Last updated: 2021-11-17
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Knit directory: HenriqueDGen/
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Rmd | 97d638d | LucianoRogerio | 2021-11-02 | Update of html links |
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Rmd | 7286357 | LucianoRogerio | 2021-11-02 | Insercao do caractere Area de Antracnose e PCA |
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Rmd | 170ea91 | LucianoRogerio | 2021-10-26 | BLUPs + Control Means for Diversity analysis |
Rmd | 60db375 | LucianoRogerio | 2021-10-26 | BLUPS and Means effects estimated for Diversity analysis |
html | b8ca347 | LucianoRogerio | 2021-10-19 | Build site. |
Rmd | b87ceea | LucianoRogerio | 2021-10-19 | Add Mixed Models analysis for the repository |
Rmd | 106f55c | LucianoRogerio | 2021-10-19 | Update Website |
html | 106f55c | LucianoRogerio | 2021-10-19 | Update Website |
Rmd | b9ece4f | LucianoRogerio | 2021-10-12 | Second Commit |
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library(here)
here() starts at /Users/lbd54/Documents/GitHub/HenriqueDGen
suppressMessages(library(tidyverse))
suppressMessages(library(plyr))
library(reactable)
suppressMessages(library(data.table))
suppressMessages(source(here::here("code", "MixedModelsFunctions.R")))
PhenoData <- readRDS(here::here("output", "DadosFenotipicosv2.RDS"))
PhenoData$block_number <- as.character(PhenoData$block_number)
PhenoData2 <- PhenoData %>% filter(!is.na(Y))
traits <- unique(PhenoData2$traits)
fmfit <- PhenoData2 %>% dlply(.variables = c("traits"),
.fun = analyzeTrial.lme4)
ResFixEffect <- lapply(fmfit, FUN = as.data.frame(anova))
ResAnInt <- matrix(unlist(ResFixEffect,use.names = T),
nrow = 2, byrow = F)
ResAnFin <- rbind(ResAnInt[,1:4],
ResAnInt[,5:8],
ResAnInt[,9:12],
ResAnInt[,13:16],
ResAnInt[,17:20])
colnames(ResAnFin) <- c("DF", "SumSq", "MeanSq", "Fvalue")
ResAnovaFinal <- data.frame(Trait = rep(traits, each = 2),
Factor = rep(c("Control", "Block"),
times = 5),
ResAnFin)
# Table 1. Anova of the fixed effects of Cassava foliar diseases
ResAnovaFinal %>% reactable(columns = list(
SumSq = colDef(format = colFormat(digits = 3, locales = "en-US")),
MeanSq = colDef(format = colFormat(digits = 3, locales = "en-US")),
Fvalue = colDef(format = colFormat(digits = 3, locales = "en-US"))))
rdfmfit <- PhenoData2 %>% dlply(.variables = c("traits"),
.fun = analyzeTrialrdMod.lme4)
Deviances <- NULL
for(i in traits){
Deviances[[i]] <- data.frame(Deviance.MM(fmfit[[i]], rdfmfit[[i]]))[2,6:8]
rownames(Deviances[[i]]) <- i
}
refitting model(s) with ML (instead of REML)
refitting model(s) with ML (instead of REML)
refitting model(s) with ML (instead of REML)
refitting model(s) with ML (instead of REML)
refitting model(s) with ML (instead of REML)
ResDeviances <- data.frame(t(sapply(Deviances, FUN = rbind)))
# Table 2. Deviance Analysis for cassava foliar disease
ResDeviances %>% reactable()
H2 <- sapply(fmfit, FUN = getVarComp.lme4) %>% t() %>% as.data.frame()
colnames(H2) <- c("VarClone", "VarRes")
H2 <- H2 %>% mutate(VarClone = as.numeric(VarClone),
VarRes = as.numeric(VarRes),
VarFen = VarClone + VarRes,
H2 = VarClone/VarFen)
# Table 3. Heritabilities of cassava foliar disease
H2 %>% reactable(columns = list(
VarClone = colDef(format = colFormat(digits = 4, locales = "en-US")),
