基于R语言的多变量预测glm可视化

我想使用以下数据集来拟合glm并可视化predict（）

然后使用

myglm

预测

new.y

：

new.y = predict(myglm, newdata=new.x, se.fit=TRUE)

将新的.x和新的.y组合起来：

addThese <- data.frame(new.x, new.y)

---

我仍然想知道这是否是创建

新.data

的正确方法，但我会尝试一下。因此，对于您的数据，稍微修改一下您的代码：

myglm <- glm(y ~ x1 + x2 + x3 + x1:x2, family = gaussian, data = mydata)

minx <- min(mydata$x1)
maxx <- max(mydata$x1)

# create data with all combinations of x1, x2, x3

new.data <- expand.grid(x1 = seq(minx, maxx, length.out = 60), 
                        x2 = unique(mydata$x2), 
                        x3 = unique(mydata$x3)
                        )

# visualize data

data.frame(predict(myglm, newdata = new.data, se.fit = T)[1:2]) %>% 
  bind_cols(new.data) %>% 
  mutate(d15N = exp(fit), lwr = fit - 1.96 * se.fit, upr = fit + 1.96 * se.fit) %>%
  ggplot(aes(x = x1, y = fit, colour = interaction(x2, x3))) +
  geom_point(size = 1, alpha = .75, pch = 19, position = "jitter") +
  geom_smooth(aes(ymin = lwr, ymax = upr), stat = "identity", alpha = .5) +
  facet_wrap(~interaction(x2, x3, sep = " : "), nrow = 5) +
  ggthemes::theme_few() +
  labs(y = "Predicted value", x = bquote(x[1])) +
  theme(legend.position = "none")

---

myglm您想在
new.x
中使用与
mydata
中相同的
x2
和
x3
吗？@ANG，是的，我想在
mydata
中保持因子
x2
和
x3
与
mydata$x2
相同。因此，在创建
new.x
时，只需使用
x2=mydata$x2
和
x3=mydata$x3
，亲爱的乌图邦，请检查并编辑答案，是
lwr=fit-1.96*se.fit
，应该是
lwr=exp>（fit-1.96*se.fit）
？与
upr
相同。非常感谢！
new.x <- data.frame(
     x1=seq(min, max, length=60),
     x2= ???
     x3= ???)

new.y = predict(myglm, newdata=new.x, se.fit=TRUE)

addThese <- data.frame(new.x, new.y)

addThese <- mutate(addThese,
                   d15N=exp(fit),
                   lwr=exp(fit-1.96*se.fit),
                   upr=exp(fit+1.96*se.fit))

ggplot(addThese, aes(x1, fit))+
  geom_point(shape=21, size=3)+
  geom_smooth(data=addThese,
              aes(ymin=lwr, ymax=upr),
              stat='identity')

myglm <- glm(y ~ x1 + x2 + x3 + x1:x2, family = gaussian, data = mydata)

minx <- min(mydata$x1)
maxx <- max(mydata$x1)

# create data with all combinations of x1, x2, x3

new.data <- expand.grid(x1 = seq(minx, maxx, length.out = 60), 
                        x2 = unique(mydata$x2), 
                        x3 = unique(mydata$x3)
                        )

# visualize data

data.frame(predict(myglm, newdata = new.data, se.fit = T)[1:2]) %>% 
  bind_cols(new.data) %>% 
  mutate(d15N = exp(fit), lwr = fit - 1.96 * se.fit, upr = fit + 1.96 * se.fit) %>%
  ggplot(aes(x = x1, y = fit, colour = interaction(x2, x3))) +
  geom_point(size = 1, alpha = .75, pch = 19, position = "jitter") +
  geom_smooth(aes(ymin = lwr, ymax = upr), stat = "identity", alpha = .5) +
  facet_wrap(~interaction(x2, x3, sep = " : "), nrow = 5) +
  ggthemes::theme_few() +
  labs(y = "Predicted value", x = bquote(x[1])) +
  theme(legend.position = "none")