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DATA ANALYSIS USINGHIERARCHICAL GENERALIZED
LINEAR MODELS WITH R
Youngjo Lee, Lars RΓΆnnegΓ₯rd & Maengseok Noh
What did John Nelder achieve in the 1970βs?
Gaussian
Poisson Binomial
Gamma
πΏπΏ = οΏ½ππ=1
ππ12ππππ
ππ12ππ2 π¦π¦ππβππ 2
ππ = ππππ
πΏπΏ = οΏ½ππ=1
πππππ¦π¦ππππβπππ¦π¦ππ!
log(ππ) = ππππ
πΏπΏ = οΏ½ππ=1
πππππ¦π¦ππ πππ¦π¦ππ(1 β ππ)ππβπ¦π¦ππ
ππππππππππ(ππ) = ππππ
πΏπΏ = οΏ½ππ=1
ππ1
Ξ(ππ) πππππ¦π¦ππππβ1ππ
βπ¦π¦ππππ
kππ β‘ ππ; 1/ππ = ππππ
Gaussian
Poisson Binomial
Gamma
πΏπΏ = οΏ½ππ=1
ππ12ππππ
ππ12ππ2 π¦π¦ππβππ 2
ππ = ππππ
πΏπΏ = οΏ½ππ=1
πππππ¦π¦ππππβπππ¦π¦ππ!
log(ππ) = ππππ
πΏπΏ = οΏ½ππ=1
πππππ¦π¦ππ πππ¦π¦ππ(1 β ππ)ππβπ¦π¦ππ
ππππππππππ(ππ) = ππππ
πΏπΏ = οΏ½ππ=1
ππ1
Ξ(ππ) πππππ¦π¦ππππβ1ππ
βπ¦π¦ππππ
kππ β‘ ππ; 1/ππ = ππππ
GLM
Gaussian
Poisson Binomial
Gamma
πΏπΏ = οΏ½ππ=1
ππ12ππππ
ππ12ππ2 π¦π¦ππβππ 2
ππ = ππππ
πΏπΏ = οΏ½ππ=1
πππππ¦π¦ππππβπππ¦π¦ππ!
log(ππ) = ππππ
πΏπΏ = οΏ½ππ=1
πππππ¦π¦ππ πππ¦π¦ππ(1 β ππ)ππβπ¦π¦ππ
ππππππππππ(ππ) = ππππ
πΏπΏ = οΏ½ππ=1
ππ1
Ξ(ππ) πππππ¦π¦ππππβ1ππ
βπ¦π¦ππππ
kππ β‘ ππ; 1/ππ = ππππ
GLMCommon estimation algorithm using iterativeregression - Fast and easy to implement- Linear regression model checking tools!!!
Lee Y. & Nelder J. A. (1996) βHierarchicalgeneralized linear modelsβ JRSS B 619-678
GLM approach for fittingβ’ Linear mixed modelsβ’ Generalized linear mixed models (Laplace approximation)
β’ Mixed models with non-Gaussian random effectsβ’ Above models + dispersion modelling
Coming out July 2017
Linear model
Generalized linear model (GLM)
Joint GLM Generalized linear model
including dispersion model with fixed effects
Linear mixed model (LMM)
Generalized linear mixedmodel (GLMM)
Generalized linear model including Gaussian random effects
Hierarchical GLM (HGLM)Generalized linear model including Gaussian
and/or non-Gaussian random effects. Dispersion can be modelled using fixed
effects.
Linear model
Generalized linear model (GLM)
Joint GLM Generalized linear model
including dispersion model with fixed effects
Linear mixed model (LMM)
Generalized linear mixedmodel (GLMM)
Generalized linear model including Gaussian random effects
Double HGLM (DHGLM)HGLM including dispersion model with both fixed and random effects
Frailty HGLMHGLMs for survival analysis including
competing risk models
Structural Equation Models (SEM)
Hierarchical GLM (HGLM)Generalized linear model including Gaussian
and/or non-Gaussian random effects. Dispersion can be modelled using fixed
effects.
HGLMs with correlated random effects
Including spatial, temporal correlations, splines, GAM.
Factor analysis
Why use HGLMs?
β’ Fast deterministic algorithms for complex models
β’ All parts of the model are checkableβ’ Predictions of unobservables
Crack growth data
Hudak et al. (1978) presented data from an experiment where crack lengths are measured on a compact tension steel test.
β’ There are 21 metallic specimens with the crack lengths recorded every 104 cycles.
