Prepare data for GWA
Clay Morrow
5/13/2019
Intro
This Document will create the full phenotype and covariates table for GWA analysis (See this) as well as preparing the genotype data for use in R (see this).
Setup
Load Data Objects
data.env <- new.env() # create new environment
data(list = params$data, envir = data.env) #load data objects into environmentlookup table
This section may also not be needed if the full trait data contains the GWA ID information.
# load in lookup tables
lookup.tab <- read.csv(params$lookuptable)
## relable the GWA ID column to exaclty GWA.ID (in case it is something else)
names(lookup.tab)[match(params$GWA.ID,names(lookup.tab))] <- "GWA.ID"These are the individuals whose genet has been updated in some way or another and the number of individuals in each genet that have been changedr:
lookup.tab$tmp.Genet.2014 <- factor(ifelse(is.na(lookup.tab$Genet.2014),
yes = "NA",
no = as.character(lookup.tab$Genet.2014)))
# rows who's 2014 genet does not equal the SSR genet
(tmp <- lookup.tab[as.character(lookup.tab$Genet.SSRrev) !=
as.character(lookup.tab$tmp.Genet.2014),
c("Genet.2014","Genet.SSRrev","GWA.ID")])## Genet.2014 Genet.SSRrev GWA.ID
## 9 748 548.748 UME_303602_P14_WA09
## 12 357 357.358359 UME_303602_P11_WG02
## 30 210 112.21 UME_303602_P14_WD09
## 56 628 627.62863 UME_303602_P13_WE11
## 70 Wau-3 Wau-2.3 <NA>
## 77 364 364.365 UME_303602_P11_WG06
## 94 PG-3 PI-3 UME_301101_P01_WH02
## 95 PG-3 PI-3 UME_301101_P01_WH02
## 98 316 316.317 UME_303602_P11_WD05
## 115 365 364.365 UME_303602_P11_WG06
## 124 100 100.991 UME_303602_P09_WF05
## 134 359 357.358359 UME_303602_P11_WG02
## 145 101 100.101 UME_303602_P09_WF06
## 154 317 316.317 UME_303602_P11_WD05
## 159 66 66.67 UME_303602_P09_WD09
## 170 341 338.341 UME_303602_P11_WE11
## 181 27 26.991 <NA>
## 194 370 369.37 UME_303602_P12_WB09
## 195 370 369.37 UME_303602_P12_WB09
## 222 221 221.991 UME_303602_P10_WF09
## 237 Wau-2 Wau-2.3 <NA>
## 247 39 39.4 UME_303602_P09_WC05
## 252 46 46.47 UME_303602_P09_WC10
## 260 387 387.991 <NA>
## 265 285 285.991 <NA>
## 275 92 92.93 UME_303602_P09_WE11
## 286 728 511 UME_303602_P13_WA11
## 329 369 369.37 UME_303602_P12_WB09
## 340 334 334.991 <NA>
## 351 328 326.328329 UME_303602_P11_WE01
## 363 112 112.21 UME_303602_P14_WD09
## 365 627 627.62863 UME_303602_P13_WE11
## 377 326 326.328329 UME_303602_P11_WE01
## 384 26 26.27 UME_303602_P09_WB11
## 387 210 112.21 UME_303602_P14_WD09
## 388 329 326.328329 UME_303602_P11_WE01
## 392 338 338.341 UME_303602_P11_WE11
## 400 47 46.47 UME_303602_P09_WC10
## 411 548 548.748 UME_303602_P14_WA09
## 445 369 369.37 UME_303602_P12_WB09
## 461 166 166.991 <NA>
## 463 196 197 UME_303602_P10_WD12
## 466 341 338.341 UME_303602_P11_WE11
## 486 358 357.358359 UME_303602_P11_WG02
## 487 101 100.101 UME_303602_P09_WF06
## 507 326 326.328329 UME_303602_P11_WE01
## 513 748 548.748 UME_303602_P14_WA09
## 530 Wau-3 Wau-2.3 <NA>
## 544 26 26.27 UME_303602_P09_WB11
## 572 316 316.317 UME_303602_P11_WD05
## 582 359 357.358359 UME_303602_P11_WG02
## 589 39 39.4 UME_303602_P09_WC05
## 602 92 92.93 UME_303602_P09_WE11
## 633 328 326.328329 UME_303602_P11_WE01
## 645 628 627.62863 UME_303602_P13_WE11
## 654 46 46.47 UME_303602_P09_WC10
## 659 27 26.27 UME_303602_P09_WB11
## 667 753 755 UME_303602_P14_WB04
## 674 548 548.748 UME_303602_P14_WA09
## 680 317 316.317 UME_303602_P11_WD05
## 684 66 66.67 UME_303602_P09_WD09
## 701 Wau-2 Wau-2.3 <NA>
## 704 40 39.4 UME_303602_P09_WC05
## 715 338 338.341 UME_303602_P11_WE11
## 721 627 627.62863 UME_303602_P13_WE11
## 723 364 364.365 UME_303602_P11_WG06
## 733 365 364.365 UME_303602_P11_WG06
## 741 47 46.47 UME_303602_P09_WC10
## 746 100 100.991 UME_303602_P09_WF05
## 764 112 112.21 UME_303602_P14_WD09
## 766 210 112.21 UME_303602_P14_WD09
## 768 312X 316.317 UME_303602_P11_WD05
## 773 370 369.37 UME_303602_P12_WB09
## 774 370 369.37 UME_303602_P12_WB09
## 806 34 32 UME_303602_P14_WC11
## 813 329 326.328329 UME_303602_P11_WE01
## 818 PG-3 PI-3 UME_301101_P01_WH02
## 819 PG-3 PI-3 UME_301101_P01_WH02
## 831 387 387.992 UME_303602_P11_WH12
## 845 227 227.991 UME_303602_P10_WG02
## 861 628 627.62863 UME_303602_P13_WE11
## 869 39 39.4 UME_303602_P09_WC05
## 873 338 338.341 UME_303602_P11_WE11
## 876 370 369.37 UME_303602_P12_WB09
## 877 370 369.37 UME_303602_P12_WB09
## 891 540 539.54 UME_303602_P13_WC03
## 892 40 39.4 UME_303602_P09_WC05
## 905 PG-2 PG-1 PG1-1B4
## 906 PG-2 PG-1 PG1-1B4
## 923 210 112.21 UME_303602_P14_WD09
## 926 93 92.93 UME_303602_P09_WE11
## 930 748 548.748 UME_303602_P14_WA09
## 947 198 198.991 <NA>
## 959 47 46.47 UME_303602_P09_WC10
## 971 539 