- x
A marssMLE object.
- digits
Number of digits for printing.
- ...
Other arguments for print.
- what
What to print. Default is "fit". If you input what as a vector, print returns a list. See examples.
- "model"
The model parameters with names for the estimated parameters. The output is customized by the form of the model that was fit. This info is in attr(x$model, "form") .
- "par"
A list of only the estimated values in each matrix. Each model matrix has it's own list element. Standard function: coef(x)
- "start" or "inits"
The values that the optimization algorithm was started at. Note, x$start shows this in form="marss" while print shows it in whatever form is in attr(x$model, "form") .
- "paramvector"
A vector of all the estimated values in each matrix. Standard function: coef(x, type="vector"). See coef().
- "par.se","par.bias","par.lowCIs","par.upCIs"
A vector the estimated parameter standard errors, parameter bias, lower and upper confidence intervals. Standard function: MARSSparamCIs(x) See MARSSparamCIs().
- "xtT" or "states"
The estimated states conditioned on all the data. x$states
- "data"
The data. This is in x$model$data
- "logLik"
The log-likelihood. Standard function: x$logLik. See MARSSkf() for a discussion of the computation of the log-likelihood for MARSS models.
- "ytT"
The expected value of the data conditioned on all the data. Returns the data if present and the expected value if missing. This is in x$ytT (ytT is analogous to xtT).
- "states.se"
The state standard errors. x$states.se
- "states.cis"
Approximate confidence intervals for the states. See MARSSparamCIs().
- "model.residuals"
The one-step ahead model residuals or innovations. \(\mathbf{y}_t - \textrm{E}[\mathbf{Y}_t|\mathbf{y}_1^{t-1}]\), aka actual data at time \(t\) minus the expected value of the data conditioned on the data from \(t=1\) to \(t-1\). Standard function: residuals(x, type="tt1") See MARSSresiduals() for a discussion of residuals in the context of MARSS models.
- "state.residuals"
The smoothed state residuals. \(\mathbf{x}_t^T- \textrm{E}[\mathbf{X}_t|\mathbf{x}_{t-1}^T]\), aka the expected value of the states at time \(t\) conditioned on all the data minus the expected value of the states at time \(t\) conditioned on \(\mathbf{x}_{t-1}^T]\). Standard function: residuals(x, type="tT") See MARSSresiduals().
- parameter name
Returns the parameter matrix for that parameter with fixed values at their fixed values and the estimated values at their estimated values. Standard function: coef(x, type="matrix")$elem
- "kfs"
The Kalman filter and smoother output. See MARSSkf() for a description of the output. The full kf output is not normally attached to the output from a MARSS() call. This will run the filter/smoother if needed and return the list invisibly. So assign the output as foo=print(x,what="kfs")
- "Ey"
The expectations involving y conditioned on all the data. See MARSShatyt() for a discussion of these expectations. This output is not normally attached to the output from a MARSS() call--except ytT which is the predicted value of any missing y. The list is returned invisibly so assign the output as foo=print(x,what="Ey").