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Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(gkwreg) > > test_check("gkwreg") ===== COMPATIBILITY ASSESSMENT ===== Fitted values correlation: 0.9992588 Log-likelihood difference: 0.4048308 Relative LL difference: 0.3664967 % ===================================== Using TMB model: kwreg Optimizing with nlminb... Computing standard errors... Call: gkwreg(formula = y ~ x1 | x2, data = data, family = "kw", control = gkw_control(hessian = TRUE)) Deviance Residuals: Min 1Q Median 3Q Max -0.406 -0.143 -0.017 0.120 0.481 Coefficients: alpha:(Intercept) alpha:x1 beta:(Intercept) beta:x2 0.66 0.29 1.15 -0.27 Link functions: alpha = log, beta = log Degrees of Freedom: 119 Total (i.e. Null); 116 Residual Residual Deviance: -63 AIC: -55 Log-Lik: 31, BIC: -43 R-squared: 0.17, RMSE: 0.19 Warning: Algorithm did not converge Number of Fisher Scoring iterations: 22 Message: relative convergence (4) Call: gkwreg(formula = y ~ x1 | x2, data = data, family = "kw", control = gkw_control(hessian = TRUE)) Deviance Residuals: Min 1Q Median 3Q Max -0.406229 -0.142906 -0.016983 0.120336 0.480620 Coefficients: alpha:(Intercept) alpha:x1 beta:(Intercept) beta:x2 0.65970 0.28887 1.14685 -0.27356 Link functions: alpha = log, beta = log Degrees of Freedom: 119 Total (i.e. Null); 116 Residual Residual Deviance: -62.616 AIC: -54.616 Log-Lik: 31.308, BIC: -43.466 R-squared: 0.16718, RMSE: 0.19366 Warning: Algorithm did not converge Number of Fisher Scoring iterations: 22 Message: relative convergence (4) Call: gkwreg(formula = y ~ x1 | x2, data = data, family = "kw", control = gkw_control(hessian = TRUE)) Deviance Residuals: Min 1Q Median 3Q Max -0.406229238 -0.142906480 -0.016983454 0.120336486 0.480619770 Coefficients: alpha:(Intercept) alpha:x1 beta:(Intercept) beta:x2 0.65970104 0.28886632 1.14684734 -0.27355883 Link functions: alpha = log, beta = log Degrees of Freedom: 119 Total (i.e. Null); 116 Residual Residual Deviance: -62.61627 AIC: -54.61627 Log-Lik: 31.308135, BIC: -43.466303 R-squared: 0.16717708, RMSE: 0.19365818 Warning: Algorithm did not converge Number of Fisher Scoring iterations: 22 Message: relative convergence (4) Call: gkwreg(formula = y ~ x1 | x2, data = data, family = "kw", control = gkw_control(hessian = TRUE)) Deviance Residuals: Min 1Q Median 3Q Max -0.40623 -0.14291 -0.01698 0.12034 0.48062 Coefficients: alpha:(Intercept) alpha:x1 beta:(Intercept) beta:x2 0.6597 0.2889 1.1468 -0.2736 Link functions: alpha = log, beta = log Degrees of Freedom: 119 Total (i.e. Null); 116 Residual Residual Deviance: -62.62 AIC: -54.62 Log-Lik: 31.31, BIC: -43.47 R-squared: 0.1672, RMSE: 0.1937 Warning: Algorithm did not converge Number of Fisher Scoring iterations: 22 Message: relative convergence (4) Generalized Kumaraswamy Regression Model Summary Family: kw Call: gkwreg(formula = y ~ x1 | x2, data = data, family = "kw", control = gkw_control(hessian = TRUE)) Residuals: Min Q1.25% Median Mean Q3.75% Max -0.4062 -0.1429 -0.0170 -0.0050 0.1203 0.4806 Coefficients: Estimate Std. Error z value Pr(>|z|) alpha:(Intercept) 0.65970 0.09148 7.212 5.53e-13 *** alpha:x1 0.28887 0.06218 4.646 3.39e-06 *** beta:(Intercept) 1.14685 0.14736 7.783 7.10e-15 *** beta:x2 -0.27356 0.15990 -1.711 0.0871 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Confidence intervals (95%): 3% 98% alpha:(Intercept) 0.4804 0.8390 alpha:x1 0.1670 0.4107 beta:(Intercept) 0.8580 1.4357 beta:x2 -0.5870 0.0398 Link functions: alpha: log beta: log Fitted parameter means: alpha: 2.043 beta: 3.192 gamma: 1 delta: 0 lambda: 1 Model fit statistics: Number of observations: 120 Number of parameters: 4 Residual degrees of freedom: 116 Log-likelihood: 31.31 AIC: -54.62 BIC: -43.47 RMSE: 0.1937 Efron's R2: 0.1672 Mean Absolute Error: 0.1573 Convergence status: Successful Iterations: 22 Generalized Kumaraswamy Regression Model Summary Family: kw Call: gkwreg(formula = y ~ x1 | x2, data = data, family = "kw", control = gkw_control(hessian = TRUE)) Residuals: Min Q1.25% Median Mean Q3.75% Max -0.4062 -0.1429 -0.0170 -0.0050 0.1203 0.4806 Coefficients: Estimate Std. Error z value Pr(>|z|) alpha:(Intercept) 0.65970 0.09148 7.212 5.53e-13 alpha:x1 0.28887 0.06218 4.646 3.39e-06 beta:(Intercept) 1.14685 0.14736 7.783 7.10e-15 beta:x2 -0.27356 0.15990 -1.711 0.0871 Confidence intervals (95%): 3% 98% alpha:(Intercept) 0.4804 0.8390 alpha:x1 0.1670 0.4107 beta:(Intercept) 0.8580 1.4357 beta:x2 -0.5870 0.0398 Link functions: alpha: log beta: log Fitted parameter means: alpha: 2.043 beta: 3.192 gamma: 1 delta: 0 lambda: 1 Model fit statistics: Number of observations: 120 Number of parameters: 4 Residual degrees of freedom: 116 Log-likelihood: 31.31 AIC: -54.62 BIC: -43.47 RMSE: 0.1937 Efron's R2: 0.1672 Mean Absolute Error: 0.1573 Convergence status: Successful Iterations: 22 Call: gkwreg(formula = y ~ x1 | x2, data = data, family = "kw", control = gkw_control(hessian = TRUE)) Deviance Residuals: Min 1Q Median 3Q Max -0.40623 -0.14291 -0.01698 0.12034 0.48062 Coefficients: alpha:(Intercept) alpha:x1 beta:(Intercept) beta:x2 0.6597 0.2889 1.1468 -0.2736 Link functions: alpha = log, beta = log Degrees of Freedom: 119 Total (i.e. Null); 116 Residual Residual Deviance: -62.62 AIC: -54.62 Log-Lik: 31.31, BIC: -43.47 R-squared: 0.1672, RMSE: 0.1937 Warning: Algorithm did not converge Number of Fisher Scoring iterations: 22 Message: relative convergence (4) Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 50 iterations): .......... Done! Simulating envelope ( 200 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Using different family (beta) than what was used to fit the model (kw) for diagnostics. Using different family (beta) than what was used to fit the model (kw). Recalculating fitted values... Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Simulating envelope ( 100 iterations): .......... Done! Using different family (beta) than what was used to fit the model (kw). Recalculating fitted values... [ FAIL 0 | WARN 0 | SKIP 0 | PASS 1177 ] > > proc.time() user system elapsed 16.98 1.87 18.87