similar to: nls convergence trouble

Dear R helpers, I would like to ask why polr occasionally generates results that look very odd. I have been trying to compare the power of proportional odds logistic regression with the Wilcoxon test. I generated random samples, applied both tests and extracted and compared the p-values, thus:- library(MASS) c1=rep(NA,100); c2=c1 for (run in 1:100) { dat=c(rbinom(20,12,0.65),rbinom(20,12,0.35))

I tried to compare if cch() and coxph() can generate same result for same case cohort data Use the standard data in cch(): nwtco Since in cch contains the cohort size=4028, while ccoh.data size =1154 after selection, but coxph does not contain info of cohort size=4028. The rough estimate between coxph() and cch() is same, but the lower and upper CI and P-value are a little different. Can we

I am trying to do a priori ANOVA analysis for a class assignment. The professor uses SPSS and does not know R. I want to do a simple planned comparison but have been unable to find a function or specific help. There is a grouping variable with five levels and a subsequent response variable. I also created a few columns that contain my group contrasts to see if anything could come of that. My first

Hola buenas. al final corri el siguiente código en mi máquina de casa. El problema es que ha habido algún cambio en la librería lme4, que hace incompatible los nuevos objetos lmer con la funcioón pamer.fnc. En este tipo de situaciones imagino que lo propio sería ponerme en contacto con el autor o intentar corregir yo mismo el código o incluso ambas. ¿Es decortes escribir al autor reportandole el

Greetings, all: I am using factanal in R. When I enter a matrix or a formula, the print method winds up with something like this: Test of the hypothesis that 6 factors are sufficient. The chi square statistic is 28.1 on 22 degrees of freedom. The p-value is 0.172 But when I enter a covmat, the print method winds up with something like this: The degrees of freedom for the model is 22 and the

On 23.11.2012, at 15:12, john skaller <skaller at users.sourceforge.net> wrote: > > On 23/11/2012, at 5:46 PM, Sean Silva wrote: > >> Adding LLVMdev, since this is intimately related to the optimization passes. >> >>> I think this is roughly because some function level optimisations are >>> worse than O(N) in the number of instructions. >>

Javier, Creo que aquí aplica la ley de Linus que dice: "Dado un número suficientemente elevado de ojos, todos los errores se convierten en obvios". La persona que revisa y encuentra un error no necesariamente tiene que ser la misma que la que lo escribe. Una motivación muy importante al compartir un código es la de recibir los beneficios del control de calidad por parte de tus pares.

Hello, I'm currently trying to model the movement of a slope (v.obs) with a regression model. The data can be found following the given links: either http://www.sendspace.com/file/dnugwc or http://rapidshare.com/files/420569660/sel.day.txt I want to use the Box-Cox transformation to normalize the response as well as the predictor variables. The scatterplot looks like this: library(zoo)

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

Dear, We are trying to determine the (one-sided) CI for the coefficient of variation in a small sample (say n = 10), with mean 100 and standard deviation 21. It appears though that the R-function ci.cv() and our simulation do not agree. The R-code: library(MBESS) n = 10 ci.cv(mean = 100, sd = 21, n = 10, conf.level = 0.9) U10.95 <- 0.3551754 ci.cv(mean = 100, sd = 21, n = 10, conf.level =

dear R experts: sorry, I have to ask this again. I know that the answer is in section 7.2 of "S Programming," but I don't have the book (and I plan to buy the next edition---which I hope will be titled S/R programming ;-) ). I believe the following yields a standard fixed-effects estimation: fixed.effects = as.factor( as.integer( runif(100)*10 ) ) y=rnorm(100); x=rnorm(100);

Estimado Javier Villacampa Gonzáles Si una persona se toma el trabajo de desarrollar y compartir, como es el caso de R, seguramente conoce de problemas que tuvo que decidir mientras escibía el código, y estará agradecido porque hay gente que utiliza su aporte, su trabajo no fué a la nada, todo lo contrario. En mi caso un solo autor tuvo una respuesta negativa para con migo. Javier Marcuzzi

Gracias a todos por las recomendaciones. Ya me he puesto en contacto con el autor y le explicado donde esta el error. Es tan fácil como que han cambiado la estructura de los objetos tipos lmer desde la versión 2.15.3 con lo que su paquete dejo de funcionar. Por suerte la nueva estructura conserva por lo menos la parte necesaria para hacer sus funciones. Se localizar el fallo y como arreglarlo, lo

Hace unos meses os escribir para comunicaros que había un fallo en esta función. Como os prometí os comento la respuesta por si alguno está interesado en utilizar el paquete LMERconvenientsfucntions Dear Javier, The package has been updated and should work for you fine now. Note that function mcp.fnc does not return the fourth plot (dffits) anymore. We still have to figure out a way to compute