If you run each variable individually, you'll discover that the NAs in
your data are causing problems. It's up to you to figure out what the
best way to handle those missing values for your research is.
Sarah
On Tue, Jan 5, 2016 at 12:39 PM, DIGHE, NILESH [AG/2362]
<nilesh.di...@monsanto.com> wrote:
> Sarah: Thanks for pointing out the errors in my function.
>
> Below are the errors I am getting after I run the corrected quote:
> Error in if (s) { : missing value where TRUE/FALSE needed
> In addition: Warning message:
> In qtukey(1 - alpha, ntr, DFerror) : NaNs produced
>
> You are right, I have no idea to handle these errors.
>
> Do you recommend any other approach to solve my problem?
>
> Thanks for your time.
> Nilesh
>
>
>
> -----Original Message-----
> From: Sarah Goslee [mailto:sarah.gos...@gmail.com]
> Sent: Tuesday, January 05, 2016 11:20 AM
> To: DIGHE, NILESH [AG/2362]
> Cc: r-help@r-project.org
> Subject: Re: [R] store results from loop into a dataframe
>
> Leaving aside the question of whether this is the best way to approach your
> problem (unlikely), there's a couple of errors in your code involving
> indexing. Once fixed, the code demonstrates some errors in your use of
> HSD.test that will be harder for you to deal with.
>
> Thanks for the complete reproducible example.
>
> fun2 <- function (x)
>
> {
>
> trait_names <- c("yield", "lp", "lnth")
>
> d = data.frame(yield = rep(0, 6), lp = rep(0, 6), lnth = rep(0,
>
> 6))
>
> for (i in trait_names) {
> # your formula has all the trait names, not the selected one
> # mod <- aov(formula(paste(trait_names, "~ PEDIGREE + FIELD +
> PEDIGREE*FIELD + FIELD%in%REP")), data = x)
> mod <- aov(formula(paste(i, "~ PEDIGREE + FIELD + PEDIGREE*FIELD +
> FIELD%in%REP")), data = x)
>
> out <- HSD.test(mod, "PEDIGREE", group = TRUE, console = FALSE)
>
> # you're indexing by the trait name, instead of its position
> # d[, i] <- out$means[, 1]
> d[, which(trait_names == i)] <- out$means[, 1]
>
> }
>
> d
>
> }
>
> Sarah
>
> On Tue, Jan 5, 2016 at 11:48 AM, DIGHE, NILESH [AG/2362]
> <nilesh.di...@monsanto.com> wrote:
>> Dear R users:
>>
>> I am trying to create a function that will loop over three dependent
>> variables in my aov model, and then get the HSD.test for each variable. I
>> like to store the results from each loop in a data frame.
>>
>>
>>
>> When I run my function (funx) on my data (dat), results from only yield gets
>> populated in all three columns of the dataframe. I am not able to store the
>> results for each variable in a dataframe. Any help will be highly
>> appreciated.
>>
>>
>>
>>
>>
>>
>>
>> function (x)
>>
>> {
>>
>> trait_names <- c("yield", "lp", "lnth")
>>
>> d = data.frame(yield = rep(0, 6), lp = rep(0, 6), lnth = rep(0,
>>
>> 6))
>>
>> for (i in trait_names) {
>>
>> mod <- aov(formula(paste(trait_names, "~ PEDIGREE + FIELD +
>> PEDIGREE*FIELD + FIELD%in%REP")),
>>
>> data = x)
>>
>> out <- HSD.test(mod, "PEDIGREE", group = TRUE, console =
>> FALSE)
>>
>> d[, i] <- out$means[, 1]
>>
>> }
>>
>> d
>>
>> }
>>
>>
>> structure(list(FIELD = structure(c(1L, 1L, 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, 2L, 2L, 3L, 3L, 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, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
>> 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
>> 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label =
>> c("FYLS", "HKI1", "KIS1", "LMLS", "SELS", "SGL1"), class = "factor"),
>> REP = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
>> 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
>> 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
