Printing marssMODEL Objects

print(MODELobj), where MODELobj is a marssMODEL object, will print out information on the model in short form (e.g. 'diagonal and equal').

summary(marssMODEL), where marssMODEL is a marssMODEL object, will print out detailed information on each parameter matrix showing where the estimated values (and their names) occur.

Usage

# S3 method for marssMODEL
print(x, ...)
# S3 method for marssMODEL
summary(object, ..., silent = FALSE)

Arguments

x

A marssMODEL object.

object

A marssMODEL object.

...

Other arguments .

silent

TRUE/FALSE Whether to print output.

Value

print(marssMODEL) prints out of the structure of each parameter matrix in 'English' (e.g. 'diagonal and unequal') and returns invisibly the list. If you assign the print call to a value, then you can reference the output.

summary(marssMODEL) prints out of the structure of each parameter matrix in as list matrices showing where each estimated value occurs in each matrix and returns invisibly the list. The output can be verbose, especially if parameter matrices are time-varying. Pass in silent=TRUE and assign output (a list with each parameter matrix) to a variable. Then specific parameters can be looked at.

Author

Eli Holmes, NOAA, Seattle, USA.

Examples

dat <- t(harborSeal)
dat <- dat[c(2, 11), ]
fit <- MARSS(dat)
#> Success! abstol and log-log tests passed at 26 iterations.
#> Alert: conv.test.slope.tol is 0.5.
#> Test with smaller values (<0.1) to ensure convergence.
#> 
#> MARSS fit is
#> Estimation method: kem 
#> Convergence test: conv.test.slope.tol = 0.5, abstol = 0.001
#> Estimation converged in 26 iterations. 
#> Log-likelihood: 11.74016 
#> AIC: -9.480311   AICc: -6.3692   
#>  
#>                                           Estimate
#> R.diag                                      0.0115
#> U.X.CoastalEstuaries                        0.0613
#> U.X.OR.NorthCoast                           0.0510
#> Q.(X.CoastalEstuaries,X.CoastalEstuaries)   0.0147
#> Q.(X.OR.NorthCoast,X.OR.NorthCoast)         0.0122
#> x0.X.CoastalEstuaries                       7.3823
#> x0.X.OR.NorthCoast                          6.2707
#> Initial states (x0) defined at t=0
#> 
#> Standard errors have not been calculated. 
#> Use MARSSparamCIs to compute CIs and bias estimates.
#> 

print(fit$model)
#> 
#> Model form is marxss. Model Structure is
#> m: 2 state process(es) named X.CoastalEstuaries X.OR.NorthCoast
#> n: 2 observation time series named CoastalEstuaries OR.NorthCoast
#> 
#> Z :  design matrix with rows: X.CoastalEstuaries X.OR.NorthCoast (2 x 2) 
#> A :  fixed and zero (2 x 1) 
#> R :  diagonal and equal (2 x 2) 
#> B :  identity (2 x 2) 
#> U :  unconstrained (2 x 1) 
#> Q :  diagonal and unequal (2 x 2) 
#> x0 :  unconstrained (2 x 1) 
#> V0 :  fixed and zero (2 x 2) 
#> D :  fixed and zero (2 x 1) 
#> C :  fixed and zero (2 x 1) 
#> d :  fixed and zero (1 x 1) 
#> c :  fixed and zero (1 x 1) 
#> G :  identity (2 x 2) 
#> H :  identity (2 x 2) 
#> L :  identity (2 x 2) 
# this is identical to
print(fit, what = "model")
#> 
#> Model form is marxss. Model Structure is
#> m: 2 state process(es) named X.CoastalEstuaries X.OR.NorthCoast
#> n: 2 observation time series named CoastalEstuaries OR.NorthCoast
#> 
#> Z :  design matrix with rows: X.CoastalEstuaries X.OR.NorthCoast (2 x 2) 
#> A :  fixed and zero (2 x 1) 
#> R :  diagonal and equal (2 x 2) 
#> B :  identity (2 x 2) 
#> U :  unconstrained (2 x 1) 
#> Q :  diagonal and unequal (2 x 2) 
#> x0 :  unconstrained (2 x 1) 
#> V0 :  fixed and zero (2 x 2) 
#> D :  fixed and zero (2 x 1) 
#> C :  fixed and zero (2 x 1) 
#> d :  fixed and zero (1 x 1) 
#> c :  fixed and zero (1 x 1) 
#> G :  identity (2 x 2) 
#> H :  identity (2 x 2) 
#> L :  identity (2 x 2)