similar to: R example for ancova

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

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 ***

Hi everyone, I've only been using R for a week or so now, and am now required to work with some pretty mixed data at my hospital. Basically, we're looking to see if hypertension status (nominal) affects cardiac function (multiple & continuous). In order to do this, we've collected multiple echocardiograph data reports on each patient. 1. Each patient is coded either as 0 or 1

Hello, I am trying to test some results I have for significance. It has been recommended that I use R and I am completely new to this. Set-up: Groups: two groups of 8 subjects (16 total) Two conditions: alert and passive Measurements: responses for three different stimuli (A, B, and C) measured in each condition Experiment: Testing the order of conditions Group one: Alert A, B

Hello R experts, I have having a difficult time figuring out how to perform and interpret an ANCOVA of my nested experimental data and would love any suggestions that you might have. Here is the deal: 1) I have twelve tanks of fish (1-12), each with a bunch of fish in them 2) I have three treatments (1-3); 4 tanks per treatment. (each tank only has one treatment applied to it) 3) I sampled

I am trying to run an ANCOVA with defined error terms. Thus I have to use AOV and not lm. my response variable is proportion of mice paw prints on track plates. These plates were placed on plots that had vegetation and fruit manipulated to two levels each (present or absent), and were sampled monthly for 14 months (repeated measures). The fully crossed factor design was also blocked. My sample

I initially posted this question to one of the StackExchange sites, and they suggested that I repost my problem here. After using ezANOVA as my primary way of specifying mixed ANOVAs, I've hit a stumbling block when it come to adding a covariate to the model. I am using an ANCOVA in order to determine if there is a developmental trajectory in my data; namely, I need to be able to see the

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

Hi I compouted a multiple linear regression with repeated measures on one explanatory variable: BOLD peak (blood oxygenation) as dependent variable, and as independent variables I have: -age.group (binaray:young(0)/old(1)) -and task-difficulty measured by means of the reaction-time 'rt'. For 'rt' I have repeated measurements, since each subject did 12 different tasks. -> so

Hello, since 3 days now I try to get pgfSweave running using Eclipse + StatET. I created a super simple .Rnw file which can be viewed here: http://www.text-upload.com/read.php?id=147992&c=6441045 test.Rnw When I use sweave it works perfectly fine but when I use pgfSweave I get an error. The R output can be viewed here: http://www.text-upload.com/read.php?id=147995&c=3804886 R output

Hi all, I am trying to fit an ANOVA model in R using the aov/lm commands. I have a set of observational (i.e. no fixed experimental effects) data, in which I have identified high and low clusters of the response variable. The design is unbalanced, with 773 high cluster observations, and 523 low cluster observations. I would like to test a set of 7 correlates to see if there are significant

Hello, Despite my search, I didn't find a post-hoc test for an ANCOVA. I used the functions aov() and lm() to run the ANCOVA then I tried TukeyHSD() but it didn't work (because of the covariable is a continuous variable?). Furthermore, I would like to plot the adjusted values (i.e. the values of the tested variable taking into account the covariable). Thanks for your help! N. Poulet

Hi there, The following data is obtained from a long-term experiments. > mydata <- read.table(textConnection(" + y year Trt + 9.37 1993 A + 8.21 1995 A + 8.11 1999 A + 7.22 2007 A + 7.81 2010 A + 10.85 1993 B + 12.83 1995 B + 13.21 1999 B + 13.70 2007 B + 15.15 2010 B + 5.69 1993 C + 5.76 1995 C + 6.39 1999

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

Dear R user: I have a question about doing ANCOVA in S-plus or R. I know that many users use lm to do the regression and check the ANCOVA. But is there a way to get the traditional Table form of the ANCOVA test through S-plus (like what we would get from SPSS or SAS)? The problem I’m interested in is whether or not there is a treatment effect on some medical measurement. I will

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'm quite sure that this is a stupid question, but I'll ask anyway. I want to perform an ANCOVA with two continuous factors and three categorical factors. Plant population growth rate (GR) = dependent variable Seed reduction due to herbivory (SR) = continuous explanatory variable Herbivore species (HS, 2 levels) = categorical explanatory variable Population (Pop, 24 levels) =

Dear R-helpers, I have an ANCOVA with a significant effect of the factor, which has three levels. I wish to determine which of the levels are different from each other but, because my model was fitted with lm(), I cannot use TukeyHSD. For some reason, I get different results (no significant effect of the factor) when I fit the model using aov() so, for the moment, I am using lm(). Could anyone

Hi, I did a aov and used summary to obtain the p-value. I tried many ways to extract the p-value from the summary result but failed. Among others I tried the following: > test.summary <- > summary(aov(data[,1]~time.points+Error(subject/time.points))) > test.summary Error: subject Df Sum Sq Mean Sq F value Pr(>F) Residuals 9 0.27467 0.030518 Error:

Hello! If I have a vector vec <- c(FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE FALSE) I can I create the following order of numbers based on vector vec: 1, 2, 2, 3, 3, 3, 4, 5 Whenever there is a FALSE I increase the number (starting with 1). Whenever there is a TRUE I set the same number as the previous FALSE has been assigned to. I would be happy for any input Cheers, Syrvn -- View