539.54 UME_303602_P13_WC03
## 995 630 627.62863 UME_303602_P13_WE11
## 1013 27 26.991 <NA>
## 1040 358 357.358359 UME_303602_P11_WG02
## 1047 174 174.991 <NA>
## 1048 328 326.328329 UME_303602_P11_WE01
## 1049 PG-3 PI-3 UME_301101_P01_WH02
## 1050 PG-3 PI-3 UME_301101_P01_WH02
## 1061 364 364.365 UME_303602_P11_WG06
## 1064 329 326.328329 UME_303602_P11_WE01
## 1073 92 92.93 UME_303602_P09_WE11
## 1099 66 66.67 UME_303602_P09_WD09
## 1109 627 627.62863 UME_303602_P13_WE11
## 1137 365 364.365 UME_303602_P11_WG06
## 1138 359 357.358359 UME_303602_P11_WG02
## 1152 326 326.328329 UME_303602_P11_WE01
## 1159 Wau-3 Wau-2.3 <NA>
## 1163 317 316.317 UME_303602_P11_WD05
## 1167 101 100.101 UME_303602_P09_WF06
## 1176 370 369.37 UME_303602_P12_WB09
## 1177 370 369.37 UME_303602_P12_WB09
## 1203 369 369.37 UME_303602_P12_WB09
## 1213 628 627.62863 UME_303602_P13_WE11
## 1218 Wau-2 Wau-2.3 <NA>
## 1219 112 112.21 UME_303602_P14_WD09
## 1248 46 46.47 UME_303602_P09_WC10
## 1261 112 112.21 UME_303602_P14_WD09
## 1264 210 112.21 UME_303602_P14_WD09
## 1269 248 248.991 <NA>
## 1271 70 68 UME_303602_P09_WD10
## 1273 312 316.317 UME_303602_P11_WD05
## 1278 628 627.62863 UME_303602_P13_WE11
## 1285 364 364.365 UME_303602_P11_WG06
## 1287 66 66.67 UME_303602_P09_WD09
## 1292 13 13.991 <NA>
## 1293 Wau-3 Wau-2.3 <NA>
## 1300 150 503 <NA>
## 1305 196 197 UME_303602_P10_WD12
## 1318 764 769 UME_303602_P14_WB10
## 1319 101 100.101 UME_303602_P09_WF06
## 1326 47 46.47 UME_303602_P09_WC10
## 1329 748 548.748 UME_303602_P14_WA09
## 1340 548 548.748 UME_303602_P14_WA09
## 1353 540 539.54 UME_303602_P13_WC03
## 1363 630 627.62863 UME_303602_P13_WE11
## 1371 39 39.4 UME_303602_P09_WC05
## 1373 317 316.317 UME_303602_P11_WD05
## 1376 100 100.101 UME_303602_P09_WF06
## 1392 370 369.37 UME_303602_P12_WB09
## 1393 370 369.37 UME_303602_P12_WB09
## 1399 95 95.991 <NA>
## 1403 326 326.328329 UME_303602_P11_WE01
## 1451 14 14.991 <NA>
## 1461 115 115.991 <NA>
## 1470 763 736 UME_303602_P14_WA01
## 1476 627 627.62863 UME_303602_P13_WE11
## 1483 336 336.991 <NA>
## 1484 387 387.993 <NA>
## 1496 27 26.991 <NA>
## 1530 28 7 UME_303602_P09_WA07
## 1536 92 92.93 UME_303602_P09_WE11
## 1547 338 338.341 UME_303602_P11_WE11
## 1568 365 364.365 UME_303602_P11_WG06
## 1592 PG-3 PI-3 UME_301101_P01_WH02
## 1593 PG-3 PI-3 UME_301101_P01_WH02
## 1596 197 196.999 <NA>
## 1600 40 39.4 UME_303602_P09_WC05
## 1606 210 112.21 UME_303602_P14_WD09
## 1607 328 326.328329 UME_303602_P11_WE01
## 1615 316 316.317 UME_303602_P11_WD05
## 1625 Wau-2 Wau-2.3 <NA>
## 1628 46 46.47 UME_303602_P09_WC10
## 1634 383 383.991 <NA>
## 1636 198 198.991 <NA>
## 1640 27 26.991 <NA>
## 1648 628 627.62863 UME_303602_P13_WE11
## 1660 29 29.991 <NA>
## 1671 40 39.4 UME_303602_P09_WC05
## 1674 365 364.365 UME_303602_P11_WG06
## 1681 27 26.991 <NA>
## 1683 748 548.748 UME_303602_P14_WA09
## 1686 338 338.341 UME_303602_P11_WE11
## 1687 66 66.67 UME_303602_P09_WD09
## 1690 67 66.67 UME_303602_P09_WD09
## 1700 112 112.991 <NA>
## NA <NA> <NA> <NA>
## 1712 197 196 UME_303602_P10_WD11
## 1716 196 197 UME_303602_P10_WD12
## 1735 174 174.992 <NA>
## 1737 370 369.37 UME_303602_P12_WB09
## 1738 370 369.37 UME_303602_P12_WB09
## 1763 539 539.54 UME_303602_P13_WC03
## 1821 ? 338.341 UME_303602_P11_WE11
## 1830 <NA> 156 UME_303602_P10_WA10
## 1835 368 368.991 <NA>
## 1842 92 92.93 UME_303602_P09_WE11
## 1843 47 46.47 UME_303602_P09_WC10
## 1863 317 316.317 UME_303602_P11_WD05
## 1867 101 100.101 UME_303602_P09_WF06
## 1871 316 316.317 UME_303602_P11_WD05
## 1894 210 112.21 UME_303602_P14_WD09
## 1916 ? Sau-1 Sau1-1B10
## 1920 334 334.991 <NA>
## 1923 316 316.317 UME_303602_P11_WD05
## 1925 100 100.101 UME_303602_P09_WF06
## 1928 336 336.991 <NA>
## 1933 150 503 <NA>
## 1935 92 92.93 UME_303602_P09_WE11
## 1936 326 326.328329 UME_303602_P11_WE01
## 1937 95 95.991 <NA>
## 1945 <NA> 93.991 <NA>
## 1946 518 517 UME_303602_P13_WB04
## 1949 PG-3 PI-3 UME_301101_P01_WH02
## 1950 PG-3 PI-3 UME_301101_P01_WH02tmp %>% group_by(Genet.2014,Genet.SSRrev,GWA.ID) %>% tally()## # A tibble: 81 x 4
## # Groups: Genet.2014, Genet.SSRrev [81]
## Genet.2014 Genet.SSRrev GWA.ID n
## <fct> <fct> <fct> <int>
## 1 ? 338.341 UME_303602_P11_WE11 1
## 2 ? Sau-1 Sau1-1B10 1
## 3 100 100.101 UME_303602_P09_WF06 2
## 4 100 100.991 UME_303602_P09_WF05 2
## 5 101 100.101 UME_303602_P09_WF06 5
## 6 112 112.21 UME_303602_P14_WD09 4
## 7 112 112.991 <NA> 1
## 8 115 115.991 <NA> 1
## 9 13 13.991 <NA> 1
## 10 14 14.991 <NA> 1
## # … with 71 more rowsThis also shows that, as of now (2019-05-15), the GWA IDs are assigned incorrectly (See Genet.2014 “100” and “101”).
