 Ευχαριστώ για την συμφωνία μας για το τετωριό μας. Στην επόμενη 10 μήνες θα σας δημιουργήσω για τις παγκάτσεις του RNMA, Προσχεία criticisms, αφού επίσηςέ για την παγκάτσεις τουού τον νέτορου μετασχέδοση, Στην ευχαριστή εσρόγραμμα για τα κολληγόμενη情况κάτστά μου, Χλυσός Τουμος Καλίβας, και Κατέρνα Πάπα Τεμμιτροπουλου, για την συγχωτή και καταπολίγουση σε αυτά τα παγκάτσια. Εντάξει, μόνος εποχέδοση του rop, για τις παγκάτσεις τουού του γόννατονού του, μου δείξε έναν μικρό ξαναδική ρεφρασία και στην επόμενη εργασία εγγραφή διεθνώστησα με συνεχασμότητα με συνεχασμότητα με μυαλή συμφωνή. Η συνεχασμότητα είναι πιο σύνθεσης της στουλς. Είναι οδηγιστή να προσπαθεί ευκαιρία για τα εξοδοσιασμότητα, δεν συμβείται στις τραγές σε κάθε στουλ της δημότητας. Τι κάνει αυτή τη στουλή είναι ακόμα μεγάλι, είναι ότι μετά τη συνεχασμότητα της στουλς, μπορεί να προσπαθεί πιο στις τραγές, διεθνώστητας με ανάγκησης, για τα ίδια συνεχασμότητα. Είναι μόνο να δημιουργήσει και διάφορα εξοδοσιασμότητα για την συνεχασμότητα και την εξοδοσιασμότητα. Η συνεχασμότητα είναι πολύ σπρεσμένη στον κλείνικο τραγέντρο, όπου οι συνεχασμότητας ας αφήσουν πιο σύντομα για τις εξοδοσιασμότητες ή δεν είναι το κοντάξο της τραγέντρος. Επίσης, οι τραγότεροι εξοδοσιασμοκρατές είναι δοκιμάτιες στην τραγία της κλείνικηςyour, που μπορεί να είναι χονούς σε συνεχασμότητα. Συ nodi, η εξοδοσιασμότητα, κυριά, συμπαγετρίζει ό,τι είναι εξοδοσιασμότητα χρήσης που δημιουργεί να εξοδοσιασμότητα για να κάνει κάτι σημασίου για την εξοδοσιασμότητα και να ντήσει αυτά τα συνεχασμότητα για εξοδοσιασμότητα. Για παράδειγμα, όλοι οι παρτήσεις πρέπει να έχουν δημιουργήσει το σχέδιο της ενδιαφέρονσης. Λευταία, ένας δημιουργικός σχέδιο είναι να χρησιμοποιήσει το σχέδιο του Πατρενμίκτου. Το σχέδιο του Πατρενμίκτου είναι ένα μοτοσχέδιο της αγγητικής δημιουργίας στον κομπλίτες και των παρτήσεις. Η ΡNMA Μόδο Αρπακάτς εμπλεμένει αυτή τη σχέδια στις φαγητικές. Η ΡNMA Μόδο Αρπακάτς εμπλεμένει να εμπλεμένει δημιουργίας το σχέδιο του Πατρενμίκτου για να διαφορετούν περισσότερες υποδοχές στον κομπλίτες και νέτος μεταγράφων στην παρτήσια μας. Αυτή ήταν κατά 2021. Τώρα, από τέτοις, έναν τέτοιο κατά πολύ και εμπλεμένουμε τις ζωές μας να φτιάξουμε ένας κομπλίτες των κομπλίτες για να υποτισθεί και να δεδοποιήσει εξηγένειται και εξηγένειται. In this teaser tutorial we will present you the core functions of the packets for modeling and visualization. You may install and load the packets directly from Khan, by running these lines. However, we do recommend installing the development version to experience the latest advances of the packets. We will use the data set of Baker and colleagues on COPD. The outcome is whether the patients experience COPD exacerbation after receiving the randomized intervention. You can run this line to see the first six trials. You see that the data set has this one trial per row format, which is the typical format found in Bach's language model. To create a network plot, use the netflow function. The function currently calls the NMA network plot function for the PCNMETA-R packets. The netflow function gives further insight into the network evidence by printing three tables on the console. This is a table that describes the network evidence. The next is a table that summarizes the trials, randomized sample, and outcome for each intervention. And likewise, for each observed comparison in the network plot. Well, this is a snapshot of a quite lengthy table. The Baker data set has many missing participants in most trials, interventions, and comparisons. We will use the hit map missing data set function to view the missing participants in percent in the data set. You can see that most trials have interventions with high level of missingness, judging by the reddish color, with only a few trials having zero or low missingness rate, the green color. Next, we use the hit map missing network function to view the median and rates of missing participants in percent for each intervention on the main diagonal of the table and observe comparison in the lower of diagonal. This network summarizes all factions of the packets responsible for running the models and presenting the results. The run model function corresponds to the largest node, because it is literally the backbone of the entire RNA-MA mod architecture. This function conducts Bayesian network analysis while accounting for missing participants using the pattern mid-to-model. Most functions in the packets cannot work without the run model function. On its own, the run model function simply turns a bulk of raw results for the model parameters. The magic happens once the run model function is fed into the other functions of the packets. So, let's run a Bayesian random effects network analysis while addressing the missing participants. The effect measure of interest is the odd ratio. We assign a half normal prior distribution on the between trial start deviation with a scale parameter equal to 1. We will estimate one more parameter for each intervention in the network. The model operates under the missing at random assumption on average with a variance equal to 1. The outcome is harmful. The function run model and all other model functions run in jacks via the R2 jacks are packets. Of course, do not forget to assign a name to the function, here under res, to be used as an object in the other functions of the packets. Use the mcmc-diagnostics function to check the model's convergence. This function returns several such panel of bar plots for the parameters of the model. However, here we illustrate only for the local ratio of all possible comparisons in the network. mcmc-diagnostics also call the mcmc plot function of the mcmc plots are packets that returns a html files with several diagnostic plots for the monitor parameters that we have specified in the par argument here. Namely, the local ratio of all possible comparisons, em, and the common between trial start deviation. This is just a snapshot of a lengthy html file. Now we move on to the core visualization of the netromethanalty results. The leap