print.csFSS {costat} | R Documentation |

`csFSS`

object.
Print information about a `csFSS`

object.

```
## S3 method for class 'csFSS'
print(x, ...)
```

`x` |
The |

`...` |
Other arguments. |

None

Guy Nason

Cardinali, A. and Nason, Guy P. (2013) Costationarity of
Locally Stationary Time Series Using costat.
*Journal of Statistical Software*, **55**, Issue 1.

Cardinali, A. and Nason, G.P. (2010) Costationarity of locally stationary
time series. *J. Time Series Econometrics*, **2**, Issue 2, Article 1.

`findstysols`

, `plot.csFSS`

,
`summary.csFSS`

```
#
# Create dummy data
#
x1 <- rnorm(32)
y1 <- rnorm(32)
#
# Find stationary combinations. Note: normally Nsims would be much bigger
#
## Not run: ans <- findstysols(Nsims=100, tsx=x1, tsy=y1)
#
# Print this csFSS object
#
## Not run: print(ans)
#Class 'csFSS' : Stationary Solutions Object from costat:
# ~~~~~ : List with 13 components with names
# startpar endpar convergence minvar pvals tsx tsy tsxname tsyname filter.number
# family spec.filter.number spec.family
#
#
#summary(.):
#----------
#Name of X time series: x1
#Name of Y time series: y1
#Length of input series: 32
#There are 100 sets of solutions
#Each solution vector is based on 3 coefficients
#Some solutions did not converge, check convergence component for more information.
#Zero indicates successful convergence, other values mean different things and
#you should consult the help page for `optim' to discover what they mean
#For size level: 0.05
# 0 solutions appear NOT to be stationary
# 97 solutions appear to be stationary
#Range of p-values: ( 0.885 , 0.975 )
#
#Wavelet filter for combinations: 1 DaubExPhase
#Wavelet filter for spectrum: 1 DaubExPhase
```

[Package *costat* version 2.4 Index]