## keep only SerialNO, Genet.SSRrev and GWA.ID
lookup.tab <- lookup.tab %>%
select(SerialNo,Genet.2014, Genet.SSRrev,GWA.ID, params$exclcol)# load in full data
full.data <- data.env[[params$full.data]]
## index the lookup table rows that match full data SerialNo.
SN.index <- match(full.data$SerialNo,lookup.tab$SerialNo)
n.old <- nrow(full.data)
# re-assign the full gwa data file to the "full.data" identifier
full.data <- cbind(lookup.tab[SN.index,] %>% select(-SerialNo),
full.data %>% select(-Genet.SSRrev))
# check that n has not changed!
stopifnot(nrow(full.data) == n.old)write phenotype and covariates file
Here, we’ll write the tab-separated phenos-and-covars.txt file as well as a .csv counterpart
write.csv(full.data,"data/phenos-and-covars.csv",row.names = FALSE)
write.table(full.data,"data/phenos-and-covars.txt",row.names = FALSE)
# copy data file to CHTC folder
system("cp data/phenos-and-covars.txt CHTC/data")Example: here we will read the file, with only individual identifiers, common insects, and columns present in our trait data:
tmp <- fread("data/phenos-and-covars.txt",
header = TRUE,
stringsAsFactors = TRUE,
# only read in columns of interest
select = unique(c("SerialNo","GWA.ID","Genet", #identifiers
data.env$common.insects, #insects
names(data.env$trait.data) #trait data
)))
names(tmp);dim(tmp)## [1] "SerialNo" "GWA.ID"
## [3] "Genet" "Harmandia"
## [5] "Phyllocolpa" "Petiole.Gall"
## [7] "Leaf.Edge.Mine" "Blotch.Mine"
## [9] "Lombardy.Mine" "Weevil.Mine"
## [11] "Blackmine" "Cottonwood.Leaf.Mine"
## [13] "Casebearer.Moth" "Leafhoppers"
## [15] "Green.Aphids" "Smokey.Aphids"
## [17] "Ants" "Pale.Green.Notodontid"
## [19] "Aspen.Leaf.Beetle" "Green.Sawfly"
## [21] "Cotton.Scale" "Genet.SSRrev"
## [23] "Latitude" "Longitude"
## [25] "Dist.Edge" "Ploidy"
## [27] "Hybrid.01" "Hobs"
## [29] "PlantingYear" "BA.2012"
## [31] "Vol.2012" "GrowLn.1516"
## [33] "GrowLn.1617" "GrowLn.1718"
## [35] "GrowLn.1518" "Sex.TEMP"
## [37] "Venturia.2017" "year"
## [39] "SLA" "ALA"
## [41] "CT" "PG"
## [43] "Npct" "CN"
## [45] "BA" "Vol"
## [47] "BBreakDegDay" "FlTwigs"
## [49] "Flprev" "EFNMean"
## [51] "LeafAreaSum" "DiseaseEdgePct"
## [53] "ScrapHolePct" "DamagePct"
## [55] "BA.2012sqrt" "BAsqrt"
## [57] "Age"## [1] 6272 57Prepare model selection data
Here we will prepare and save the data objects needed for model selection
mod.selection.env <- new.env()
# lists of names from full.data to use
mod.selection.env$all.vars <- names(full.data)
mod.selection.env$common.insects <- data.env$common.insects
mod.selection.env$all.insects <- data.env$all.insects
mod.selection.env$ins.func.grps <- c("Free.Feeding_cnt","Leaf.Modifying_cnt",
"Wood.Modifying_cnt","ecto_cnt","endo_cnt")
mod.selection.env$environmental.vars <- c("Block","Dist.Edge",
"Survey.Year","Survey.Month" #,"survey.event"
)
mod.selection.env$weather.vars <- c("avg.temp_F","high.wind_mph")
mod.selection.env$tree.traits.of.interest <- c("Hobs","Age","BA.2012sqrt", #"BA.2012
"Sex.TEMP","SLA","ALA",
"CT","PG","Npct","CN",
"BAsqrt","Vol", #"BA"
"BBreakDegDay","Flprev"#,"FlTwigs",
#"Genet.SSRrev"
#"Latitude","Longitude"
)
mod.selection.env$offset <- "Min.per.Tree"
mod.selection.env$all.covars <- c(mod.selection.env$environmental.vars,
mod.selection.env$weather.vars,
mod.selection.env$tree.traits.of.interest)
mod.selection.env$random.terms <- c("Survey.Year","Survey.Month","Genet.SSRrev")
## check that all variables are valid
stopifnot(
all(mod.selection.env$ins.func.grps %in% names(full.data)) &
all(mod.selection.env$environmental.vars %in% names(full.data)) &
all(mod.selection.env$weather.vars %in% names(full.data)) &
all(mod.selection.env$tree.traits.of.interest %in% names(full.data))
)
# save the objects
save(list = names(mod.selection.env),file = "data/variable-groups.RData",
envir = mod.selection.env)
## and to the CHTC folder
# save(list = names(mod.selection.env),file = "CHTC/data/variable-groups.RData",
# envir = mod.selection.env)
# create all combinations of variables
# len <- length(mod.selection.env$all.covars)
len = length(mod.selection.env$all.covars[! mod.selection.env$all.covars %in%
mod.selection.env$weather.vars])
# my computer can't do this part:
all.models <- as.data.frame(expand.grid(replicate(len, c(TRUE,FALSE),simplify = FALSE)))
names(all.models) <- mod.selection.env$all.covars[! mod.selection.env$all.covars %in%
mod.selection.env$weather.vars]
all.models$model.names <- paste0("model.",1:nrow(all.models))
write.table(all.models,file = "data/model-variable-inclusions.txt",row.names = FALSE)
# write.table(all.models,file = "CHTC/data/model-variable-inclusions.txt",row.names = FALSE)This R code is used by this model selection script
Pipeline:
Exhaustive model selection will be done on UW-Madison’s CHTC (high-throughput computing center) which uses a job-handling program called HTCondor.
The job submission (.sub) file for HTCondor is here and it’s purpose is to run as many tasks (models, in our case) in the shortest amount of time. For our models, we are considering 18 terms + Genet as predictor variables. Therefore, there are \(2^{18} = 2.62144\times 10^{5}\) possible models for each of our 18 insect response variables for a total of \(2^{18}\times18=4.718592\times 10^{6}\) models. Therefore, it is very important to parallelize the process.
the model-selection-submission.sub (HTCondor_files/) file executes run-model-selection.sh, which then calls model-selection-script.R (both in scripts/), passing the arguments from the “arguments” fields to each script. Currently, the submission file is set to run 9438 jobs, each of which will fit 500 models. Each job will create a .csv file called <insect>_proc<job>_mods<first.model>-<last.model>.csv. These csv files will each contain 500 rows.
In order to combine all csv files for a given insect, use:
bash `scripts/stack-CHTC-csv-files.sh` <insect>Extract Covariates for Each insect
This step uses the result of model the model selection step (REFERENCE NEEDED) to determine which covariates should be included in each insect GWA model.
