我通过flexmix
以下方式用于预测:
pred = predict(mod, NPreg)
clust = clusters(mod,NPreg)
result = cbind(NPreg,data.frame(pred),data.frame(clust))
plot(result$yn,col = c("red","blue")[result$clust],pch = 16,ylab = "yn")
和混淆矩阵:
table(result$class,result$clust)
为了获得 的预测值yn
,我选择数据点所属的集群的组件值。
for(i in 1:nrow(result)){
result$pred_model1[i] = result[,paste0("Comp.",result$clust[i],".1")][i]
result$pred_model2[i] = result[,paste0("Comp.",result$clust[i],".2")][i]
}
实际结果与预测结果显示了拟合(此处仅添加其中一个,因为您的两个模型相同,您将pred_model2
用于第二个模型)。
qplot(result$yn, result$pred_model1,xlab="Actual",ylab="Predicted") + geom_abline()
RMSE = sqrt(mean((result$yn-result$pred_model1)^2))
给出均方根误差5.54
.
该答案基于我在使用flexmix
. 它对我的问题很有效。
您可能还对可视化这两个分布感兴趣。我的模型如下,它显示了一些重叠,因为组件的比例不接近1
.
Call:
flexmix(formula = yn ~ x, data = NPreg, k = 2,
model = list(FLXMRglm(yn ~ x, family = "gaussian"),
FLXMRglm(yn ~ x, family = "gaussian")))
prior size post>0 ratio
Comp.1 0.481 102 129 0.791
Comp.2 0.519 98 171 0.573
'log Lik.' -1312.127 (df=13)
AIC: 2650.255 BIC: 2693.133
我还使用直方图生成密度分布,以可视化这两个组件。这是受到betareg
.
a = subset(result, clust == 1)
b = subset(result, clust == 2)
hist(a$yn, col = hcl(0, 50, 80), main = "",xlab = "", freq = FALSE, ylim = c(0,0.06))
hist(b$yn, col = hcl(240, 50, 80), add = TRUE,main = "", xlab = "", freq = FALSE, ylim = c(0,0.06))
ys = seq(0, 50, by = 0.1)
lines(ys, dnorm(ys, mean = mean(a$yn), sd = sd(a$yn)), col = hcl(0, 80, 50), lwd = 2)
lines(ys, dnorm(ys, mean = mean(b$yn), sd = sd(b$yn)), col = hcl(240, 80, 50), lwd = 2)
# Joint Histogram
p <- prior(mod)
hist(result$yn, freq = FALSE,main = "", xlab = "",ylim = c(0,0.06))
lines(ys, p[1] * dnorm(ys, mean = mean(a$yn), sd = sd(a$yn)) +
p[2] * dnorm(ys, mean = mean(b$yn), sd = sd(b$yn)))