本文整理汇总了Python中pyvttbl.DataFrame.anova方法的典型用法代码示例。如果您正苦于以下问题：Python DataFrame.anova方法的具体用法？Python DataFrame.anova怎么用？Python DataFrame.anova使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyvttbl.DataFrame的用法示例。
在下文中一共展示了DataFrame.anova方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test02
# 需要导入模块: from pyvttbl import DataFrame [as 别名]
# 或者: from pyvttbl.DataFrame import anova [as 别名]
def test02(self):
"""using loftus and masson error bars"""
# a simple plot
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
aov = df.anova('WORDS', wfactors=['AGE','CONDITION'])
aov.plot('WORDS','AGE', seplines='CONDITION',
errorbars='ci', output_dir='output')示例2: test1
# 需要导入模块: from pyvttbl import DataFrame [as 别名]
# 或者: from pyvttbl.DataFrame import anova [as 别名]
def test1(self):
df=DataFrame()
fname='error~subjectXtimeofdayXcourseXmodel.csv'
df.read_tbl(fname)
aov=df.anova('ERROR',wfactors=['TIMEOFDAY','COURSE','MODEL'])#,transform='windsor05')
aov.output2html(fname[:-4]+'RESULTS.htm')示例3: test1
# 需要导入模块: from pyvttbl import DataFrame [as 别名]
# 或者: from pyvttbl.DataFrame import anova [as 别名]
#.........这里部分代码省略.........
Error(COURSE * Sphericity Assumed 3.593 - 6.588 0.545
MODEL) Greenhouse-Geisser 3.593 0.327 2.153 1.669
Huynh-Feldt 3.593 0.327 2.153 1.669
Box 3.593 0.500 3.294 1.091
------------------------------------------------------------------------------------------------------------------------------------------------------
TIMEOFDAY * Sphericity Assumed 2.222 - 4 0.556 1.318 0.355 0.063 3 0.458 0.898 2.400 0.125
COURSE * Greenhouse-Geisser 2.222 0.336 1.343 1.654 1.318 0.387 0.063 3 0.458 0.898 2.400 0.080
MODEL Huynh-Feldt 2.222 0.336 1.343 1.654 1.318 0.387 0.063 3 0.458 0.898 2.400 0.080
Box 2.222 0.500 2 1.111 1.318 0.380 0.063 3 0.458 0.898 2.400 0.093
------------------------------------------------------------------------------------------------------------------------------------------------------
Error(TIMEOFDAY * Sphericity Assumed 2.778 - 6.588 0.422
COURSE * Greenhouse-Geisser 2.778 0.336 2.212 1.256
MODEL) Huynh-Feldt 2.778 0.336 2.212 1.256
Box 2.778 0.500 3.294 0.843
TABLES OF ESTIMATED MARGINAL MEANS
Estimated Marginal Means for TIMEOFDAY
TIMEOFDAY Mean Std. Error 95% Lower Bound 95% Upper Bound
==================================================================
T1 5.704 0.433 4.855 6.552
T2 2.593 0.215 2.171 3.014
Estimated Marginal Means for COURSE
COURSE Mean Std. Error 95% Lower Bound 95% Upper Bound
===============================================================
C1 5.167 0.584 4.021 6.312
C2 4.444 0.532 3.403 5.486
C3 2.833 0.414 2.021 3.645
Estimated Marginal Means for MODEL
MODEL Mean Std. Error 95% Lower Bound 95% Upper Bound
==============================================================
M1 5.222 0.645 3.959 6.485
M2 4.278 0.535 3.229 5.327
M3 2.944 0.328 2.301 3.588
Estimated Marginal Means for TIMEOFDAY * COURSE
TIMEOFDAY COURSE Mean Std. Error 95% Lower Bound 95% Upper Bound
===========================================================================
T1 C1 7.111 0.588 5.959 8.263
T1 C2 6 0.726 4.576 7.424
