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Home/ Questions/Q 6845957
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Editorial Team
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Editorial Team
Asked: May 27, 20262026-05-27T00:33:53+00:00 2026-05-27T00:33:53+00:00

I have some time-series data that I’m fitting a loess curve in ggplot2, as

  • 0

I have some time-series data that I’m fitting a loess curve in ggplot2, as seen attached. The data takes the shape of an “S” curve. What I really need to find out is the date where the data starts to level off, which looks to be right around time ‘550’ or ‘600’

Is there some kind of quantitative way that this can be marked off in the graph?

A link to the dataset: http://dl.dropbox.com/u/75403/stover_data.txt

A dput() of the dataset:

structure(list(date = c(211L, 213L, 215L, 217L, 218L, 221L, 222L, 
223L, 224L, 225L, 226L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 
235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 244L, 246L, 247L, 
248L, 249L, 250L, 251L, 253L, 254L, 255L, 256L, 258L, 259L, 260L, 
261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 
272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 281L, 282L, 283L, 
285L, 286L, 287L, 288L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 
297L, 298L, 299L, 300L, 301L, 302L, 304L, 305L, 306L, 307L, 308L, 
309L, 310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 
320L, 321L, 322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 
331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 
342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 
353L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 
364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 
375L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 
386L, 387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 
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597L, 598L, 599L, 600L, 601L, 602L, 603L, 604L, 605L, 606L, 607L, 
608L, 609L, 610L, 611L, 612L, 613L, 614L, 615L, 616L, 617L, 618L, 
619L, 620L, 621L, 622L, 623L, 624L, 625L, 626L, 627L, 628L, 629L, 
630L, 631L, 632L, 634L, 635L, 636L, 637L, 638L, 639L, 640L, 641L, 
642L, 643L, 644L, 645L, 646L, 647L, 648L, 649L, 650L, 651L, 652L, 
653L, 654L, 655L, 656L, 657L, 658L, 659L, 660L, 661L, 662L, 663L, 
664L, 665L, 666L, 667L, 668L, 669L, 670L, 671L, 672L, 673L, 674L, 
675L, 676L, 677L, 678L, 679L, 680L, 681L, 684L, 685L, 686L, 687L, 
688L, 689L, 690L, 691L, 692L, 693L, 694L, 695L, 696L, 697L, 698L, 
699L, 700L, 701L, 702L, 703L, 704L, 705L, 706L, 707L, 708L, 709L, 
710L, 711L, 712L, 713L, 714L, 715L, 716L, 717L, 718L, 719L, 720L, 
721L, 722L, 723L, 724L, 725L, 726L, 727L, 728L, 729L, 730L, 731L, 
732L, 733L, 734L, 735L, 736L, 737L, 738L, 739L, 740L, 741L, 742L, 
743L, 744L, 745L, 746L, 747L, 748L, 749L, 750L, 751L, 752L, 753L, 
754L, 755L, 756L, 757L, 758L, 759L, 760L, 761L, 762L, 763L, 764L, 
765L, 766L, 767L, 768L, 769L, 770L, 771L, 772L, 773L, 774L, 775L, 
776L, 777L, 778L, 781L, 782L, 783L, 784L, 785L, 786L, 787L, 788L, 
789L, 790L, 791L, 792L, 793L, 794L, 795L, 796L, 797L, 798L, 799L, 
800L, 801L, 802L, 803L, 804L, 805L, 806L, 807L, 808L, 809L, 810L, 
811L, 812L, 813L, 814L, 815L, 816L, 817L, 818L, 819L, 820L, 821L, 
822L, 823L, 824L, 825L, 826L, 827L, 828L, 829L, 830L, 831L, 832L, 
833L, 834L, 835L, 836L, 837L, 838L, 839L, 840L, 841L), org_count = c(2L, 
1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 2L, 5L, 3L, 2L, 1L, 4L, 1L, 
1L, 10L, 10L, 4L, 5L, 4L, 1L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 2L, 
1L, 3L, 6L, 4L, 2L, 1L, 3L, 1L, 2L, 4L, 4L, 6L, 3L, 2L, 6L, 12L, 
13L, 14L, 8L, 7L, 5L, 11L, 11L, 1L, 40L, 13L, 1L, 2L, 4L, 2L, 
5L, 2L, 1L, 2L, 3L, 5L, 1L, 3L, 4L, 1L, 4L, 7L, 12L, 3L, 3L, 
2L, 2L, 2L, 2L, 2L, 3L, 4L, 2L, 5L, 6L, 4L, 5L, 6L, 3L, 6L, 4L, 
