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Spotkania Entuzjastów R mają na celu integrację środowiska użytkowników i entuzjastów analizy danych z użyciem programu R. Spotkania organizowane są co miesiąc w czwartki w godzinach popołudniowych. Każde spotkanie składa się z dwóch półgodzinnych prelekcji rozdzielonych półgodzinną przerwą na spokojną dyskusję w kuluarach.

Udział w spotkaniach jest bezpłatny, wymagana jest jednak wcześniejsza rejestracja.

Zapraszamy nowicjuszy i weteranów, każdy znajdzie coś dla siebie!

YouTube channel: https://www.youtube.com/channel/UCq1v6SmgtmJeWDUtIKr0nVw

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Współdzielimy konto na meetupie z R-Ladies Warsaw inicjatywą promującą kobiety programujące w R.

Enthusiasts R Meetup aims at integrating community of users and data analysis enthusiasts using R. Meetings are held every month on Thursdays. Each meeting consists of two half-hour lectures separated by a half-hour break for a quiet discussion on the sidelines.

Participation in meetings is free, but requires pre-registration.

We invite novices and veterans, everyone will find something for everyone!

Meetup account is shared with R-Ladies Warsaw promoting women in rstats.

YouTube channel: https://www.youtube.com/channel/UCq1v6SmgtmJeWDUtIKr0nVw

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Upcoming events (1)

February Warsaw R Enthusiasts Meetup

Politechnika Warszawska, Wydział MINI

We are excited to announce the next meetup. We have a pleasure to host two international guests and active members of R community. The meeting will be in room 107. Agenda: 18:00-18:05 Welcoming 18:05 - 18:45 - Causal Inference with Missing Values: Treatment Effect Estimation of Tranexamic Acid on Mortality for Traumatic Brain Injury Patients - Julie Josse 18:45-19:15 Pizza break sponsored by Appsilon 19:15-19:55 - Nonparametric imputation by data depth - Pavlo Mozharovskyi 19:55-20:00 - Few words from Appsilon Afterparty Julie's abstract: In healthcare or social sciences research, prospective observational studies are frequent, relatively easily put in place (compared to experimental randomized trial studies for instance) and can allow for different kinds of posterior analyses such as causal inferences. Average treatment effect (ATE) estimation, for instance, is possible through the use of propensity scores which allow to correct for treatment assignment biases in the non-randomized study design. However, a major caveat of large observational studies is their complexity and incompleteness: the covariates are often taken at different levels and stages, they can be heterogeneous – categorical, discrete, continuous – and almost inevitably contain missing values. The problem of missing values in causal inference has long been ignored and only recently gained some attention due to the non-negligible impacts in terms of power and bias induced by complete case analyses. We propose several consistent doubly robust average treatment effect estimators which directly account for missing values and compare them to complete case ATE estimators applied on imputed data, i.e. on complete data obtained by replacing every missing value by at least one plausible one, and to the recently proposed method of Kallus et al. [2018]. We assess the performance of our estimators on a large prognostic database containing detailed information about over 15,000 severely traumatized patients in France. Using the proposed ATE estimators and this database we study the effect on mortality of tranexamic acid administration to patients with traumatic brain injury in the context of critical care management. Pavlo's abstract: We present single imputation method for missing values which borrows the idea of data depth-a measure of centrality defined for an arbitrary point of a space with respect to a probability distribution or data cloud. This consists in iterative maximization of the depth of each observation with missing values, and can be employed with any properly defined statistical depth function. For each single iteration, imputation reverts to optimization of quadratic, linear, or quasiconcave functions that are solved analytically by linear programming or the Nelder-Mead method. As it accounts for the underlying data topology, the procedure is distribution free, allows imputation close to the data geometry, can make prediction in situations where local imputation ($k$-nearest neighbors, random forest) cannot, and has attractive robustness and asymptotic properties under elliptical symmetry. It is shown that a special case-when using the Mahalanobis depth-has direct connection to well-known methods for the multivariate normal model, such as iterated regression and regularized PCA. The methodology is extended to multiple imputation for data stemming from an elliptically symmetric distribution. Simulation and real data studies show good results compared with existing popular alternatives. The method has been implemented as an R-package. Meetup sessions will be recorded and available on the YouTube channel thanks to Appsilon. Appsilon delivers the most advanced R Shiny apps, data science consulting services and support with R Shiny and Python Dash technologies. www: https://appsilon.com/ fb: @appsilon.company https://www.facebook.com/appsilon.company/ twitter: @appsilonds https://twitter.com/appsilonds linkedin: https://www.linkedin.com/company/appsilon/

Past events (40)

December'18 Warsaw R Enthusiasts

Sala 107 Wydziału Matematyki i Nauk Informacyjnych Politechniki Warszawskiej

Photos (86)