Lags vectors and covariates correctly so that an autoregressive model can be estimated by regression.

Usage

make.lags(x, lags, cov,nobs=3500)

Arguments

x Vector or matrix representing a univariate or multivariate time series. (rows are assumed to idex time)
lags Vector of time delays used in reconstruction.
nobs Maximum length of time series.
cov A vector or matrix of covariates that will be matched with the times for the independent varaible

Description

This function is used to create the appropriate data structure for a nonlinear autoregressive process of the form X_t = F(X_t-1) + e_t.

Value

x Matrix of lagged values of the time series, independent variables. The covaraites are the last columns of this matrix
y Vector of time series values, dependent variables.
nvar Number of variables or dimension of x matrix.
lags Time delays used in constructing the x matrix.
start Observation number of univariate time series used for the start of the y vector.
end Observation number of univariate time series used for the end of the y vector.
skip Information about which columns of the returned X matrix are covariates.

See Also

nnreg, rossler

Examples


make.lags(rossler.state[,1],c(1,2,3)) -> data  
# create  
# 3-d time delay vector model of the x variable of rossler system.
nnreg(data$x,data$y,5,5) -> fit # fit time series model using nnreg.

# fitting a state space model to the rossler state vector
# only one lag is neede in this case. 
make.lags(rossler.state, lags=c(1))-> data
nnreg( data$x, data$y[,1], 5,5)-> fit1
nnreg( data$x, data$y[,2], 5,5)-> fit2
nnreg( data$x, data$y[,3], 5,5)-> fit3


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