# Get the results:
results.folder <- "CHTC/output/full-run_27-May/full-run_27-May"
insect.folders = list.files(file.path(results.folder,"Insects-specific-results"))
for(i in 1:length(insect.folders)){
## current insect
insect <- insect.folders[i]
## current file
file.location = file.path(results.folder,"Top-30-AIC",
paste0(insect,"-top-models.csv")
)
# Read in the files and remove duplicated rows
TOP.mods <- read.csv(file.location)
names(TOP.mods)[1] <- "model.names"
TOP.mods <- TOP.mods %>% distinct
# Get the variable table for each model
model.table <- NULL # empy frame
## read in each model row individually and then combine (speed)
for(j in TOP.mods$model.row){
row.j <- fread("CHTC/data/model-variable-inclusions.txt",
header = FALSE, skip = j, nrows = 1)
model.table <- rbind(model.table,row.j)
}
# add the column names to the model table
names(model.table) <- unname(unlist(
fread("CHTC/data/model-variable-inclusions.txt",
nrows = 1, header = FALSE)
))
# relevel the model names according to decreasing AIC
lvls <- TOP.mods[order(TOP.mods$AIC,decreasing = FALSE),"model.names"]
model.table$model.names <- factor(model.table$model.names, levels = lvls)
# Print the AIC of the top models
top.aic = min(TOP.mods$AIC)
full.mod <- TOP.mods %>% filter(model.row == 1)
full.aic = full.mod$AIC
top.table <- TOP.mods %>% select(model.names,AIC) %>%
mutate("dif.frm.top" = abs(AIC - top.aic) >= 2,
"dif.frm.full" = abs(AIC - full.aic) >= 2)
sig.col <- ifelse(top.table$dif.frm.top,yes = "plain",no = "bold")
# Plot a variable plot of the top 30 models (plus the full model)
library(ggplot2)
g <- ggplot(data = melt(model.table, id.vars = "model.names"),
aes(x = variable, y = model.names)) +
labs(y = "model", subtitle = paste("Top 30 Models by AIC for",insect)) +
geom_tile(aes(fill = value), colour = "white") +
theme(axis.text.x = element_text(angle = 35, vjust = 1, hjust = 1),
axis.text.y = element_text(face = sig.col),
axis.text = element_text(face = "bold",size = 10, colour = "black"),
text = element_text(face = "bold",colour = "black",size = 12),
legend.key = element_rect(colour = "black")) +
scale_fill_manual(values = c("TRUE" = "black", "FALSE" = "white"))
print(top.table)
plot(g)
}## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang## model.names AIC dif.frm.top dif.frm.full
## 1 model.230065 6794.285 FALSE TRUE
## 2 model.230129 6794.369 FALSE TRUE
## 3 model.197297 6794.700 FALSE TRUE
## 4 model.197361 6794.891 FALSE TRUE
## 5 model.229937 6794.904 FALSE TRUE
## 6 model.230385 6794.999 FALSE TRUE
## 7 model.230001 6795.114 FALSE TRUE
## 8 model.197169 6795.133 FALSE TRUE
## 9 model.230321 6795.292 FALSE TRUE
## 10 model.197233 6795.459 FALSE TRUE
## 11 model.230257 6795.564 FALSE TRUE
## 12 model.246449 6795.656 FALSE TRUE
## 13 model.197617 6795.675 FALSE TRUE
## 14 model.230193 6795.722 FALSE TRUE
## 15 model.230049 6795.764 FALSE TRUE
## 16 model.229553 6795.765 FALSE TRUE
## 17 model.229617 6795.860 FALSE TRUE
## 18 model.197553 6795.888 FALSE TRUE
## 19 model.230113 6795.896 FALSE TRUE
## 20 model.230033 6795.983 FALSE TRUE
## 21 model.197265 6796.008 FALSE TRUE
## 22 model.197489 6796.065 FALSE TRUE
## 23 model.99057 6796.100 FALSE TRUE
## 24 model.98993 6796.114 FALSE TRUE
## 25 model.197425 6796.135 FALSE TRUE
## 26 model.229425 6796.190 FALSE TRUE
## 27 model.246321 6796.204 FALSE TRUE
## 28 model.197281 6796.205 FALSE TRUE
## 29 model.196785 6796.224 FALSE TRUE
## 30 model.164529 6796.284 FALSE TRUE
## 31 model.1 6802.838 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.45681 2756.252 FALSE TRUE
## 2 model.45617 2756.816 FALSE TRUE
## 3 model.111217 2757.510 FALSE TRUE
## 4 model.176753 2757.605 FALSE TRUE
## 5 model.45689 2757.716 FALSE TRUE
## 6 model.41585 2757.751 FALSE TRUE
## 7 model.37489 2757.953 FALSE TRUE
## 8 model.45169 2758.071 FALSE TRUE
## 9 model.12913 2758.093 FALSE TRUE
## 10 model.45649 2758.152 FALSE TRUE
## 11 model.45665 2758.178 FALSE TRUE
## 12 model.111153 2758.208 FALSE TRUE
## 13 model.45625 2758.234 FALSE TRUE
## 14 model.41521 2758.272 TRUE TRUE
## 15 model.37425 2758.480 TRUE TRUE
## 16 model.176689 2758.578 TRUE TRUE
## 17 model.45105 2758.673 TRUE TRUE
## 18 model.12849 2758.724 TRUE TRUE
## 19 model.45601 2758.742 TRUE TRUE
## 20 model.45585 2758.806 TRUE TRUE
## 21 model.107121 2758.838 TRUE TRUE
## 22 model.176761 2758.942 TRUE TRUE
## 23 model.103025 2759.028 TRUE TRUE
## 24 model.172657 2759.135 TRUE TRUE
## 25 model.78449 2759.196 TRUE TRUE
## 26 model.110705 2759.304 TRUE TRUE
## 27 model.168561 2759.335 TRUE TRUE
## 28 model.242289 2759.350 TRUE TRUE
## 29 model.33401 2759.374 TRUE TRUE
## 30 model.111185 2759.409 TRUE TRUE
## 31 model.1 2767.650 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.205747 3794.420 FALSE TRUE
## 2 model.205235 3794.706 FALSE TRUE
## 3 model.205107 3794.927 FALSE TRUE
## 4 model.205619 3794.974 FALSE TRUE
## 5 model.209843 3795.248 FALSE TRUE
## 6 model.201651 3795.373 FALSE TRUE
## 7 model.205715 3795.598 FALSE TRUE
## 8 model.238515 3795.615 FALSE TRUE
## 9 model.209715 3795.684 FALSE TRUE
## 10 model.201139 3795.775 FALSE TRUE
## 11 model.209331 3795.807 FALSE TRUE
## 12 model.238003 3795.855 FALSE TRUE
## 13 model.201523 3795.868 FALSE