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ggplot2geom-ribbon

Adding geom_ribbon to geom_line from secondary dataframe

I'm trying to display my model fit and data points on the same plot with ggplot, but can't figure out how to add my calculated SE bands (pred$conf.low, pred$conf.high) to my model line (pred$predicted).

> head(toad2,5)
    num   DO
753   0 8.41
755   0 8.27
756   0 8.31
757   0 8.04
758   0 7.77

> str(toad2)
'data.frame':   640 obs. of  2 variables:
 $ num: int  0 0 0 0 0 0 0 0 0 0 ...
 $ DO : num  8.41 8.27 8.31 8.04 7.77 7.43 7.54 7.37 7.33 6.75 ...
 - attr(*, "na.action")= 'omit' Named int [1:1005] 1 2 3 4 5 6 7 8 9 10 ...
  ..- attr(*, "names")= chr [1:1005] "1" "2" "3" "4" ...

fit <- glmmTMB(num ~ DO, family=nbinom2, ziformula = ~0, dispformula = ~1, toad2, REML = FALSE)

pred <- ggpredict(fit, terms = "DO")

> pred
# Predicted counts of num

DO | Predicted |       95% CI
-----------------------------
 0 |      0.11 | [0.05, 0.24]
 2 |      0.15 | [0.09, 0.25]
 4 |      0.20 | [0.14, 0.27]
 6 |      0.26 | [0.21, 0.33]
 8 |      0.35 | [0.25, 0.49]
10 |      0.46 | [0.27, 0.80]
12 |      0.62 | [0.29, 1.33]
16 |      1.10 | [0.32, 3.79]

How would I add the 95% confidence intervals to this geom_line from ggeffects/data.frame "pred"?

ggplot(data = toad2, aes(x = DO, y = num)) + 
  geom_point(color='blue') +
  geom_line(color='red',data = pred, aes(x=x, y=predicted))

enter image description here

My attempt:

ggplot(data = toad2, aes(x = DO, y = num)) + 
  geom_point(color='blue') +
  geom_line(color='red',data = pred, aes(x=x, y=predicted, ymin = conf.low, ymax = conf.high)) 

Warning message:
In geom_line(color = "red", data = pred, aes(x = x, y = predicted,  :
  Ignoring unknown aesthetics: ymin and ymax

"pred" data:

