r - Is there a way to weight geom_smooth by different quantiles? - Stack Overflow

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I am depicting the 14 day spei conditions leading up to and following wildfires. I'm looking for trends in variability or dips. In my study region, n = 399. As one could expect, there is a lot of variability.

geom_smooth oversimplifies the data and effectively drives the trend to the zero line, while geom_quantile misses the very non-linear trends and dips I am seeking. I wish there was a way to do multiple smoother lines representing the smoother weighted by different quantiles. I've looked over the documentation for geom_smooth, and geom_quantile, and don't see anything relating one to the other. Is this possible? Right now a stripped down version of my plot looks like this:

Here's the code to create it.

    
    ggplot(data = drought3,
            aes(x = diff_days, y = spei14d))+
            geom_smooth(span = 0.1)+
            geom_quantile(quantiles = c(0.2,0.5,0.7,0.9))+
            geom_vline(xintercept = 0)+
            geom_hline(yintercept = 0)+
            theme_bw(10)+
            labs(x = "Days till/since fire",
             y = "14 Day SPEI")

And here's some sample data (about 10 fires out of my 399).

structure(list(diff_days = c(-49, -49, -49, -49, -49, -49, -49, 
-49, -49, -49, -48, -48, -48, -48, -48, -48, -48, -48, -48, -48, 
-47, -47, -47, -47, -47, -47, -47, -47, -47, -47, -46, -46, -46, 
-46, -46, -46, -46, -46, -46, -46, -45, -45, -45, -45, -45, -45, 
-45, -45, -45, -45, -44, -44, -44, -44, -44, -44, -44, -44, -44, 
-44, -43, -43, -43, -43, -43, -43, -43, -43, -43, -43, -42, -42, 
-42, -42, -42, -42, -42, -42, -42, -42, -41, -41, -41, -41, -41, 
-41, -41, -41, -41, -41, -40, -40, -40, -40, -40, -40, -40, -40, 
-40, -40, -39, -39, -39, -39, -39, -39, -39, -39, -39, -39, -38, 
-38, -38, -38, -38, -38, -38, -38, -38, -38, -37, -37, -37, -37, 
-37, -37, -37, -37, -37, -37, -36, -36, -36, -36, -36, -36, -36, 
-36, -36, -36, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, 
-34, -34, -34, -34, -34, -34, -34, -34, -34, -34, -33, -33, -33, 
-33, -33, -33, -33, -33, -33, -33, -32, -32, -32, -32, -32, -32, 
-32, -32, -32, -32, -31, -31, -31, -31, -31, -31, -31, -31, -31, 
-31, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -29, -29, 
-29, -29, -29, -29, -29, -29, -29, -29, -28, -28, -28, -28, -28, 
-28, -28, -28, -28, -28, -27, -27, -27, -27, -27, -27, -27, -27, 
-27, -27, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -25, 
-25, -25, -25, -25, -25, -25, -25, -25, -25, -24, -24, -24, -24, 
-24, -24, -24, -24, -24, -24, -23, -23, -23, -23, -23, -23, -23, 
-23, -23, -23, -22, -22, -22, -22, -22, -22, -22, -22, -22, -22, 
-21, -21, -21, -21, -21, -21, -21, -21, -21, -21, -20, -20, -20, 
-20, -20, -20, -20, -20, -20, -20, -19, -19, -19, -19, -19, -19, 
-19, -19, -19, -19, -18, -18, -18, -18, -18, -18, -18, -18, -18, 
-18, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -16, -16, 
-16, -16, -16, -16, -16, -16, -16, -16, -15, -15, -15, -15, -15, 
-15, -15, -15, -15, -15, -14, -14, -14, -14, -14, -14, -14, -14, 
-14, -14, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -12, 
-12, -12, -12, -12, -12, -12, -12, -12, -12, -11, -11, -11, -11, 
-11, -11, -11, -11, -11, -11, -10, -10, -10, -10, -10, -10, -10, 
-10, -10, -10, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -8, -8, 
-8, -8, -8, -8, -8, -8, -8, -8, -7, -7, -7, -7, -7, -7, -7, -7, 
-7, -7, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -5, -5, -5, -5, 
-5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, 
-3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, 
-2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 
8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 
12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 
13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 
15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 
