similar to: Question about Tukey HSD (stat package)

Dear list members, I'm running some tests looking at differences between means for various levels of a factor, using Tukey's HSD method. I would like to plot the data as boxplots or dotplots, with horizontal significance lines indicating which groups are statistically significantly different, according to Tukey HSD. Here's a nice image showing an example of such a graphical

Hi, I need to determinate HSD value from a matrix like that Thesis Days A1 Cx 0 66.07 Cx 0 60.24 Cx 0 42.86 Tw 0 66.07 Tw 0 60.24 Tw 0 42.86 Aa 0 66.07 Aa 0 60.24 Aa 0 42.86 Qe 0 66.07 Qe 0 60.24 Qe 0 42.86 Cx 56 310.86 Cx 56 223.17 Cx 56 186.77 Tw 56 149.42 Tw 56 127.75 Tw 56 138.59 Aa 56 130.24 Aa 56 214.83 Aa 56 137.95 Qe 56 186.64 Qe 56 189.09 Qe 56 187.87 this is my script

Hi everyone I'm using Tukey HSD as post-hoc test following a lme analysis. I'm measuring hemicelluloses in different species treated with three different CO2 concentrations (l=low, m=medium, h=high). The whole experiment is a split-plot design and the Tukey-function from the package multcomp is suitable for lme-analysis with random factors. The analysis works fine but I get a non

Dear all Is there any way of doing a Tukey HSD post-hoc test after a Kruskal- Wallis or Friedman rank sum test (in the ctest package)? Thanks in advance, Albertus Dr. Albertus J. Smit Department of Botany University of Cape Town Private Bag Rondebosch 7700 South Africa Tel. +27 21 689 3032

Hi, I have been trying to work with error terms given back from aov to make confidence intervals. However, the numbers seem to be incorrect. If there is more than one term in the ANOVA then the error terms can be inflated by the number of factors in the extra terms. The F's are correct so it is right back to the SS. I was wondering if this is standard practice for stats programs or

Dear all, This is a pure statistical question, not necessarly related to R. I could not find it in literature. Suppose I'm intersted in a parameter rho, say, equal to: r=beta1/beta2, where beta1 and beta2 come from a linear model y=beta0+beta1X1+beta2X2+.... Fitting the model I can get the (biased) estimate of r=b1/b2, where b1 and b2 are the estimates in the regression model; I can get the

The help for the round function states: round rounds the values in its first argument to the specified number of decimal places (default 0). Note that for rounding off a 5, the IEEE standard is used, ``go to the even digit''. Therefore round(0.5) is 0 and round(-1.5) is -2. Could someone provide a reference for this standard? Thanks, Anne ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Anne E.

Hey Folks: New to R and learning as I go, but really, I know just enough to get myself into trouble. I've waded through everything up till now, and don't see anything in the search that is directly helpful for the POS that I've created. The GOAL: All I want in the world is a program that performs 1-way ANOVA's on every column in a data set (taking the first column as the

Hi everyone, I am fairly new to R, and I am aware that others have had this problem before, but I have failed to solve the problem from previous replies I found in the archives. As this is such a standard procedure in psychological science, there must be an elegant solution to this...I think. I would much appreciate a solution that even I could understand... ;-) Now, I want to calculate a

Dear colleague: Actually that is what is done. When using the z-test between proportions in two different groups, or using chi-squared to test the null hypothesis of equal proportions of two or more groups, the null hypothesis is that H0: p1=p2=p3.....=p where this p is the pooled proportion, the proportion in the summed groups = n1+n2+n3+..../N1+N2+N3.... in the z-test between two

Hi, i am working on a data set with EDA. That includes QQ-Plots of residuals vs expected normal distribution. What puzzles me is that the range of ordinate and abscissae is so different: while the theoretical quantiles range from [-2, 2] the sample quantiles on the ordinate do extent from [-20, 50]. Quite obviously some kind of transformation is done. Although i intensively RTFM i could not

Is there a "canned" function in R for finding the standard error of the mean? I have tried > sem <- function(x) c(mean =mean(x), + SEM = stdev(x)/sqrt(length(x))) > sem(pnet.lai) Error in sem(pnet.lai) : couldn't find function "stdev" It looks like there is no stdev function in R Thanks, Kirk Kirk R. Wythers email: kwythers at umn.edu University of

Hi I try to implement a simple 2-factorial repeated-measure anova in the lme framework and would be grateful for a short feedback -my dependent var is a reaction-time (rt), -as dependent var I have -the age-group (0/1) the subject belongs to (so this is a between-subject factor), and -two WITHIN experimental conditions, one (angle) having 5, the other 3 (hands) factor-levels;

Hi, I am getting some strange results using round - it seems that it depends if the number before the decimal point is odd or even .... For example: > round(1.5)[1] 2> round(2.5)[1] 2 While i would expect that round(2.5) be 3 and not 2. Do you have any explanation for that? I really appreciate your input, Monica

Hi, I'm designing a experiment in order to compare the growing of several clones of a tree specie. It will be a complete randomized block design. How can I decide what model of mean comparision to choose? LSD, HSD,TukeyHSD, Duncan,...? Thanks in advance

Dear all, I am doing a one way between subjects anova in an unbalanced data set. Suppose we have "a" levels of the one factor. I want to merge the not so significantly different levels into the same cluster. Can I do a Tukey Kramer HSD and then use the following algorithm: For i in 2 : "a" For j in 1 : i-1 if mean of level i is not significantly different

Hi, I was wondering if you could help me: The following are the first few lines of my data set: subject group condition depvar s1 c ver 114.87 s1 c feet 114.87 s1 c body 114.87 s2 c ver 73.54 s2 c feet 64.32 s2 c body 61.39 s3 a ver 114.87 s3 a feet 97.21 s3 a body 103.31 etc. I entered the following ANOVA command: >dat <- read.table("mydata.txt", header=T)

dear all effect of a, b and c on d, total 48 comparisons, got one anova result in model1=aov(d~A*B*C). can we get all the result in one command? or can we interpret the whole comparisons from this result? how it work in Tukey HSD? jose ____________________________________________________________________________________ Access over 1 million songs.

Drug US1 US2 Aptecha Celebrex 235.54 269.99 121.02 Detrol LA 157.99 190.99 55.3 Flomax 166.00 190.99 93.45 Lipitor 174.99 200.99 137.7 Novaldex 108.6 129.99 22.48 Norvasc 186.66 203.99 161.93 Plavix 107.99 106.99 64.53 Prevacid 117.39 134.99 59.83 Prilosec 115.99 126.99 57.75 Zyrtec 181.1 200.99 58.79 US1=c(235.54,157.99,166,174.99,108.60,186.66,107.99,117.39,115.99,181.10)

Hello, I am a little out of date and am still using S-Plus instead of R. I haven't been able to find the right place to ask this question, so I thought I would ask it here and hope that someone can help. I am unable to located the TukeyHSD() function in S-Plus. I have been able to use the GUI to run Tukey's test, but the results don't provide a p-value. Does anyone know how I can