similar to: SURVEY PREDICTED SEs: Problem

If I do fit<-lm(y~x) Is it possible to access the SE of the slope? (Analogous to getting the slope like this: fit$coef[2]) If not, it would be handy. (I want SE of 1/slope, and an approx way is fit$se[2]/(fit$coef[2]^2)) Thanks for any help. Bill Simpson -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

So I'm curious how SAS JBOD arrays and linux MDraid as implemented in CentOS6, and SES (SCSI/SAS Enclosure Services) backplane controllers 'get along' and how much configuration is needed to get the warning lights to work properly. scenario: whitebox server with a SAS backplane or two, daisy chained on a SAS HBA (like an LSI Logic 2008), and disks organized as several raid5/6

Can someone direct me to an R function that properly computes standard errors of data obtained from a complex survery design, i.e. perform alnalyses similiar to those that can be performed with SUDAAN, particularly for a non-parametric one-way ANOVA, e.g. signed rank test? Thanks, John John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics Baltimore VA Medical Center GRECC, University of

Dear friends I am analysing the leaf expansion of a grass species and am interested in the speed of expansion. I produced exponential models for each of the treatments and got the equation for leaf size in function of time. I want to compare the coeficients that gives the initial inclination of the exponential curve, but as they are result of the previous modelling, I have them as means and SE. Is

Hi i'm student and my final project is related to Voip. I have Asterisk almost fully configured. The next step is to accept login of users, that data is in Universitys database which uses ActiveDirectory and also Ldap and Kerberos. It's possible? I don't want authentications in sip.conf, but in other remote database. The problem is i don't have ideas how to start with. I would

The first one, i.e. "optimal"; check help for match.arg() for the idiom. -pd > On 2 Oct 2017, at 11:48 , Ashim Kapoor <ashimkapoor at gmail.com> wrote: > > Dear All, > > I am trying to use the function ses from the forecast package. > > From its help I have : > > Usage: > > ses(y, h = 10, level = c(80, 95), fan = FALSE, initial =

predict.lm(..., type="terms") gives wrong standard errors. Below, I have provided what I believe are the necessary fixes. However, there are subtleties, and the code needs careful checking. Some of the looping is surely not necessary, but it is surely best to begin with the minimum necessary changes. My tests, including checks against S-PLUS, have extended to fitting spline curves. I

Dear All, I am trying to use the function ses from the forecast package. >From its help I have : Usage: ses(y, h = 10, level = c(80, 95), fan = FALSE, initial = c("optimal", "simple"), alpha = NULL, lambda = NULL, biasadj = FALSE, x = y, ...) My query is that if I do not mention the initial value will its default value be "optimal". A MWE would be

>From e980153 Tue Apr 25 14:42:27 2000 To: r-help@stat.math.ethz.ch Subject: Wrong SEs in predict.lm(..., type="terms") For what it is worth, I am using RW-1.0.0 under Windows 98. I submitted this earlier to r-help. There is one change below to my proposed corrected code: predict.lm(..., type="terms") gives wrong standard errors. Below, I have provided what I believe are

I'm trying to replicate a time-series cross-sectional analysis (countries over years) with SEs clustered by country. ?The original analysis was done in Stata 10 with: xtreg [DV] [IVs] fe cluster(country). Using plm() in R (cran.r-project.org/web/packages/plm/index.html), I've replicated the coefficients. I sought to estimate country-clustered SEs with vcovHC(), and tried a variety of

R 2.3 Windows XP I am trying to understand lme. My aim is to run a random effects regression in which the intercept and jweek are random effects. I am comparing output from SAS PROC MIXED with output from R. The point estimates and the SEs are the same, however the DFs and the p values are different. I am clearly doing something wrong in my R code. I would appreciate any suggestions of how I can

Hi, This code: n=40 x=17 phat=x/n SE=sqrt(phat*(1-phat)/n) zstar=qnorm(0.995) E=zstar*SE phat+c(-E,E) Gives this result: [1] 0.2236668 0.6263332 The TI Graphing calculator gives the same result. Whereas this test: prop.test(x,n,conf.level=0.99,correct=FALSE) Give this result: 0.2489036 0.6224374 I'm wondering why there is a difference. D. -- View this message in context:

Thank you so much for your help. The function "dbinom" seems to work very well. However, I'm a bit lost with the "dnorm" function. Apparently, I have to compute the mean "mu" and the standard deviation "sd" but what does it mean exactly? I only have a vector of predicted response and a vector of observed response that I would like to compare! What

Simple-mindedly I tried getting MLE and SE for one-parameter model in the same way as for multi-param models. out<-nlm(fn,p=c(2),hessian=T) But sqrt(diag(solve(out$hessian))) gives the answer 1. The Hessian has only one entry, not really a matrix. diag(x) gives 1 if x is just a single number. Is this what I should be doing to get SE for MLE? sqrt(solve(out$hessian)) Thanks very much for

Simple-mindedly I tried getting MLE and SE for one-parameter model in the same way as for multi-param models. out<-nlm(fn,p=c(2),hessian=T) But sqrt(diag(solve(out$hessian))) gives the answer 1. The Hessian has only one entry, not really a matrix. diag(x) gives 1 if x is just a single number. Is this what I should be doing to get SE for MLE? sqrt(solve(out$hessian)) Thanks very much for

Hi, I've two fitted models, one binomial model with presence-absence data that predicts probability of presence and one gaussian model (normal or log-normal abundances). I would like to evaluate these models not on their capability of adjustment but on their capability of prediction by calculating the (log)likelihood between predicted and observed values for each type of model. I found

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Hi all, Im a little bit confused concerning the effect() command, effects package. I have done several glm models with family=quasipoisson: model <-glm(Y~X+Q+Z,family=quasipoisson) and then used results.effects <-effect("X",model,se=TRUE) to get the "adjusted means". I am aware about the debate concerning adjusted means, but you guys just have to trust me - it

Hi, there I am a graduate student new to coding in S who is hitting a bit of a wall at present using an "optim" function. I am running into some troubles, and was hoping someone might be able to recognize where I am going wrong. As background: I have constructed a loop that carries out a 365-day calculation for a mass-balance model. Basically, the model depends on 2 variables (p,

Hello , i am working in the functional time series using the multivariate time series data(hourly time series data). Sir? i am using FAR model more than one order for which no statistical package is available in R, so for this i convert my data into functional form and obtained the functional principle component and from those FPCA i extract their corresponding? FPCscores. Know i use the VAR model