[R] spatial adjustment using checks

DIGHE, NILESH [AG/2362] nilesh.dighe at monsanto.com
Thu Oct 22 19:10:29 CEST 2015


Hi,
I have yield data for several varieties and a randomly placed check (1 in every 8 column or "cols") in a field test arranged in a rows*cols grid format (see image attached).  Both "rows" & "cols" are variables in the data set.  I like to adjust "yield" variable for each row listed as "variety" in variable "linecode" by dividing its yield with the average yield of four nearest "check" (on the rows*cols field grid) in variable "linecode".  I like to have two checks on the same row where one check is on the left and the other is on the right side of a given variety.  The other two checks should come from the two neighboring columns ("cols").  If a check is missing on one or more sides of a given variety, then I like to proceed with the calculation with only the available checks around that given variety.  If two checks on the neighboring column are equidistance from a given variety then use position of the variety to choose which one to use (If variety is in cols 1-8 then use check from those cols; if variety is in cols 9-16 then use check from cols 9-16).

Below is the function I wrote which adjust yield values for each "variety" (variable "linecode") by dividing its yield with the average yield of all checks in the field.  Instead of using average check across the whole field, I like to use the four neighboring checks to make this adjustment.  I am struggling with specifying the four nearest checks in this loop.  I played around using "dist" function but without any success.  I tried searching for any packages that can do these nearest check adjustments without any success.  Any help will be appreciated.

-------------------function------------------------------------------
function (dataset, trait, control) {
    m <- c()
    x <- length(trait)
    chkmean <- tapply(trait, control, mean, na.rm = T)
    for (i in 1:x) {
        m[i] <- ifelse(control[i] == "variety", trait[i]/chkmean[1],
            trait[i]/trait[i])
    }
    head(as.data.frame(m))
}

---------------------data----------------------------------------------------------------------

dput(dat)

structure(list(rows = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,

1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,

2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,

3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,

4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("1", "2", "3",

"4"), class = "factor"), cols = structure(c(1L, 2L, 3L, 4L, 5L,

6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 16L, 15L,

14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L,

1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,

15L, 16L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L,

5L, 4L, 3L, 2L, 1L), .Label = c("1", "2", "3", "4", "5", "6",

"7", "8", "9", "10", "11", "12", "13", "14", "15", "16"), class = "factor"),

    plotid = c(289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L,

    297L, 298L, 299L, 300L, 301L, 302L, 303L, 304L, 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, 397L, 398L, 399L, 400L,

    465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L,

    475L, 476L, 477L, 478L, 479L, 480L), yield = c(5.1, 5.5,

    5, 5.5, 6.2, 5.1, 5.5, 5.2, 5, 5, 3.9, 4.6, 5, 4.4, 5.1,

    4.3, 4.4, 4.2, 3.9, 4.6, 4.8, 5.4, 4.7, 5.5, 5.3, 4.8, 5.8,

    4.6, 5.8, 5.5, 5.3, 5.6, 5.6, 5, 4.8, 4.9, 5.2, 5.3, 4.6,

    4.8, 5.3, 4.2, 4.6, 4.2, 4.2, 4, 3.9, 4.5, 5.4, 4.8, 4.6,

    5.2, 4.9, 5.1, 4.5, 5.8, 5.2, 4.7, 4.8, 5.3, 5.8, 4.9, 5.9,

    4.5), line = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,

    9L, 1L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,

    20L, 1L, 21L, 22L, 1L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,

    30L, 31L, 32L, 33L, 1L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,

    41L, 42L, 1L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 1L,

    51L, 52L, 53L, 54L, 1L, 55L, 56L, 57L), .Label = c("CHK",

    "V002", "V003", "V004", "V005", "V006", "V007", "V008", "V009",

    "V010", "V011", "V012", "V013", "V014", "V015", "V016", "V017",

    "V018", "V019", "V020", "V021", "V022", "V023", "V024", "V025",

    "V026", "V027", "V028", "V029", "V030", "V031", "V032", "V033",

    "V034", "V035", "V036", "V037", "V038", "V039", "V040", "V041",

    "V042", "V043", "V044", "V045", "V046", "V047", "V048", "V049",

    "V050", "V051", "V052", "V053", "V054", "V055", "V056", "V057"

    ), class = "factor"), linecode = structure(c(1L, 2L, 2L,

    2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,

    2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,

    2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,

    2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,

    2L), .Label = c("check", "variety"), class = "factor")), .Names = c("rows",

"cols", "plotid", "yield", "line", "linecode"), row.names = c(NA,

-64L), class = "data.frame")

-------------------------------------------------------------------------------------------------
My expected output is in column "adj_yield" below:
    rows cols plotid yield line linecode  adj_yield
1     1    1    289   5.1  CHK    check     check
2     1    2    290   5.5 V002  variety     1.071
3     1    3    291   5.0 V003  variety     0.974
4     1    4    292   5.5 V004  variety     1.071
5     1    5    293   6.2 V005  variety     1.208
6     1    6    294   5.1 V006  variety     0.994
7     1    7    295   5.5 V007  variety     1.071
8     1    8    296   5.2 V008  variety     1.013
9     1    9    297   5.0 V009  variety     0.974
10    1   10    298   5.0  CHK    check     check
11    1   11    299   3.9 V010  variety     0.750
12    1   12    300   4.6 V011  variety     0.885
13    1   13    301   5.0 V012  variety     0.962
14    1   14    302   4.4 V013  variety     0.846
15    1   15    303   5.1 V014  variety     0.981
16    1   16    304   4.3 V015  variety     0.827
17    2   16    369   4.4 V016  variety     check
18    2   15    370   4.2 V017  variety     0.881
19    2   14    371   3.9 V018  variety     0.818
20    2   13    372   4.6 V019  variety     0.965
21    2   12    373   4.8 V020  variety     1.007
22    2   11    374   5.4  CHK    check     check
23    2   10    375   4.7 V021  variety     0.959
24    2    9    376   5.5 V022  variety     1.053
25    2    8    377   5.3  CHK    check     check
26    2    7    378   4.8 V023  variety     0.923
27    2    6    379   5.8 V024  variety     1.115
28    2    5    380   4.6 V025  variety     0.885
29    2    4    381   5.8 V026  variety     1.115
30    2    3    382   5.5 V027  variety     1.058
31    2    2    383   5.3 V028  variety     1.019
32    2    1    384   5.6 V029  variety     1.077


-----------------session info------------------------------------------------------------------------

R version 3.2.1 (2015-06-18)

Platform: i386-w64-mingw32/i386 (32-bit)

Running under: Windows 7 x64 (build 7601) Service Pack 1



locale:

[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252

[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C

[5] LC_TIME=English_United States.1252



attached base packages:

[1] stats     graphics  grDevices utils     datasets  methods   base



other attached packages:

[1] rlist_0.4.5.1 mapplots_1.5  agridat_1.12



loaded via a namespace (and not attached):

 [1] magrittr_1.5     plyr_1.8.3       tools_3.2.1      reshape2_1.4.1   Rcpp_0.12.0      stringi_0.5-5

 [7] grid_3.2.1       data.table_1.9.4 stringr_1.0.0    chron_2.3-47     lattice_0.20-31



Nilesh Dighe
(806)-252-7492 (Cell)
(806)-741-2019 (Office)


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