similar to: How to test significant differences for non-linearrelationships for two locations

Hi List, I have to compare a relationship between y and x for two locations. I found logistic regression fits both datasets well, but I am not sure how to test if relationships for both sites are significantly different. I searched the r site, however no answers exactly match the question. I used Tukey's HSD to compare two means, but the relationship in my study was not simply linear. So I

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

Hello there, I am using nls the first time for a non-linear regression with a logistic growth function: startparam <- c(alpha=3e+07,beta=4000,gamma=2) fit <- nls(dataset$V2~(( alpha / ( 1 + exp( beta - gamma * dataset$V1 ) ) ) ),data=dataset,start=startparam) Everything works fine and i get good results. Now I would like to improve the results using my DUMMY Variable (dataset$V6) the

I have insect data from twelve sites and like most environmental data it is non-normal mostly. I would like to preform an anova and a means seperation like tukey's HSD in a nonparametric sense (on some sort of central tendency measure - median?). I am searching around at this time on the internet. Any suggestions, books, etc. would be greatly appreciated. -- Stephen Sefick Research

Hi, The problem is that I would like to do an interaction plot with intervals based on Tukey's honestly significant difference (HSD) procedure, but I do not know how to do it in R. I have 3 factors "A", "B" and "C" and a response variable "response". I would like to study a model where there are main effects and second order interaction effects.

If I want to test for a salesman effect on average sale, I model it as "Sale ~ Salesman". But what if I want to test for a salesman effect on total sales? How do I model that? Given a significant salesman effect, how do I follow up with the equivalent of Tukey's HSD to determine who the (indistinguishly) top salesmen are? If I add another factor, say "region" to the

I have a model that looks like this (with various other methods) class HsdPair attr_accessor :hsd def initialize(hsd) @hsd = hsd @current_display = 0 @has_changed_flag = false end end After initializing this object in the controller @hsd_pairs = HsdPair.new(@job.hsd_pairs) using it in the view various times and in various ways, when I try to pass this object back to the

Hi I asked a while ago about multiple comparison tests for use with multi--way ANOVAs. Thanks to all those who replied and gave me some ideas and functions. What I ended up doing was writing my own function for Tukey's HSD test (ref: Zar's Biostatistics pg 186-190), as this was the procedure I could best understand. I've included my function here for those who may be interested.

Hi, A client has a consumption measure on each of four products. The sample size is 75. The consumption distributions are highly skewed for each product. He would like a pairwise comparison test of the products, much like Tukey's HSD but using medians rather than means. Is there such a median comparison test in R? Thanks, Walt ________________________ Walter R. Paczkowski, Ph.D.

Question 1) I am using the summary.formula function, from the Hmisc, package, to construct a table of the mean establishment rates of multiple plant ecotypes. I do not want the count (N) columns included in the table. Is there a way to suppress that statistic? Question 2) The table will have multiple columns, each representing a different location. Is there a way to include Tukey's HSD at

This is a simple question, but I'm going on the supposition that the only stupid question is the one not asked. 1. I have many sets of 5 proportions that are different from each other (prop.test), and want to know which proportions are different from each other. In other words, I want the equivalent of Tukey's HSD test, but for proportions rather than anova. Here is one of the

Hello, I am really new to R and it's still a challenge to me. Currently I'm working on my Master's Thesis. My supervisor works with SAS and is not familiar with R at all. I want to run an Anova, a tukey-test and as a result I want to have the tukey-grouping ( something like A - AB - B) I came across the HSD.test in the agricolae-package, but... unfortunately I do not get an output

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

I'm using SAS for my statistical purpose and I'm triyng to jump in the R' world. How can i implement Tukey linear test in R? In sas-code my lines are : PROC GLM; BY PARCELLA; CLASS TESI; MODEL PROD=TESI; MEANS TESI/TUKEY lines; IN R: ab<-type.convert(a$tesi) a<-cbind(a,ab) cc<-anova(lm(prod~ab,data=a)) cc2<-aov(prod~ab,data=a) tt<-TukeyHSD(cc2,ordered=TRUE) but how can

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

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 R-people, I am wanting to run Factorial ANOVA followed by Scheffe tests on some spatial subjective data. I'm comparing X-Y independent coordinates against x-y dependent coordinates. There are only four independent spatial coordinates that form a square. I am wondering whether I am doing the right thing, because there doesn't seem to be a simple way of doing this. I have attempted to

Hi everyone, I woudl like to apply a Tukey post hoc after a repeated measure ANOVA. I followed the suggestions that I found in this help -list especially this one: /[R] Tukey HSD (or other post hoc tests) following repeated measures ANOVA You want to use lme() in package nlme, then glht() in the multcomp package. This will give you multiplicity adjusted p-values and confidence intervals.

Dear all, I have a serious problem in applying Tukey post hoc after a repeated measure ANOVA. I followed the suggestions that I found in this help -list especially this one: //[R] Tukey HSD (or other post hoc tests) following repeated measures ANOVA You want to use lme() in package nlme, then glht() in the multcomp package. This will give you multiplicity adjusted p-values and confidence

Dear R community I currently try to get post hoc multiple comparisons with the package multcomp from a cox mixed-effects model, where the survival is explained by two variables (cover with levels: nocover and cover; treatment with levels: tx, uv, meta), whose interaction is significant. I read Hothorn, T. 2011: Additional multcomp Examples and there is an example involving a two-way ANOVA with