similar to: Specifying State Space model to decompose structural shocks

Hi, This small mail to share my observation about a recent move of mailboxes between two servers and ask about explanation and/or improvement about UIDL in dovecot. SV1 : Dovecot 0.99.14 / Red Hat Fedore Core 4 SV2 : Dovecot 1.07 / CentOS 5.2 Mailboxes in /var/spool/mail on the twoo servers. Test will be done with outlook express with option "leave message on server" checked. Goal is

Dear R-help! I have encountered strange behaviour (that is, far-off filtering, smoothing and forecast distributions under certain conditions) in the `dlm` package by Giovanni Petris. Here is an example: I use the annual hotel bookings time series data, which I model using a second order polinomial DLM. First I perform the analysis with the data in logarithmic form and everything seems to be

Hi Group, I'm trying to build a model to predict a product's sale price. I'm researching the dlm package. Looks like I should use dlmModPoly, dlmMLE, dlmFilter, dlmSmooth, and finally dlmForecast. I'm looking at the Nile River example and I have a few questions: 1. If I only want to predict future sale price based on observed sale price, I should use a univariate model,

Hi all R gurus out there, Im a kind of newbie to kalman-filters after some research I have found that the dlm package is the easiest to start with. So be patient if some of my questions are too basic. I would like to set up a beta estimation between an asset and a market index using a kalman-filter. Much littarture says it gives superior estimates compared to OLS estimates. So I would like to

Hi All, I have a problem trying to forecast using the dlm package, can anyone offer any advise? I setup my problem as follows, (following the manual as much as possible) data for example to run code CostUSD <- c(27.24031,32.97051, 38.72474, 22.78394, 28.58938, 49.85973, 42.93949, 35.92468) library(dlm) buildFun <- function(x) { dlmModPoly(1, dV = exp(x[1]), dW = exp(x[2])) } fit <-

Hello, I would be very grateful if somebody more knowledgeable then me could assist me in the following. I have two (three actually but for simplicity I will say two) models which I would like to fit jointly as a state space object. Here are the equations: (1) w = a1 + b1*(p) + e1 a1 = a1[t-1] + g1 g1 = g1[t-1] + e2 b1 = b1[t-1] + e3 (2) d = a2 + b2*(w) + e3 a2 = a2[t-1] + e4 b2 = b2[t-1] + e5

Hi all, Please help Ive been wasting days at this... I am not trying to create a PDC, I am not joining an active directory, I am not needing samba schema in my openldap (or do I?), I am not trying to use winbind and idmaps for translations from SID to UID/GID, I just want my samba server which shares linux home directories to authenticate user+password against my existing linux based openldap

Hello All, WIndows DC reports (not for the entire directory, just a portion. See outputs below). "The replication operation failed because of a schema mismatch between the servers involved.? I suspect the error is a red herring. Running several Samba DCs at multiple sites. Version 4.11.6-Debian from Louis?s repo (on Ubuntu 18) ?vdcw00? is the Windows 2012 R2 server ?cdcx15? is the Samba

I'm sure this has been asked a zillion times so I searched for hours before posting this. How can there be so little documentation on this. Point me to what to read. rsh is good: [g3p:~] smith% rsh -l a1012 sv1.domain.com Password: {a1012@sv1.domain.com:65} rsync not good: [g3p:~] smith% rsync -av a1012@sv1.domain.com:kkk.txt sync/ rsync: open connection using rsh -l a1012 sv1.domain.com

I've been working on a new package and I have a few questions regarding the behaviour of the nlm function. I've been (for better or worse) using the nlm function to fit a linear model without suppling the hessian or gradient attributes in the objective function. I'm curious as to why the nlm requires 31 iterations (for the linear model), and then it doesn't work when I try to add

Hi I am getting error : Error in plot.new() : figure margins too large Can you please help -----code is ------------------------------------------------------------ temp <- seq(5500000/2, 22000000, 5500000/2) sV2 <- c(1, 1+temp[-length(temp)]) eV2 <- temp i=1 bitmap(paste("c:/vidhu/poster/poster_56half_2", LETTERS[i], ".png", sep = ""), "png256",

