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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(rmsMD) > > test_check("rmsMD") Loading required package: Hmisc Attaching package: 'Hmisc' The following object is masked from 'package:testthat': describe The following objects are masked from 'package:base': format.pval, units Iteration 1 Iteration 2 Iteration 3 Iteration 4 Iteration 5 Iteration 6 Iteration 7 Iteration 8 Wald Statistic Information Variance Inflation Factors Due to Imputation: Intercept age age' age'' bmi 1.05 1.10 1.10 1.11 1.19 bmi' sex=Male smoking=Former smoking=Current 1.19 1.15 1.12 2.06 Fraction of Missing Information: Intercept age age' age'' bmi 0.05 0.09 0.09 0.10 0.16 bmi' sex=Male smoking=Former smoking=Current 0.16 0.13 0.11 0.51 d.f. for t-distribution for Tests of Single Coefficients: Intercept age age' age'' bmi 1477.18 448.01 445.50 408.58 155.68 bmi' sex=Male smoking=Former smoking=Current 157.29 249.12 331.36 15.10 The following fit components were averaged over the 5 model fits: fitted.values stats linear.predictors Wald Statistic Information Variance Inflation Factors Due to Imputation: Intercept age age' age'' bmi 1.08 1.07 1.07 1.07 1.06 bmi' sex=Male smoking=Former smoking=Current 1.10 1.09 1.98 4.16 Fraction of Missing Information: Intercept age age' age'' bmi 0.07 0.06 0.07 0.07 0.06 bmi' sex=Male smoking=Former smoking=Current 0.09 0.08 0.50 0.76 d.f. for t-distribution for Tests of Single Coefficients: Intercept age age' age'' bmi 784.73 992.09 846.51 839.26 1234.56 bmi' sex=Male smoking=Former smoking=Current 481.52 608.83 16.30 6.93 The following fit components were averaged over the 5 model fits: stats linear.predictors RCS overall p-values displayed are from Wald tests. To use LR test set `MI_lrt = TRUE` in modelsummary_rms(), and set `lrt = TRUE` in fit.mult.impute() when fitting the model. Iteration 1 Iteration 2 Iteration 3 Iteration 4 Iteration 5 Iteration 6 Iteration 7 Iteration 8 RCS overall p-values displayed are from Wald tests. To use the recommended test for this model type (LR test), please set 'x = TRUE, y = TRUE' when fitting the model. RCS overall p-values displayed are from Wald tests. To use the recommended test for this model type (LR test), please set 'x = TRUE, y = TRUE' when fitting the model. RCS overall p-values displayed are from Wald tests. To use the recommended test for this model type (LR test), please set 'x = TRUE, y = TRUE' when fitting the model. Wald Statistic Information Variance Inflation Factors Due to Imputation: Intercept age age' age'' bmi 1.05 1.10 1.10 1.11 1.19 bmi' sex=Male smoking=Former smoking=Current 1.19 1.15 1.12 2.06 Fraction of Missing Information: Intercept age age' age'' bmi 0.05 0.09 0.09 0.10 0.16 bmi' sex=Male smoking=Former smoking=Current 0.16 0.13 0.11 0.51 d.f. for t-distribution for Tests of Single Coefficients: Intercept age age' age'' bmi 1477.18 448.01 445.50 408.58 155.68 bmi' sex=Male smoking=Former smoking=Current 157.29 249.12 331.36 15.10 The following fit components were averaged over the 5 model fits: fitted.values stats linear.predictors Wald Statistic Information Variance Inflation Factors Due to Imputation: Intercept age age' age'' bmi 1.08 1.07 1.07 1.07 1.06 bmi' sex=Male smoking=Former smoking=Current 1.10 1.09 1.98 4.16 Fraction of Missing Information: Intercept age age' age'' bmi 0.07 0.06 0.07 0.07 0.06 bmi' sex=Male smoking=Former smoking=Current 0.09 0.08 0.50 0.76 d.f. for t-distribution for Tests of Single Coefficients: Intercept age age' age'' bmi 784.73 992.09 846.51 839.26 1234.56 bmi' sex=Male smoking=Former smoking=Current 481.52 608.83 16.30 6.93 The following fit components were averaged over the 5 model fits: stats linear.predictors RCS overall p-values displayed are from Wald tests. To use LR test set `MI_lrt = TRUE` in modelsummary_rms(), and set `lrt = TRUE` in fit.mult.impute() when fitting the model. Wald Statistic Information Variance Inflation Factors Due to