16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 
18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 
20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 
21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 
23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 
26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 
28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 
29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 
31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
32, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 
34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 36, 
36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 37, 37, 
37, 37, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 
39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 40, 40, 40, 40, 
40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 
42, 42, 42, 42, 42, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 44, 
44, 44, 44, 44, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 45, 45, 
45, 45, 45, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 47, 47, 47, 
47, 47, 47, 47, 47, 47, 47, 48, 48, 48, 48, 48, 48, 48, 48, 48, 
48, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49), spei14d = c(-1.69000005722046, 
-0.811219203758178, -0.46000000834465, -0.992645950939344, 0.480137197209931, 
0.239999994635582, 0.319999992847443, 0.081293444126801, -0.43774733383111, 
2.08999991416931, 0.0842905125601223, 0.727154719448018, 0.372192969120908, 
-1.27999997138977, -1.52379684205999, -1.45000004768372, 0.701203443580892, 
-0.801965666256297, -2.08999991416931, -0.922905455244348, 0.554863375985872, 
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-0.191171079054627, -0.620000004768372, -1.27999997138977, 0.319999992847443, 
0.256744179684062, -0.239999994635582, -0.516524758691481, 0.0299999993294477, 
-0.411032553994378, -0.539569748624948, -0.18516886616112, 0.446381573883915, 
0.640398811378366, -0.389999985694885, -0.800000011920929, -1.60603115347658, 
0.381357679900069, -1.00999999046326, 0.781996125097187, 0.306382127146863, 
0.210153843316582, 0.100000001490116, 0.737539561100032, 0.762423325122289, 
0.0689926661132017, 1.12999999523163, 0.0325697497758746, 1.45000004768372, 
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)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-990L), groups = structure(list(diff_days = c(-49, -48, -47, 
-46, -45, -44, -43, -42, -41, -40, -39, -38, -37, -36, -35, -34, 
-33, -32, -31, -30, -29, -28, -27, -26, -25, -24, -23, -22, -21, 
-20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10, -9, -8, 
-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 
27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 
43, 44, 45, 46, 47, 48, 49), .rows = structure(list(1:10, 11:20, 
    21:30, 31:40, 41:50, 51:60, 61:70, 71:80, 81:90, 91:100, 
    101:110, 111:120, 121:130, 131:140, 141:150, 151:160, 161:170, 
    171:180, 181:190, 191:200, 201:210, 211:220, 221:230, 231:240, 
    241:250, 251:260, 261:270, 271:280, 281:290, 291:300, 301:310, 
    311:320, 321:330, 331:340, 341:350, 351:360, 361:370, 371:380, 
    381:390, 391:400, 401:410, 411:420, 421:430, 431:440, 441:450, 
    451:460, 461:470, 471:480, 481:490, 491:500, 501:510, 511:520, 
    521:530, 531:540, 541:550, 551:560, 561:570, 571:580, 581:590, 
    591:600, 601:610, 611:620, 621:630, 631:640, 641:650, 651:660, 
    661:670, 671:680, 681:690, 691:700, 701:710, 711:720, 721:730, 
    731:740, 741:750, 751:760, 761:770, 771:780, 781:790, 791:800, 
    801:810, 811:820, 821:830, 831:840, 841:850, 851:860, 861:870, 
    871:880, 881:890, 891:900, 901:910, 911:920, 921:930, 931:940, 
    941:950, 951:960, 961:970, 971:980, 981:990), ptype = integer(0), class = c("vctrs_list_of", 
"vctrs_vctr", "list"))), row.names = c(NA, -99L), .drop = TRUE, class = c("tbl_df", 
"tbl", "data.frame")))

