基于逐步公式的_R_代码用于GLM和GLMM [英] Step-by-step formula based _R_ codes for GLM and GLMM
问题描述
我知道如何使用中来自lme4包中的glm
和glmer
将广义线性模型( GLM )和广义线性混合模型( GLMM )拟合> R .作为统计专业的学生,我有兴趣按照逐步的公式基础 R 代码学习如何适合 GLM 和 GLMM .如果您在这方面有任何资源和/或参考,我将不胜感激.预先感谢.
I know how to fit generalized linear models (GLMs) and generalized linear mixed models (GLMMs) with glm
and glmer
from lme4 package in R. Being a student of statistics, I'm interested in learning how to fit GLM and GLMM following step-by-step formula bases R codes. I'd highly appreciate if you point out any resource and/or reference in this regard. Thanks in advance.
EDiT
我想使用公式逐步进行 GLM 和 GLMM ,就像我们使用矩阵方法进行 LM 一样.有没有使用这种方法的 R 书籍或教程?谢谢
I'd like to do GLM and GLMM step by step using formula as we do LM using matrix approach. Is there any R book or tutorial available that use this approach? Thanks
推荐答案
这可能有帮助
**泊松回归:GLM **
* 建议阅读:安妮特·J·多布森(Annette J. Dobson),第二版,第4章,第4.3和4.4节*
This may help
** Poisson regression: GLM**
*Suggested reading: An Introduction to generalized linear model, by Annette J. Dobson, 2nd Edition, Chapter 4, section 4.3 and 4.4 *
library(MASS)
poisreg = function(n, b1, y, x1, tolerence) { # n is the number of iteration
x0 = rep(1, length(x1))
x = cbind(x0, x1)
y = as.matrix(y)
w = matrix(0, nrow = (length(y)), ncol = (length(y)))
b0 = b1
result = b0
for (i in 1:n) {
mu = exp(x %*% b0)
diag(w) = mu
eta = x %*% b0
z = eta + (y - mu) * (1/mu) # dot product of (y - mu) & (1/mu)
xtwx = t(x) %*% w %*% x
xtwz = t(x) %*% w %*% z
b1 = solve(xtwx, xtwz)
if(sqrt(sum(b0 - b1)^2) > tolerence) (b0 <- b1)
result<- cbind(result,b1) # to get all the iterated values
}
result
}
x1 <- c(-1,-1,0,0,0,0,1,1,1) # x1 is the explanatory variable
y<- c(2,3,6,7,8,9,10,12,15) # y is the dependent variable
b1 = c(1,2) # initial value
poisreg (10, b1, y, x1, .001) # Nicely converge after 10 iterations
glm(y~x1, family=poisson(link="log")) # check your result with the R GLM program
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