search for: sigma_l

...ted as random and to assess components of variation. However, I have an extral question about it. From the analysis, we can evaluate the variance from different labs. Let's say a training program was done to the labs, I want to see whether after the trainning program, the variance from the lab (sigma_L in the book) becomes smaller or not. Basically I need to compare \sigma_L before and after the training program. How can I do that? I think there should already be some model for this type of analysis. Thank you in advance. Dave [[alternative HTML version deleted]]

...quot;)) > aov(Conc ~ Lab + Error(Lab / Bat), data=coop, subset = Spc=="S1") However, as shown in V&R, raov also equated the expected and observed mean squares, to solve for and display the variance components associated with the random factors, \sigma_\epsilon^2, \sigma_B^2, and \sigma_L^2 in a column labeled "Est. Var.". Given the analytical forms of the expected mean squares for each stratum, I can obviously do this manually. But is there way to get R to do it automatically, a la raov? This would be particularly useful for mixed cases in which the analytical formula...

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? -

...el is described by the following > (discretisation<http://www.dict.cc/englisch-deutsch/discretisation.html>) > stochastic differential equation > > > > Lambda[t]=lambda[t-1]+kappa*lambda[t]*delta_t+epsilon_l > > R[t]=R[t-1]+mu*delta_t+epsilon_r > > epsilon_l=sigma_l*sqroot(delta_t) > > epsilon_r=sigma_r*sqroot(delta_t) > > > > Ln(S[t])=lambda[t]+R[t] > > > > The paramters for estimation are: > > kappa > > mu > > sigma_l > > sigma_r > > > > The state-space-model for this problem is:...