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minor snap difference, probably caused by glm
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@DanChaltiel
DanChaltiel committed Aug 26, 2024
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24 changes: 12 additions & 12 deletions tests/testthat/_snaps/effects_snap.md
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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:
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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:
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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]
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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]
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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]
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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]
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