[R] store results from loop into a dataframe
DIGHE, NILESH [AG/2362]
nilesh.dighe at monsanto.com
Tue Jan 5 20:12:43 CET 2016
Sarah: Thanks a lot for taking time to guide me to the right direction. I now see how the missing data is causing the problem.
Thanks again!
Nilesh
-----Original Message-----
From: Sarah Goslee [mailto:sarah.goslee at gmail.com]
Sent: Tuesday, January 05, 2016 12:13 PM
To: DIGHE, NILESH [AG/2362]
Cc: r-help at r-project.org
Subject: Re: [R] store results from loop into a dataframe
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.dighe at 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.goslee at gmail.com]
> Sent: Tuesday, January 05, 2016 11:20 AM
> To: DIGHE, NILESH [AG/2362]
> Cc: r-help at 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.dighe at 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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