> dput(pred)
structure(list(x = c(0, 2, 4, 6, 8, 10, 12, 14, 16), predicted = c(0.110979768096301, 
0.147788011747898, 0.196804307587347, 0.262077654519131, 0.348999967735793, 
0.464751478728963, 0.618893859452371, 0.824160065752538, 1.09750614520922
), std.error = c(0.385361502587751, 0.270114672803532, 0.167239363548879, 
0.116295711211178, 0.1721307632046, 0.276191828818464, 0.391769667585664, 
0.511110201467043, 0.632085771961832), conf.low = c(0.0521463010143289, 
0.0870396588397944, 0.141801294508668, 0.208659856560919, 0.2490619680673, 
0.270474215214499, 0.287171428211178, 0.302659126928022, 0.317961528540038
), conf.high = c(0.236191420812848, 0.250934995696594, 0.27314232651499, 
0.329170632676038, 0.489038846134357, 0.798574965119925, 1.33380124775568, 
2.24424031376646, 3.78825622175962), group = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = "1", class = "factor")), row.names = c(NA, 
-9L), class = c("ggeffects", "data.frame"), legend.labels = "1", x.is.factor = "0", continuous.group = FALSE, rawdata = structure(list(
    response = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
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    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
    0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
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    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 
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    0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L), 
    x = c(8.41, 8.27, 8.31, 8.04, 7.77, 7.43, 7.54, 7.37, 7.33, 
    6.75, 6.3, 6.64, 6.85, 6.63, 7.09, 6.53, 7.04, 6.56, 6.93, 
    6.93, 8.17, 6.4, 6.76, 6.57, 7.72, 7.21, 7.52, 7.35, 7.56, 
    7.02, 7.3, 7.03, 6.58, 6.49, 6.09, 5.44, 7.3, 5.04, 5.77, 
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    5.61, 5.97, 5.22, 6.33, 9.09, 6.45, 6.11, 4.96, 4.8, 4.59, 
    5.07, 6.19, 5.77, 5.71, 5.75, 5.6, 5.95, 8.06, 7.8, 5.88, 
    5.13, 5.97, 7.17, 5.91, 5.97, 5.82, 5.02, 5.93, 5.79, 4.72, 
    4.89, 5.69, 3.95, 3.47, 3.48, 9.64, 7.01, 6.13, 5.81, 4.28, 
    4.42, 3.18, 3.91, 2.4, 7.42, 9.88, 9.22, 7.77, 7.32, 7.03, 
    9.98, 7.15, 7.49, 8.26, 6.94, 7.05, 5.05, 6.65, 7.63, 9.48, 
    9.3, 9.26, 8.97, 12.67, 10.21, 14.43, 10.71, 6.01, 7.25, 
    5.83, 5.48, 4.85, 5.23, 3.74, 8.19, 6.38, 5.17, 8.11, 8.15, 
    5.72, 5.63, 5.48, 4.29, 9.74, 9.88, 7.32, 8.69, 7.26, 5.05, 
    9.22, 9.51, 8.94, 8.68, 8.82, 8.14, 5.57, 8.09, 7.92, 11.72, 
    7.2, 7.84, 12.55, 7.18, 6.47, 7.12, 7.29, 4.94, 12.07, 8.95, 
    11.45, 10.5, 6.88, 5.8, 7.16, 6.4, 5.25, 4.68, 5.14, 7.05, 
    4.46, 4.4, 11.91, 9.32, 7.8, 7.85, 8.04, 7.98, 6.96, 6.7, 
    3.56, 8.2, 5.74, 6.78, 7.08, 6.99, 4.9, 6.55, 3.51, 6.23, 
    4.93, 4.83, 4.17, 3.96, 4.45, 3.43, 2.74, 2.62, 1.56, 4.01, 
    4.1, 4.93, 4.17, 9.61, 6.55, 4.43, 4.79, 3.92, 3.43, 3.31, 
    2.56, 0.82, 1.54, 5.17, 7.17, 3.28, 4.14, 1.79, 2.87, 3.52, 
    2.06, 0.66, 10.28, 8.22, 7.41, 5.17, 4.29, 1.29, 10.2, 6.8, 
    5, 6.6, 5.5, 5.3, 5.2, 4.5, 4, 2.7, 8.6, 7.7, 7.2, 6.1, 5.3, 
    6.2, 5.5, 5.6, 5, 4, 9.4, 8.4, 8.1, 7.6, 7.2, 6.9, 6.7, 5.9, 
    6.1, 5.3, 5.3, 5.3, 5.4, 5.1, 5.9, 4.9, 5.4, 5.2, 7.8, 7.7, 
