Last updated on 2024-11-23 09:49:46 CET.
| Package | NOTE | OK |
|---|---|---|
| DChaos | 10 | 3 |
Current CRAN status: NOTE: 10, OK: 3
Version: 0.1-7
Check: Rd files
Result: NOTE
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64