search for: esim

...0.694 (Dispersion parameter for binomial family taken to be 1) Null deviance: 62.687 on 49 degrees of freedom Residual deviance: 62.530 on 48 degrees of freedom AIC: 66.53 Number of Fisher Scoring iterations: 4 But I would like to get the p-value [column heading Pr(>|z|)] for the esimate. I can get the coefficient estimates with myprobit$coefficients. Is there something similar to get the p-value? Thank you in advance. -- View this message in context: http://r.789695.n4.nabble.com/Getting-p-value-from-summary-output-tp3300503p3300503.html Sent from the R help mailing list arch...

Hi list, I have a group of data. It looks like they follow a exponential distribution. In R, how can I esimate lamda, that is the rate in pexp, of the distribution and can I use Kolmogorov-Smirnov for hypothesis testing in such a situation? I have read the "8.2 Examing the distribution of a set of data" of "An Introduction to R" but I did not find any clues on this issue. (The data i...

Anyone know how to esimate parameters in the system: x[k]=Ax[k-1]+ B + Gv[k-1] y[k]=x[k]+Du[k]+Hw[k] a system with exogenous u[k] in the measurement eq., v,w are iid, both eq. are gaussian. Thanks, Oyvind --------------------------------- [[alternative HTML version deleted]]

...subset=(id==1)) but ,you can see the variable "id" is quite irregular,they are not arranaged in order and many number missing,if I write a loop by using "for",it will give me a lot "NA", and for sure ,I dont want to type id=## for about 500 times so,how to get all the esimates for each id,and exclude the NA,then record those estimate in one table? great thanks ~~ [[alternative HTML version deleted]]

Hi, I am new to time-dependent Cox model to estimate time dependent hazard ratios. Let me use aml dataset from survival package: > aml3<-survSplit(aml2,cut=c(5,10,20),end="time",start="start", event="status",episode="i") If I want to esimate hazard ratio for each of the time intervals 0-5, 5-10, 10-20 and >=20, would the following calculate such HRs? > coxph(Surv(start,time,status)~x*as.factor(i),data=aml3) Call: coxph(formula = Surv(start, time, status) ~ x * as.factor(i), data = aml3) c...

...ple site. I was trying to use lme function to discribe relationship between y and a few covariates. Both y and covariates were measured at the sample site level. My question is when I use exactlly the same model but choose different estimation method (ML vs REML), I got quite different coefficients esimate for fixed effect and variance estimate for random effect(see below). Can anyone here please help me to understand why? Thank you very much.       1) Using REML lme(y~ Region*(x1+x2+x3), random=~1|unit, data=temp)   Linear mixed-effects model fit by REML  Data: temp         AIC      BIC    logLik...

Hi, is there any similar function in R to S function qq.loglogistic, which produces a Q-Q plot? Thanks a lot Pete

Hello, I am trying to optimise a nonlinear model to derive 'best-fit' parameter esimates using the genoud function. I have been using the genetic algorithm - gafit - in order to do this, but I am getting parameter estimates that do not always reach the global minimum. I am very keen to apply genoud to optimising this model to see if my results will improve, and also out of personal...

....040177 sigma1hat=0.01222 sigma2hat=0.00325 My problem is - and thats why I feel uncomfortable about relying on the values- that the ouput of boot.se(normalmix) varies quite strong. So without chanigng the code an drerun it (with the same normalmix, so normalmix is not rerun again) I get different esimates of the standard errors. I increased the default value for B from 100 to 1000. In the manual there is nothing said about any other randomness. So where does it com from? What should I do now? [[alternative HTML version deleted]]

Hi, I would appreciate of someone could explain how the residual standard error is computed for rlm models (MASS package). Usually, one would expect to get the residual standard error by > sqrt(sum((y-fitted(fm))^2)/(n-2)) where y is the response, fm a linear model with an intercept and slope for x and n the number of observations. This does not seem to work for rlm models and I am wondering

Problem: Based on a n x 2 data matrix i want a kernel estimate of the bivariate density. However, i also wish to specify wich points the density should be calculated at. I can offcourse just write the full kernel density estimate as a R-code, but surely there must already exist some package for this operation? The package density(), seems to create a new matrix (depending on n), where the

Dear R-helpers, I'm not a speciallist in writing complex functions, and the function still very rusty (any kind of suggestions are very welcome). I want to implement Gale-Shapley algorithm for R Language. It is based on http://www.jstor.org/stable/10.2307/2312726 Gale and Shapley (1962) , and it has evolved to