similar to: Non-linear Weibull model for aggregated parasite data

Announcing the the 0.2.0 release of parasite and the camping_generator. Parasite (from the project page -- http://parasite.rubyforge.org/): Camping app developers no longer have any reason to envy their Ruby on Rails friends: Parasite brings generators, environments, and other Rails-y goodness to the world of Camping app development. Parasite is currently at version 0.2. The Parasite package is

All, I see this item on the merb-0.0.8 release notes Added rails session parasite mode. I am assuming merbs can piggy back off the rails current session. How does one achieve that ? Thanks Fernand -------------- next part -------------- An HTML attachment was scrubbed... URL: http://rubyforge.org/pipermail/merb-devel/attachments/20070112/0803f46e/attachment.html

Hi all, I want to arrange two ggplot2 plots on the same page with their x-axes lined up - even though one is a boxplot and the other is a line plot. Is there a simple way to do this? I know I could do this using facetting if they were both the same type of plot (for example, if they were both boxplots), but I haven't been able to figure it out for two different types of plots. Below is my

When syncing one backup system (running dirvish, but that's not really relevant) to a second long-term backup system which uses btrfs snapshots, I get the following output: rsync: link "/parasite.oob.telegraaf.net/vservers-var-www/tree/elo/var/www/lokaalx/openx-htdocs/etc/xmlcache/.svn/prop-base/cache_etc-changes_schema-tables-core-12934.bin.svn-base" (in backup-aquagirl) =>

Dear all, I have tried to calculate a GLMM fit with lmer (lme4) and glmmPQL (MASS), I also used glm for comparison. I am getting very different results from different functions, and I suspect that the problem is with our dataset rather than the functions, but I would appreciate help in deciding whether my suspicions are right. If indeed we are attempting the wrong type of analysis, some

I have a host-parasite association matrix in which parasite species are rows and host species columns and cells contain the frequency of interactions. Some parasites are associated with many hosts, and some hosts harbor several parasites, and I want to repeatedly select only one single representative host per "generalized" (multi-host) parasite to create a new matrix in which no hosts

Dear All, Apologies for sending this email to both list, but at this point I'm not sure which one could help me the most. I have 4 sets of data, 1 test and 3 different sets of controls. The measurements are binary, with a matrix of 0 and 1 I'm measuring across time (rows, ~815) the behaviour of organelles in the cell by microscopy in response to different stimuli (several measurements

Hi everyone, I determined the presence of three types parasites in a passerine bird over two years. I would like to create a bar chart that shows the proportion infected on the y and year/parasite on the x such that each type of parasite is grouped together (single label) and a bar for each year . This would show if there have been changes in the prevalence of a the parasite over two years.

Hello all, I am hoping to use survival analysis to examine whether parasite attack increases nest death in a species of social wasp. I therefore have data for 1. Whether the nest "died" in the 6 week census period ("Status", where 1=died, 0=survived) 2. The day number of death/last recorded day it was observed alive. 3. Whether the nest was attacked by the parasite (0/1 as

Dear R community, I'm trying to analyze a model with an ordinal response variable. I wonder if clm()s (Cumulative Link Models) are appropriate in my case. The study compares parasite infestation of porpoises in 1995 and 2009. The degree of infestation is a rank (mild to severe, as ordered factor). In some parasite species clm gives meaningful results. But in one case I started to wonder. In

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

Dear all, I'm building binomial mixed-model using lme4 package. I'm able to obtain outputs properly except when I include two particular variables: date (from 23 to 34; 1 being to first sampling day) and Latitude (UTM/100 000, from 55.42 to 56.53). No "NA" is any of those variables. In those cases, I get the warning message: "nlminb failed to converge" I tried to

Hello campers, I wanted to let you know that I''ve just released my camping generator, which uses the rails generator system to create new camping apps. $ gem install camping_generator It''s the first part of my Parasite project, which aims to leverage rails for better, faster, stronger camping development. Feel free to take issue with my coding style, but submit issues at

I'm getting errors when running what seems to be a simple Weibull distribution function: This works: x <- c(23,19,37,38,40,36,172,48,113,90,54,104,90,54,157,51,77,78,144,34,29,45,16,15,37,218,170,44,121) rate <- c(.01,.02,.04,.05,.1,.2,.3,.4,.5,.8,.9) year <- c(100,50,25,20,10,5,3.3,2.5,2,1.2,1.1) library(MASS) x <- sort(x) tryCatch( f<-fitdistr(x, 'weibull'), error

Hello, I'm quite new to R and want to make a Weibull-regression with the survival package. I know how to build my "Surv"-object and how to make a standard-weibull regression with "survreg". However, I want to fit a translated or 3-parametric weibull dist to account for a failure-free time. I think I would need a new object in survreg.distributions, but I don't know how

Hi all I try to make a weibull survival analysis on R. I know make this on GLIM, and now I try to make the GLIM exercice GLEX8 on R to learning and compare the test. The variables are: time censor group bodymass In GLIM I make: $calc %s=1 $ to fit weibull rather than exponential $input %pcl weibull $ $macro model group*bodymass $endmac$ $use weibull t w %s $ Then, GLIM estimate an alpha for the

Hello What is the appropriate method to test if a given distribution is a weibull thank you meriema email meriema.belaidouni at int-evry.fr

Hello, is there a quick way of estimating Weibull parameters for some data points that are assumed to be Weibull-distributed? I guess I'm just too lazy to set up a Maximum-Likelihood estimation... ...but maybe there is a simpler way? Thanks for any hint (and yes, I've read help(Weibull) ;) Kaspar Pflugshaupt -- Kaspar Pflugshaupt Geobotanical Institute ETH Zurich, Switzerland

Dear list, dear Frank, I try to fit a Weibull survival regression model with package Design: sclear <- psm(sobj~V1+V2,dist="weibull") sobj is a one-dimensional survival object (no event indicators), V1 and V2 are factors. I get the following result: Parametric Survival Model: Weibull Distribution psm(formula = sobj ~ V1 + V2, dist = "weibull") Obs Events

Hi I am using R to fit a survival function to my data (with a weibull distribution). Data: Survival of individuals in relation to 4 treatments ('a','b','c','g') syntax: ---- > survreg(Surv(date2)~males2, dist='weibull') But I have some problems interpreting the outcome and getting the parameters for each curve. --------- Value Std.