similar to: Robust linear models and unequal variance

Hi, I am trying to fit a ANCOVA model using lmRob. The P-values of the variables in the model differ hugely between the summary() function and the anova() function (from >0.8 in the summary to <0.001in the anova for the same variable). I understand that with an ANCOVA the order in which the variables are added to the model matters and that this influences the P-value, but can this make such

Hi, I have fitted a model using a glm() approach and using a gls() approach (but without correcting for spatially autocorrelated errors). I have noticed that although these models are the same (as they should be), the AIC value differs between glm() and gls(). Can anyone tell me why they differ? Thanks, Geertje ~~~~ Geertje van der Heijden PhD student Tropical Ecology School of Geography

Hi, I have collected data on trees from 5 forest plots located within the same landscape. Data within the plots are spatially autocorrelated (calculated using Moran's I). I would like to do a ANCOVA type of analysis combining these five plots, but the assumption that there is no autocorrelation in the residuals is obviously violated. Does anyone have any ideas how to incorporate these spatial

Hi, I am trying to calculate Moran's I test for the residuals for a regression equation, but I have trouble converting my coordinates into nb format. I have used the dnearneigh() funtion now with an arbitrarily high upper distance to make it include all plots. However, when I do the lm.morantest() I get a Moran's I value which is the same as the expected value and a P-value of 1. I

Hi, I'd like some advice on bootstrapping in R. I have a species x with 20 individuals and a factor containing 0 and 1's (in this case 5 zeros and 15 ones). I want to compare the frequency of the occurrence of 1 with a probability value. This code seems to work to do this in R. attach(test) p <- c(0.5272, (1-0.5272)) sp1_1 <- length(subset(x, x==1)) sp1_0 <- length(subset(x,

Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley

I found this paper on ANOVA on unequal error variance. Has this be incorporated to any R package? Is there any textbook that discuss the problem of ANOVA on unequal error variance in general? http://www.jstor.org/stable/2532947?cookieSet=1

I'am trying to develop some code if R, which would correspond to what I did in SAS. The data look like: Treatment Replicate group1 GSI Control A 1 0.81301 Control B 1 1.06061 Control C 1 1.26350 Control D 1 0.93284 Low A 2 0.79359 Low B

Hi all, right now i am simply comparing means. obviously this can be done by the simple t.test respectively the welch test, if var.equal is set to FALSE. just like this t.test( Y ~ group) t.test( Y ~ group, var.equal = FALSE) now that i need to compare weighted means i am using the lm function as an adjusted t-test: like lmtest <- ( Y ~ group ) anova(lmtest)

Hello- I have run an ANOVA on 4 treatments with unequal sample sizes (n=9,7,10 and 10). I want to determine where my sig. differences are between treatments using a Bonferroni test, and have run the code: pairwise.t.test(Wk16, Treatment, p.adf="bonf") I receive an error message stating that my arguments are of unequal length: Error in tapply(x, g, mean, na.rm = TRUE) :

Hello, I have some patient data for my masters thesis with three groups (n=16, 19 & 20) I have completed compiling the results of 7 tests, for which one of these tests the variances are unequal. I wish to perform an ANOVA between the three groups but for the one test with unequal variance (<0.001 by both bartlett and levene's test) I am not sure what to do. I thought i would run

Hello, I?ve tried several times to learn R, but have never gotten past a particular gate. My data are organized by column in Excel, with column headers in the first row. The columns are of unequal lengths. I export them as CSV, then import the CSV file into R. I wish to summarize the data by column. R inserts NA for missing values, then refuses to operate on columns with NA. R is importing

Hi, I am doing multiple regression, and have several X variables that are categorical. I read that I can use dummy or contrast codes for that, but are there any special rules when there're unequal #observations in each groups (4 females vs 7 males in a "gender" variable)? Also, can R generate these codes for me? THanks.

Hi, How can I read data of unequal number of observations (rows) as is (i.e. without introducing NA for columns of less observations than the maximum. Example: A B C D 1 10 1 12 2 10 3 12 3 10 4 12 4 10 5 10 Thanks in advance. Sachin --------------------------------- [[alternative HTML version deleted]]

Has anyone created or rebuilt a Linux Software RAID having mirrored partitions on unequal sized hard disks ? There is a CentOS 5 server having two 400 GB hard disks with five mirrored partitions (software RAID 1) and one of the hard disks is dying. Since new 400 GB HDDs are not available here, we are exploring the possibility of replacing the faulty hard disk with one of a higher capacity (500 GB

I am using levelplot() from lattice with grids that have unequal cell sizes. This means that the boundary between two cells is not always half-way between nodes, as levelplot() assumes. The result is that some cell sizes are rendered incorrectly, which can be painfully obvious if using relatively large cells. Is there any work-around? I am using the conditioning capability of lattice and

Dear R users, I need to merge two data frames based on both equal and unequal comparisons. The "sqldf" package used to work well , but today, I cannot resolve the following error by reinstallation of the sqldf package. Can anyone suggest a different way to perform this kind of merge function? Thank you, Ding > DMRlog2pbde47DMS <- sqldf("select * from DMR_log2pbde47 as a

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Hello, I am hoping someone could shed some light on power calculations for me. I have two small data sets of unequal sample size after NA removal (m = 5, f = 7). m <- c(2.0863, 2.1340, 2.1008, 1.9565, 2.0413, NA, NA) f <- c(1.8938, 1.9709, 1.8613, 2.0836, 1.9485, 2.0630, 1.9143) In a R help message/reply from Sep 30, 2001, it was noted that the "power.t.test" function

Well fellow R users, I throw myself on your mercy. Help me, the unworthy, satisfy my employer, the ungrateful. My feeble ramblings follow... I've searched R-Help, the R Website and done a GOOGLE without success for a one way ANOVA procedure to analyse data that are both non-normal in nature and which exhibit unequal variances and unequal sample sizes across the 4 treatment levels. My