similar to: Statistical question re assessing fit of distribution functions.

I have run a t.test in R, and received these results: Two Sample t-test data: rsa and umple t = 0.9819, df = 10, p-value = 0.3493 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -76.1541 196.1541 sample estimates: mean of x mean of y 508.3333 448.3333 Can someone give me a detailed interpretation of the above results? Specifically,

Hello! I want to run windows in xen. My linux distrution in ubuntu 6.06 amd 64. I downloaded and installed xen 3.0.2 from binaries. I found that hvmloader is not included in binary distribution :-( So I try to compile it from sources. I get following error: cpp -P -DDEBUG -DTEXTADDR=0x000D0000 vmxassist.ld > vmxassist.tmp ld -o vmxassist -m elf_i386 -nostdlib --fatal-warnings -N -T

Hi, As part of an R code assingment I have been asked to find a quantitative procedure for assessing whether or not the data are normal? I have previously used the graphical procedure using the qqnorm command. Any help/tips would be greatly appreciated as to how I should start going about this! Henry _________________________________________________________________

Hi friends I just started using wine on my Linux-machine. Mostly because I want to switch finally from Windows to Linux on my computer at work. There is just one single application I want to "take with me" and that is "GraphPad Prism" (http://www.graphpad.com/prism/Prism.htm). So I installed wine0.9.20 on my Mandriva2007. Then I used wine to install Prism in its most recent

I am analyzing a 1-to-2 matched case-control study using clogit in the survival package. I am interested in comparing and assessing fit of non-nested models. I don't want to program all the diagnostics described in Hosmer/Lemeshow (2000). Can someone proficient with clogit and assessing fit for non-nested models point me in the right direction. Many thanks! Tomas Tomas Aragon, MD, DrPH Tel:

Hello! I am trying to model data on species abundance (count data) with a poisson error distribution. I have a fixed and a random variables and thus needs a mixed model. I strongly doubt that my model is overdispersed but I don't know how to get the overdispersion parameter in a mixed model. Maybe someone can help me on this point. Secondly, it seems that quasi models cannot be implemented

Hi, using polynomial contrasts for the ordered factors in an experiment leads to much nicer covariance structure than using treatment contrasts. It is easy to assess the mean effect for each of the experimental groups. However, standard errors are provided only for the components of the orthogonal contrasts. I wonder how to assess the standard errors not of the components, but of the respective

This is a reminder. Please distribute this email. Data on both agile and plan-driven projects are welcome. To Whom It May Concern, My name is Donald Buresh, and I am a Ph.D. student at Northcentral University located in Prescott Valley, Arizona. The reason that I am writing to you is because I would like you to participate in an internet survey for my dissertation. The topic of my

Dear R-Users! I am using the svm function (e1071 package) to classify two groups using a set of 180 indicator variables. Now I am confused about the cross-validation procedure. (A) On one hand I use the setting cross=10 in the svm function to run 10 cross-validation iterations and to get an estimate of the svm's performance in prediction. (B) On the other hand most tutorials I found

Dear R-Helpers, I am looking for ways to assess quality of a predictor of variance of a random variable. Here a two related, but yet distinct, setups. 1. I observe y_t, t=1,...,T which is normally distributed with unknown variance v_t (note that the variance is time-dependent). I have two "predictors" for v_t, dubbed v1_t and v2_t, and I want to tell which predictor is better. Here

I have data like so: time datum 30 12 60 24 90 37 120 41 150 8 In addition to standard deviation, I want to measure the average of the differences in data for each time interval, i.e. average of 24-12, 37-24, 41-37, 8-41. Is there a statistical term for this task? Which package should I use please? rhelp at conference.jabber.org

Dear colleagues, I'm trying (and failing) to write the script required to generate a chart that would help me assess the forecasting accuracy of a logistic regression model by plotting the cumulative proportion of observed events occurring in cases across the range of possible predicted probabilities. In other words, let: x = any value on 0-1 scale phat_i = predicted probability of event Y

I am trying to trouble shoot one of my ISP's network and compare to my other ISPs offering. Although network 1 is reasonably fast and has low enough latency, voice quality is not good and the reason for this is not readily apparent using standard network tools. What tools can be used to assess the quality of the network in terms of it's suitability for voice? I am using ping, mtr,

Dear R users, Recent changes to the MuMIn package now means that the model averaging command (model.avg) no longer returns confidence intervals, but instead returns zvalues and corresponding pvalues for fixed effects included in models. Previously I have used this package for model selection/averaging following Greuber et al (2011) where it suggests that one should use confidence intervals from

I am trying to find methods for testing and visualizing calibration to Cox models with time-depended coefficients. I have read this nice article <http://journals.sagepub.com/doi/10.1177/0962280213497434>. In this paper, we can fit three models: fit0 <- coxph(Surv(futime, status) ~ x1 + x2 + x3, data = data0) p <- log(predict(fit0, newdata = data1, type = "expected")) lp

Hi all, I am attempting to apply a nonlinear model developed using nls to a new dataset and assess the fit of that model. At the moment, I am using the fitted model from my fit dataset as the starting point for an nls fit for my test dataset (see below). I would like to be able to view the t-statistic and p-values for each of the iterations using the trace function, but have not yet worked out

Dear R gurus I am running a GLMM that looks at whether chimpanzees spend time in shade more than sun (response variable 'y': used cbind() on counts in the sun and shade) based on the time of day (Time) and the availability of shade (Tertile). I've included some random factors too which are the chimpanzee in question (Individual) and where they are in a given area (Zone). There are

Dear All, I would like to ask a couple of questions on a LME model. I tested 4 selection lines at 4 food concentrations against a standard competitor stock. I had 3 replicate cages per selection line. In each cage I have 10 vials. I counted the number of wild type flies and competitor stock emerging in each vial. My main question is: is there any difference between selection lines? I did fit

Hi all , I have a dataframe of 200 columns and 2 rows. The first row in each column contains the frequency of cases in group I . The second row in each column contains the frequency of cases in group II. The frequency of trails is a fixed value for group I(e.g.200) and it is also another fixed values for group II (e.g. 100). The dataset looks like this :- > Mydata

Hi anonymous, ?prop.test states that it returns a list. And one of the element is 'p.value'. str() on the output of prop.test() reveals that too. So prop.test()$p.value or prop.test()["p.value"] should work. Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR