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Sure the difference between Little Miss Sunshine or Juno which are the most recent indie film equivalents of Facebook, return millions, instead of billions, the raw likelihood for a net positive outcome is probably closer than you think.
Using the raw likelihood method as implemented in Mx, the different models were compared using the log-likelihood ratio test.
In 76 of the 100 simulated datasets, raw likelihood values were greater for the IM model, by an average of just over one log-likelihood unit.
Bapteste et al. (2005) and Susko et al. (2006) adapted this method, using AU or SH test P values in the place of raw likelihood values.
Since different networks have different complexities, it is common to use a score that takes into account the network complexity instead of the raw likelihood to compare the networks.
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The advantage of this approach is that participants with incomplete data can be included in the analyses, as Mx provides a method for handling incomplete data by using raw maximum likelihood estimation, in which a likelihood statistic (−2LL) of the data for each observation is calculated.
Raw data likelihood procedures were used to allow for partial missingness.
The use of raw maximum likelihood estimation implies that there is no overall measure of fit (such as a χ-value with corresponding p-value for the number of degrees of freedom, as obtained by fitting directly on observed variance covariance matrices).
An artificial neural network (ANN) is then used to transform raw log-likelihood ratio scores into manageable terms for easy incorporation.
P values from AU or SH tests, as well as raw tree likelihoods, have also been used in data set exploration methods.
The q-values (false discovery rate) were calculated from raw log-likelihood ratio test P-values to account for multiple hypothesis testing and only mRNAs with a q-value <1% were considered significant.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com