similar to: Adjusted Means

I've run an ancova, edadysexo is a factor with 3 levels,and log(lcc) is the covariate (continous variable) I get this results > ancova<-aov(log(peso)~edadysexo*log(lcc)) > summary(ancova) Df Sum Sq Mean Sq F value Pr(>F) edadysexo 2 31.859 15.9294 803.9843 <2e-16 *** log(lcc) 1 11.389 11.3887 574.8081 <2e-16 ***

Dear R users, i'm using a custom function to fit ancova models to a dataset. The data are divided into 12 groups, with one dependent variable and one covariate. When plotting the data, i'd like to add separate regression lines for each group (so, 12 lines, each with their respective individual slopes). My 'model1' uses the group*covariate interaction term, and so the coefficients

Hi all, I have data from an experiment with 3 independent variables, 2 are within and 1 is between. In addition to the dependent variable, I have a covariate that is a single measure per subject. Below I provide an example generated data set and my approach to implementing the ANCOVA. However the output confuses me; why does the covariate only appear in the first strata? Presumably it should

Hi! as my subject says I am struggling with the different of a two-way ANOVA and a (two-way) ANCOVA. I found the following examples from this webpage: http://www.statmethods.net/stats/anova.html # One Way Anova (Completely Randomized Design) fit <- aov(y ~ A, data=mydataframe) # Randomized Block Design (B is the blocking factor) fit <- aov(y ~ A + B, data=mydataframe) # Two Way

Could you please help me on the following ANCOVA issue? This is a part of my dataset: sampling dist h 1 wi 200 0.8687212 2 wi 200 0.8812909 3 wi 200 0.8267464 4 wi 0 0.8554508 5 wi 0 0.9506721 6 wi 0 0.8112781 7 wi 400 0.8687212 8 wi 400 0.8414646 9 wi 400 0.7601675 10 wi 900 0.6577048 11 wi 900

Dear All, I am a researcher doing research in plant growth and I have a statistical problem that seems to not be able to handle. Recently, I conducted an experiment about plant growing in three different nutrient-level sediments. I harvested these every three week (three harvests in all). Some growth traits of these plants were recorded (e.g. total biomass, leaf biomass and stem biomass). In

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

Please help me interpret the following results. The full model (Schwa~Dialect*Prediction*Reduction) was reduced via both update() and step(). The minimal adequate model is: ancova<-lm(Schwa~Dialect+Prediction+Reduction+Dialect:Prediction) Schwa is response variable Dialect is factor, two levels ("QF","SF") Prediction is factor, two levels ("High","Low")

Dear list, I would like to run an ANCOVA using car::Anova with repeated measures factors, but I can't figure out how to do it. My (between-subjects) covariate always interacts with my within-subject factors. As far as I understand ANCOVA, covariates usually do not interact with the effects of interest but are simply additive (or am I wrong here?). More specifically, I can add a covariate as

I'm trying to do an ANCOVA with two factors (clipping treatment with two levels, and plot with 4 levels) and a covariate (stem diameter). The response variable is fruit number. The minimal adequate model looks like this: model3<-lm(fruit~clip + plot + st.dia + clip:plot) I'd like to get some multiple comparisons like the ones from TukeyHSD, but TukeyHSD doesn't work with the

I am interested in knowing whether and how I can test the significance of the relationship between my continuous predictor variable (a covariate) and my binary response variable according to two different groups, my categorical predictor variable, in a logistic regression model (glm). Specifically, can I determine whether the relationships are identical (the hypothesis of coincidence), or whether

Hello,   Could you please help me in the following question: I have 16 persons 6 take 0.5 mg, 6 take 0.75 mg and 4 take placebo! Can I use the ANCOVA and t-test in this case? Is it possible in R?   Thank you in advance, Samuel [[alternative HTML version deleted]]

Dear r helpers, I would like to look at the interaction between two two-level factors, one between and one within participants, after accounting for any variance due to practice (31 trials in each of two blocks) in the task. It seems to require treating practice as a covariate. All the examples I noticed for handling covariates (i.e. ANCOVA, including the ones in Faraway's "Practical

I am performing a sequential bonferroni adjustment on the results of an ANCOVA but the equation I have for calculating p-values from the t-values is not working. I can't seem to find it anywhere else. This is the code I have now: 2*(1-pt(t,df)) where t=t-value and df=degrees of freedom from the ANCOVA. Is there an error in the code? maybe another way to find the p-values? Help! -- View this

Hello R gurus, I am a beginner with R. I am doing an ANCOVA analysis using 'aov,' and need some help understanding how 'aov' works. I have a dataset (taken from http://faculty.vassar.edu/lowry/ch17pt2.html) looking at hypnotic induction. The variable 'X' is a measure of how susceptible the subject is to being hypnotized, the variable 'Y' is how well the

I have some data with two categorises plus/minus (p53) and a particular time (Time) and the outcome is a continuous vairable (Result). I set up a maximum model. ancova <- lm(Result~Time*p53) > summary(ancova) .. Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.05919 0.55646 0.106 0.916 Time -0.02134 0.01785 -1.195 0.241 p53plus

I do not think anyone has answered this. > I'm trying to run a simple one-way ANCOVA with the lmer > function in R package lme4, but have encountered some > conceptual problem. The data file MyData.txt is like this: > > Group Subj Cov Resp > A 1 3.90 4.05 > A 2 4.05 4.25 > A 3 4.25 3.60 > A 4 3.60 4.20 > A 5 4.20 4.05 > A 6 4.05 3.85

Hi R-Users, I am working with sm.ancova (in the package sm) and I have two problems with the graph, which is automatically generated when sm.ancova() is run. 1-Besides of the fitted lines, the observed data appeared automatically in the graph. I prefer that only fitted lines appear. I check the sm.options, but I could not find the way that the observed data do not appear in the graph. 2-I

Hi, i am trying to run an ANCOVA and a bootstrapped ANCOVA analysis on a specific data set. I am using the ancova and ancboot functions as in the following code: setwd("C:/Users/User/Desktop/Rdatabilingualstudy2012") bilingualismdata<-read.spss("bilingualdataforconferences2012.sav", use.value.labels = TRUE, to.data.frame = TRUE)

How to Ancova in R? I know this has got to be an FAQ, because I see it asked in the lists, but I haven't seen an answer to it. I see the R-sm has the ancova thing happening, but I kind of doubt that what I'm trying to do is "smoothing"... -- Pete Hurd phurd at uts.cc.utexas.edu http://www.zo.utexas.edu/research/phurd Section of Integrative Biology, University of Texas, Austin