PSM (version 0.8-6)

PSM.smooth: Smoothing of model states based on estimated population parameters.

Description

Gives estimates of model states and random effects $\eta$. The function is intended to be used based on population parameters found using PSM.estimate or to check initial values before parameter estimation.

Usage

PSM.smooth(Model, Data, THETA, subsample = 0, trace = 0, etaList = NULL)

Arguments

Model

Model list.*

Data

Data list.*

THETA

Vector of population parameters used for the state estimation.

subsample

Number of points to estimate states in between measurements. The extra points are linearly spaced.

trace

Non-negative integer. If positive, tracing information on the progress of the optimization is produced. Higher values produces more tracing information.

etaList

Matrix where each column contains an etimate of $\eta_i$. etaList has the same format as the output of PSM.estimate. If ommitted, the function will evalutate the population li

Value

• An unnamed list with one element for each individual. Each element contains the following elements:
• Time
  Possibly subsampled time-vector corresponding to the estimated states
• Xs, Ps
  Smoothed state and state co-variance estimate
• Ys
  Response based on smoothed state: Ys = g(Xs).
• Xf, Pf
  Filtered state and state co-variance estimate
• Xp, Pp
  Predicted state and state co-variance estimate
• Yp, R
  Predicted observations and observation variances
• eta
  Estimated eta
• negLogL
  Value of the negative log-likelihood function at THETA (thus same value for all individuals).

Details

The function produces three types of estimates.

[object Object],[object Object],[object Object] If subsample>0 then the data is automatically subsampled to provide estimated of the model states between observation time points.

References

Please visit http://www.imm.dtu.dk/psm or refer to the help page for PSM.

See Also

PSM, PSM.estimate, PSM.simulate, PSM.plot, PSM.template

Examples

cat("Examples are included in the package vignette.
")