pandas/_libs/lib.pyx in pandas._libs.lib.map_infer() TypeError: 'NoneType' object is not callable . Any suggestions on what to do here?
I think it's better to report the bug on a new issue. Try to describe the exact steps to reproduce it, since the automated tests don't seem to be covering your case.
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pingouin/parametric.py", line 177
warnings.warn("x and y have unequal sizes. Switching to "
UserWarning: x and y have unequal sizes. Switching to paired == False.; could you please tell me what the implications are? Is this automatically corrected ?
x and y. Pingouin therefore automatically switched to an independent T-test (= no assumption that the data are repeated measures from the same individuals). Paired T-test have more statistical power than independent T-test (and therefore you should expect higher T-values if there is indeed an effect), but the results from the two tests should go in the same direction and be consistent. I'd recommend checking your input data, is it paired or not?
x and y do not have the same size from the beginning, and therefore the data cannot be interpreted as paired. Do you see what I mean? I would check all the steps of your code to make sure that you are not removing some values at any point (careful, putting a missing value in a list is NOT the same as removing this value!). If you still cannot resolve your issue, please post your data and code here so that I can have a look. Thanks
import pandas as pd
import pingouin as pg
intrusions = pd.read_csv('Intrusions.csv', sep=';')
pg.rm_anova(data=intrusions[intrusions.Condition=='No-Think'],
subject='Subject',
within=['Emotion', 'Sessions'],
dv='Intrusions_per')Anova Table (Type 3 tests)
Response: Intrusions_per
Effect df MSE F ges p.value
1 Emotion 1.60, 43.18 593.70 3.82 * .007 .04
2 Sessions 3.21, 86.62 724.90 21.00 *** .08 <.0001
3 Emotion:Sessions 7.41, 200.10 276.18 0.78 .003 .62
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG
pg.pairwise_corr(data=df, columns=[['X'], ['Y1', 'Y2', ..., 'Yn']], covar=['x1'], method='pearson')
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pingouin/distribution.py", line 464
warnings.warn("Epsilon values might be innaccurate in "
UserWarning: Epsilon values might be innaccurate in two-way repeated measures design where each factor has more than 2 levels. Please double-check your results.__Any idea(s) about the implication(s)?
eps value of the interaction term in the output dataframe, and, by extension, to the Greenhouse-Geisser corrected p-value of the interaction term (p-GG-corr). Results are slightly different than R, and I haven't still figured out exactly why. That said, given the number of levels in both of your factors (8 and 12), I don't think an ANOVA is the most appropriate analysis here, instead, you might want to check linear mixed modeling, as implemented in the lme4 R package (no implementation yet in Python). Hope this helps! Thanks!
Hi @raphaelvallat & team,
First of all, thanks for creating this helpful library. I faced an error while using the ANCOVA function.
This is the error:
--> 168 set_use_numexpr(get_option("compute.use_numexpr"))
OptionError: "No such keys(s): 'compute.use_numexpr'"
My pandas is version '0.24.2'
Need your kind suggestion on this. Thanks!
pip install --upgrade numpy statsmodels pandas pingouin, 2) check that your input dataframe looks "right", 3) try running the ANCOVA with only one covariate. Thanks
- Updated the packages you listed above, but the error still persists.
- These are the columns of the dataframe: customer_id group trx_cnt_pre trx_cnt_post
I also made sure that each group has same number of customers. - I run the function with only 1 covariate, which is trx_cnt_pre from the dataframe above
Hi guys,
I just have to say thanks for wrapping statsmodels into such a great interface for N-Way ANOVA.
I have a the data from full factorical 3 factor 3 level experiment in a df and it just runs fine.
But I'm wondering if it does the same thing as
model = ols('Shearforce ~ C(US_Power)*C(BondForce)*C(BOndTime)', df_shear).fit()
sm.stats.anova_lm(model, typ= 2)in the background?
print(aov.formula_) (formula_ is a hidden attribute of the dataframe and should be accessible for as long as you don't directly modify your output anova dataframe). Thanks!
Sum to be consistent with the statsmodels documentation (https://www.statsmodels.org/stable/anova.html), my understanding is that it changes the way that the Patsy design matrix is created, by forcing to use a sum (deviation) coding, as you can read here: https://www.statsmodels.org/devel/contrasts.html#sum-deviation-coding I've validated the results of the N-way anova against JASP: https://github.com/raphaelvallat/pingouin/blob/424a65e0dd0f4c4b64aa1c98012747adceff2975/pingouin/tests/test_parametric.py#L140 Hope this helps!