I've been trying to figure out how to build a list of terms from a nonlinear model (terms() returns a error). I need to compute and evaluate the partial derivatives (Jacobian) for each equaiton in a set of equations. For example: > eqn <- q ~ s0 + s1 * p + s2 * f + s3 * a > sv2 <- c(d0=3,d1=4.234,d2=4,s0=-2.123,s1=0.234,s2=2.123,s3=4.234) > names( sv2 ) [1] "d0"

Folks: I've replaced an outer for-loop with lapply and it works great. But, I can't seem to do the following type of exception handling: tryCatch(dlmMLE(x)$value==Inf,error = function(e) NULL) which basically says if the likelihood is Inf, throw an error. But what I want it to do is just go to the next index in the list. When I was using a for-loop I used:

Jul 19 01:05:27 sv1 dovecot: auth: Error: passwd-file(aa at ddd,89...24,<>): stat(/usr/dovecot-cfg/dom-home/ddd/etc/ddd/shadow) failed: Permission denied (euid=33454(dovecot) egid=33454(dovecot) missing +x perm: /usr/dovecot-cfg/dom-home/ddd, we're not in group 33795(sysgroup), dir owned by 32072:33795 mode=0710) root at sv1 [~]# sudo -u dovecot groups dovecot sysgroup root at sv1 [~]#

On Tue, Jan 21, 2014 at 05:18:27PM +0100, Pino Toscano wrote: > + sv = caml_copy_string (fields->subkey ? fields->subkey : ""); > Store_field (v, 1, sv); Heh, sure would be nice if this was an option type :-) I believe the following should work: (1) Change CAMLlocal4 (..) at the top of the function to: CAMLlocal5 (rv, v, sv, sv2, fv); (2) Then the new code

I've been trying to figure out how to get the names of the parameter vector variables when inside the function that nlm calls to return the objective function value: knls <- function( theta, eqns, data, fitmethod="OLS", instr=NULL, S=NULL ) { ## print( names( theta ) ) # returns NULL ## get the values of the parameters for( i in 1:length( theta ) )

Dear R programming folks: I'm trying to finish off a package for non-linear simultaneous system estimation and I've been trying to figure out how to get the names of the parameter vector variables when inside the function that nlm calls to return the objective function value: knls <- function( theta, eqns, data, fitmethod="OLS", instr=NULL, S=NULL ) { ## print(

I have built a Kalman Filter model for flu forecasting as shown below. Y - Target Variable X1 - Predictor1 X2 - Predictor2 While forecasting into the future, I will NOT have data for all three variables. So, I am predicting X1 and X2 using two Kalman filters. The code is below x1.model <- dlmModSeas(52) + dlmModPoly(1, dV=5, dW=10) x2.model <- dlmModSeas(52) + dlmModPoly(1, dV=10,

If I have a web log file?as follows: #Software: Microsoft Internet Information Services 5.0 #Version: 1.0 #Date: 2007-12-03 13:50:17 #Fields: date time c-ip cs-username s-ip s-port cs-method cs-uri-stem cs-uri-query sc-status sc-bytes cs-bytes time-taken cs(User-Agent) cs(Cookie) cs(Referer) "2007-12-03 13:50:17 200.40.203.197 - 200.40.51.20 80 GET /localidades/img/nada.gif - 200 328 447 0

Dear all, I am currently trying to use the "dlm" package for Kalman filtering. My model is very simple: Y_t = F'_t Theta_t + v_t Theta_t = G_t Theta_t-1 + w_t v_t ~ N(0,V_t) = N(0,V) w_t ~ N(0,W_t) = N(0,W) Y_ t is a univariate time series (1x1) F_t is a vector of factor returns (Kx1) Theta_t is the state vector (Kx1) G_t is the identity matrix My first