similar to: New package for multivariate Kalman filtering, smoothing, simulation and forecasting

> On Jul 30, 2017, at 5:10 AM, Spencer Graves <spencer.graves at effectivedefense.org> wrote: > > > > On 2017-07-29 11:26 PM, Staff wrote: >> I found an example at >> http://www.bearcave.com/finance/random_r_hacks/kalman_smooth.html > > That example is signed by "Ian Kaplan". There's a box at the bottom of the page for you to email

On 2017-07-29 11:26 PM, Staff wrote: > I found an example at > http://www.bearcave.com/finance/random_r_hacks/kalman_smooth.html That example is signed by "Ian Kaplan". There's a box at the bottom of the page for you to email him. > shown > below. But it seems the structSSM function has been removed from KFAS > library or it never was part of

> structSSM Is no longer part of KFAS. All you needed to do was: library(KFAS) ?KFAS and you would have seen that if you went to the index. A structural state space model is now built up from its components, much like in LM. Look at; ?SSModel -Roy > On Jul 29, 2017, at 9:26 PM, Staff <rbertematti at gmail.com> wrote: > > I found an example at >

I found an example at http://www.bearcave.com/finance/random_r_hacks/kalman_smooth.html shown below. But it seems the structSSM function has been removed from KFAS library so it won't run. Does anyone know how to fix the code so that it runs? library(KFAS) library(tseries) library(timeSeries) library(zoo) library(quantmod) getDailyPrices = function( tickerSym, startDate, endDate ) {

Dear all, I'd like to discuss about a possible bug in function StructTS of stats package. It seems that the function returns wrong value of the log-likelihood, as the added constant to the relevant part of the log-likelihood is misspecified. Here is an simple example: > data(Nile) > fit <- StructTS(Nile, type = "level") > fit$loglik [1] -367.5194 When computing the

Dear R users, Just to clarify. I am not offering to pay someone to do my Dissertation. These 4-5 commands on Kalman Filter would be only a tiny part of my 10,000 words dissertation. A part that even after trying for a few days, I am still stuck on. I am offering ?50, just to say thanks. Regards -- View this message in context:

Hello, I would like use Kalman filter for estimating parameters of a stochastic model. I have developed the state space model but I don’t know the correct way use Kalman filter for parameter estimation. Has anybody experience in work with Kalman filter in R. I don’t know the correct function. Maybe it is - KalmanLike; but what is the correct Input? - tsmooth? -

I cannot find the equivalent of cex.axis for lattice. How does one change the size of the labels of the axis tick marks in xyplot et al.? Thanks in advance for any help. Pedro. p.s.: I looked in the FAQ and Trellis manual, but I diidn't find an answer... so I hope I am not asking about something obvious. I am using R 1.4.1 on windows 9X. Lattice 0.4-0 --

Dear all, I am confused. I have encountered some strange behaviour of gls > data(co2) > co2.y <- aggregate(co2,1,mean) > co2.y.data <- data.frame(co2=as.numeric(co2.y),year=seq(1959-1980,along=co2.y)) > co2.1.gls <- gls(co2~year+I(year^2), co2.y.data) > co2.2.gls <- update(CO2.1.gls, corr=corAR1()) > summary(CO2.2.gls) > plot(CO2.2.gls) plot shows standardized

Hi, I cant log into my Fedora core 2 box from another linux machine or an OSX machine It worked the very first time I tried then never since. I have downl?oaded the newest openssh sources, and done ./configure make make install as root but seems to still use the old openssh. I stopped and restarted the sshd any Ideas? cheers Dan I get this message with the -v flag on [daniel:~] dan%

To R programming experts, I am a undergraduate student, and now doing research personally. I apply diagonal bivariate poisson (R package "bivpois") with stochatics weighted function (refer to dixoncoles97 section 4.5 to 4.7). However I dont know how to fit this stochatical weighted function to the completed bivariate poisson model. I know that some other references for dynamic soccer

This is only partly a question about R, as I am not quite sure about the underlying statistical theory either. I have fitted a non-linear mixed-effects model with nlme. In the fixed part of the model I have a factor with three levels as explanatory variable. I would like to use Tukey HSD or a similar test to test for differences between these three levels. I have two grouping factors:

Hello: What's the preferred way to link to package documentation from a vignette using markdown? My current draft includes "[cumsum](https://stat.ethz.ch/R-manual/R-devel/library/base/html/cumsum.html)" to link to the help page for cusum{base} and "[KFAS](https://rweb.crmda.ku.edu/cran/web/packages/KFAS/KFAS.pdf)" to link to the pdf documentation for

Dear R Users, I am new to state-space modeling. I am using SSPIR package for Kalman Filter. I have a data set containing one dependent variable and 7 independent variables with 250 data points. I want to use Kalman Filter for forecast the future values of the dependent variable using a multiple regression framework. I have used ssm function to produce the state space (SS)

Hi, Duncan: On 10/18/2015 8:18 PM, Duncan Murdoch wrote: > On 18/10/2015 5:51 PM, Spencer Graves wrote: >> Hello: >> >> >> What's the preferred way to link to package documentation from a >> vignette using markdown? >> >> >> My current draft includes >>

Dear all, I have tried to calculate a GLMM fit with lmer (lme4) and glmmPQL (MASS), I also used glm for comparison. I am getting very different results from different functions, and I suspect that the problem is with our dataset rather than the functions, but I would appreciate help in deciding whether my suspicions are right. If indeed we are attempting the wrong type of analysis, some

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,

CRAN (and crantastic) updates this week New packages ------------ Updated packages ---------------- New reviews ----------- This email provided as a service for the R community by http://crantastic.org. Like it? Hate it? Please let us know: cranatic at gmail.com.

Hello everyone, is there a package/packages for factor analysis, particularly PCA? thanks, Katja Katja L??ytynoja Taitoniekantie 9 A 218 40 740 Jyv??skyl?? Finland tel.+35814 608058 cell.+35850 336 0174 kaloytyn at jyu.fi

  Hello All,   I am working with dlm for the purpose of estimating and forecasting with a Kalman filter model. I have succesfully set up the model and started generating results. Of course, I need to somehow be sure that the results make sense. Without any apparent target to compare with, my natural selection is the results by odinary least square. The idea being that if I choose a diffuse prior,