I am depicting the 14 day spei conditions leading up to and following wildfires. I'm looking for trends in variability or dips. In my study region, n = 399. As one could expect, there is a lot of variability.

geom_smooth oversimplifies the data and effectively drives the trend to the zero line, while geom_quantile misses the very non-linear trends and dips I am seeking. I wish there was a way to do multiple smoother lines representing the smoother weighted by different quantiles. I've looked over the documentation for geom_smooth, and geom_quantile, and don't see anything relating one to the other. Is this possible? Right now a stripped down version of my plot looks like this:

Here's the code to create it.

    
    ggplot(data = drought3,
            aes(x = diff_days, y = spei14d))+
            geom_smooth(span = 0.1)+
            geom_quantile(quantiles = c(0.2,0.5,0.7,0.9))+
            geom_vline(xintercept = 0)+
            geom_hline(yintercept = 0)+
            theme_bw(10)+
            labs(x = "Days till/since fire",
             y = "14 Day SPEI")

And here's some sample data (about 10 fires out of my 399).

structure(list(diff_days = c(-49, -49, -49, -49, -49, -49, -49, 
-49, -49, -49, -48, -48, -48, -48, -48, -48, -48, -48, -48, -48, 
-47, -47, -47, -47, -47, -47, -47, -47, -47, -47, -46, -46, -46, 
-46, -46, -46, -46, -46, -46, -46, -45, -45, -45, -45, -45, -45, 
-45, -45, -45, -45, -44, -44, -44, -44, -44, -44, -44, -44, -44, 
-44, -43, -43, -43, -43, -43, -43, -43, -43, -43, -43, -42, -42, 
-42, -42, -42, -42, -42, -42, -42, -42, -41, -41, -41, -41, -41, 
-41, -41, -41, -41, -41, -40, -40, -40, -40, -40, -40, -40, -40, 
-40, -40, -39, -39, -39, -39, -39, -39, -39, -39, -39, -39, -38, 
-38, -38, -38, -38, -38, -38, -38, -38, -38, -37, -37, -37, -37, 
-37, -37, -37, -37, -37, -37, -36, -36, -36, -36, -36, -36, -36, 
-36, -36, -36, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, 
-34, -34, -34, -34, -34, -34, -34, -34, -34, -34, -33, -33, -33, 
-33, -33, -33, -33, -33, -33, -33, -32, -32, -32, -32, -32, -32, 
-32, -32, -32, -32, -31, -31, -31, -31, -31, -31, -31, -31, -31, 
-31, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -29, -29, 
-29, -29, -29, -29, -29, -29, -29, -29, -28, -28, -28, -28, -28, 
-28, -28, -28, -28, -28, -27, -27, -27, -27, -27, -27, -27, -27, 
-27, -27, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -25, 
-25, -25, -25, -25, -25, -25, -25, -25, -25, -24, -24, -24, -24, 
-24, -24, -24, -24, -24, -24, -23, -23, -23, -23, -23, -23, -23, 
-23, -23, -23, -22, -22, -22, -22, -22, -22, -22, -22, -22, -22, 
-21, -21, -21, -21, -21, -21, -21, -21, -21, -21, -20, -20, -20, 
-20, -20, -20, -20, -20, -20, -20, -19, -19, -19, -19, -19, -19, 
-19, -19, -19, -19, -18, -18, -18, -18, -18, -18, -18, -18, -18, 
-18, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -16, -16, 
-16, -16, -16, -16, -16, -16, -16, -16, -15, -15, -15, -15, -15, 
-15, -15, -15, -15, -15, -14, -14, -14, -14, -14, -14, -14, -14, 
-14, -14, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -12, 
-12, -12, -12, -12, -12, -12, -12, -12, -12, -11, -11, -11, -11, 
-11, -11, -11, -11, -11, -11, -10, -10, -10, -10, -10, -10, -10, 
-10, -10, -10, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -8, -8, 
-8, -8, -8, -8, -8, -8, -8, -8, -7, -7, -7, -7, -7, -7, -7, -7, 
-7, -7, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -5, -5, -5, -5, 
-5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, 
-3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, 
-2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 
8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 
12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 
13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 
15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 
16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 
18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 
20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 
21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 
23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 
26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 
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• r
• ggplot2
• loess
• quantile-regression

Share Improve this question Follow asked 7 hours ago Ivey_MichaellaIvey_Michaella 2311 silver badge55 bronze badges 1

• What exactly do you mean by "weighted by different quantiles"? If you were doing this outside the ggplot framework, what would you do? – DaveArmstrong Commented 6 hours ago

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1 Answer 1

Sorted by: Reset to default 2

The help page for geom_quantile suggest that smooth quantile regression can be drawn using:

geom_quantile(method = "rqss")

Do note the warning in those same docs:

Note that rqss doesn't pick a smoothing constant automatically, so you'll need to tweak lambda yourself.

Here is a comparison, for your data:

ggplot(data = drought3,
       aes(x = diff_days, y = spei14d))+
  geom_smooth(span = 0.1, data = \(d) mutate(d, facet = 'smooth'))+
  geom_quantile(method = "rqss", quantiles = c(0.2,0.5,0.7,0.9), 
                data = \(d) mutate(d, facet = 'smooth quantile')) +
  geom_quantile(quantiles = c(0.2,0.5,0.7,0.9), 
                data = \(d) mutate(d, facet = 'quantile'))+
  geom_vline(xintercept = 0)+
  geom_hline(yintercept = 0)+
  facet_grid(cols = vars(facet)) + 
  theme_bw(10)+
  labs(x = "Days till/since fire",
       y = "14 Day SPEI")

本文标签： rIs there a way to weight geomsmooth by different quantilesStack Overflow