The lm function name stands for "linear model." Linear regression is a subset of techniques called general linear models. Interpreting the Results The summary command displays just the basic results ...

R 2 is a statistical measure of the goodness of fit of a linear regression model (from 0.00 to 1.00), also known as the coefficient of determination. In general, the higher the R 2 , the better ...

One option for working with survey data in R is to use the “survey” package. For an introduction on working with survey data in R, see our earlier blog post. The first step involves creating a survey ...

Prerequisites. For this tutorial, I'm making some assumptions: You have a Teton account (see the Teton: Beginner's Guide for more on how that works).. You are using a Linux-based system for ...

Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous or discrete (e.g. binary or frequency).This course covers: ... How to use R to fit GLMs using ...

This can be visually examined through scatterplots, where deviations from a linear pattern may suggest that the relationship is not adequately captured by the model [11] 4].

Nevertheless, we can obtain an approximation of the allelic log-OR and corresponding variance from the linear model, 10 given by. and. where is the maximum-likelihood estimate of the intercept.

Figure 1: The results of multiple linear regression depend on the correlation of the predictors, as measured here by the Pearson correlation coefficient r (ref. 2). ( a ) Simulated values of ...