similar to: Different deviance residuals in a (similar?!?) glm example

Hi all, I met with a problem about the weighted least square regression. 1. I simulated a Normal vector (sim1) with mean 425906 and standard deviation 40000. 2. I simulated a second Normal vector with conditional mean b1*sim1, where b1 is just a number I specified, and variance proportional to sim1. Precisely, the standard deviation is sqrt(sim1)*50. 3. Then I run a WLS regression without the

Hello, All: ????? The default "simulate" method for lm and glm seems to ignore the sampling variance of the parameter estimates;? see the trivial lm and glm examples below.? Both these examples estimate a mean with formula = x~1.? In both cases, the variance of the estimated mean is 1. ??? ??????? * In the lm example with x0 = c(-1, 1), var(x0) = 2, and

I don't understand what is happening! I have a (large) object sim1, an matrix list with dim c(101,101) where each element is an 3*3 matrix. I am subsetting that with a matrix coo, of dim c(100,2), of unique indices, but the resulting object has length 99, not 100 as expected. Code reproducing the problem follows: library(RandomFields) set.seed(123) sim0 <- GaussRF(x=seq(0, 100, by=1),

I want to load many files in the R. The names of the files are "Sim1.txt", " Sim2.txt", "Sim3.txt", "Sim4.txt", "Sim5.txt" and so on. Can I read them at one time? What should I do? I can give the same names in R. Thanks. For example: > tst=paste("Sim",1:20,".txt",sep="") # the file names > tst [1]

On 2019-12-27 04:34, Duncan Murdoch wrote: > On 26/12/2019 11:14 p.m., Spencer Graves wrote: >> Hello, All: >> >> >> ? ????? The default "simulate" method for lm and glm seems to ignore the >> sampling variance of the parameter estimates;? see the trivial lm and >> glm examples below.? Both these examples estimate a mean with formula = >>

Hi, Can anyone help to write a more elegant version of my code? I am sure this can be put into a loop but I am having trouble creating the objects b1,b2,b3,...,etc. b1 <- rigamma(50,1,1) theta1 <- rgamma(50,0.5,(1/b1)) sim1 <- rpois(50,theta1) b2 <- rigamma(50,1,1) theta2 <- rgamma(50,0.5,(1/b2)) sim2 <- rpois(50,theta2) b3 <- rigamma(50,1,1) theta3 <-

Dear all, I use Trixbox as my PBX. Until a couple of days I've installed a GSM Gateway to communicate with our three cellular phones: 15 64227777 15 64228888 15 64229999 The GSM Gateway has just one SIM. I use the Free PBX web interface in order to set up the route and trunk parameters: Trunk: ******* Name: SIM1 Peer details: host=10.10.1.2 (IP from GSM Gateway) port=5060 type=peer

I have a dataframe with many rows like this: > df X1 X2 X3 X4 X5 X6 X7 week d sim1 FALSE TRUE TRUE TRUE TRUE TRUE TRUE 1 0.3064985 sim1 is the rowname, X1..X7,week,d are the column names. X1..X7 are factors, booleans in this case. I need to add another row, represented by the following list: list(rep(T,7),5,0.0) -- i.e, TRUE in all boolean columns,

Hi, I am using the FracSim library to simulate a time series. However, the simulate function ignores my attempt to set the RNG seed I need for reproducible research. The published docs and google have not yielded an answer, so any help greatly received. Thanks, Selwyn ## Example code snippet library(FracSim) ## simulate some 1d fractal data set.seed(1234) sim1 = fracsim.1d(h=0.5,k=1000,n=5000)

Hi all: I'm working on a project that I have packaged for ease of distribution. The different simulations in the package share code, so obviously I have those parts organized as functions. Now, I want to show people my code, but the structure with the internal functions might be a little confusing to follow. One thing I tried was to have the code of the functions as their own R files in the

Hi Folks, I've facing an intermittent hang with a USB port which seems cam related: Event's that happen are: o USB modem (HUAWEI E220) plugged into PC ugen3.2: <HUA WEI> at usbus3 u3g0: <3G Modem> on usbus3 u3g0: Found 3 ports. umass0: <USB MASS STORAGE> on usbus3 umass0: SCSI over Bulk-Only; quirks = 0x0000 umass0:6:0:-1: Attached to scbus6 umass1: <USB

Hi, all. I'm working with published paleodemographic data (counts of skeletons that have been assigned into an age-range category, based upon observed morphological characteristics). For example, the following is the age distribution from a prehistoric cemetery in Egypt: naga <-

On 26/12/2019 11:14 p.m., Spencer Graves wrote: > Hello, All: > > > ????? The default "simulate" method for lm and glm seems to ignore the > sampling variance of the parameter estimates;? see the trivial lm and > glm examples below.? Both these examples estimate a mean with formula = > x~1.? In both cases, the variance of the estimated mean is 1. That's how

dear list members, my question concerns computation of confidence intervals in nonlinear fits with `nls' when weigthing the fit. the seemingly correct procedure does not work as (I) expected. I'm posting this here since: (A) the problem might suggest a modification to the `m' component in the return argument of `nls' (making this post formally OK for this list) and (B) I got no

Hello, I need some help with the simulatedSNPs function from scrime package. I am trying to simulate some genotype of a case/control disease locus. The allele frequence are cases/controls Sample cases controls 2000 .5 .10 1500 .6 .40 In each of the row, i need to simulate 100 snp and calculate the pvalue ##############Download Scrime

Dear useRs, I want to simulate a time series (stationary; the distribution of values is skewed to the right; quite a few ARMA absolute standardized residuals above 2 - about 8% of them). Is this the right way to do it? #-------------------------------- load("rdtb") #the time series > summary(rdtb) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.11800 -0.65010 -0.09091

Good afternoon/morning readers. This is the first time I am trying to run some Bayesian computation in R, and am experiencing a few problems. I am working on a Poisson model for cancer rates which has a conjugate Gamma prior. 1) The first question is precisely how I work out the parameters. #Suppose I assign values to theta with *seq()* *theta<-seq(0,1,len=500)* #Then I try out the

2015-02-27 10:25 GMT-06:00 A J Stiles <asterisk_list at earthshod.co.uk>: > O.K. So what does your existing Dial() statement in extensions.conf look > like? > apology, put the gateway was sangoma but is a openvox , all my outgoing calls out for this context: [my-mobile-out] exten => _NXXXXXXX,n,Dial(SIP/1003/${EXTEN},55,rT) exten =>

dear R wizards:? I am wrestling with reshape.? I have a long data set that I want to convert into a wide data set, in which rows are firms and columns are years. > summary(rin) firm fyear sim1 Min. :1004.00 Min. :1964.0 Min. : -1.00000 1st Qu.:1010.00 1st Qu.:1979.0 1st Qu.: -0.14334 Median :1016.00 Median :1986.0 Median : 0.00116 Mean

Hello, I have fitted a Poisson Process model in spatstat using >fit1<-ppm(points, ~elevation, covariates=list(elevation=elevation.im)) This far, everything went well, but I try to simulate the fitted model using the function: >sim1<-rmh(fit1) But I get the error message: "Extracting model information...Evaluating trend...done. Checking arguments..determining simulation