Managing the Sample Clauses

Managing the folders The TCs and TSPs are responsible for:  Moving all files from their own In\Temp folder to the In\New folder  Emptying their own In\Temp, In\Rejected and Out\New folder All other folders are managed by the EGH according to the agreed schedule described in chapter 5.
Managing the interconnector between the integrated LNG network and another NWIS network, including managing energy and power flows, and power quality, across the interconnector; and
Managing the sta ObjectStoring large sets of samples for each participant can greatly increase the size of sta objects. The stSample defaults (assuming stored monotonic samples are not allowed to grow too large) do not cause problems, but if an object contains data from a large number of participants, issues may arise, such as very slow loading and saving times for sta objects. The staManage function (see Figure 5) can be used to reduce the number of stored samples in such cases (Number of encom-passing model samples to keep, Number of trace model samples to keep and Number of monoton- ic model samples to keep). It also includes the option to keep only samples for the “best” (i.e., most frequently occurring, and hence most probable) monotonic order rather than all samples with monotonic orders (Keep only samples for the best monotonic model?). Figure 5: GUI for the staManage function, which can be used to combine sta objects, check the convergence of MCMC trace chains and reduce the number of stored samples. As stSample can add extra stored samples staManage also allows for Gelman’s multivariate convergence analysis to be run on the expanded sets (Check convergence of trace model MCMC chains?). However, as stSample also provides the option of modifying the number of trace sam- ples drawn per chain, the Length of each MCMC chain must be specified; this value must be some multiple of the total number of trace samples and result in there being at least two chains. staManage also allows the expanded sample sets to be exported as a list of mcmc.list objects3 by specifying a name for the object (Name of saved MCMC samples; e.g., “DFIE.mcmc”). Ex- porting this object allows the user to run further convergence checks provided by Plummer et al.’s (2006) coda package (see the package documentation for details). For example, visual in- spection of the chain convergence can be assessed using > plot(DFIE.mcmc[[1]])which will plot the results for the first participant. If the chains have converged then this plot should look like a “fat hairy caterpillar” and the separate chains should not be distinguishable from each other (see also summary(DFIE.mcmc[[1]]) to obtain additional summary statis- tics from coda). Finally, staManage can be used to bind multiple sta objects. In contrast to the previous functions the required input of an sta object name (* sta object name/s (character string)) may either speci- fy a single character name, in which case the stored samples are managed, or a ...

Related to Managing the

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