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Results of ordered logit models are therefore unidirectional (show either an increase or decrease in severity) and are thus very easy to interpret.
The following code creates an array from a raw image file of unsigned 16-bit integers with a header of 1024 bytes: For every raw data specification, it is thus very easy to write a reader using np.memmap (see for example https://github.com/jni/python-redshirt). hdf5 files are accessed using modules such as h5py, pytables.
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On the other hand, sBEFs produced consistent results from a clustering point of view, both for the yeast and the DCs datasets, independently from the clustering method, thus making very easy the identification of similar datasets.
For a given value of α (0.05 shown here, and used in [ Bushdid et al., 2014 ]), the fraction correctly discriminated required to reach this threshold varies greatly with T. Rejecting the null hypothesis can thus be very easy or very hard depending on T (or the number of subjects S, not shown), or on α.
Some people may be very light sleepers and thus be very easy to wake up.
Thus it is very easy to find out the corresponding node to construct the histogram at a specific resolution level.
They have one single tunable parameter with a transparent effect on the closed-loop performance, and thus they are very easy to tune.
Thus, it was very easy to calculate the exact time to take tacrolimus in a different time zone.
The proof of Lemma 1 is very easy, thus we omit it.
Thus, the interface is very easy to master and requires no technical skills: only a simple action on the device for each permission.
To prove Theorem 3.10 below, we need Lemma 3.7, which is very easy and thus its proof is omitted.
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