我正在使用MCMCpack::MCMClogit
运行贝叶斯logit。语法很简单,遵循lm()
或glm()
,但我找不到predict.glm
函数的任何等价物。有没有办法在MCMClogit
中预测数据框中每个观察单位的结果概率? predict()
对于验证来自新数据的训练数据特别有用,这是我最终必须做的。
df = read.csv("http://dl.dropbox.com/u/1791181/MCMC.csv")#Read in data
model.glm = glm(SECONDARY.LEVEL ~ AGE + SEX, data=df, family=binomial(link=logit))
glm.predict = predict(model.glm, type="response")
对于MCMClogit():
model.mcmc = MCMClogit(SECONDARY.LEVEL ~ AGE + SEX, data=df, mcmc=1000)
答案 0 :(得分:2)
您可以使用MCMC生成的模型参数的后验分布来获得预测分布,使用逻辑函数。
例如,如果您的模型公式为y ~ x1 + x2 + x3
,并且您的MCMC输出存储在变量posterior.mcmc
中,那么您可以使用
function(x1, x2, x3) 1 / (1 + exp(-posterior.mcmc %*% rbind(1, x1, x2, x3)))
给出类似于predict.glm(., 'response')
单个输入变量案例的更详细示例:
library(extraDistr)
library(MCMCpack)
# Take x uniformly distributed between -100 and 100
x <- runif(2000, min=-100, max=100)
# Generate a response which is logistic with some noise
beta <- 1/8
eps <- rnorm(length(x), 0, 1)
p <- function(x, eps) 1 / (1 + exp(-beta*x + eps))
p.x <- p(x, eps)
y <- sapply(p.x, function(p) rbern(1, p))
df1 <- data.frame(x, y)
# Fit by logistic regression
glm.logistic <- glm(y ~ x, df1, family=binomial)
# MCMC gives a distribution of values for the model parameters
posterior.mcmc <- MCMClogit(y ~ x, df1, verbose=2000)
densplot(posterior.mcmc)
# Thus, we have a distribution of model predictions for each x
predict.p.mcmc <- function(x) 1 / (1 + exp(-posterior.mcmc %*% rbind(1,x)))
interval.p.mcmc <- function(x, low, high) apply(predict.p.mcmc(x), 2,
function(x) quantile(x, c(low, high)))
predict.y.mcmc <- function(x) posterior.mcmc %*% rbind(1,x)
interval.y.mcmc <- function(x, low, high) apply(predict.y.mcmc(x), 2,
function(x) quantile(x, c(low, high)))
## Plot the data and fits ##
plot(x, p.x, ylab = 'Pr(y=1)', pch = 20, cex = 0.5, main = 'Probability vs x')
# x-values for prediction
x_test <- seq(-100, 100, 0.01)
# Blue line is the logistic function we used to generate the data, with noise removed
p_of_x_test <- p(x_test, 0)
lines(x_test, p_of_x_test, col = 'blue')
# Green line is the prediction from logistic regression
lines(x_test, predict(glm.logistic, data.frame(x = x_test), 'response'), col = 'green')
# Red lines indicates the range of model predictions from MCMC
# (for each x, 95% of the distribution of model predictions lies between these bounds)
interval.p.mcmc_95 <- interval.p.mcmc(x_test, 0.025, 0.975)
lines(x_test, interval.p.mcmc_95[1,], col = 'red')
lines(x_test, interval.p.mcmc_95[2,], col = 'red')
# Similarly for the log-odds
plot(x, log(p.x/(1 - p.x)), ylab = 'log[Pr(y=1) / (1 - Pr(y=1))]',
pch = 20, cex = 0.5, main = 'Log-Odds vs x')
lines(x_test, log(p_of_x_test/(1 - p_of_x_test)), col = 'blue')
lines(x_test, predict(glm.logistic, data.frame(x = x_test)), col = 'green')
interval.y.mcmc_95 <- interval.y.mcmc(x_test, 0.025, 0.975)
lines(x_test, interval.y.mcmc_95[1,], col = 'red')
lines(x_test, interval.y.mcmc_95[2,], col = 'red')
答案 1 :(得分:0)
该功能的描述说:
此函数使用随机游走Metropolis算法从逻辑回归模型的后验分布生成样本。
因此,我认为您的model.mcmc
已经包含了MCMClogit()
模拟的点数。
您可以使用str
查看其中包含的内容以及summary
和plot
功能,如下例所示:http://cran.r-project.org/web/packages/MCMCpack/MCMCpack.pdf