similar to: Quantative procedure assessing if data is normal

Hello, I would like to post the below position on your site. Thanks, Quantitative Analyst/Quantitative Developer MSIM Global Risk & Analysis, Quantitative Research & Model Review group Morgan Stanley Investment Management (MSIM), together with its investment advisory affiliates, has more than 680 investment professionals around the world and approximately $279 billion in assets under

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

I am in a situation where I have to fit a distrution, such as cauchy or normal, to an empirical dataset. Well and good, that is easy. But I wanted to assess just how good the fit is, using ks.test. I am concerned about the following note in the docs (about the example provided): "Note that the distribution theory is not valid here as we have estimated the parameters of the normal

Dear all: My logistic regression model includes one qualitative and one quantitative predictor variable, aes <- glm(p.a ~ spp * log(light), family=binomial(link=logit)), where spp is abundance of 3 species and light is subcanopy light availability varying from 0 1. I want to test differences among levels of the quantitative variable at a value of x other than the current log(light)=0.

Hi all, I am trying to produce a half-normal plot of residuals from a GLM. I have found the qqnorm function for producing a normal plot but can't figure out how to produce a half-normal. Can anyone help with this? Thanks Malcolm ---------------------- MJ Price, Social Medicine epmjp at bristol.ac.uk

Hi, there: I have a variable whose distribution is far from normal and its qqnorm is S-shape, like a logisitic plot. My purpose is to discretize it into 2 or 3 classes. (basically, a transformation from quantative to discrete). I am wondering if there is a good way to do that. thanks, Ed

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:

This question might seem silly, because I felt that it MUST be in the mailing list archives or help files somewhere, but I simply couldn't find it. I want to make some simple CDF (cumulative distribution function) plots to check whether distributions are Gaussian / normal. But in order to check how "normal" the distribution is, I really need the y-axis to be Gaussian as well

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

Hi all, I am trying to test wheater the distribution of my samples is normal with QQ plot. I have a values of water content in clays in around few hundred samples. Is the code : qqnorm(w) #w being water content qqline(w) sufficient? How do I know when I get the plots which distribution is normal and which is not? Thanks, m [[alternative HTML version

Hello, I once used the metawin software to perform a meta-analysis (see metawinsoft, Rosenberg et al.) and produced normal qqplot to test for a potential bias in the dataset. I now want to re-use the same dataset with the package metafor by W. Viechtbauer (great package btw). I run the qqnorm.rma.uni function. I use standardized effect sizes as in metawin. QQplot generated with metafor differs

Hi, I am mediocre at R, maybe 1000 hours experience, but I received an 8GB dataset and I don't know what to do with it. I have to do extensive analysis over it for my Honours thesis. I can't even import it. I've tried; - Splitting it up using the free csv-splitter-1.1.zip that seems to be working for everyone else (it doesn't work for me, it just outputs 1 single line). -

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

> De : Bill Venables <venables at acland.qld.cmis.csiro.au> > Objet : Re: [R] paste ? > Date?: mardi 25 avril 2000 08:45 > (...) > Secondly, I'm curious about the history of this kind of plot. > I've only heard it called a "Tukey mean difference" plot, (and > Trellis graphics has a function, tmd(), that does it, but no one > knows about it...).

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

RDQA is a package for Qualitative Data Analysis built upon R. It works both on the Windows and Linux/FreeBSD platforms. RQDA is an easy-to-use tool to assist in the analysis of textual data. At the present, it supports only plain text format data. All the information is stored in SQLite database via the R package of RSQLite. The GUI is based on RGtk2, via the aid of gWidgetsRGtk2. It includes a

RDQA is a package for Qualitative Data Analysis built upon R. It works both on the Windows and Linux/FreeBSD platforms. RQDA is an easy-to-use tool to assist in the analysis of textual data. At the present, it supports only plain text format data. All the information is stored in SQLite database via the R package of RSQLite. The GUI is based on RGtk2, via the aid of gWidgetsRGtk2. It includes a

Hello! I am trying with this question again: I would like to test few distributional assumptions for some behavioral response data. There are few theories about true distribution of those data, like: normal, lognormal, gamma, ex-Gaussian (exponential-Gaussian), Wald (inverse Gaussian) etc. The best way would be via qq-plot, to show to students differences. First two are trivial: qqnorm(dat$X)

I know this topic has had plenty of discussion in the last couple of days, but.... I've been trying to compare the effects of different fitted methods for systems of equations (OLS, SUR, 2SLS, 3SLS ) and would like to compare the residual plots (easy) and the qqnorm/qqplot of the fits for the different fitted methdos. For example, qqnorm( residuals( lm( q ~ p + f + a ) ) ) par( new = TRUE )