How to plot confidence interval for yintercept in ggplot

Question

I have two datasets. One with quantile estimates for multiple quantiles and the other with ols estimate.

d1 <- structure(
  list(
    tau = c(0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95),
    term = c("xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz", "xyz"),
    Value = c(
      0, 9.13682889869627e-16, 0.115868979841894, 0.177249380780954, 0.19607591773447, 0.195687830776138, 0.200114980211708, 0.194414277006126,
      0.187632176725543, 0.187173613121143, 0.188976803954401, 0.179502655354206, 0.181229125428629, 0.174872004143586, 0.152074703983541, 0.146260827630119,
      0.138590448328074, 0.128650158320059, 0.150026804023301
    ),
    Std..Error = c(
      6.2062850595809e-16, 0.0233470616183061, 0.0307059321347318, 0.0282162883685036, 0.0200725341931686, 0.0183116985228486, 0.0191468932241407,
      0.0202374458735842, 0.0189676742252486, 0.0211745244254686, 0.0198311584152537, 0.0216761318683201, 0.017955669024132, 0.0190371929314243,
      0.0183546066197098, 0.0194123534398986, 0.0190844067896042, 0.0213743223242021, 0.0219487120022877
    ),
    t.value = c(
      0, 3.91348129716342e-14, 3.77350471998319, 6.28181065014945, 9.76836884907147, 10.6864926009985, 10.451564014542, 9.60666075257518, 9.89220789525049,
      8.83956632792239, 9.52928719529789, 8.281120286805, 10.0931424601924, 9.1858082635139, 8.28536983300085, 7.53442018676139, 7.26197307864806, 6.01891168144255, 6.83533521272976
    ),
    Pr...t.. = c(1, 0.999999999999969, 0.000162587936320957, 3.59170471142534e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.6621374255883e-14, 4.32098801184111e-13, 1.86426696302533e-09, 9.02655727941237e-12)
  ),
  class = "data.frame", row.names = c(NA, -19L)
)

d2 <- structure(
  list(
    rowname = "zzz",
    `Std. Error` = 0.0155334622532616,
    Estimate = 0.280017767279397
  ),
  row.names = c(NA, -1L), class = "data.frame"
)

How to plot the confidence interval of ols estimate?

ggplot(data = d1, aes(
  x = tau, y = Value,
  ymin = Value - Std..Error,
  ymax = Value + Std..Error
)) +
  geom_ribbon(alpha = 0.5) +
  geom_line(color = "blue") +
  labs(x = "Quantiles", y = "xyz") + # adds the confidence intervals as error bars
  geom_hline(
    data = d2,
    aes(yintercept = Estimate),
    color = "red"
  ) # add OLS estimate - note it is from another data set which is totally OK

This is what I tried

ggplot(data = d1, aes(
  x = tau, y = Value,
  ymin = Value - Std..Error,
  ymax = Value + Std..Error
)) +
  geom_ribbon(alpha = 0.5) +
  geom_line(color = "blue") +
  labs(x = "Quantiles", y = "xyz") + # adds the confidence intervals as error bars
  geom_hline(
    data = d2,
    aes(
      yintercept = Estimate,
      yinterceptmin = Estimate - `Std. Error`,
      yinterceptmax = Estimate + `Std. Error`,
    ),
    color = "red"
  ) +
  geom_ribbon(alpha = 0.5)

score 2 · Answer 1 · answered Apr 26 '23 at 07:35

One option would be to use e.g. a geom_rect to add the confidence band for your OLS estimates:

library(ggplot2)

ggplot(data = d1, aes(
  x = tau, y = Value,
  ymin = Value - Std..Error,
  ymax = Value + Std..Error
)) +
  geom_ribbon(alpha = 0.5) +
  geom_line(color = "blue") +
  labs(x = "Quantiles", y = "xyz") + # adds the confidence intervals as error bars
  geom_rect(
    data = d2, aes(
      ymin = Estimate - `Std. Error`,
      ymax = Estimate + `Std. Error`
    ), fill = "red", alpha = .5,
    xmin = -Inf, xmax = Inf, inherit.aes = FALSE
  ) +
  geom_hline(
    data = d2,
    aes(
      yintercept = Estimate,
    ),
    color = "red"
  ) +
  geom_ribbon(alpha = 0.5)

How to plot confidence interval for yintercept in ggplot

1 Answers1