I wanted to see the lm.summary() of the fit result but I can't edit the parametric.py succesfully as the import fails with
Traceback (most recent call last):
File "C:\Anaconda\envs\ThesisWireBond\lib\site-packages\IPython\core\interactiveshell.py", line 3325, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-1-b1ff2590f725>", line 12, in <module>
import pingouin as pg
File "C:\Anaconda\envs\ThesisWireBond\lib\site-packages\pingouin\__init__.py", line 7, in <module>
from .equivalence import *
File "C:\Anaconda\envs\ThesisWireBond\lib\site-packages\pingouin\equivalence.py", line 5, in <module>
from .parametric import ttest
File "C:\Anaconda\envs\ThesisWireBond\lib\site-packages\pingouin\parametric.py", line 1155
print(lm.summary())
^
TabError: inconsistent use of tabs and spaces in indentation
During one of my computation (rm_anova), I got a negative F value. A bit surprised I would like to know if it's a bug or something normal? (in this case, any idea why?)
Subject column that does not match the repeated measures design. Please have a look at previous messages in this thread (around Jul 10) for more details on this. Thanks!
Hi! just wanted to say first of all, what a pleasure it is to work with Pingouin - really straightforward and sensible API that allows you to do what you want to do. Also appreciate all the attention and care that has gone into the documentation - the detailed explanations are great and super-useful.
I just have a question about pairwise_ttests(). I have a dataset with repeated measures on the same group of subjects in different conditions (condition is in the 'condid' column). The data failed normality tests, so I want a non-parametric test. And the design is unbalanced - has missing values for some subjects in some conditions. I've tried:
posthocs = pg.pairwise_ttests(dv='value', within='condid', subject='subid', data=df2, parametric=False, padjust='fdr_bh'),
expecting that this would call mwu() as a non-parametric test.
However, this fails, with an error raised from wilcoxon() instead of mwu().
Looking at the source for pairwise_ttests, I see you have the line (executed when parametric=False):
paired = True if contrast == 'simple_within' else False
And hence it tries to call wilcoxon(), then fails (I presume because of the unequal samples).
So it's true that this is a simple within - there is only the 'condid' factor within subjects, and no between factor. But there are missing values, so a paired test can't be used.
Just wondering why it works like this? I could just repeatedly call mwu and then do multicomp myself, but I guess I'm missing something more fundamental.
wilcoxon and not the mwu function. If you want to use the non-paired non-parametric test, then you can just use pairwise_ttest(dv='value', between='condid', data=df2, parametric=False, padjust='fdr_bh'). Can you please let me know what exactly is the error that you get in the wilcoxon function? In case of unbalanced repeated measures data, Pingouin will apply a listwise deletion, meaning removing all the rows for which there is at least one missing value. You might want to check your input data / the number of non-missing rows before running the function. Thanks!
Thanks for that quick reply ! I should just clarify that I probably misstated what my data looks like: it's not really missing values so much as missing rows. So if a subject didn't take part in a condition, there actually is no row in the dataframe for that combination at all (I only have one dependent variable). To get the Wilcoxon test working with the listwise deletion behaviour you described, should I set up the data frame differently?
The error trace I get is
File "/Users/leib0006/anaconda2/envs/mseqpaper/lib/python3.7/site-packages/pingouin/pairwise.py", line 272, in pairwise_ttests
df_ttest = wilcoxon(x, y, tail=tail)
File "/Users/leib0006/anaconda2/envs/mseqpaper/lib/python3.7/site-packages/pingouin/nonparametric.py", line 407, in wilcoxon
correction=True, alternative=tail)
File "/Users/leib0006/anaconda2/envs/mseqpaper/lib/python3.7/site-packages/scipy/stats/morestats.py", line 2848, in wilcoxon
raise ValueError('The samples x and y must have the same length.')
ValueError: The samples x and y must have the same length.
And just to make sure I understand what you meant with the listwise deletion: Suppose we had
Condition A: Sub 1, 2, 3, 4
Condition B: Sub 1, 3, 4
Condition C: Sub 1, 2, 4
Does it do the list deletion on a per-comparison basis? i.e. compare A vs B with Subs 1, 3, 4; A vs C with 1, 2, 4, etc?
Or would it see that only Subs 1 and 4 have all conditions, and only use those two for all comparisons?
OK, I have actually managed to answer my own questions - I've added rows with NaNs in them for all the missing values, and now wilcoxon works.
And I've stepped through the source code and I've seen that it does the second form of listwise deletion that I described.
So now I have a follow-up question: would it actually be valid to do it in the first, per-comparison way I described, where for each pairwise comparison, it finds the subjects that are in common for those two conditions only (rather than the subjects that occur in all conditions), and does that particular Wilcoxon test on the data from those subjects? (And then the next pairwise comparison might be on a different set of subjects. ) And then at the end do the correction for multiple comparisons withmulticomp? Obviously it's possible to do it with the software as multicomp doesn