heatmap function creates the popular leap table. The table is read as row versus columns and shows the posterior-mediano ratio a 95% credible interval for each comparison. The interventions are sorted in decreasing order by the posterior mean sucra value, which appears in the main diagonal of the table. The larger the treatment effect, the darker the color shade. The leap heatmap function can also display the treatment effects from two outcomes or a selected subset of interventions, which is ideal when we deal with the huge networks. The leap heatmap print function has the same arguments as the leap heatmap function, but as you may suspect, it displays the predictions for all possible perguise comparisons in the network. A compact alternative to leap table is to create a forest plot of comparisons with a selected comparator intervention. For illustration, we have selected buddhasiton as the reference and we run the following code to obtain the forest plots where results on estimation and prediction co-appear. In plot A, the 95% credible intervals, the black lines, overlaid with the 95% prediction intervals, the orange lines. Now, in both plots, the interventions found in the PSE axis are sorted from best to worst based on the sucra value, found in plot B. The ranko-sucra plot function creates a plot for each intervention that combines two popular hierarchy plots, namely the racongram and the sucra plot. The interventions are sorted from best to worst based on the sucra value, the number on the top left corner of each plot. The ranko-sucra plot function can also display the hierarchy results from two outcomes. We can explore the implications of assuming consistency a common heterogeneity as opposed to performing perguise metanalysis separately for each observed comparison in the network. First, use the run series meta function to contact separate perguise metanalysis for all observed comparisons in the network. Next, insert meta into the function series meta plot to experience the visualization toolkit for this analysis. This function returns the same results in two formats. One, a table that is printed on the console, and second, a panel of forest plots. Here we present the plots. We can see in plot A that the network metanalysis produced more precise results than perguise metanalysis for all observed comparisons. The same benefits are also evident for the heterogeneity parameter, which was estimated at a reasonable level, judging by the color key, and with greater precision than in any perguise metanalysis. By the way, the plot vertical lines refer to the posterior median a 95% credible interval of the common heterogeneity parameter. For the local evaluation of consistency, we have considered the node splitting approach. The run node split function applies the node splitting approach with the synergy of the R-package KMTC to select the proper nodes to split. Next, we insert the node into the node splitting plot to get the necessary results both in tabular and graphical format. The first graph is a panel of interval plots to check consistency through the inconsistency factor, namely the difference between direct and indirect estimate. For each split node, we can see the estimated direct effect for perguise metanalysis, the indirect effect after removing the corresponding comparison from the network, and the inconsistency factor, indicated as IF. The devian's information criterion for each model appear on the top left of each plot. The plots have been sorted in ascending order of the devian's information criterion. We see that the 95% credible interval for the consistency factor crosses the vertical line of consistency in order plots. Now, one may conclude that the consistency assumption holds. However, we see that the point estimate is not even close to zero in order plots. The next graph is a so-to-say reversed forest plot on the between-trial standard deviation after each split node. Again, the split nodes have been sorted in ascending order of the devian's information criterion. The blue horizontal lines refer to the posterior median, a 95% credible interval of the common heterogeneity parameter. As a global evaluation of consistency, we apply the unrelated mean effects model using the UME model function. Next, we insert UME into the UME plot function to set light on the validity of consistency globally through a variety of results in tabular and graphical format. The first panel of graphs consists of a scattered plot on the left and a blood-album plot on the right, on the posterior mean deviants under the natural metanalysis and unrelated mean effects model. The next panel includes the leverage plot under natural metanalysis on the left and unrelated mean effects model on the right. Lastly, a panel of interval plots on the UME ratio under both models, but only for the pairwise comparisons that we observe in the network. We conclude this teaser tutorial with the network metagression. First, we used the runMetallic function to run the model, and as you may have guessed by now, we insert REC into the function MetallicPlot to obtain several results in tabular and graphical format. We will glimpse into the plots, we will present the results for publication year 2002 and for BUDESIDON as the selector comparator. In plot A, the 95% crendable intervals, the black lines overlap with the 95% prediction intervals, colored lines. The odd ratios refer to comparisons with BUDESIDON, the selector comparator intervention. In both plots, the interventions are sorted from best to worst based on the secret value found in plot B. The hierarchy of the interventions does not seem to materially change when comparing the two models regarding the secret values for the publication year 2002. With this teaser tutorial, we have presented the main functionalities of the RNMA mode are packets. We highly recommend to browse through the manual to dive in the technical details of the functions. For any questions about the tutorial and the RNMA mode are packets, feel free to contact me. Thank you very much.