Скачать с ютуб Statistics with R (3) - Generalized, linear, and generalized least squares models (LM, GLM, GLS) в хорошем качестве

Statistics with R (3) - Generalized, linear, and generalized least squares models (LM, GLM, GLS)

In this video, I show how how to implement linear models, generalized linear models and generalized least squares models in R. Using the "airquality" dataset, I show how to fit and interpret the models. The core of the session is the interpretation of partial slope coefficients in Poisson generalized linear models. Finally, I give an outlook on generalized additive models which will be covered in one of the next sessions. The R code used in this video is: airquality plot(Ozone~Wind,airquality) model1=lm(Ozone~Wind,airquality) plot(model1) coef(model1) (Intercept) Wind 96.872895 -5.550923 #predictions for Wind speeds of 19 and 20 mph: Ozone1=coef(model1)[1]+coef(model1)[2]*19 Ozone2=coef(model1)[1]+coef(model1)[2]*20 Ozone1 Ozone2 ## model2=glm(Ozone~Wind,airquality,family=poisson) coef(model2) (Intercept) Wind 5.0795877 -0.1488753 Ozone1.glm=exp(coef(model2)[1]+coef(model2)[2]*19) Ozone2.glm=exp(coef(model2)[1]+coef(model2)[2]*20) Ozone2.glm/Ozone1.glm 0.8616765 exp(coef(model2)[2]) #exp(-0.1488753 ) #0.8616765 ## library(nlme) model3=gls(Ozone~Wind,airquality) summary(airquality$Ozone) model3=gls(Ozone~Wind,airquality,na.action=na.exclude) head(airquality) airquality$Date= as.Date(paste(1973,airquality$Month,airquality$Day,sep="-")) library(lattice) xyplot(Ozone~Date,airquality) model4=gls(Ozone~Wind*Date,airquality,na.action=na.exclude) air2=subset(airquality,complete.cases(Ozone)) model5=gls(Ozone~Wind*Date,air2) plot(ACF(model5,form=~Date),alpha=0.05) model6=update(model5,correlation=corAR1()) library(MuMIn) AICc(model5,model6) df AICc model5 5 1099.40 model6 6 1095.21 summary(model6)