最新消息:请大家多多支持

Linear Regression, GLMs and GAMs with R

其他教程 dsgsd 150浏览 0评论


Linear Regression, GLMs and GAMs with R

MP4 | Video: AVC (.mp4) 1280×720 | Audio: AAC 44KHz 2ch | Duration: 8 Hours | 2.16 GB
Genre: eLearning | Language: English
How to extend linear regression to specify and estimate generalized linear models and additive models.

Linear Regression, GLMs and GAMs with R demonstrates how to use R to extend the basic assumptions and constraints of linear regression to specify, model, and interpret the results of generalized linear (GLMs) and generalized additive (GAMs) models. The course demonstrates the estimation of GLMs and GAMs by working through a series of practical examples from the book Generalized Additive Models: An Introduction with R by Simon N. Wood (Chapman & Hall/CRC Texts in Statistical Science, 2006). Linear statistical models have a univariate response modeled as a linear function of predictor variables and a zero mean random error term. The assumption of linearity is a critical (and limiting) characteristic. Generalized linear models (GLMs) relax this assumption of linearity. They permit the expected value of the response variable to be a smoothed (e.g. non-linear) monotonic function of the linear predictors. GLMs also relax the assumption that the response variable is normally distributed by allowing for many distributions (e.g. normal, poisson, binomial, log-linear, etc.). Generalized additive models (GAMs) are extensions of GLMs. GAMs allow for the estimation of regression coefficients that take the form of non-parametric smoothers. Nonparametric smoothers like lowess (locally weighted scatterplot smoothing) fit a smooth curve to data using localized subsets of the data. This course provides an overview of modeling GLMs and GAMs using R. GLMs, and especially GAMs, have evolved into standard statistical methodologies of considerable flexibility. The course addresses recent approaches to modeling, estimating and interpreting GAMs. The focus of the course is on modeling and interpreting GLMs and especially GAMs with R. Use of the freely available R software illustrates the practicalities of linear, generalized linear, and generalized additive models.

003ad974

Download uploaded
http://uploaded.net/file/nsn734ky/Linear%20Regression%2C%20GLMs%20and%20GAMs%20with%20R.part1.rar
http://uploaded.net/file/1yjrihp9/Linear%20Regression%2C%20GLMs%20and%20GAMs%20with%20R.part2.rar
http://uploaded.net/file/7h52j6wo/Linear%20Regression%2C%20GLMs%20and%20GAMs%20with%20R.part3.rar
http://uploaded.net/file/07jdd368/Linear%20Regression%2C%20GLMs%20and%20GAMs%20with%20R.part4.rar
http://uploaded.net/file/wmdkx31j/Linear%20Regression%2C%20GLMs%20and%20GAMs%20with%20R.part5.rar
http://uploaded.net/file/8o83jlfw/Linear%20Regression%2C%20GLMs%20and%20GAMs%20with%20R.part6.rar
http://uploaded.net/file/c1f3akai/Linear%20Regression%2C%20GLMs%20and%20GAMs%20with%20R.part7.rar

Download nitroflare
http://nitroflare.com/view/21A6DBD2ACF0BF8/Linear_Regression%2C_GLMs_and_GAMs_with_R.part1.rar
http://nitroflare.com/view/A7EED05525D471D/Linear_Regression%2C_GLMs_and_GAMs_with_R.part2.rar
http://nitroflare.com/view/DB86346C187E958/Linear_Regression%2C_GLMs_and_GAMs_with_R.part3.rar
http://nitroflare.com/view/01C3F8F77F98E96/Linear_Regression%2C_GLMs_and_GAMs_with_R.part4.rar
http://nitroflare.com/view/FA317787D87E36E/Linear_Regression%2C_GLMs_and_GAMs_with_R.part5.rar
http://nitroflare.com/view/2E4409121936F77/Linear_Regression%2C_GLMs_and_GAMs_with_R.part6.rar
http://nitroflare.com/view/BEF4F692F782667/Linear_Regression%2C_GLMs_and_GAMs_with_R.part7.rar

Download 百度云

你是VIP 1个月(1 month)赞助会员,

资源下载此资源仅限VIP下载,请先

转载请注明:0daytown » Linear Regression, GLMs and GAMs with R

您必须 登录 才能发表评论!