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One approach to quantifying model fit is to compute Bayesian posterior predictive p values (ppp value).
Then, the ppp value is defined as the proportion of chi-square values obtained in the simulated data that exceed that of the actual data.
Six of these display no evidence against colocalisation (all posterior predictive P > 0.01 ), the exception being 2q33.2/ CTLA4/ ICOS in which the ppp value for the BMA approach is 6 × 10 − 3 (two SNP models) or 8 × 10 − 5 (three SNP models).
Analogous to equation (3), posterior predictive P values are therefore defined by (4) ppp = ∑ m ∈ M p * (m ) P (m ) where M is the set of models under consideration, P * (m ) is the colocalisation testing P or ppp value under the SNP model m, and P (m ) is the posterior probability of model m given the data and under the assumption that one of M is the true model.
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Fig. 1 Real per capita GDP in PPP values (1970 2014).
Fig. 16 Real per capita GDP in PPP values in Spain and Portugal vs Greece and OECD.
Fig. 14 Real per capita GDP in PPP values: Greece vs Euro Area. a Annual data over the period 1995 2014.
In fact, using revisions to intended migration duration over time, as well as observed event realizations, this study shows that the realizations of ppp values lead to revisions to intended migration duration.
Data on provincial GDP mer and population (including urban and rural) come from Jiangxi Statistical Yearbooks (Jiangxi National Bureau of Statistics, 1996-2010); basin-aggregated GDP mer was converted to GDP ppp using the annual ratio of national GDP mer to GDP ppp values for Jiangxi (World Bank, 2001).
The ppp values around.50 indicate a well-fitting model.
PPP values were obtained from the German Federal Statistical Office [ 19].
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Justyna Jupowicz-Kozak
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