β’ y = increment of crack lengthβ’ Covariate for the mean part of the model: crack length
previously recorded (crack0)β’ Covariate for the dispersion part of the model: number of cycles
(cycle)
## GLM ##res_glm <- glm(y ~ crack0, family= Gamma(link=log), data=data_crack_growth)
## GLM ##res_glm <- glm(y ~ crack0, family= Gamma(link=log), data=data_crack_growth)
library(hglm)## GLMM ##res_glmm1 <- hglm2(y ~ crack0 + (1|specimen), family = Gamma(link=log) , data = data_crack_growth )
## GLM ##res_glm <- glm(y ~ crack0, family= Gamma(link=log), data=data_crack_growth)
library(hglm)## GLMM ##res_glmm1 <- hglm2(y ~ crack0 + (1|specimen), family = Gamma(link=log) , data = data_crack_growth )
library(dhglm)## HGLM I ##model_mu <- DHGLMMODELING(Model="mean", Link="log",
LinPred = y ~ crack0 + (1|specimen), RandDist = "inverse-gamma")model_phi <- DHGLMMODELING(Model="dispersion")res_hglm1 <- dhglmfit(RespDist="gamma", DataMain=data_crack_growth,
MeanModel=model_mu, DispersionModel=model_phi)
## GLM ##res_glm <- glm(y ~ crack0, family= Gamma(link=log), data=data_crack_growth)
library(hglm)## GLMM ##res_glmm1 <- hglm2(y ~ crack0 + (1|specimen), family = Gamma(link=log) , data = data_crack_growth )
library(dhglm)## HGLM I ##model_mu <- DHGLMMODELING(Model="mean", Link="log",
LinPred = y ~ crack0 + (1|specimen), RandDist = "inverse-gamma")model_phi <- DHGLMMODELING(Model="dispersion")res_hglm1 <- dhglmfit(RespDist="gamma", DataMain=data_crack_growth,
MeanModel=model_mu, DispersionModel=model_phi)## HGLM II ##model_mu <- DHGLMMODELING(Model="mean", Link="log",
LinPred = y ~ crack0 + (1|specimen), RandDist="inverse-gamma")model_phi <- DHGLMMODELING(Model = "dispersion", Link = "log",
LinPred = phi ~ cycle)res_hglm2 <- dhglmfit(RespDist = "gamma", DataMain = data_crack_growth,
MeanModel = model_mu, DispersionModel = model_phi)
V(y)= ππππ2log(ππ)=Xb
## GLM ##res_glm <- glm(y ~ crack0, family= Gamma(link=log), data=data_crack_growth)
library(hglm)## GLMM ##res_glmm1 <- hglm2(y ~ crack0 + (1|specimen), family = Gamma(link=log) , data = data_crack_growth )
library(dhglm)## HGLM I ##model_mu <- DHGLMMODELING(Model="mean", Link="log",
LinPred = y ~ crack0 + (1|specimen), RandDist = "inverse-gamma")model_phi <- DHGLMMODELING(Model="dispersion")res_hglm1 <- dhglmfit(RespDist="gamma", DataMain=data_crack_growth,
MeanModel=model_mu, DispersionModel=model_phi)## HGLM II ##model_mu <- DHGLMMODELING(Model="mean", Link="log",
LinPred = y ~ crack0 + (1|specimen), RandDist="inverse-gamma")model_phi <- DHGLMMODELING(Model = "dispersion", Link = "log",
LinPred = phi ~ cycle)res_hglm2 <- dhglmfit(RespDist = "gamma", DataMain = data_crack_growth,
MeanModel = model_mu, DispersionModel = model_phi)## DHGLM I ##model_mu <- DHGLMMODELING(Model="mean", Link="log",
LinPred = y ~ crack0 + (1|specimen), RandDist="inverse-gamma")model_phi <- DHGLMMODELING(Model="dispersion", Link="log",
LinPred = phi ~ cycle + (1|specimen), RandDist="gaussian")res_dhglm1 <- dhglmfit(RespDist = "gamma", DataMain = data_crack_growth,
MeanModel = model_mu, DispersionModel = model_phi)
hglm1
hglm2
dhglm1
Use HGLMs!
β’ Fast deterministic algorithms for complex models
β’ All parts of the model are checkableβ’ Predictions of unobservables
LRN@DU.SE
www.larsronnegard.se
LRN@DU.SE
DATA ANALYSIS USINGHIERARCHICAL GENERALIZED
LINEAR MODELS WITH R
Lee, RΓΆnnegΓ₯rd, Noh
www.larsronnegard.se
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