    5.5, 7.2, 7.5, 6.1, 4.9, 5.5, 5.9, 2.95, 3.09, 3.08, 3.55, 
    2.47, 3.53, 2.95, 2.84, 3.98, 2.28, 0.78, 6.02, 4.06, 2.7, 
    1.03, 1.32, 2.12, 7.29, 5.89, 7.71, 8.63, 8.5, 8.13, 7.13, 
    8.33, 6.87, 5.83, 10.77, 6.9, 6.9, 5.55, 5.38, 5.76, 4.64, 
    3.83, 4.36, 4.77, 4.45, 5.08, 3.96, 4.4, 4.7, 5.84, 5.87, 
    4.4, 4.47, 4.85, 8.5, 7.4, 6.9, 7.3, 7.1, 8.6, 7.5, 7.2, 
    7.8, 8.1, 7.4, 8.1, 7.5, 8.2, 6.6, 6.5, 4.7, 6.3, 4.9, 5.4, 
    5.6, 7.7, 6.3, 7.4, 5.1, 5.7, 6, 5, 4.1, 3.3, 4.1, 9.5, 7.6, 
    5.8, 6.6, 5.3, 5, 4.8, 4.8, 4.3, 4.9, 5.6, 6.8, 6.7, 6.3, 
    5.1, 6, 6.7, 7.9, 5.9, 6.8, 6.2, 5.4, 5.2, 5.2, 4.1, 5.9, 
    7.3, 4.8, 5, 6, 5.3, 4.5, 4.4, 6.4, 5.7, 6, 6, 4.4, 4.3, 
    4.1, 3.7, 3.2, 6.4, 4.9, 5, 4.7, 4.4, 5.6, 5, 2.9, 3.6, 3.5, 
    3.1, 3.1, 5.5, 5.4, 2.7, 2.2, 1.7, 4, 3.6, 5.9, 6, 6.9, 6.9, 
    7.4, 7.2, 7.1, 6.1, 7.1, 6.5, 6.3, 5.8, 7.5, 8, 8.3, 8.4, 
    8.9, 8.5, 7.8, 7.7, 7.1, 8.9, 8.6, 11, 13.5, 12.8, 8, 9, 
    8.5, 7.9, 7.6, 7.7, 9.5, 6.8, 6.6, 6.1, 6.7, 6.6, 6.3, 6.6, 
    7.9, 6.5, 5.2, 5.9, 6.7, 6.3, 4.4, 4.7, 6.5, 3.67, 5.2, 6.24, 
    5.76, 6.73, 6.16, 6.9, 7.16, 3.92, 2.03, 2.17, 3.63, 1.81, 
    1.85, 4.25, 0.69, 3.52, 2.25, 7.83, 2.45, 3.41, 2.49, 6.24, 
    6.77, 5.26, 4.92, 5.03, 4.3, 4.33, 3.67, 10.73, 7.43, 6.11, 
    6.3, 5.11, 3.56, 11.02, 8.99, 6.68, 10.7, 6.88, 5.92, 5.5, 
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    3.14, 3.76, 4.6, 5.03, 4.04, 2.77, 4.11, 3.06, 4.32, 4.19, 
    4.32, 4.36, 4.2, 3.03, 5.31, 5.4, 4.83, 4.68, 4.7, 4.27, 
    4.4, 4.61, 5.28, 4.11, 3.95, 3.51, 3.59, 3.12, 2.5, 2.32, 
    2.55, 3.05, 4.43, 4.05, 3.42, 1.26, 3.91, 3.93, 3.41, 4.61, 
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    4.77, 5.16, 4.55, 4.6, 5.73, 5.16, 5.41, 5.88, 1.63, 2.71, 
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    4.65, 3.93, 3.76, 3.73, 6.62, 6.7, 5.49, 0.93, 1.12, 0.9, 
    1.96, 1.5, 1.32, 2.98, 3.2, 3.23, 3.52, 3.08, 3.73, 3.95, 
    3.21, 4.14, 4.34, 3.48, 3.25, 0.61), group = structure(c(1L, 
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    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), class = "factor", levels = "1"), 
    facet = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
    ), class = "factor", levels = "1")), class = "data.frame", row.names = c(NA, 
-640L)), title = "Predicted counts of num", x.title = "DO", y.title = "num", legend.title = NA_character_, constant.values = structure(list(), names = character(0)), terms = "DO", original.terms = "DO", at.list = list(
    DO = c(0, 2, 4, 6, 8, 10, 12, 14, 16)), prediction.interval = FALSE, ci.lvl = 0.95, type = "fe", response.name = "num", family = "nbinom2", link = "log", logistic = "0", link_inverse = function (eta) 
pmax(exp(eta), .Machine$double.eps), link_function = function (mu) 
log(mu), is.trial = "0", fitfun = "glm", model.name = "fit")
Solution

  • Using a geom_ribbon you could do:

    library(ggplot2)

ggplot() +
  geom_ribbon(data = pred, aes(x=x, ymin = conf.low, ymax = conf.high), alpha = .3, fill = "red") +
  geom_line(color = "red", data = pred, aes(x = x, y = predicted))

    enter image description here