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Specifically, the data pattern is identified by using some complex network measures.
The predicted data pattern is observed to follow the actual data pattern closely, with little disagreement.
Noting that the observed data was generated through interactions of several known/unknown factors, and thus when a data pattern is obtained through some configurations of learnt factors, additional (unseen) data patterns can likely be described through new configurations of the learnt factors and patterns[5],[24].
On the other hand, an insignificant result means the test is hinting that this data pattern is quite possible in a random sample, hence your data pattern is nothing special.
This explanation, as well as the overall data pattern, is by and large compatible with the premotor theory of attention (Rizzolatti et al., 1987; Rizzolatti, Riggio & Sheliga, 1994), which maintains that covert spatial attention derives from the activation of brain maps engaged in sensorimotor transformation processes.
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The numerical Markov chain Monte Carlo (MCMC) method of this procedure was chosen because the missing data pattern was not monotonic.
If the missing data pattern was already monotone, then this step was skipped.
With respect to the correlation between corrected and clean data this data pattern was also present: It was largest ICA corrected data, smaller for EMCP w/s and even smaller for EMCPs (Figure 5).
The missing data pattern was very nearly monotone; to simplify analysis, we made it completely monotone by replacing 5 intermittent missing values at the 4th, 5th and 6th observations with the previously observed values.
Furthermore, a new technique to access data patterns is also proposed to improve the encoding time.
In all models individuals with partially missing item level data were included, since estimation of missing data patterns is possible under traditional ML and WLSMV.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com