trim_mean not defined for 'est'

[paul-hawkins]

On Python 3.7 on Windows 10 (Anaconda/Jupyter notebook)

for compare_groups_with_single_factor.bootdpci the only arugment to the est function allowable is trim_mean:

https://alcampopiano.github.io/hypothesize/function_guide/#bootdpci

est: function

Measure of location (currently only trim_mean is supported)

However on

import hypothesize as hypo
hypo.compare_groups_with_single_factor.bootdpci(xdf,est=trim_mean)

NameError: name 'trim_mean' is not defined

If other estimators, such as numpy.mean, are used

hypo.compare_groups_with_single_factor.bootdpci(xdf,est=np.mean)

the following error is thrown:

analysis is being done on difference scores each confidence interval has probability coverage of 1-alpha.
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-39-c2264eb089c1> in <module>
      1 # https://alcampopiano.github.io/hypothesize/function_guide/#bootdpci
----> 2 hypo.compare_groups_with_single_factor.bootdpci(xdf,est=np.mean)

~\anaconda3\envs\oepython37\lib\site-packages\hypothesize\compare_groups_with_single_factor\_compare_groups_with_single_factor.py in bootdpci(x, est, nboot, alpha, dif, BA, SR, *args)
   1024 
   1025     results=rmmcppb(x, est, *args, nboot=nboot,alpha=alpha,
-> 1026                    SR=SR, dif=dif, BA=BA)
   1027 
   1028     col_names = ['con_num', 'psihat', 'p_value', 'p_crit', 'ci_lower', 'ci_upper']

~\anaconda3\envs\oepython37\lib\site-packages\hypothesize\compare_groups_with_single_factor\_compare_groups_with_single_factor.py in rmmcppb(x, est, alpha, con, dif, nboot, BA, hoch, SR, seed, *args)
    322 
    323         temp=rmmcppbd(x,est, *args, alpha=alpha,con=con,
--> 324                       nboot=nboot,hoch=True)
    325 
    326         if called_directly:

~\anaconda3\envs\oepython37\lib\site-packages\hypothesize\compare_groups_with_single_factor\_compare_groups_with_single_factor.py in rmmcppbd(x, est, alpha, con, nboot, hoch, seed, *args)
    646 
    647     for ic in range(ncon):
--> 648       output[ic, 1] = tmeans[ic]
    649       output[ic, 0] = ic
    650       output[ic, 2] = test[ic]

IndexError: invalid index to scalar variable.

[Alcampopiano]

Hi Paul,

Thanks for the report and for using the library!

A couple things that might help.

1. trim_mean has to be imported first.
2. est is a actually a positional argument. This is stated in the docs for bootdpci, but perhaps it could be made more clear:

Note that arguments up to and including args are positional arguments

If you import trim_mean and specify it as positional, it should work. Example:

from hypothesize.utilities import trim_mean
results=bootdpci(df, trim_mean, .2)

---

Also, in the docs there is a button you can click that allows you to see an example of every function being run. Example:

This can be a helpful way of seeing how the functions can be run and playing around with them straight away. Everything seems to work as expected in that context.

Does this help?

[Alcampopiano]

@paul-hawkins Closing but feel free to reopen if you feel I've not addressed this issue.