TRUE
## 14 model.205203 3795.885 FALSE TRUE
## 15 model.209203 3795.929 FALSE TRUE
## 16 model.201011 3795.941 FALSE TRUE
## 17 model.205731 3795.966 FALSE TRUE
## 18 model.197555 3795.973 FALSE TRUE
## 19 model.74675 3795.991 FALSE TRUE
## 20 model.237875 3796.100 FALSE TRUE
## 21 model.205745 3796.105 FALSE TRUE
## 22 model.197043 3796.178 FALSE TRUE
## 23 model.238387 3796.195 FALSE TRUE
## 24 model.205075 3796.204 FALSE TRUE
## 25 model.205491 3796.216 FALSE TRUE
## 26 model.242611 3796.229 FALSE TRUE
## 27 model.205587 3796.247 FALSE TRUE
## 28 model.74163 3796.291 FALSE TRUE
## 29 model.140211 3796.406 FALSE TRUE
## 30 model.196915 3796.446 TRUE TRUE
## 31 model.1 3806.702 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.102369 3490.719 FALSE TRUE
## 2 model.102337 3491.006 FALSE TRUE
## 3 model.233441 3491.325 FALSE TRUE
## 4 model.233409 3491.692 FALSE TRUE
## 5 model.101857 3491.958 FALSE TRUE
## 6 model.101825 3492.247 FALSE TRUE
## 7 model.69569 3492.313 FALSE TRUE
## 8 model.100321 3492.438 FALSE TRUE
## 9 model.69601 3492.442 FALSE TRUE
## 10 model.232929 3492.524 FALSE TRUE
## 11 model.102385 3492.567 FALSE TRUE
## 12 model.102113 3492.595 FALSE TRUE
## 13 model.101345 3492.674 FALSE TRUE
## 14 model.102305 3492.713 FALSE TRUE
## 15 model.233457 3492.715 FALSE TRUE
## 16 model.36833 3492.718 FALSE TRUE
## 17 model.100289 3492.723 TRUE TRUE
## 18 model.102353 3492.812 TRUE TRUE
## 19 model.102273 3492.841 TRUE TRUE
## 20 model.232897 3492.893 TRUE TRUE
## 21 model.102081 3492.915 TRUE TRUE
## 22 model.101313 3492.945 TRUE TRUE
## 23 model.36801 3493.005 TRUE TRUE
## 24 model.231393 3493.022 TRUE TRUE
## 25 model.233425 3493.039 TRUE TRUE
## 26 model.200641 3493.087 TRUE TRUE
## 27 model.200673 3493.094 TRUE TRUE
## 28 model.233185 3493.230 TRUE TRUE
## 29 model.232417 3493.275 TRUE TRUE
## 30 model.167905 3493.301 TRUE TRUE
## 31 model.1 3506.191 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.178680 5019.073 FALSE TRUE
## 2 model.178616 5019.173 FALSE TRUE
## 3 model.178676 5020.033 FALSE TRUE
## 4 model.178360 5020.224 FALSE TRUE
## 5 model.178420 5020.269 FALSE TRUE
## 6 model.178615 5020.326 FALSE TRUE
## 7 model.174584 5020.358 FALSE TRUE
## 8 model.178678 5020.504 FALSE TRUE
## 9 model.170488 5020.614 FALSE TRUE
## 10 model.178424 5020.657 FALSE TRUE
## 11 model.174520 5020.690 FALSE TRUE
## 12 model.145912 5020.716 FALSE TRUE
## 13 model.178614 5020.719 FALSE TRUE
## 14 model.145848 5020.825 FALSE TRUE
## 15 model.170424 5020.860 FALSE TRUE
## 16 model.176632 5020.984 FALSE TRUE
## 17 model.176568 5020.989 FALSE TRUE
## 18 model.178648 5021.050 FALSE TRUE
## 19 model.178584 5021.055 FALSE TRUE
## 20 model.47608 5021.064 FALSE TRUE
## 21 model.178664 5021.072 FALSE TRUE
## 22 model.47544 5021.141 TRUE TRUE
## 23 model.178418 5021.162 TRUE TRUE
## 24 model.178600 5021.170 TRUE TRUE
## 25 model.178674 5021.262 TRUE TRUE
## 26 model.244216 5021.283 TRUE TRUE
## 27 model.178679 5021.299 TRUE TRUE
## 28 model.178644 5021.449 TRUE TRUE
## 29 model.179704 5021.512 TRUE TRUE
## 30 model.178359 5021.521 TRUE TRUE
## 31 model.1 5041.753 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.146674 2687.060 FALSE TRUE
## 2 model.146641 2687.397 FALSE TRUE
## 3 model.15602 2687.411 FALSE TRUE
## 4 model.146642 2687.484 FALSE TRUE
## 5 model.146676 2687.498 FALSE TRUE
## 6 model.146673 2687.578 FALSE TRUE
## 7 model.15570 2687.689 FALSE TRUE
## 8 model.145650 2687.774 FALSE TRUE
## 9 model.15569 2687.825 FALSE TRUE
## 10 model.146643 2687.831 FALSE TRUE
## 11 model.146644 2687.879 FALSE TRUE
## 12 model.15604 2687.930 FALSE TRUE
## 13 model.145617 2687.941 FALSE TRUE
## 14 model.146675 2688.052 FALSE TRUE
## 15 model.145649 2688.066 FALSE TRUE
## 16 model.147186 2688.133 FALSE TRUE
## 17 model.15601 2688.141 FALSE TRUE
## 18 model.15572 2688.167 FALSE TRUE
## 19 model.14578 2688.173 FALSE TRUE
## 20 model.147188 2688.243 FALSE TRUE
## 21 model.145618 2688.264 FALSE TRUE
## 22 model.146578 2688.278 FALSE TRUE
## 23 model.15571 2688.329 FALSE TRUE
## 24 model.14545 2688.424 FALSE TRUE
## 25 model.145652 2688.498 FALSE TRUE
## 26 model.147153 2688.509 FALSE TRUE
## 27 model.14546 2688.519 FALSE TRUE
## 28 model.16114 2688.562 FALSE TRUE
## 29 model.147155 2688.596 FALSE TRUE
## 30 model.147185 2688.599 FALSE TRUE
## 31 model.1 2701.331 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.179188 3168.667 FALSE TRUE
## 2 model.177908 3168.743 FALSE TRUE
## 3 model.177140 3168.759 FALSE TRUE
## 4 model.178164 3168.799 FALSE TRUE
## 5 model.166900 3169.148 FALSE TRUE
## 6 model.146420 3169.506 FALSE TRUE
## 7 model.177396 3169.578 FALSE TRUE
## 8 model.176628 3169.627 FALSE TRUE
## 9 model.144372 3169.675 FALSE TRUE
## 10 model.176884 3169.700 FALSE TRUE
## 11 model.178676 3169.748 FALSE TRUE
## 12 model.177652 3169.809 FALSE TRUE
## 13 model.170996 3169.822 FALSE TRUE
## 14 model.194548 3169.855 FALSE TRUE
## 15 model.145140 3169.886 FALSE TRUE
## 16 model.145396 3169.887 FALSE TRUE
## 17 model.194036 3169.995 FALSE TRUE
## 18 model.166388 3170.094 FALSE TRUE
## 19 model.48116 3170.125 FALSE TRUE
## 20 model.178932 3170.190 FALSE TRUE
## 21 model.243444 3170.255 FALSE TRUE
## 22 model.161780 3170.294 FALSE TRUE
