Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>")
options(rmarkdown.html_vignette.check_title = FALSE)
library(statgenHTP)
library(ggplot2)
## ----importPhenovator---------------------------------------------------------
data("PhenovatorDat1")
## ----headPhenovato, echo=FALSE, message=FALSE---------------------------------
knitr::kable(head(PhenovatorDat1), align=c('c','c'), booktabs = TRUE)
## ----createTP-----------------------------------------------------------------
## Create a TP object containing the data from the Phenovator.
phenoTP <- createTimePoints(dat = PhenovatorDat1,
experimentName = "Phenovator",
genotype = "Genotype",
timePoint = "timepoints",
repId = "Replicate",
plotId = "pos",
rowNum = "y", colNum = "x",
addCheck = TRUE,
checkGenotypes = c("check1", "check2", "check3", "check4"))
summary(phenoTP)
## ----getTimepoints------------------------------------------------------------
## Extract the time points table.
timepoint <- getTimePoints(phenoTP)
## ----getTimepointsbis, echo=FALSE, message=FALSE------------------------------
knitr::kable(head(timepoint), align=c('c','c'), padding = 0)
## ----layoutPlot, fig.height=4, fig.width=5, fig.align = 'center'--------------
## Plot the layout for the third time point.
plot(phenoTP,
plotType = "layout",
timePoints = 3)
## ----layoutPlotHL, fig.height=4, fig.width=5, fig.align = 'center'------------
## Plot the layout for the third time point with the check genotypes highlighted.
plot(phenoTP,
plotType = "layout",
timePoints = 3,
highlight = c("check1", "check2", "check3", "check4"))
## ----layoutPlotSG, fig.height=7, fig.width=8, fig.align = 'center'------------
## Plot the layout for the third time point.
plot(phenoTP,
plotType = "layout",
timePoints = 3,
highlight = c("check1", "check2", "check3", "check4"),
showGeno = TRUE)
## ----layoutPlotheatmap, fig.height=7, fig.width=8, fig.align = 'center'-------
## Plot the layout for the third time point.
plot(phenoTP,
plotType = "layout",
timePoints = 3,
traits = "EffpsII")
## ----rawVator, include=TRUE, fig.height=3, fig.width=7, fig.align = 'center'----
## Create the raw data time courses for three genotypes.
plot(phenoTP,
traits = "EffpsII",
plotType = "raw",
genotypes = c("G001", "G002", "check1"))
## ----boxPlot, fig.height=4.5, fig.width=7, fig.align = 'center'---------------
## Create a boxplot for "EffpsII" using the default all time points.
plot(phenoTP,
plotType = "box",
traits = "EffpsII")
## ----corPlot, fig.height=5, fig.width=6, fig.align = 'center'-----------------
## Create a correlation plot for "EffpsII" for a selection of time points.
plot(phenoTP,
plotType = "cor",
traits = "EffpsII",
timePoints = seq(from = 1, to = 73, by = 5))
## ----setParamVator------------------------------------------------------------
# First select a subset of plants, for example here 4 plants.
plantSel <- c("c1r17","c13r17","c6r51","c21r24")
# Then run on the subset
resuVatorHTP <- detectSingleOut(TP = phenoTP,
trait = "EffpsII",
plotIds = plantSel,
confIntSize = 3,
nnLocfit = 0.1)
## ----headOutVator, echo=FALSE, message=FALSE----------------------------------
knitr::kable(head(resuVatorHTP), align = c('c','c'), padding = 0)
## ----plotOutVatorFA1, fig.height=1.5, fig.width=5, fig.align = 'center'-------
plot(resuVatorHTP,
outOnly = FALSE)
## ----rmSingleOutVator---------------------------------------------------------
phenoTPOut <- removeSingleOut(phenoTP,
resuVatorHTP)
## ----fitSp, message=FALSE-----------------------------------------------------
## Fit a model for a few time points.
modPhenoSp <- fitModels(TP = phenoTPOut,
trait = "EffpsII",
timePoints = c(1, 33, 36, 54, 73))
summary(modPhenoSp)
## ----plotSpatPerc, fig.height=4, fig.width=7.3, message=FALSE----------------
plot(modPhenoSp,
timePoints = 36,
plotType = "spatial",
spaTrend = "percentage")
## ----plotTimeLapse, fig.height=5, fig.width=6, message=FALSE, eval=FALSE-----
# plot(modPhenoSp,
# plotType = "timeLapse",
# outFile = "TimeLapse_modPhenoSp.gif")
## ----plotSpPred, message=FALSE, fig.height=2, fig.width=6, fig.align = 'center'----
plot(modPhenoSp,
plotType = "rawPred",
genotypes = c("G007", "G058"))
## ----plotSpCorr, message=FALSE, fig.height=2, fig.width=6, fig.align = 'center'----
plot(modPhenoSp,
plotType = "corrPred",
genotypes = c("G007", "G058"))
## ----plotSpHerit, message=FALSE, fig.height=2.5, fig.width=3.5, fig.align = 'center'----
plot(modPhenoSp,
plotType = "herit",
yLim = c(0.5, 1))
## ----plotSpVar, fig.height=3, fig.width=5, message=FALSE, fig.align = 'center'----
plot(modPhenoSp,
plotType = "variance")
## ----plotSpED, message=FALSE, fig.height=3, fig.width=5, fig.align = 'center'----
plot(modPhenoSp,
plotType = "effDim",
whichED = c("colId", "rowId", "fColRow","colfRow", "surface"),
EDType = "ratio")
## ----getFun, message=FALSE----------------------------------------------------
## Extract the genotypic predictions for one time point:
genoPredSp <- getGenoPred(modPhenoSp)
## ----getPred, echo=FALSE, message=FALSE---------------------------------------
knitr::kable(head(genoPredSp$genoPred), align = "c", padding = 0)
## ----fitSplineVator, message=FALSE, warning=FALSE-----------------------------
data(spatCorrectedVator)
