similar to: who can explain the difference between the R and SAS on the results of GLM

Dear friends, I used R to analyze my data with the models of generalized linear models, and found three models were relatively good, but i can't decide which is the best,how should i do ? *Model1:* glm(formula = snail ~ grass + gheight + humidity + altitude + soiltem + airtem + grass:altitude, *family = Gamma(link = inverse*), data = model, na.action = na.exclude, control =

Dear friends, summary() doesn't give a good ouput style,e.g.: grass soiltem airtem gheight humidity altitude diluo :38 Min. :15.90 Min. :17.70 Min. : 8.00 Min. : 0.2360 high: 43 huanghuacai:32 1st Qu.:19.32 1st Qu.:22.60 1st Qu.:40.00 1st Qu.: 0.3190 low :119 hucao :46 Median :20.20 Median :25.30 Median :60.00

Import "DisplayState interface change" from qemu mainstream: the patch has been adapted to qemu-xen and merged with several following fixes. The original qemu svn commit is the following: git-svn-id: svn://svn.savannah.nongnu.org/qemu/trunk@6336 c046a42c-6fe2-441c-8c8c-71466251a162 Signed-off-by: Stefano Stabellini <stefano.stabellini@eu.citrix.com> --- diff --git a/console.c

Hi! I'm running an anova on a lm and I get the message Error: invalid dimnames given for data frame Any idea where it comes from? Many thanks. Here is the fit and the attempted anova: > fit Call: lm(formula = as.formula(paste(chemical, "a~", cmd, sep = "")), singular.ok = TRUE) Coefficients: (Intercept) Odom Displace Weight

Hi, I'm trying to match peaks between chromatographic runs. I'm able to match peaks when they are chromatographed with the same method, but not when there are different methods are used and spectra comes in to play. While searching I found the ALS package which should be usefull for my application, but I couldn't figure it out. I made some dummy chroms with R, which mimic my actual

Recently, I ran a series of Kruskal-Wallace tests [kruskal.test()] using by() to group by site Output is a list: >Herb.KW Herb.df$ID: 10-1 Kruskal-Wallis rank sum test data: Indicator_Rating by Year Kruskal-Wallis chi-squared = 15.24, df = 7, p-value = 0.03302 ----------------------------------------------------------------------------------------------------- Herb.df$ID: 18-1

Hi all, I wrote a r program as below: x <- 1:10 y <- c(3,3,3,3,3,3,3,3,3,3) fit <- lm(log(y) ~ x) summary(fit) And I expect to get some error message from R, because "y" is constant. But, I got the message as below: > summary(fit) Call: lm(formula = log(y) ~ x) Residuals: Min 1Q Median 3Q Max -6.802e-17 -3.933e-17 -1.063e-17 1.807e-17

Hello, I need to perform a mixed-model (with nesting) MANCOVA, using Type III sums of squares. I know how to perform each of these types of tests individually, but I am not sure if performing a mixed-model MANCOVA is possible. Please let me know. Erika <>< <>< <>< <>< <>< <>< <>< Erika Crispo, PhD candidate

Problem: I am sorting through model selection process for first time and want to make sure that I have used glm, stepAIC, and update correctly. Something is strange because I get a different result between: 1) a glm of 12 predictor variables followed by a stepAIC where all interactions are considered and then an update to remove one specific interaction. vs. 2) entering all the terms