VarRes = colDef(format = colFormat(digits = 4, locales = "en-US")),
VarFen = colDef(format = colFormat(digits = 4, locales = "en-US")),
H2 = colDef(format = colFormat(digits = 4, locales = "en-US"))))
### Obter estimativas de médias + Blups dos clones
MediasFix <- as.matrix(sapply(fmfit, FUN = (fixef)))
MediasFix[2:32, ] <- MediasFix[2:32,] +
matrix(rep(MediasFix[1, 1:5], each = 31), nrow = 31, ncol = 5,
byrow = F)
MediasFix <- as.data.frame(MediasFix)
rownames(MediasFix)[1] <- "controlClones"
MediasFix$CLONE <- rownames(MediasFix)
MediasFix %<>% filter(CLONE %like% "control") %>%
as.data.frame() %>% dplyr::select(CLONE, everything())
rownames(MediasFix) <- NULL
MediasFix$CLONE <- gsub(pattern = "control", replacement = "", x = MediasFix$CLONE)
MediasFix %>% filter(CLONE != "Clones") -> MediasFix
#### Obter os efeitos aleatorio dos Clones
BLUPsAle <- lapply(fmfit, FUN = getBLUPs.lme4)
BLUPSDisea <- data.frame(CLONE = rownames(BLUPsAle[1]$AnAr))
for(i in names(BLUPsAle)){
drg<-data.frame(CLONE = rownames(BLUPsAle[[i]]), stringsAsFactors=F)
drg[,i] <-BLUPsAle[[i]]
BLUPSDisea<-merge(BLUPSDisea,drg,by="CLONE",all.x=T)
}
BLUPSDisea <- BLUPSDisea %>% filter(CLONE %like% ":1")
BLUPSDisea$CLONE <- gsub(pattern = ":1", replacement = "", BLUPSDisea$CLONE)
BLUPS <- rbind(BLUPSDisea, MediasFix)
saveRDS(BLUPS, here::here("output", "BLUPsDisease.RDS"))
# Table 3. Blups of the accessions for cassava foliar diseases
BLUPS %>% reactable(defaultPageSize = 25, columns = list(
AnAr = colDef(format = colFormat(digits = 4, locales = "en-US")),
Anth = colDef(format = colFormat(digits = 4, locales = "en-US")),
BlLS = colDef(format = colFormat(digits = 4, locales = "en-US")),
BrLS = colDef(format = colFormat(digits = 4, locales = "en-US")),
WhLS = colDef(format = colFormat(digits = 4, locales = "en-US"))))
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sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lme4_1.1-27.1 sommer_4.1.4 crayon_1.4.2 lattice_0.20-45
[5] MASS_7.3-54 Matrix_1.3-4 data.table_1.14.2 reactable_0.2.3
[9] plyr_1.8.6 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[13] purrr_0.3.4 readr_2.1.0 tidyr_1.1.4 tibble_3.1.6
[17] ggplot2_3.3.5 tidyverse_1.3.1 here_1.0.1
loaded via a namespace (and not attached):
[1] httr_1.4.2 sass_0.4.0 jsonlite_1.7.2 splines_4.1.1
[5] modelr_0.1.8 bslib_0.3.1 assertthat_0.2.1 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.6.4 backports_1.3.0 glue_1.5.0
[13] digest_0.6.28 promises_1.2.0.1 minqa_1.2.4 rvest_1.0.2
[17] colorspace_2.0-2 htmltools_0.5.2 httpuv_1.6.3 reactR_0.4.4
[21] pkgconfig_2.0.3 broom_0.7.10 haven_2.4.3 scales_1.1.1
[25] whisker_0.4 later_1.3.0 tzdb_0.2.0 git2r_0.28.0
[29] generics_0.1.1 ellipsis_0.3.2 withr_2.4.2 cli_3.1.0
[33] magrittr_2.0.1 readxl_1.3.1 evaluate_0.14 fs_1.5.0
[37] fansi_0.5.0 nlme_3.1-153 xml2_1.3.2 tools_4.1.1
[41] hms_1.1.1 lifecycle_1.0.1 munsell_0.5.0 reprex_2.0.1
[45] compiler_4.1.1 jquerylib_0.1.4 rlang_0.4.12 nloptr_1.2.2.3
[49] grid_4.1.1 rstudioapi_0.13 htmlwidgets_1.5.4 crosstalk_1.2.0
[53] rmarkdown_2.11 boot_1.3-28 gtable_0.3.0 DBI_1.1.1
[57] R6_2.5.1 lubridate_1.8.0 knitr_1.36 fastmap_1.1.0
[61] utf8_1.2.2 workflowr_1.6.2 rprojroot_2.0.2 stringi_1.7.5
[65] Rcpp_1.0.7 vctrs_0.3.8 dbplyr_2.1.1 tidyselect_1.1.1
[69] xfun_0.28