## 23 model.46836 3170.302 FALSE TRUE
## 24 model.46068 3170.315 FALSE TRUE
## 25 model.194484 3170.341 FALSE TRUE
## 26 model.166644 3170.376 FALSE TRUE
## 27 model.168948 3170.398 FALSE TRUE
## 28 model.194516 3170.408 FALSE TRUE
## 29 model.183284 3170.415 FALSE TRUE
## 30 model.47092 3170.417 FALSE TRUE
## 31 model.1 3189.619 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.231093 3633.947 FALSE TRUE
## 2 model.100021 3634.026 FALSE TRUE
## 3 model.67253 3634.207 FALSE TRUE
## 4 model.198325 3634.315 FALSE TRUE
## 5 model.230581 3634.445 FALSE TRUE
## 6 model.165557 3634.479 FALSE TRUE
## 7 model.99509 3634.626 FALSE TRUE
## 8 model.34485 3634.639 FALSE TRUE
## 9 model.66741 3634.794 FALSE TRUE
## 10 model.165045 3634.829 FALSE TRUE
## 11 model.231061 3634.840 FALSE TRUE
## 12 model.197813 3634.856 FALSE TRUE
## 13 model.165525 3634.982 FALSE TRUE
## 14 model.99989 3634.992 FALSE TRUE
## 15 model.132789 3635.033 FALSE TRUE
## 16 model.33973 3635.128 FALSE TRUE
## 17 model.230069 3635.160 FALSE TRUE
## 18 model.232117 3635.160 FALSE TRUE
## 19 model.101045 3635.185 FALSE TRUE
## 20 model.231605 3635.255 FALSE TRUE
## 21 model.34453 3635.281 FALSE TRUE
## 22 model.1717 3635.292 FALSE TRUE
## 23 model.98997 3635.303 FALSE TRUE
## 24 model.100533 3635.444 FALSE TRUE
## 25 model.132277 3635.464 FALSE TRUE
## 26 model.198293 3635.496 FALSE TRUE
## 27 model.230549 3635.499 FALSE TRUE
## 28 model.66229 3635.528 FALSE TRUE
## 29 model.68277 3635.538 FALSE TRUE
## 30 model.165013 3635.578 FALSE TRUE
## 31 model.1 3686.772 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.81649 2881.442 FALSE TRUE
## 2 model.81633 2881.773 FALSE TRUE
## 3 model.81905 2882.071 FALSE TRUE
## 4 model.81889 2882.366 FALSE TRUE
## 5 model.79601 2882.576 FALSE TRUE
## 6 model.78561 2882.819 FALSE TRUE
## 7 model.78577 2882.840 FALSE TRUE
## 8 model.81521 2882.892 FALSE TRUE
## 9 model.79585 2882.907 FALSE TRUE
## 10 model.66273 2883.067 FALSE TRUE
## 11 model.80625 2883.087 FALSE TRUE
## 12 model.81585 2883.104 FALSE TRUE
## 13 model.81137 2883.119 FALSE TRUE
## 14 model.81505 2883.146 FALSE TRUE
## 15 model.66289 2883.246 FALSE TRUE
## 16 model.77553 2883.278 FALSE TRUE
## 17 model.81121 2883.286 FALSE TRUE
## 18 model.80609 2883.289 FALSE TRUE
## 19 model.81569 2883.421 FALSE TRUE
## 20 model.79857 2883.423 FALSE TRUE
## 21 model.73457 2883.427 FALSE TRUE
## 22 model.81617 2883.427 FALSE TRUE
## 23 model.16113 2883.442 FALSE TRUE
## 24 model.81777 2883.548 TRUE TRUE
## 25 model.77537 2883.572 TRUE TRUE
## 26 model.81841 2883.572 TRUE TRUE
## 27 model.79841 2883.718 TRUE TRUE
## 28 model.73441 2883.745 TRUE TRUE
## 29 model.81601 2883.753 TRUE TRUE
## 30 model.16097 2883.765 TRUE TRUE
## 31 model.1 2891.832 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.164587 5936.220 FALSE TRUE
## 2 model.33515 5936.803 FALSE TRUE
## 3 model.164459 5937.115 FALSE TRUE
## 4 model.164585 5937.330 FALSE TRUE
## 5 model.131819 5937.414 FALSE TRUE
## 6 model.164075 5937.626 FALSE TRUE
## 7 model.747 5937.878 FALSE TRUE
## 8 model.164523 5937.901 FALSE TRUE
## 9 model.33513 5937.901 FALSE TRUE
## 10 model.33387 5937.987 FALSE TRUE
## 11 model.164603 5938.020 FALSE TRUE
## 12 model.164555 5938.105 FALSE TRUE
## 13 model.164457 5938.135 FALSE TRUE
## 14 model.33003 5938.224 TRUE TRUE
## 15 model.163947 5938.308 TRUE TRUE
## 16 model.131691 5938.334 TRUE TRUE
## 17 model.131817 5938.492 TRUE TRUE
## 18 model.33451 5938.558 TRUE TRUE
## 19 model.164395 5938.679 TRUE TRUE
## 20 model.33483 5938.699 TRUE TRUE
## 21 model.164073 5938.826 TRUE TRUE
## 22 model.131307 5938.853 TRUE TRUE
## 23 model.164521 5938.883 TRUE TRUE
## 24 model.33531 5938.906 TRUE TRUE
## 25 model.745 5938.924 TRUE TRUE
## 26 model.164427 5938.946 TRUE TRUE
## 27 model.33385 5939.000 TRUE TRUE
## 28 model.164601 5939.040 TRUE TRUE
## 29 model.131755 5939.043 TRUE TRUE
## 30 model.619 5939.083 TRUE TRUE
## 31 model.1 5964.450 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.176369 4277.768 FALSE TRUE
## 2 model.176305 4278.343 FALSE TRUE
## 3 model.143601 4278.481 FALSE TRUE
## 4 model.179697 4278.635 FALSE TRUE
## 5 model.178673 4278.899 FALSE TRUE
## 6 model.176625 4278.921 FALSE TRUE
## 7 model.178417 4278.927 FALSE TRUE
## 8 model.143537 4279.095 FALSE TRUE
## 9 model.176561 4279.173 FALSE TRUE
## 10 model.179633 4279.296 FALSE TRUE
## 11 model.178609 4279.359 FALSE TRUE
## 12 model.176353 4279.520 FALSE TRUE
## 13 model.179441 4279.526 FALSE TRUE
## 14 model.178353 4279.667 FALSE TRUE
## 15 model.146929 4279.690 FALSE TRUE
## 16 model.176337 4279.723 FALSE TRUE
## 17 model.45297 4279.750 FALSE TRUE
## 18 model.143857 4279.764 FALSE TRUE
## 19 model.172273 4279.768 FALSE TRUE
## 20 model.168177 4279.768 FALSE TRUE
## 21 model.145649 4279.788 TRUE TRUE
## 22 model.145905 4279.835 TRUE TRUE
## 23 model.171505 4280.017 TRUE TRUE
## 24 model.143793 4280.049 TRUE TRUE
## 25 model.176289 4280.116 TRUE TRUE
## 26 model.177649 4280.184 TRUE TRUE
## 27 model.179681 4280.186 TRUE TRUE
## 28 model.143585 4280.245 TRUE TRUE
## 29 model.143569 4280.282 TRUE TRUE
## 30 model.45233 4280.297 TRUE TRUE
## 31 model.1 4292.093 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.142481 6976.995 