课程

Generalized Linear Models in R

中级技能水平

更新时间 2024年8月

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

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RProbability & Statistics4 小时14 视频51 练习4,050 经验值21,558成就声明

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课程描述

Linear regression serves as a workhorse of statistics, but cannot handle some types of complex data. A generalized linear model (GLM) expands upon linear regression to include non-normal distributions including binomial and count data. Throughout this course, you will expand your data science toolkit to include GLMs in R. As part of learning about GLMs, you will learn how to fit model binomial data with logistic regression and count data with Poisson regression. You will also learn how to understand these results and plot them with ggplot2.

先决条件

Intermediate Regression in R

1

GLMs, an extension of your regression toolbox

This chapter teaches you how generalized linear models are an extension of other models in your data science toolbox. The chapter also uses Poisson regression to introduce generalize linear models.

Limitations of linear models

50 经验值

Assumptions of linear models

50 经验值

Refresher on fitting linear models

100 经验值

Poisson regression

50 经验值

Fitting a Poisson regression in R

100 经验值

Comparing linear and Poisson regression

100 经验值

Intercepts-comparisons versus means

100 经验值

Basic lm() functions with glm()

50 经验值

Applying summary(), print(), and tidy() to glm

100 经验值

Extracting coefficients from glm()

100 经验值

Predicting with glm()

100 经验值

2

Logistic Regression

This chapter covers running a logistic regression and examining the model outputs.

Overview of logistic regression

50 经验值

Fitting a logistic regression

100 经验值

Examining & interpreting logistic regression outputs

100 经验值

Bernoulli versus binomial distribution

50 经验值

Bernoulli versus binomial

50 经验值

Simulating binary data

100 经验值

Long-form logistic regression input

100 经验值

Wide-form input logistic regression

100 经验值

Comparing logistic regression outputs

50 经验值

Link functions-Probit compared to logit

50 经验值

Probit versus logit

50 经验值

Fitting probits and logits

100 经验值

Simulating a logit

100 经验值

Simulating a probit

100 经验值

3

Interpreting and visualizing GLMs

This chapter teaches you about interpreting GLM coefficients and plotting GLMs using ggplot2.

Poisson regression coefficients

50 经验值

Poisson link

50 经验值

lm vs. Poisson coefficients

100 经验值

Plotting Poisson regression

50 经验值

Poisson regression plotting

100 经验值

Understanding output from logistic regression

50 经验值

Understanding odds ratios

50 经验值

Extracting and interpreting odds-ratios

100 经验值

Odds-ratios & confidence intervals in the Tidyverse

100 经验值

ggplot2 and binomial regression

50 经验值

Default trend lines

100 经验值

Methods for trend lines

100 经验值

Comparing probits and logits

100 经验值

4

Multiple regression with GLMs

In this chapter, you will learn how to do multiple regression with GLMs in R.

Multiple logistic regression

50 经验值

Fitting a multiple logistic regression

100 经验值

Building two models

100 经验值

Comparing regression outputs

50 经验值

Comparing variable order

100 经验值

Formulas in R

50 经验值

Multiple slopes

100 经验值

100 经验值

Multiple intercepts

100 经验值

Assumptions of multiple logistic regression

50 经验值

Simpson's paradox

100 经验值

Non-linear logistic regression

100 经验值

50 经验值

Generalized Linear Models in R

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