Imputation: age age' age'' bmi bmi' 1.17 1.24 1.25 1.03 1.08 sex=Male smoking=Former smoking=Current 1.12 1.06 1.22 Fraction of Missing Information: age age' age'' bmi bmi' 0.14 0.19 0.20 0.03 0.07 sex=Male smoking=Former smoking=Current 0.10 0.06 0.18 d.f. for t-distribution for Tests of Single Coefficients: age age' age'' bmi bmi' 197.88 106.67 99.50 3962.57 794.37 sex=Male smoking=Former smoking=Current 375.55 1281.05 119.52 The following fit components were averaged over the 5 model fits: linear.predictors means stats center RCS overall p-values displayed are from Wald tests. To use LR test set `MI_lrt = TRUE` in modelsummary_rms(), and set `lrt = TRUE` in fit.mult.impute() when fitting the model. Imputation 1 Imputation 2 Imputation 3 Imputation 4 Imputation 5 Wald Statistic Information Variance Inflation Factors Due to Imputation: Intercept age age' age'' bmi 1.08 1.07 1.07 1.07 1.06 bmi' sex=Male smoking=Former smoking=Current 1.10 1.09 1.98 4.16 Fraction of Missing Information: Intercept age age' age'' bmi 0.07 0.06 0.07 0.07 0.06 bmi' sex=Male smoking=Former smoking=Current 0.09 0.08 0.50 0.76 d.f. for t-distribution for Tests of Single Coefficients: Intercept age age' age'' bmi 784.73 992.09 846.51 839.26 1234.56 bmi' sex=Male smoking=Former smoking=Current 481.52 608.83 16.30 6.93 The following fit components were averaged over the 5 model fits: stats linear.predictors Imputation 1 Imputation 2 Imputation 3 Imputation 4 Imputation 5 Wald Statistic Information Variance Inflation Factors Due to Imputation: age age' age'' bmi bmi' 1.17 1.24 1.25 1.03 1.08 sex=Male smoking=Former smoking=Current 1.12 1.06 1.22 Fraction of Missing Information: age age' age'' bmi bmi' 0.14 0.19 0.20 0.03 0.07 sex=Male smoking=Former smoking=Current 0.10 0.06 0.18 d.f. for t-distribution for Tests of Single Coefficients: age age' age'' bmi bmi' 197.88 106.67 99.50 3962.57 794.37 sex=Male smoking=Former smoking=Current 375.55 1281.05 119.52 The following fit components were averaged over the 5 model fits: linear.predictors means stats center [ FAIL 0 | WARN 0 | SKIP 37 | PASS 76 ] ══ Skipped tests (37) ══════════════════════════════════════════════════════════ • On CRAN (37): 'test-ggrmsMD_snapshot.R:18:5', 'test-ggrmsMD_snapshot.R:31:5', 'test-ggrmsMD_snapshot.R:44:5', 'test-ggrmsMD_snapshot.R:58:5', 'test-ggrmsMD_snapshot.R:72:5', 'test-ggrmsMD_snapshot.R:85:5', 'test-ggrmsMD_snapshot.R:99:5', 'test-ggrmsMD_snapshot.R:113:5', 'test-ggrmsMD_snapshot.R:126:5', 'test-ggrmsMD_snapshot.R:140:5', 'test-ggrmsMD_snapshot.R:153:5', 'test-ggrmsMD_snapshot.R:168:5', 'test-snapshot.R:32:3', 'test-snapshot.R:37:3', 'test-snapshot.R:49:3', 'test-snapshot.R:55:3', 'test-snapshot.R:62:3', 'test-snapshot.R:69:3', 'test-snapshot.R:74:3', 'test-snapshot.R:79:3', 'test-snapshot.R:84:3', 'test-snapshot.R:89:3', 'test-snapshot.R:94:3', 'test-snapshot.R:104:3', 'test-snapshot.R:110:3', 'test-snapshot.R:116:3', 'test-snapshot.R:125:3', 'test-snapshot.R:130:3', 'test-snapshot.R:135:3', 'test-snapshot.R:140:3', 'test-snapshot.R:150:3', 'test-snapshot.R:156:3', 'test-snapshot.R:162:3', 'test-snapshot.R:172:3', 'test-snapshot.R:178:3', 'test-snapshot.R:202:3', 'test-snapshot.R:228:3' [ FAIL 0 | WARN 0 | SKIP 37 | PASS 76 ] Deleting unused snapshots: • ggrmsMD_snapshot/combined-multi-panel-spline-plot.svg • ggrmsMD_snapshot/cox-spline-on-age-hazard-ratio.svg • ggrmsMD_snapshot/custom-x-y-labels-and-titles.svg • ggrmsMD_snapshot/logistic-spline-on-age-odds-ratio.svg • ggrmsMD_snapshot/logistic-spline-on-age-predicted-probability.svg • ggrmsMD_snapshot/logistic-spline-with-inferior-shading-higher.svg • ggrmsMD_snapshot/logistic-spline-with-inferior-shading-lower.svg • ggrmsMD_snapshot/ols-spline-log-x-axis.svg • ggrmsMD_snapshot/ols-spline-log-y-axis.svg • ggrmsMD_snapshot/ols-spline-np-10.svg • ggrmsMD_snapshot/ols-spline-on-age-default-options.svg • ggrmsMD_snapshot/ols-spline-with-custom-axis-limits.svg > > proc.time() user system elapsed 22.76 0.82 23.57