16L, 79L, 61L, 31L, 43L, 40L, 38L, 25L, 22L, 29L, 22L, 5L, 6L, 
11L, 6L, 6L, 8L, 7L, 4L, 7L, 11L, 4L, 18L, 10L, 13L, 10L, 8L, 
12L, 14L, 11L, 22L, 13L, 16L, 16L, 6L, 5L, 11L, 17L, 11L, 11L, 
16L, 15L, 13L, 16L, 15L, 12L, 16L, 14L, 9L, 15L, 18L, 20L, 13L, 
15L, 21L, 16L, 6L, 22L, 20L, 13L, 19L, 15L, 23L, 19L, 18L, 21L, 
21L, 12L, 15L, 41L, 26L, 14L, 12L, 11L, 11L, 9L, 9L, 8L, 7L, 
5L, 2L, 7L, 6L, 2L, 3L, 4L, 2L, 2L, 1L, 7L, 3L, 3L, 4L, 2L, 3L, 
1L, 2L, 1L, 2L, 2L, 2L, 6L, 5L, 7L, 8L, 6L, 5L, 8L, 6L, 5L, 5L, 
4L, 4L, 8L, 5L, 3L, 6L, 6L, 6L, 6L, 5L, 6L, 4L, 1L, 4L, 2L, 5L, 
1L, 2L, 1L, 1L, 1L, 2L, 3L, 5L, 1L, 1L, 3L, 3L, 4L, 3L, 4L, 6L, 
6L, 1L, 2L, 3L, 6L, 4L, 7L, 17L, 6L, 5L, 2L, 4L, 6L, 8L, 1L, 
3L, 2L, 4L, 4L, 2L, 3L, 4L, 3L, 3L, 7L, 9L, 6L, 14L, 12L, 12L, 
6L, 15L, 33L, 19L, 13L, 17L, 12L, 16L, 10L, 7L, 7L, 6L, 20L, 
20L, 8L, 14L, 9L, 22L, 21L, 6L, 6L, 8L, 54L, 44L, 22L, 21L, 14L, 
13L, 64L, 34L, 26L, 21L, 61L, 43L, 47L, 42L, 37L, 57L, 46L, 38L, 
33L, 32L, 51L, 76L, 36L, 31L, 45L, 35L, 27L, 17L, 17L, 12L, 7L, 
77L, 69L, 18L, 28L, 37L, 35L, 40L, 47L, 36L, 37L, 33L, 17L, 24L, 
13L, 19L, 28L, 22L, 27L, 49L, 37L, 25L, 30L, 35L, 20L, 16L, 20L, 
10L, 15L, 67L, 35L, 32L, 28L, 48L, 66L, 76L, 68L, 38L, 16L, 18L, 
37L, 29L, 37L, 53L, 31L, 30L, 20L, 48L, 36L, 35L, 31L, 33L, 16L, 
13L, 32L, 56L, 47L, 32L, 39L, 20L, 27L, 53L, 62L, 60L, 49L, 41L, 
17L, 25L, 26L, 42L, 33L, 48L, 34L, 25L, 24L, 51L, 31L, 44L, 37L, 
27L, 17L, 35L, 32L, 34L, 28L, 28L, 28L, 28L, 53L, 48L, 58L, 49L, 
25L, 25L, 34L, 33L, 63L, 75L, 112L, 74L, 29L, 36L, 36L, 42L, 
42L, 44L, 49L, 16L, 24L, 27L, 47L, 40L, 37L, 33L, 13L, 25L, 31L, 
45L, 40L, 53L, 51L, 30L, 41L, 43L, 60L, 46L, 39L, 24L, 39L, 48L, 
59L, 43L, 71L, 31L, 21L, 37L, 45L, 41L, 45L, 34L, 19L, 19L, 25L, 
45L, 40L, 28L, 33L, 19L, 25L, 25L, 31L, 25L, 29L, 31L, 30L, 27L, 
40L, 31L, 25L, 42L, 29L, 18L, 11L, 27L, 34L, 35L, 59L, 32L, 23L, 
22L, 29L, 38L, 39L, 35L, 47L, 21L, 16L, 33L, 22L, 15L, 18L, 16L, 
20L, 16L, 36L, 44L, 58L, 35L, 21L, 20L, 14L, 55L, 34L, 30L, 40L, 
27L, 34L, 31L, 47L, 53L, 42L, 59L, 55L, 41L, 43L, 29L, 26L, 32L, 
40L, 33L, 28L, 27L, 47L, 40L, 52L, 48L, 58L, 38L, 35L, 29L, 37L, 
19L, 19L, 22L, 15L, 16L, 21L, 31L, 25L, 31L, 23L, 32L, 30L, 80L, 
45L, 49L, 32L, 18L, 29L, 35L, 23L, 27L, 21L, 21L, 29L, 43L, 106L, 
58L, 117L, 49L, 28L, 24L, 43L, 49L, 34L, 23L, 28L, 16L, 21L, 
45L, 37L, 29L, 32L, 26L, 16L, 18L, 26L, 24L, 21L, 18L, 16L, 23L, 
10L, 19L, 24L, 29L, 11L, 26L, 15L, 14L, 19L)), .Names = c("date", 
"org_count"), class = "data.frame", row.names = c(NA, -599L))

Graph:
enter image description here

Code:

> p<-qplot(date,org_count, data=christi)

> p+stat_smooth(method="loess",size=1.5)
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  1. Editorial Team
    Editorial Team
    2026-05-27T00:33:53+00:00Added an answer on May 27, 2026 at 12:33 am

    If you are asking for a way of determining the point where the curve is a maximum (i.e. flat), this is the same as finding the point where the slope of the line is at its maximum (from basic calculus).

    First, read your data:

    christi <- read.table("http://dl.dropbox.com/u/75403/stover_data.txt", sep="\t", header=TRUE)
    

    Next, use loess to fit a smoothed model:

    fit <- loess(org_count~date, data=christi)
    

    Then, predict the values in your range of x-values (with predict.loess), determine the slope (diff is close enough`), and find the

    x <- 200:800
    px <- predict(fit, newdata=x)
    px1 <- diff(px)
    
    which.max(px1)
    [1] 367
    

    Since the start value of x is 200, this means the curve is flat at position 200+367=567.


    If you wanted to plot this:

    par(mfrow=c(1, 2))
    plot(x, px, main="loess model")
    
    plot(x[-1], px1, main="diff(loess model)")
    abline(v=567, col="red")
    

    enter image description here

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