ggeffects: Tidy Data Frames of Marginal Effects from Regression Models
نویسندگان
چکیده
منابع مشابه
Estimation of Count Data using Bivariate Negative Binomial Regression Models
Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...
متن کاملRegression Discontinuity Marginal Threshold Treatment Effects
In regression discontinuity models, where the probability of treatment jumps discretely when a running variable crosses a threshold, an average treatment effect can be nonparametrically identi ed. We show that the derivative of this treatment effect with respect to the threshold is also nonparametrically identi ed and easily estimated, in both sharp and fuzzy designs. This marginal threshold tr...
متن کاملRank Regression Analysis of Multivariate Failure Time Data Based on Marginal Linear Models
Multivariate failure time data arises when each study subject can potentially experience several types of failures or recurrences of a certain phenomenon, or when failure times are sampled in clusters. We formulate the marginal distributions of such multivariate data with semiparametric accelerated failure time models (i.e. linear regression models for log-transformed failure times with arbitra...
متن کاملBayesian learning from marginal data in bionetwork models.
In studies of dynamic molecular networks in systems biology, experiments are increasingly exploiting technologies such as flow cytometry to generate data on marginal distributions of a few network nodes at snapshots in time. For example, levels of intracellular expression of a few genes, or cell surface protein markers, can be assayed at a series of interim time points and assumed steady-states...
متن کاملVisualizing Marginal Effects from Interactions in Generalized Linear Models
Interactions are often included in generalized linear models (GLM). Interpreting these interactions in the transformed scale of the linear equation is like interpreting an interaction in an OLS regression. However, this is rarely the scale in which results are discussed, and interpreting interactions in the non-transformed scale is not straightforward. Focusing on a continuous by categorical in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Open Source Software
سال: 2018
ISSN: 2475-9066
DOI: 10.21105/joss.00772