T1 C3 4 0.577 2.868 5.132
T2 C1 3.222 0.401 2.437 4.007
T2 C2 2.889 0.261 2.378 3.400
T2 C3 1.667 0.236 1.205 2.129
Estimated Marginal Means for TIMEOFDAY * MODEL
TIMEOFDAY MODEL Mean Std. Error 95% Lower Bound 95% Upper Bound
==========================================================================
T1 M1 7.222 0.760 5.733 8.711
T1 M2 6.111 0.512 5.107 7.115
T1 M3 3.778 0.465 2.867 4.689
T2 M1 3.222 0.434 2.372 4.073
T2 M2 2.444 0.338 1.782 3.107
T2 M3 2.111 0.261 1.600 2.622
Estimated Marginal Means for COURSE * MODEL
COURSE MODEL Mean Std. Error 95% Lower Bound 95% Upper Bound
=======================================================================
C1 M1 6.500 0.992 4.556 8.444
C1 M2 5.167 1.195 2.825 7.509
C1 M3 3.833 0.601 2.656 5.011
C2 M1 6 1.095 3.853 8.147
C2 M2 4.167 0.792 2.614 5.720
C2 M3 3.167 0.477 2.231 4.102
C3 M1 3.167 0.872 1.457 4.877
C3 M2 3.500 0.764 2.003 4.997
C3 M3 1.833 0.307 1.231 2.436
Estimated Marginal Means for TIMEOFDAY * COURSE * MODEL
TIMEOFDAY COURSE MODEL Mean Std. Error 95% Lower Bound 95% Upper Bound
===================================================================================
T1 C1 M1 8.667 0.333 8.013 9.320
T1 C1 M2 7.667 0.333 7.013 8.320
T1 C1 M3 5 0.577 3.868 6.132
T1 C2 M1 8.333 0.667 7.027 9.640
T1 C2 M2 5.667 0.882 3.938 7.395
T1 C2 M3 4 0.577 2.868 5.132
T1 C3 M1 4.667 1.202 2.311 7.022
T1 C3 M2 5 0.577 3.868 6.132
T1 C3 M3 2.333 0.333 1.680 2.987
T2 C1 M1 4.333 0.333 3.680 4.987
T2 C1 M2 2.667 0.882 0.938 4.395
T2 C1 M3 2.667 0.333 2.013 3.320
T2 C2 M1 3.667 0.333 3.013 4.320
T2 C2 M2 2.667 0.333 2.013 3.320
T2 C2 M3 2.333 0.333 1.680 2.987
T2 C3 M1 1.667 0.333 1.013 2.320
T2 C3 M2 2 0.577 0.868 3.132
T2 C3 M3 1.333 0.333 0.680 1.987
"""
df=DataFrame()
fname='data/error~subjectXtimeofdayXcourseXmodel.csv'
df.read_tbl(fname)
aov=df.anova('ERROR',wfactors=['TIMEOFDAY','COURSE','MODEL'],transform='windsor05')
## print(aov)
self.assertEqual(str(aov),R)示例4: test0
# 需要导入模块: from pyvttbl import DataFrame [as 别名]
# 或者: from pyvttbl.DataFrame import anova [as 别名]
#.........这里部分代码省略.........
-----------------------------------------------------------------------------------------------------------------------------------------------------
Error(COURSE * Sphericity Assumed 4.667 - 8 0.583
MODEL) Greenhouse-Geisser 4.667 0.354 2.830 1.649
Huynh-Feldt 4.667 0.354 2.830 1.649
Box 4.667 0.500 4 1.167
-----------------------------------------------------------------------------------------------------------------------------------------------------
TIMEOFDAY * Sphericity Assumed 2.778 - 4 0.694 1.923 0.200 0.067 3 0.408 0.800 2.885 0.152
COURSE * Greenhouse-Geisser 2.778 0.290 1.159 2.397 1.923 0.293 0.067 3 0.408 0.800 2.885 0.087
MODEL Huynh-Feldt 2.778 0.290 1.159 2.397 1.923 0.293 0.067 3 0.408 0.800 2.885 0.087
Box 2.778 0.500 2 1.389 1.923 0.260 0.067 3 0.408 0.800 2.885 0.109
-----------------------------------------------------------------------------------------------------------------------------------------------------
Error(TIMEOFDAY * Sphericity Assumed 2.889 - 8 0.361
COURSE * Greenhouse-Geisser 2.889 0.290 2.318 1.246
