search for: esimates

I can get this summary of a model that I am running: summary(myprobit) Call: glm(formula = Response_Slot ~ trial_no, family = binomial(link = "probit"), data = neg_data, na.action = na.pass) Deviance Residuals: Min 1Q Median 3Q Max -0.9528 -0.8934 -0.8418 1.4420 1.6026 Coefficients: Estimate Std. Error z value Pr(>|z|)

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.

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

Hi, All,   My data collection is from 4 regions (a, b, c, d). Within each region, it has 2 or 3 units. Within each unit, it has measurement from about 25 sample 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

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 int...

....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