similar to: multiple comparison of interaction of ANCOVA

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

It's been a few weeks I'm racking my brains on how to use the function glht the package multcomp to test interactions. Unfortunately, the creator of the package forgot to put a sample in pdf package how to do it. I have looked in several places, but found nothing. If someone for the love of God can help me I'll be extremely grateful. The model is glm. -- View this message in context:

Dear list, im facing an issue of statistical data analysis that I consider myself unable to resolve in R so i hope to get some valuable insights from you. i run an ANOVA with four factors; factor4 is an between factor (two different groups measured), the others are withins (tested across /all/ subjects). accordingly, my model looks as follows: fm1

I am returning this to the R-help list. Please keep followups on the list. Yes, it can be done. It is not currently easy because multcomp doesn't have the syntax yet. Making this easy is on Torsten's to-do list for the multcomp package. See the MMC.WoodEnergy example in the HH package. The current version on CRAN is HH_1.17. Please see the discussion of this example in R-help:

In the model: lm.1 <- lm(variable ~ BLOC + TIL * YEAR , data=selvanera) I found TIL*YEAR interaction significant. Then I am trying to compare means of the different levels of TIL inside every YEAR using: mc.2 <- glht(lm.1, linfct = mcp(TIL*YEAR="Tukey")) summary(mc.2, test = univariate()) but it does not work. There is any way of doing this, like the SLICE option in

I have a data set for different time intervals. The data has three comment lines before data for each time interval. For each time interval there are 500 data points. I want to change the dataset such that I have the following format: t1 t2 t3 ................ 0.00208 0.00417 0.00625 ................. a1 a2 a3 ...................

If I have a table (we'll call it, "test") containing two columns (as below): i x1 x2 x3 x4 x5 y 0 1.125 0.232 7.160 0.0859 8.905 1.5563 7 0.920 0.268 8.804 0.0865 7.388 0.8976 15 0.835 0.271 8.108 0.0852 5.348 0.7482 22 1.000 0.237 6.370 0.0838 8.056 0.7160 29 1.150 0.192 6.441 0.0821 6.960 0.3130 37 0.990 0.202 5.154 0.0792 5.690 0.3617 44 0.840 0.184 5.896 0.0812 6.932 0.1139 58

Hi all, I try to understand the output of the summary(model). I make a simple model. > summary(m1anova) Call: glm(formula = peso ~ gen) Deviance Residuals: Min 1Q Median 3Q Max -4.3114 -2.3788 -0.9167 2.1581 5.7856 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.8660 0.9808 4.961 0.000101 *** gen2g 3.8504

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

Hello, in the data.frame "resultsfuzzy" I would like to replace the characters in the second column ("5a", "5b", ... "5e") with numbers from 1 to 5. The data.frame has 39150 entries. I seems to work on samples that are << nrow(resultsfuzzy) but it takes suspicously long. Do you have any suggestions how to make the character replacing more

I have a fairly large set of data with the following attributes: >str(raw.data) `data.frame': 1429 obs. of 16 variables: $ TStamp :`POSIXlt', format: chr "2001-11-25 02:00:00" "2001-11-25 01:55:00" "2001-11-25 01:50:00" "2001-11-25 01:45:00" ... $ iPDT.AHU14.14: num 0.0122 0.0125 0.0120 0.0120 0.0122 ... $ iPDT.AHU14.15: num 0.0121

Hello all, I am trying to minimize a function which contains a double integral, using "nlminb" for the minimization and "adapt" for the integral. The integral is over two variables (thita and radiusb) and the 3 free parameters I want to derive from the minimization are counts0, index and radius_eff. I have used both tasks in the past successfully but this is the first time

I've looked at how to speed up the transpose function in R (ie, t(X)). The existing code does the work with loops like the following: for (i = 0; i < len; i++) REAL(r)[i] = REAL(a)[(i / ncol) + (i % ncol) * nrow]; It seems a bit optimistic to expect a compiler to produce good code from this. I've re-written these loops as follows: for (i = 0, j = 0; i<len; i +=

I am trying to interpolate missing values using spline and am running into some strange problems. first, this works just fine: x<- c(1:10, rep(NA, 3), 14:20) y<- c(rnorm(10), rep(NA,3), rnorm(7)) s<- spline(x,y, n=length(x)) plot(x,y) lines(s, col="blue") but look at what happens with my real data (sorry for the long vectors here): x<- c(-9.816, -9.801, -9.801, -9.795,

Hi, I am using ccf but I could not figure out how to calculate the actual lag in number of periods from the returned results. The documentation for ccf says:"The lag is returned and plotted in units of time". What does "units of time" mean? For example: > x=ldeaths > x1=lag(ldeaths,1) > results=ccf(x,x1) > results Autocorrelations of series 'X', by lag

Hello all, I would like to do a Dunnett test in glht(). However, the factor level I want to use as the control is not the first. dunn1<-glht(model3, linfct = mcp(Container = "Dunnett"), alternative = "less") The factor container has 8 levels, so it would be nice not to manually enter in all of the contrasts. I originally discovered glht() when working with a glm model

This is what my data looks like DOC TOC TKN RM119mFeb-06 1 2 3 RM61mFeb-06 2 4 6 I have this both in a .csv and .txt I have read this in with read.csv("chemodr.csv", header=T) and this is what I get X dAmon DN.N Nitrite.N DOC OP P TKN TOC 1 RM215mFeb-06 0.000 0.1300 0.0000 2.5

Dear R list, I've been attempting to interpret the results from a three-way ANOVA. I think I understand contrasts and the R defaults for these (treatment contrasts). My question is: what is the intercept in this test? As far as I can tell, its NOT the expected value of a point that belongs to the first level of all three explanatory factors (because there is only one point that satisfies these

Can anyone help explain to me why the two codes below have different result? I thought I can use log(time)~. to replace log(time)~dist+climb+timef.I am using CVlm from DAAG package. I think nihills is preloaded with the package. Thanks in advance. > CVlm(df=nihills, form.lm=formula(log(time)~.),plotit="Observed",m=2)Analysis of Variance Table Response: log(time) Df Sum Sq

Dear R-devel, There seems to be a discrepancy in the order in which lm and rlm evaluate their arguments. This causes rlm to sometimes produce an error where lm is just fine. Here is a little script that illustrate the issue: > library(MASS) > ## create data > n <- 100 > dat <- data.frame(x=rep(c(-1,0,1), n), y=rnorm(3*n)) > > ## call lm, works fine > summary(lm(y ~