# Fit P-splines using on a subset of genotypes.
subGenoVator <- c("G160", "G151")
fit.spline <- fitSpline(inDat = spatCorrectedVator,
trait = "EffpsII_corr",
genotypes = subGenoVator,
knots = 50,
useTimeNumber = TRUE,
timeNumber = "timeNumHour")
# Extracting the tables of predicted values and P-spline coefficients
predDat <- fit.spline$predDat
coefDat <- fit.spline$coefDat
## ----plotSplGeno, fig.height=4, fig.width=7, message=FALSE-------------------
plot(fit.spline,
genotypes = "G160")
## ----OutVator, message=FALSE, warning=FALSE-----------------------------------
outVator <- detectSerieOut(corrDat = spatCorrectedVator,
predDat = predDat,
coefDat = coefDat,
trait = "EffpsII_corr",
genotypes = subGenoVator,
thrCor = 0.9,
thrPca = 30)
## ----headOutPoint, echo=FALSE, message=FALSE----------------------------------
knitr::kable(head(outVator), align = "c", booktabs = TRUE, row.names = FALSE)
## ----plotOutVator, fig.height=6, fig.width=6, message=FALSE, warning=FALSE----
plot(outVator, genotypes = "G151")
## ----rmSerieOutVator----------------------------------------------------------
fit.splineOut <- removeSerieOut(fitSpline = fit.spline,
serieOut = outVator)
## ----summaryData, message=FALSE, warning=FALSE, eval=TRUE, fig.align='center'----
data(spatCorrectedVator)
spatCorrectedVator[["pop"]] <- as.factor(rep("Pop1", nrow(spatCorrectedVator)))
str(droplevels(spatCorrectedVator[spatCorrectedVator$genotype %in% subGenoVator,]))
## ----fitPsHDMVator, message=FALSE, warning=FALSE------------------------------
## Fit P-spline HDM.
fit.psHDM <- fitSplineHDM(inDat = spatCorrectedVator,
genotypes = subGenoVator,
trait = "EffpsII_corr",
useTimeNumber = TRUE,
timeNumber = "timeNumHour",
pop = "pop",
genotype = "genotype",
plotId = "plotId",
weights = "wt",
difVar = list(geno = FALSE, plot = FALSE),
smoothPop = list(nseg = 20, bdeg = 3, pord = 2),
smoothGeno = list(nseg = 20, bdeg = 3, pord = 2),
smoothPlot = list(nseg = 20, bdeg = 3, pord = 2),
trace = FALSE)
## ----predictSplineHDMVatorNum, message=FALSE, warning=FALSE-------------------
## Predict P-spline HDM.
pred.psHDM <- predict(object = fit.psHDM,
newtimes = seq(min(fit.psHDM$time[["timeNumber"]]),
max(fit.psHDM$time[["timeNumber"]]),
length.out = 100),
pred = list(pop = TRUE, geno = TRUE, plot = TRUE),
se = list(pop = TRUE, geno = TRUE, plot = FALSE),
trace = FALSE)
## ----plotPredPopVator, fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'----
plot(pred.psHDM, plotType = "popTra", themeSizeHDM = 10)
## ----plotPredGenoTraVator, fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'----
plot(pred.psHDM, plotType = "popGenoTra", themeSizeHDM = 10)
## ----plotPredGenoDevVator, fig.height=4, fig.width=5, message=FALSE, warning=FALSE, fig.align='center'----
plot(pred.psHDM, plotType = "genoDev", themeSizeHDM = 10)
## ----plotPredPlotVator, fig.height=4, fig.width=6, message=FALSE, warning=FALSE, fig.align='center'----
plot(pred.psHDM,
plotType = "genoPlotTra",
themeSizeHDM = 10)
## ----paramVator, fig.height=2, fig.width=4, message=FALSE, warning=FALSE, fig.align='center'----
paramVator1 <-
estimateSplineParameters(x = fit.splineOut,
estimate = "predictions",
what = "AUC",
timeMin = 330,
timeMax = 432,
genotypes = subGenoVator)
plot(paramVator1, plotType = "box")
## ----paramPhenoGenoPred, fig.height=2, fig.width=4, message=FALSE, warning=FALSE, fig.align='center'----
paramVator2 <-
estimateSplineParameters(x = pred.psHDM,
what = "min",
fitLevel = "plot",
estimate = "predictions")
plot(paramVator2, plotType = "box")
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