FALSE TRUE
## 2 model.142737 6977.010 FALSE TRUE
## 3 model.140433 6977.301 FALSE TRUE
## 4 model.142513 6977.486 FALSE TRUE
## 5 model.142769 6977.543 FALSE TRUE
## 6 model.140689 6977.694 FALSE TRUE
## 7 model.140465 6977.733 FALSE TRUE
## 8 model.175505 6977.774 FALSE TRUE
## 9 model.175249 6977.852 FALSE TRUE
## 10 model.139409 6977.998 FALSE TRUE
## 11 model.140721 6978.177 FALSE TRUE
## 12 model.173201 6978.227 FALSE TRUE
## 13 model.140945 6978.290 FALSE TRUE
## 14 model.11409 6978.450 FALSE TRUE
## 15 model.11665 6978.490 FALSE TRUE
## 16 model.173457 6978.513 FALSE TRUE
## 17 model.139441 6978.535 FALSE TRUE
## 18 model.140977 6978.658 FALSE TRUE
## 19 model.142993 6978.660 FALSE TRUE
## 20 model.134289 6978.683 FALSE TRUE
## 21 model.143249 6978.728 FALSE TRUE
## 22 model.141201 6978.808 FALSE TRUE
## 23 model.9361 6978.812 FALSE TRUE
## 24 model.134545 6978.844 FALSE TRUE
## 25 model.142465 6978.864 FALSE TRUE
## 26 model.142721 6978.872 FALSE TRUE
## 27 model.141457 6978.905 FALSE TRUE
## 28 model.11441 6978.935 FALSE TRUE
## 29 model.172177 6978.954 FALSE TRUE
## 30 model.141713 6979.009 TRUE TRUE
## 31 model.1 6986.899 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.172769 6460.802 FALSE TRUE
## 2 model.172785 6461.239 FALSE TRUE
## 3 model.172705 6461.287 FALSE TRUE
## 4 model.172257 6461.330 FALSE TRUE
## 5 model.172273 6461.414 FALSE TRUE
## 6 model.172721 6461.649 FALSE TRUE
## 7 model.140001 6461.661 FALSE TRUE
## 8 model.172193 6461.770 FALSE TRUE
## 9 model.172209 6461.780 FALSE TRUE
## 10 model.140017 6462.064 FALSE TRUE
## 11 model.164577 6462.085 FALSE TRUE
## 12 model.139937 6462.107 FALSE TRUE
## 13 model.139489 6462.115 FALSE TRUE
## 14 model.139505 6462.157 FALSE TRUE
## 15 model.139953 6462.433 FALSE TRUE
## 16 model.139441 6462.480 FALSE TRUE
## 17 model.139425 6462.515 FALSE TRUE
## 18 model.164593 6462.530 FALSE TRUE
## 19 model.164065 6462.537 FALSE TRUE
## 20 model.164513 6462.547 FALSE TRUE
## 21 model.164081 6462.619 FALSE TRUE
## 22 model.172737 6462.620 FALSE TRUE
## 23 model.131809 6462.724 FALSE TRUE
## 24 model.41697 6462.794 FALSE TRUE
## 25 model.164529 6462.917 TRUE TRUE
## 26 model.164001 6462.950 TRUE TRUE
## 27 model.164017 6462.958 TRUE TRUE
## 28 model.172753 6463.040 TRUE TRUE
## 29 model.131297 6463.080 TRUE TRUE
## 30 model.131313 6463.115 TRUE TRUE
## 31 model.1 6470.916 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.80689 3788.530 FALSE TRUE
## 2 model.80657 3788.645 FALSE TRUE
## 3 model.129809 3788.821 FALSE TRUE
## 4 model.81713 3788.911 FALSE TRUE
## 5 model.81681 3788.926 FALSE TRUE
## 6 model.81745 3789.401 FALSE TRUE
## 7 model.80721 3789.406 FALSE TRUE
## 8 model.81841 3789.675 FALSE TRUE
## 9 model.130833 3789.683 FALSE TRUE
## 10 model.80817 3789.717 FALSE TRUE
## 11 model.81809 3789.813 FALSE TRUE
## 12 model.79665 3789.842 FALSE TRUE
## 13 model.79633 3789.893 FALSE TRUE
## 14 model.113425 3789.900 FALSE TRUE
## 15 model.77585 3789.942 FALSE TRUE
## 16 model.80785 3789.945 FALSE TRUE
## 17 model.73489 3789.996 FALSE TRUE
## 18 model.77617 3790.024 FALSE TRUE
## 19 model.113489 3790.064 FALSE TRUE
## 20 model.73521 3790.065 FALSE TRUE
## 21 model.81873 3790.070 FALSE TRUE
## 22 model.114513 3790.140 FALSE TRUE
## 23 model.15153 3790.153 FALSE TRUE
## 24 model.76593 3790.161 FALSE TRUE
## 25 model.129937 3790.198 FALSE TRUE
## 26 model.76561 3790.204 FALSE TRUE
## 27 model.122641 3790.241 FALSE TRUE
## 28 model.114449 3790.246 FALSE TRUE
## 29 model.125713 3790.266 FALSE TRUE
## 30 model.72497 3790.277 FALSE TRUE
## 31 model.1 3801.333 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.230549 3886.851 FALSE TRUE
## 2 model.232597 3887.065 FALSE TRUE
## 3 model.231573 3887.651 FALSE TRUE
## 4 model.99477 3887.898 FALSE TRUE
## 5 model.101525 3888.155 FALSE TRUE
## 6 model.165013 3888.332 FALSE TRUE
## 7 model.197781 3888.443 FALSE TRUE
## 8 model.229525 3888.449 FALSE TRUE
## 9 model.199829 3888.709 FALSE TRUE
## 10 model.100501 3888.709 FALSE TRUE
## 11 model.230533 3888.812 FALSE TRUE
## 12 model.167061 3888.841 FALSE TRUE
## 13 model.232581 3889.083 TRUE TRUE
## 14 model.33941 3889.347 TRUE TRUE
## 15 model.166037 3889.432 TRUE TRUE
## 16 model.198805 3889.444 TRUE TRUE
## 17 model.98453 3889.508 TRUE TRUE
## 18 model.231557 3889.656 TRUE TRUE
## 19 model.66709 3889.720 TRUE TRUE
## 20 model.35989 3889.880 TRUE TRUE
## 21 model.99461 3889.881 TRUE TRUE
## 22 model.230421 3889.924 TRUE TRUE
## 23 model.68757 3889.936 TRUE TRUE
## 24 model.163989 3890.000 TRUE TRUE
## 25 model.132245 3890.014 TRUE TRUE
## 26 model.101509 3890.152 TRUE TRUE
## 27 model.196757 3890.162 TRUE TRUE
## 28 model.232469 3890.201 TRUE TRUE
## 29 model.164997 3890.306 TRUE TRUE
## 30 model.197765 3890.429 TRUE TRUE
## 31 model.1 4014.484 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.133817 6299.384 FALSE TRUE
## 2 model.2745 6299.912 FALSE TRUE
## 3 model.133801 6299.954 FALSE TRUE
## 4 model.133881 6300.061 FALSE TRUE
## 5 model.2729 6300.281 FALSE TRUE
## 6 model.2809 6300.307 FALSE TRUE
## 7 model.133865 6300.672 FALSE TRUE
## 8 model.2793 6300.709 FALSE TRUE
## 9 model.131769 6301.000 FALSE TRUE
## 10 model.133785 6301.342 FALSE TRUE
## 11 model.133305 6301.357 