glm r tutorial

Https://github.com/josephmisiti/awesome-machine-learning#julia-nlp Julia General-purpose Machine Learning Machinelearning-julia Machine Learning LibraryMlbase-a set of functions to support development of machine learningPGM-A Julia Framework for probabilistic graphical models.Da-julia Package for regularized-discriminant analysisRegression-algorithms for regression analysis (e.g. linear regression and logistic regression)Local regression-local regression, so smooooth!Naive bayes-simple Naive Bay

reflect the uncertainty caused by the missing value and multiple interpolation. Analysis based on mice packages usually conforms to the following analysis process: Library (MICE) Imp MyData is a matrix or data frame of a dietary missing value; IMP is a list object that contains m interpolation datasets and also contains information to complete the interpolation process, the default m=5 Analysis is an expression object that is used to set up a statistical analytical method applied to M inter

reflect the uncertainty caused by the missing value and multiple interpolation. Analysis based on mice packages usually conforms to the following analysis process: Library (MICE) Imp MyData is a matrix or data frame of a dietary missing value; IMP is a list object that contains m interpolation datasets and also contains information to complete the interpolation process, the default m=5 Analysis is an expression object that is used to set up a statistical analytical method applied to M inter

Same point:Both are generalized linear models GLM (generalized linear models) Different points:1. Linear regression requires that the dependent variable (assuming y) is a continuous numeric variable, while the logistic regression requires that the dependent variable is a discrete type variable, such as the most common two classification problem, 1 represents a positive sample, and 0 represents a negative sample2. Linear regression requires the self-v