similar to: [Fwd: Re: Parameterization puzzle]

Consider the following example (based on an example in Pat Altham's GLM notes) pyears <- scan() 18793 52407 10673 43248 5710 28612 2585 12663 1462 5317 deaths <- scan() 2 32 12 104 28 206 28 186 31 102 Smoke <- gl(2,1,10,labels=c("No","Yes")) Age <- gl(5,2,10,labels=c("35-44","45-54","55-64","65-74","75-84"),

Dear all, I am having a (really) hard time getting pyears to work together with a ratetable to give me the number of expected events (deaths). I have the following data: dos, date of surgery, as.Date dof, date of last follow-up, as.Date dos, date of surgery, as.Date sex, gender, as.factor (female,male) ev, event(death), 0= censored at time point dof, 1=death at time point dof Could someone

A few weeks ago I reported a problem with model.frame, whose root lay in a formula expression "....+ ratetable(x1=x1, x2=x2, ....x100=x100)" that was really long and caused model.frame to fail. Brian had some indefinite ideas on what might need to change in the base code to handle it. In survival_2.36-1 the bit of code that generated the offending expression has been changed (mostly

Henrik wrote: I think what people are also thinking about is that the policy for publishing a package on CRAN is that it have to pass R CMD check with no errors, warnings *or* notes. So, in that sense notes are no different from warnings. --------------------------------- Getting rid of these notes would be very hard in the survival package. The population survival routines (survexp, pyears)

Dear all, I used the glm.nb with the default values from the MASS package to run a negative binomial regression. Here is a simple example: set.seed(123) y <- c( rep(0, 30), rpois(70, lambda=2) ) smoke <- factor( sample( c("NO", "YES"), 100, replace=T ) ) height <- c( rnorm(30, mean=100, sd=20), rnorm(70, mean=150, sd=20) ) fit <- glm.nb( y

I am not sure if this is the right place for that kind of questions, but I wondered that the recommended package survival did not pass R's check procedure without warnings: 1) unbalanced braces: * Rd files with unbalanced braces: * man/Surv.Rd * man/cluster.Rd * man/cox.zph.Rd * man/coxph.Rd * man/coxph.detail.Rd * man/date.ddmmmyy.Rd * man/lines.survfit.Rd *

Dear List, Is there an automated way to use the survival package to generate survival rate estimates and their standard errors? To be clear, *not *the survivorship estimates (which are cumulative), but the survival *rate * estimates... Thank you in advance for any help. Best, Brian [[alternative HTML version deleted]]

Greetings, I am trying to join a EMC Celerra NS502 CIFS server to our Samba 3.0.21a domain controller. According to EMC, I was told that we need to manually create the machine account first. How is the best way to do this? We are using an openLDAP backend, using the idealx scripts. Joining a windows machine from the computer properties dialog of that machine works perfectly. Things I have

I am working with estimates of vegetation height derived from radar data. We have a nonlinear model to correct these estimates for errors associated with viewing geometry. I am trying to estimate a single parameter in this model while accounting for spatial (spherical structure) autocorrelation. I'd also like to statistically test the influence of several vegetation parameters. The gnls()

Dear R users; I'm looking for some hint about how to deal with the following situation: Response = Y Factor A = levels: 0, 1 Factor B = levels: 0, 1 Factor C = levels: 1,2,3,4 Model: Logistic 3-parms. where th1~1+A+C, th2~1+C; th3~1 For 'simplicity' (for me) I'm using the SAS contrast parameterization. The output looks like Beta p-value th1.(Intercept) 550

Hello Everyone Both the MCMCpack and the bayesm libraries allow us to make draws from the Inverse Wishart distribution. But I wanted to find out how exactly is the Inverse Wishart distribution parameterized in these libraries. The reason I ask is the following: Now its generally standard to express Inverse Wishart as IW(0.5 * DOF,0.5* Scale). (DOF-> Degree of freedom, Scale -> Scale

Dear all, Apologies if this question is bit theoretical and for the longish email. I am meta-analyzing the coefficients and standard errors from multiple studies where the raw data is not available. Each study analyst runs a model that includes an interaction term for, say, between sex and smoking and age. Here is an illustrative example example for one study: set.seed(1066) status

Dear all, I would like to fit a non-linear model of the form: y=g*x/(a+b*x) with nls(). However this model is somehow overparameterized and I get the error message about singular gradient matrix at initial parameter estimates. What I am interested in is to make inference about parameters b and g, so this has to be taken into account in the model formulation. What options do I have? Also, how is

Hi, I am trying to parameterize the following mixed model (following Piepho and Ogutu 2002), to test for a trend over time, using multiple sites: y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij] where: y[ij]= a response variable at site i and year j mu = fixed intercept Beta=fixed slope w[j]=constant representing the jth year (covariate) b[j]=random effect of jth year, iid N(0,sigma2[b]) a[i]=random

Hi all, I encountered this problem with parameterization in R: I have two factors in a regression. how about if I want to set constraint so that for each factor, the sum of their coefficients equals to zero(instead of choosing a reference category)? for example, I have factor(variable) A(with three categories) and factor(variable) B(with 4 categories), and I want to parameterize so that the sum

> On May 12, 2016, at 11:21 PM, Elena Lepilkina via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > Hi all, > > As we understood great changes will be done in LNT, so we are waiting to new LNT version and stopped our work in LNT. > > One more question about using test-suite separately with cmake. Cmake can only build all tests and generate lit tests. After that we

Hello, helpeRs, I've been trying, unsuccessfully, to change a dataframe from long to wide format using reshape (the original). I would appreciate it if someone could demonstrate the correct syntax. The script below will create a toy example. The new wide data should have a column name for each unique entry in the "fame" column. Under each column should be either the

I am trying to use Rails and an Oracle 7 database. I have the following error message: (eval):3:in `__send__'': ORA-12663: Services required by client not available on the server (OCIError) from (eval):3:in `describe'' and the line oracle_adapter,rb:361: (owner, table_name) = @connection.describe(table_name) Do I need this describe line? Can I replace with something else just

Greetings everyone, We would like to improve LNT. The following RFC describes two LNT enhancements: * Custom (extensible) metrics * Test parameterization The main idea is in document https://docs.google.com/document/d/1zWWfu_iBQhFaHo73mhqqcL6Z82thHNAoCxaY7BveSf4/edit?usp=sharing. Thanks, Elena. -------------- next part -------------- An HTML attachment was scrubbed... URL:

Dear R community, I am new to R, a reforming SAS user :) I am running R 2.10.1 on a Windows XP machine. I would like to write linear functions of my coefficient parameter estimates from a glm, but am having a difficult time understanding the parameterization R uses. In the toy example below I am running a glm on binomial data, with clones and lines within clones as fixed effects, each with 6