r - ggplot2: how to get robust confidence interval for predictions in geom_smooth? -

consider simple example

dataframe <- data_frame(x = c(1,2,3,4,5,6),                         y = c(12,24,24,34,12,15)) > dataframe # tibble: 6 x 2       x     y   <dbl> <dbl> 1     1    12 2     2    24 3     3    24 4     4    34 5     5    12 6     6    15      dataframe %>% ggplot(., aes(x = x, y = y)) +  geom_point() +  geom_smooth(method = 'lm', formula = y~x) 

here standard errors computed default option. however, use robust variance-covariance matrix available in package sandwich , lmtest

that is, using vcovhc(mymodel, "hc3")

is there way in simple way using geom_smooth() function?

i new whole robust se thing, able generate following:

zz = ' x y 1     1    12 2     2    24 3     3    24 4     4    34 5     5    12 6     6    15  '  df <- read.table(text = zz, header = true) df  library(sandwich) library(lmtest)  lm.model<-lm(y ~ x, data = df) coef(lm.model) se = sqrt(diag(vcovhc(lm.model, type = "hc3"))) fit = predict(lm.model) predframe <- with(df,data.frame(x,                                 y = fit,                                 lwr = fit - 1.96 * se,                                 upr = fit + 1.96 * se))  library(ggplot2) ggplot(df, aes(x = x, y = y))+   geom_point()+   geom_line(data = predframe)+   geom_ribbon(data = predframe, aes(ymin = lwr,ymax = upr), alpha = 0.3)