>> 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
>> 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
>> 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
>> 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("1", "2", "3"),
>> class = "factor"), PEDIGREE = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L,
>> 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 1L, 1L, 1L, 2L, 2L, 2L,
>> 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 1L, 1L, 1L, 2L, 2L,
>> 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 1L, 1L, 1L, 2L,
>> 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 1L, 1L, 1L,
>> 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 1L, 1L,
>> 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L),
>> .Label = c("A", "B", "C", "D", "E", "F"), class = "factor"), yield =
>> c(1003L, 923L, 1268L, 1226L, 1059L, 1150L, 900L, 816L, 1072L, 1158L,
>> 1026L, 1299L, 1083L, 1038L, 1236L, 1287L, 1270L, 1612L, 1513L, 1676L,
>> 1504L, 1417L, 1932L, 1644L, 1293L, 1542L, 1452L, 1180L, 1248L, 1764L,
>> 1326L, 1877L, 1788L, 1606L, 1809L, 1791L, 2294L, 2315L, 2320L, 2083L,
>> 1895L, 2284L, 2000L, 2380L, 1952L, 2414L, 2354L, 2095L, 2227L, 2093L,
>> 2019L, 2505L, 2410L, 2287L, 2507L, 2507L, 2349L, 2162L, 2108L, 2319L,
>> 2028L, 1947L, 2352L, 2698L, 2369L, 1798L, 2422L, 2509L, 2234L, 2451L,
>> 2139L, 1957L, 799L, 787L, 701L, 781L, 808L, 582L, 770L, 752L, 801L,
>> 865L, 608L, 620L, 677L, 775L, 722L, 1030L, 606L, 729L, 1638L, 1408L,
>> 1045L, 1685L, 1109L, 1210L, 1419L, 1048L, 1129L, 1549L, 1325L, 1315L,
>> 1838L, 1066L, 1295L, 1499L, 1472L, 1139L), lp = c(NA, NA, 46.31, NA,
>> NA, 43.8, NA, NA, 43.91, NA, NA, 44.47, NA, NA, 45.16, NA, NA, 43.57,
>> 40.65, NA, NA, 40.04, NA, NA, 41.33, NA, NA, 40.75, NA, NA, 42.04, NA,
>> NA, 40.35, NA, NA, 43.682, NA, NA, 41.712, NA, NA, 42.566, NA, NA,
>> 43.228, NA, NA, 43.63, NA, NA, 42.058, NA, NA, NA, 45.19, NA, NA,
>> 41.91, NA, NA, 43.86, NA, NA, 44.48, NA, NA, 44.34, NA, NA, 43.03, NA,
>> NA, NA, 44.08, NA, NA, 41.39, NA, NA, 42.48, NA, NA, 44.13, NA, NA,
>> 43.39, NA, NA, 42.82, 42.18, NA, NA, 41.42, NA, NA, 41.25, NA, NA,
>> 42.31, NA, NA, 43.22, NA, NA, 40.52, NA, NA), lnth = c(NA, NA, 1.151,
>> NA, NA, 1.135, NA, NA, 1.109, NA, NA, 1.117, NA, NA, 1.107, NA, NA,
>> 1.196, 1.255, NA, NA, 1.229, NA, NA, 1.158, NA, NA, 1.214, NA, NA,
>> 1.152, NA, NA, 1.194, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
>> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.2, NA, NA, 1.219, NA, NA, 1.115,
>> NA, NA, 1.205, NA, NA, 1.238, NA, NA, 1.244, NA, NA, NA, 1.096, NA,
>> NA, 1.021, NA, NA, 1.055, NA, NA, 1.058, NA, NA, 1.026, NA, NA, 1.115,
>> 1.202, NA, NA, 1.161, NA, NA, 1.168, NA, NA, 1.189, NA, NA, 1.204, NA,
>> NA, 1.277, NA, NA)), .Names = c("FIELD", "REP", "PEDIGREE", "yield",
>> "lp", "lnth"
>> ), row.names = c(NA, -108L), class = "data.frame")
>>
>>
>>
>>
>>
>>
>> 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] agricolae_1.2-1 asreml_3.0 lattice_0.20-31 ggplot2_1.0.1
>> dplyr_0.4.2 plyr_1.8.3
>>
>>
>>
>> loaded via a namespace (and not attached):
>>
>> [1] spdep_0.5-88 Rcpp_0.12.1 cluster_2.0.2 magrittr_1.5
>> splines_3.2.1 MASS_7.3-41
>>
>> [7] munsell_0.4.2 colorspace_1.2-6 R6_2.0.1 stringr_1.0.0
>> tools_3.2.1 parallel_3.2.1
>>
>> [13] grid_3.2.1 gtable_0.1.2 nlme_3.1-122 coda_0.17-1
>> DBI_0.3.1 deldir_0.1-9
>>
>> [19] lazyeval_0.1.10 assertthat_0.1 digest_0.6.8 Matrix_1.2-1
>> reshape2_1.4.1 sp_1.2-1
>>
>> [25] stringi_1.0-1 klaR_0.6-12 LearnBayes_2.15 scales_0.3.0
>> boot_1.3-17 combinat_0.0-8
>>
>> [31] proto_0.3-10
>>
>> Thanks.
>> Nilesh
>>
>
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