forml=as.formula("Surv(time, status) ~ x1+x2+A*x3+A*x4+B*x5+strata(sex)") #I would like to drop all instances of variable A (the main effect and its interactions). The following: updated.forml=update(forml, ~ . -A) #gives me this: #Surv(time, status) ~ x1 + x2 + x3 + x4 + B + x5 + strata(sex) + A:x3 +