MODEL) Huynh-Feldt 2.889 0.290 2.318 1.246
Box 2.889 0.500 4 0.722
TABLES OF ESTIMATED MARGINAL MEANS
Estimated Marginal Means for TIMEOFDAY
TIMEOFDAY Mean Std. Error 95% Lower Bound 95% Upper Bound
==================================================================
T1 5.778 0.457 4.882 6.674
T2 2.556 0.229 2.108 3.003
Estimated Marginal Means for COURSE
COURSE Mean Std. Error 95% Lower Bound 95% Upper Bound
===============================================================
C1 5.222 0.608 4.031 6.414
C2 4.500 0.562 3.399 5.601
C3 2.778 0.432 1.931 3.625
Estimated Marginal Means for MODEL
MODEL Mean Std. Error 95% Lower Bound 95% Upper Bound
==============================================================
M1 5.333 0.686 3.989 6.678
M2 4.222 0.558 3.129 5.315
M3 2.944 0.328 2.301 3.588
Estimated Marginal Means for TIMEOFDAY * COURSE
TIMEOFDAY COURSE Mean Std. Error 95% Lower Bound 95% Upper Bound
===========================================================================
T1 C1 7.222 0.641 5.966 8.478
T1 C2 6.111 0.790 4.564 7.659
T1 C3 4 0.577 2.868 5.132
T2 C1 3.222 0.401 2.437 4.007
T2 C2 2.889 0.261 2.378 3.400
T2 C3 1.556 0.294 0.979 2.132
Estimated Marginal Means for TIMEOFDAY * MODEL
TIMEOFDAY MODEL Mean Std. Error 95% Lower Bound 95% Upper Bound
==========================================================================
T1 M1 7.444 0.835 5.807 9.081
T1 M2 6.111 0.512 5.107 7.115
T1 M3 3.778 0.465 2.867 4.689
T2 M1 3.222 0.434 2.372 4.073
T2 M2 2.333 0.408 1.533 3.133
T2 M3 2.111 0.261 1.600 2.622
Estimated Marginal Means for COURSE * MODEL
COURSE MODEL Mean Std. Error 95% Lower Bound 95% Upper Bound
=======================================================================
C1 M1 6.667 1.085 4.540 8.794
C1 M2 5.167 1.195 2.825 7.509
C1 M3 3.833 0.601 2.656 5.011
C2 M1 6.167 1.195 3.825 8.509
C2 M2 4.167 0.792 2.614 5.720
C2 M3 3.167 0.477 2.231 4.102
C3 M1 3.167 0.872 1.457 4.877
C3 M2 3.333 0.882 1.605 5.062
C3 M3 1.833 0.307 1.231 2.436
Estimated Marginal Means for TIMEOFDAY * COURSE * MODEL
TIMEOFDAY COURSE MODEL Mean Std. Error 95% Lower Bound 95% Upper Bound
===================================================================================
T1 C1 M1 9 0.577 7.868 10.132
T1 C1 M2 7.667 0.333 7.013 8.320
T1 C1 M3 5 0.577 3.868 6.132
T1 C2 M1 8.667 0.882 6.938 10.395
T1 C2 M2 5.667 0.882 3.938 7.395
T1 C2 M3 4 0.577 2.868 5.132
T1 C3 M1 4.667 1.202 2.311 7.022
T1 C3 M2 5 0.577 3.868 6.132
T1 C3 M3 2.333 0.333 1.680 2.987
T2 C1 M1 4.333 0.333 3.680 4.987
T2 C1 M2 2.667 0.882 0.938 4.395
T2 C1 M3 2.667 0.333 2.013 3.320
T2 C2 M1 3.667 0.333 3.013 4.320
T2 C2 M2 2.667 0.333 2.013 3.320
T2 C2 M3 2.333 0.333 1.680 2.987
T2 C3 M1 1.667 0.333 1.013 2.320
T2 C3 M2 1.667 0.882 -0.062 3.395
T2 C3 M3 1.333 0.333 0.680 1.987
"""
df=DataFrame()
fname='data/error~subjectXtimeofdayXcourseXmodel.csv'
df.read_tbl(fname)
aov=df.anova('ERROR',wfactors=['TIMEOFDAY','COURSE','MODEL'])
## print(aov)
self.assertEqual(str(aov),R)示例5: test3
# 需要导入模块: from pyvttbl import DataFrame [as 别名]
# 或者: from pyvttbl.DataFrame import anova [as 别名]
#.........这里部分代码省略.........
