diff --git a/tests/testthat/_snaps/effects_snap.md b/tests/testthat/_snaps/effects_snap.md index afbee3c..27a580c 100644 --- a/tests/testthat/_snaps/effects_snap.md +++ b/tests/testthat/_snaps/effects_snap.md @@ -6,13 +6,13 @@ Warning in `crosstable()`: Cannot describe columns `dummy_na` and `dummy_na2` as they contain only missing values/blank. Warning: - A problem occured when calculating crosstable effects (glm-logit): - i "glm.fit: fitted probabilities numerically 0 or 1 occurred" + Problems occured when calculating crosstable effects (glm-logit): + i "glm.fit: fitted probabilities numerically 0 or 1 occurred" and "collapsing to unique 'x' values" * You might want to check for complete separation or extreme outliers. * Applying `forcats::fct_rev()` to some columns might help too. Warning: - Problems occured when calculating crosstable effects (glm-logit): - i "glm.fit: fitted probabilities numerically 0 or 1 occurred" and "collapsing to unique 'x' values" + A problem occured when calculating crosstable effects (glm-logit): + i "glm.fit: fitted probabilities numerically 0 or 1 occurred" * You might want to check for complete separation or extreme outliers. * Applying `forcats::fct_rev()` to some columns might help too. Warning: @@ -100,13 +100,13 @@ Warning in `crosstable()`: Cannot describe columns `dummy_na` and `dummy_na2` as they contain only missing values/blank. Warning: - A problem occured when calculating crosstable effects (glm-logit): - i "glm.fit: fitted probabilities numerically 0 or 1 occurred" + Problems occured when calculating crosstable effects (glm-logit): + i "glm.fit: fitted probabilities numerically 0 or 1 occurred" and "collapsing to unique 'x' values" * You might want to check for complete separation or extreme outliers. * Applying `forcats::fct_rev()` to some columns might help too. Warning: - Problems occured when calculating crosstable effects (glm-logit): - i "glm.fit: fitted probabilities numerically 0 or 1 occurred" and "collapsing to unique 'x' values" + A problem occured when calculating crosstable effects (glm-logit): + i "glm.fit: fitted probabilities numerically 0 or 1 occurred" * You might want to check for complete separation or extreme outliers. * Applying `forcats::fct_rev()` to some columns might help too. Warning: @@ -163,7 +163,7 @@ 6 wt Difference in means (bootstrap CI), ref='auto'\nmanual minus auto: -1.36 [-1.84 to -0.88] 7 qsec Difference in means (t-test CI), ref='auto'\nmanual minus auto: -0.82 [-2.12 to 0.48] 8 vs Odds ratio [95% Wald CI], ref='manual vs auto'\nvshaped vs straight: 0.19 [0.02 to 1.11] - 9 gear Odds ratio [95% Wald CI], ref='manual vs auto'\n4 vs 3: 1.71e+09 [6.32e-162 to NA]\n5 vs 3: 7.30e+17 [3.18e-214 to NA] + 9 gear Odds ratio [95% Wald CI], ref='manual vs auto'\n4 vs 3: 1.71e+09 [6.32e-162 to NA]\n5 vs 3: 7.30e+17 [3.29e-214 to NA] 10 carb Difference in means (bootstrap CI), ref='auto'\nmanual minus auto: 0.19 [-1.09 to 1.46] 11 hp_date Difference in means (bootstrap CI), ref='auto'\nmanual minus auto: -33.42 [-85.88 to 19.05] 12 qsec_posix Difference in means (t-test CI), ref='auto'\nmanual minus auto: -7.11e+04 [-1.83e+05 to 4.12e+04] @@ -257,7 +257,7 @@ 6 wt Difference in medians (bootstrap CI), ref='auto'\nmanual minus auto: -1.20 [-1.77 to -0.66] 7 qsec Difference in medians (bootstrap CI), ref='auto'\nmanual minus auto: -0.80 [-2.21 to 1.17] 8 vs Risk difference [95% Wald CI], ref='manual vs auto'\nvshaped vs straight: -1.66 [-3.79 to 0.11] - 9 gear Risk difference [95% Wald CI], ref='manual vs auto'\n4 vs 3: 21.26 [-371.17 to NA]\n5 vs 3: 41.13 [-491.60 to NA] + 9 gear Risk difference [95% Wald CI], ref='manual vs auto'\n4 vs 3: 21.26 [-371.17 to NA]\n5 vs 3: 41.13 [-491.56 to NA] 10 carb Difference in medians (bootstrap CI), ref='auto'\nmanual minus auto: -1.00 [-2.00 to 2.00] 11 hp_date Difference in medians (bootstrap CI), ref='auto'\nmanual minus auto: -66.00 [-109.00 to 0] 12 qsec_posix Difference in medians (bootstrap CI), ref='auto'\nmanual minus auto: -6.91e+04 [-1.86e+05 to 1.04e+05] @@ -411,7 +411,7 @@ Output .id effect 1 mpg Difference in medians (bootstrap CI), ref='FALSE'\nTRUE minus FALSE: 1.35 [-5.53 to 4.00] - 2 cyl Risk difference [95% Wald CI], ref='TRUE vs FALSE'\n6 vs 4: 5.11e+01 [CI error]\n8 vs 4: -1.49e-14 [CI error] + 2 cyl Risk difference [95% Wald CI], ref='TRUE vs FALSE'\n6 vs 4: 5.11e+01 [CI error]\n8 vs 4: -9.05e-15 [CI error] 3 disp Difference in medians (bootstrap CI), ref='FALSE'\nTRUE minus FALSE: -108.20 [-159.00 to 78.30] 4 hp Difference in medians (bootstrap CI), ref='FALSE'\nTRUE minus FALSE: -27.00 [-75.00 to 53.35] 5 drat Difference in medians (bootstrap CI), ref='FALSE'\nTRUE minus FALSE: 0.08 [-0.95 to 0.74] @@ -461,7 +461,7 @@ 7 qsec Difference in means (t-test CI), ref='A'\nB minus A: 0.42 [-0.89 to 1.72] 8 vs Odds ratio [95% Wald CI], ref='B vs A'\nvshaped vs straight: 1.09 [0.21 to 6.03] 9 am Odds ratio [95% Wald CI], ref='B vs A'\nmanual vs auto: 0.45 [0.10 to 1.88] - 10 gear Odds ratio [95% Wald CI], ref='B vs A'\n4 vs 3: 0.63 [0.13 to 2.87]\n5 vs 3: 1.31 [0.17 to 12.27] + 10 gear Odds ratio [95% Wald CI], ref='B vs A'\n4 vs 3: 0.62 [0.13 to 2.87]\n5 vs 3: 1.31 [0.17 to 12.27] 11 carb Difference in means (bootstrap CI), ref='A'\nB minus A: 0.62 [-0.49 to 1.74] 12 hp_date Difference in means (t-test CI), ref='A'\nB minus A: 20.38 [-29.37 to 70.12] 13 qsec_posix Difference in means (t-test CI), ref='A'\nB minus A: 3.60e+04 [-7.66e+04 to 1.48e+05]