Last updated on 2024-12-18 19:49:22 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 3.4.5 | 17.74 | 141.89 | 159.63 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 3.4.5 | 10.26 | 85.94 | 96.20 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 3.4.5 | 252.59 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 3.4.5 | 249.03 | NOTE | |||
| r-devel-windows-x86_64 | 3.4.5 | 18.00 | 163.00 | 181.00 | NOTE | |
| r-patched-linux-x86_64 | 3.4.5 | 15.70 | 139.18 | 154.88 | NOTE | |
| r-release-linux-x86_64 | 3.4.5 | 16.57 | 139.00 | 155.57 | NOTE | |
| r-release-macos-arm64 | 3.4.5 | 57.00 | NOTE | |||
| r-release-macos-x86_64 | 3.4.5 | 120.00 | NOTE | |||
| r-release-windows-x86_64 | 3.4.5 | 18.00 | 184.00 | 202.00 | NOTE | |
| r-oldrel-macos-arm64 | 3.4.5 | 63.00 | OK | |||
| r-oldrel-macos-x86_64 | 3.4.5 | 176.00 | OK | |||
| r-oldrel-windows-x86_64 | 3.4.5 | 22.00 | 191.00 | 213.00 | OK |
Version: 3.4.5
Check: Rd files
Result: NOTE
checkRd: (-1) testcoef.env.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.apweights.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with nonconstant errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.apweights.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with nonconstant errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.apweights.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with nonconstant errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.apweights.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with nonconstant errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.tcond.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with t-distributed errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.tcond.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with t-distributed errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.tcond.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with t-distributed errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.env.tcond.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with t-distributed errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.genv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta[[i]] R = A, versus Ha: L beta[[i]] R != A. The beta is estimated by the groupwise envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta[[i]] = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.genv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta[[i]] R = A, versus Ha: L beta[[i]] R != A. The beta is estimated by the groupwise envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta[[i]] = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.genv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta[[i]] R = A, versus Ha: L beta[[i]] R != A. The beta is estimated by the groupwise envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta[[i]] = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.genv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta[[i]] R = A, versus Ha: L beta[[i]] R != A. The beta is estimated by the groupwise envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta[[i]] = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.henv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the heteroscedastic envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.henv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the heteroscedastic envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.henv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the heteroscedastic envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.henv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the heteroscedastic envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.logit.env.Rd:18: Lost braces
18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.
| ^
checkRd: (-1) testcoef.logit.env.Rd:18: Lost braces; missing escapes or markup?
18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.
| ^
checkRd: (-1) testcoef.logit.env.Rd:18: Lost braces; missing escapes or markup?
18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.
| ^
checkRd: (-1) testcoef.logit.env.Rd:18: Lost braces
18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.
| ^
checkRd: (-1) testcoef.penv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta1 R = A, versus Ha: L beta1 R != A. The beta is estimated by the partial envelope model. If L = Ir, R = Ip1 and A = 0, then the test is equivalent to the standard F test on if beta1 = 0. The test statistics used is vec(L beta1 R - A) hat{Sigma}^{-1} vec(L beta1 R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta1 R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.penv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta1 R = A, versus Ha: L beta1 R != A. The beta is estimated by the partial envelope model. If L = Ir, R = Ip1 and A = 0, then the test is equivalent to the standard F test on if beta1 = 0. The test statistics used is vec(L beta1 R - A) hat{Sigma}^{-1} vec(L beta1 R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta1 R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.penv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta1 R = A, versus Ha: L beta1 R != A. The beta is estimated by the partial envelope model. If L = Ir, R = Ip1 and A = 0, then the test is equivalent to the standard F test on if beta1 = 0. The test statistics used is vec(L beta1 R - A) hat{Sigma}^{-1} vec(L beta1 R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta1 R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.penv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta1 R = A, versus Ha: L beta1 R != A. The beta is estimated by the partial envelope model. If L = Ir, R = Ip1 and A = 0, then the test is equivalent to the standard F test on if beta1 = 0. The test statistics used is vec(L beta1 R - A) hat{Sigma}^{-1} vec(L beta1 R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta1 R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.pois.env.Rd:18: Lost braces
18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.
| ^
checkRd: (-1) testcoef.pois.env.Rd:18: Lost braces; missing escapes or markup?
18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.
| ^
checkRd: (-1) testcoef.pois.env.Rd:18: Lost braces; missing escapes or markup?
18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.
| ^
checkRd: (-1) testcoef.pois.env.Rd:18: Lost braces
18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.
| ^
checkRd: (-1) testcoef.rrenv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.rrenv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.rrenv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.rrenv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.rrenv.apweights.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model that accommodates nonconstant error variance. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.rrenv.apweights.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model that accommodates nonconstant error variance. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.rrenv.apweights.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model that accommodates nonconstant error variance. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.rrenv.apweights.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model that accommodates nonconstant error variance. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.senv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.senv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.senv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.senv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.stenv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the simultaneous envelope model. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.stenv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the simultaneous envelope model. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.stenv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the simultaneous envelope model. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.stenv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the simultaneous envelope model. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.sxenv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model in the predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.sxenv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model in the predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.sxenv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model in the predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.sxenv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model in the predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.xenv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model in predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.xenv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model in predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.xenv.Rd:19: Lost braces; missing escapes or markup?
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model in predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) testcoef.xenv.Rd:19: Lost braces
19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model in predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
| ^
checkRd: (-1) xenv.Rd:28: Lost braces; missing escapes or markup?
28 | \item{eta}{The estimated eta. According to the envelope parameterization, beta = Gamma * Omega^{-1} * eta.}
| ^
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