Huynh-Feldt 0.386 1 63 0.006
Box 0.386 0.167 10.500 0.037
TABLES OF ESTIMATED MARGINAL MEANS
Estimated Marginal Means for CYCLE
CYCLE Mean Std. Error 95% Lower Bound 95% Upper Bound
==============================================================
1 0.220 0.022 0.177 0.262
2 0.306 0.022 0.263 0.349
3 0.307 0.024 0.259 0.354
4 0.308 0.026 0.257 0.359
Estimated Marginal Means for PHASE
PHASE Mean Std. Error 95% Lower Bound 95% Upper Bound
==============================================================
I 0.207 0.014 0.180 0.234
II 0.363 0.016 0.332 0.394
Estimated Marginal Means for GROUP
GROUP Mean Std. Error 95% Lower Bound 95% Upper Bound
==============================================================
AA 0.222 0.018 0.187 0.256
AB 0.292 0.022 0.250 0.334
LAB 0.341 0.020 0.302 0.381
Estimated Marginal Means for CYCLE * PHASE
CYCLE PHASE Mean Std. Error 95% Lower Bound 95% Upper Bound
======================================================================
1 I 0.173 0.028 0.119 0.228
1 II 0.266 0.031 0.206 0.326
2 I 0.224 0.026 0.173 0.275
2 II 0.387 0.027 0.335 0.439
3 I 0.223 0.027 0.170 0.276
3 II 0.391 0.032 0.327 0.454
4 I 0.207 0.031 0.146 0.269
4 II 0.408 0.030 0.350 0.466
Estimated Marginal Means for CYCLE * GROUP
CYCLE GROUP Mean Std. Error 95% Lower Bound 95% Upper Bound
======================================================================
1 AA 0.193 0.046 0.104 0.282
1 AB 0.177 0.025 0.128 0.225
1 LAB 0.289 0.035 0.221 0.358
2 AA 0.253 0.033 0.187 0.318
2 AB 0.323 0.035 0.254 0.392
2 LAB 0.341 0.044 0.256 0.427
3 AA 0.219 0.036 0.149 0.289
3 AB 0.331 0.039 0.255 0.406
3 LAB 0.371 0.044 0.285 0.456
4 AA 0.223 0.026 0.172 0.273
4 AB 0.337 0.057 0.225 0.449
4 LAB 0.364 0.040 0.287 0.442
Estimated Marginal Means for PHASE * GROUP
PHASE GROUP Mean Std. Error 95% Lower Bound 95% Upper Bound
======================================================================
I AA 0.209 0.024 0.162 0.255
I AB 0.179 0.023 0.134 0.225
I LAB 0.233 0.026 0.183 0.283
II AA 0.235 0.026 0.184 0.286
II AB 0.404 0.023 0.359 0.450
II LAB 0.450 0.016 0.419 0.481
Estimated Marginal Means for CYCLE * PHASE * GROUP
CYCLE PHASE GROUP Mean Std. Error 95% Lower Bound 95% Upper Bound
==============================================================================
1 I AA 0.177 0.060 0.060 0.295
1 I AB 0.126 0.036 0.056 0.196
1 I LAB 0.216 0.047 0.124 0.309
1 II AA 0.209 0.072 0.067 0.351
1 II AB 0.228 0.025 0.179 0.276
1 II LAB 0.363 0.038 0.288 0.437
2 I AA 0.224 0.039 0.148 0.300
2 I AB 0.235 0.049 0.139 0.331
2 I LAB 0.214 0.053 0.110 0.317
2 II AA 0.281 0.055 0.173 0.389
2 II AB 0.411 0.026 0.360 0.463
2 II LAB 0.469 0.026 0.417 0.520
3 I AA 0.231 0.057 0.120 0.342
3 I AB 0.200 0.031 0.140 0.260
3 I LAB 0.238 0.054 0.133 0.342
3 II AA 0.208 0.047 0.115 0.300
3 II AB 0.461 0.024 0.414 0.508
3 II LAB 0.504 0.016 0.472 0.535
4 I AA 0.203 0.039 0.126 0.279
4 I AB 0.156 0.062 0.036 0.277
4 I LAB 0.264 0.058 0.149 0.378
4 II AA 0.242 0.034 0.176 0.309
4 II AB 0.517 0.031 0.457 0.578
4 II LAB 0.465 0.020 0.425 0.505
"""
df=DataFrame()
fname='data/suppression~subjectXgroupXcycleXphase.csv'
df.read_tbl(fname)
df['SUPPRESSION']=[.01*x for x in df['SUPPRESSION']]
aov=df.anova('SUPPRESSION',wfactors=['CYCLE','PHASE'],bfactors=['GROUP'])
## print(aov)
self.assertEqual(str(aov),R)