FALSE TRUE
## 12 model.697 6301.462 TRUE TRUE
## 13 model.131753 6301.493 TRUE TRUE
## 14 model.133849 6301.585 TRUE TRUE
## 15 model.131833 6301.703 TRUE TRUE
## 16 model.681 6301.794 TRUE TRUE
## 17 model.2233 6301.852 TRUE TRUE
## 18 model.133689 6301.853 TRUE TRUE
## 19 model.2777 6301.860 TRUE TRUE
## 20 model.2713 6301.863 TRUE TRUE
## 21 model.133769 6301.887 TRUE TRUE
## 22 model.761 6301.899 TRUE TRUE
## 23 model.133289 6301.914 TRUE TRUE
## 24 model.133369 6302.030 TRUE TRUE
## 25 model.2617 6302.045 TRUE TRUE
## 26 model.133833 6302.174 TRUE TRUE
## 27 model.2697 6302.233 TRUE TRUE
## 28 model.2217 6302.257 TRUE TRUE
## 29 model.2297 6302.260 TRUE TRUE
## 30 model.2761 6302.261 TRUE TRUE
## 31 model.1 6446.457 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.179347 3000.833 FALSE TRUE
## 2 model.179859 3001.507 FALSE TRUE
## 3 model.179345 3001.530 FALSE TRUE
## 4 model.48275 3001.730 FALSE TRUE
## 5 model.196307 3001.884 FALSE TRUE
## 6 model.195795 3001.968 FALSE TRUE
## 7 model.179857 3001.978 FALSE TRUE
## 8 model.179411 3001.978 FALSE TRUE
## 9 model.244883 3001.996 FALSE TRUE
## 10 model.171155 3002.304 FALSE TRUE
## 11 model.178323 3002.329 FALSE TRUE
## 12 model.48273 3002.394 FALSE TRUE
## 13 model.162963 3002.399 FALSE TRUE
## 14 model.175251 3002.489 FALSE TRUE
## 15 model.48787 3002.497 FALSE TRUE
## 16 model.179331 3002.589 FALSE TRUE
## 17 model.163027 3002.605 FALSE TRUE
## 18 model.113811 3002.612 FALSE TRUE
## 19 model.179923 3002.625 FALSE TRUE
## 20 model.163475 3002.653 FALSE TRUE
## 21 model.244881 3002.700 FALSE TRUE
## 22 model.177299 3002.704 FALSE TRUE
## 23 model.64723 3002.780 FALSE TRUE
## 24 model.245395 3002.795 FALSE TRUE
## 25 model.146579 3002.819 FALSE TRUE
## 26 model.196305 3002.835 TRUE TRUE
## 27 model.65235 3002.851 TRUE TRUE
## 28 model.48785 3002.925 TRUE TRUE
## 29 model.179409 3002.981 TRUE TRUE
## 30 model.163539 3003.003 TRUE TRUE
## 31 model.1 3016.859 TRUE FALSE
## model.names AIC dif.frm.top dif.frm.full
## 1 model.146929 2702.395 FALSE TRUE
## 2 model.212465 2702.816 FALSE TRUE
## 3 model.146913 2703.708 FALSE TRUE
## 4 model.146865 2703.725 FALSE TRUE
## 5 model.144881 2703.793 FALSE TRUE
## 6 model.138737 2703.863 FALSE TRUE
## 7 model.212449 2703.898 FALSE TRUE
## 8 model.142833 2703.950 FALSE TRUE
## 9 model.15857 2703.983 FALSE TRUE
## 10 model.212401 2704.075 FALSE TRUE
## 11 model.81393 2704.200 FALSE TRUE
## 12 model.146673 2704.214 FALSE TRUE
## 13 model.145905 2704.239 FALSE TRUE
## 14 model.146897 2704.282 FALSE TRUE
## 15 model.204273 2704.368 FALSE TRUE
## 16 model.208369 2704.395 FALSE TRUE
## 17 model.210417 2704.510 TRUE TRUE
## 18 model.211441 2704.668 TRUE TRUE
## 19 model.212433 2704.678 TRUE TRUE
## 20 model.212209 2704.775 TRUE TRUE
## 21 model.146849 2705.065 TRUE TRUE
## 22 model.144865 2705.089 TRUE TRUE
## 23 model.143857 2705.093 TRUE TRUE
## 24 model.138721 2705.117 TRUE TRUE
## 25 model.144817 2705.151 TRUE TRUE
## 26 model.212385 2705.195 TRUE TRUE
## 27 model.142817 2705.220 TRUE TRUE
## 28 model.138673 2705.263 TRUE TRUE
## 29 model.146833 2705.271 TRUE TRUE
## 30 model.142769 2705.342 TRUE TRUE
## 31 model.1 2719.941 TRUE FALSE
Prepare SNP data
Here, we will take a SNP file and convert it into a form that is usable by R.
Prepare files
First, convert the .bed, .fam, and .map files intoa flat .ped version using plink:
plink --noweb --bfile wisasp_gwa-data --recode --tab --out wisasp_gwa-flatthen read in the necessary files and make alterations:
# phenotype file
phen.covar <- fread("data/phenos-and-covars.txt",
header = TRUE,
stringsAsFactors = TRUE,
# only read in columns of interest
select = unique(c("SerialNo","GWA.ID","Genet", #identifiers
data.env$common.insects, #insects
names(data.env$trait.data) #trait data
)))
# .ped file (flat form of .bed, contains genotype information at each SNP)
ped <- fread("data/gwa-files/wisasp_gwa-flat.ped",drop = 2:6) #only ID and genotype cols
# .bim file (containes SNP information and major/minor allele info)
bim <- fread("data/gwa-files/wisasp_gwa-data.bim")
# Rename columns by SNP name
data.table::setnames(ped,old=names(ped), new = c("IID",
unname(unlist(bim[,2]))))
# create vector with IIDs
## Rename Genet name (IID), remove .bam
IIDs <- ped[,1] <- gsub(unlist(ped[,1]), pattern = "\\.bam",replacement = "") #faster than loop
# Remove extra individuals in phenos-and-covar to match those present in SNP data.
phen.covar <- phen.covar[phen.covar$GWA.ID %in% IIDs,]
# get some statistics
obs.n <- nrow(phen.covar) # number of observations
snps.n <- (ncol(ped) - 1) # number of SNPs
IID.n <- length(unique(IIDs)) # number of Individuals (IIDs/genets)Calculate allele number
The number of alleles equal to the reference is calculated by:
# # Calculate no. allele
# ## create empty matrix with the appropriate dimensions
# SNP.add <- matrix(nr=IID.n, nc=snps.n)
#
# for(i in 2:114421) {
# tmp <- unname(unlist(ped[,..i]))
# # Reference allele from .bim
# ref.allele <- unlist(bim[i-1,5])
# SNP.add[,i-1] <- (substr(tmp,start=1,stop=1) == ref.allele) +
# (substr(tmp,start=3,stop=3) == ref.allele)
# }
#
# SNP.add <- as.data.frame(SNP.add)
# colnames(SNP.add) <- names(ped)[-1]
# rownames(SNP.add) <- ped$IID