… rms tracks modeling details related to the expanded X or … to provide regression splines and
other modeling tools), but the … of the comprehensive modeling strategy presented in this book…

… and ordinary linear models. 'rms' works with almost any regression model, but it was especially
… to work with binary or ordinal regression models, Cox regression, accelerated failure time …

… Commands in the rms package can be used to plot only what is needed. Here is an … We
can do an ANOVA by fitting a multiple regression model with an intercept and nine dummy …

… The combination of this book with its accompanying R library (rms) sets a standard for
thoughtful biomedical research using regression models and will influence the many regression …

… Poly0, Poly1, and Poly2 indicate zero, first, and second order polynomial regression model,
respectively. Exp and Gauss indicate general exponential and Gaussian correlation model, …

… Taking inspiration from the regression strategies used in object detection [31], our model
takes as input a short video chunk X = (x1,x2, ..., xT ) with length T, and predicts the temporal …

… regression neural networks for the extraction of apparent rms velocity trajectories from
semblance data. Both networks share a similar end-to-end trainable structure, except for the final …

… regression models, this chapter also describes, in general terms, how the three types of
assumptions of regression models … scale, R Hmisc and rms package functions by default divide …

… A linear regression model is often used to estimate and/or to predict the nature of the
relationship between a measured activity and some measure or calculated descriptors. Linear …

… that the GRNN model was a qualified data-intelligent tool for temperature estimation without
a need for climate-based inputs, at least in the present investigation, and this model can be …