Type:	Package
Title:	Single Trial Analysis (STA) of Field Trials
Version:	1.0.15
Date:	2025-06-24
Description:	Phenotypic analysis of field trials using mixed models with and without spatial components. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (https://vsni.co.uk/software/asreml-r/).
License:	GPL-2 | GPL-3 [expanded from: GPL]
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Depends:	R (≥ 3.3)
Imports:	emmeans, ggplot2 (≥ 3.3.0), ggrepel, gridExtra, knitr, lme4, mapproj, maps, qtl, rlang, scales (≥ 1.1.0), SpATS (≥ 1.0-18), xtable
Suggests:	asreml(≥ 3.0), rmarkdown, testthat, tibble
SystemRequirements:	pdflatex
VignetteBuilder:	knitr
URL:	https://biometris.github.io/statgenSTA/index.html, https://github.com/Biometris/statgenSTA/
BugReports:	https://github.com/Biometris/statgenSTA/issues
NeedsCompilation:	no
Packaged:	2025-06-24 12:37:59 UTC; rossu027
Author:	Bart-Jan van Rossum [aut, cre], Fred van Eeuwijk [ctb], Martin Boer [ctb], Marcos Malosetti [ctb], Daniela Bustos-Korts [ctb], Emilie J. Millet [ctb], Joao Paulo [ctb], Maikel Verouden [ctb], Willem Kruijer [ctb], Ron Wehrens [ctb], Choazhi Zheng [ctb]
Maintainer:	Bart-Jan van Rossum <bart-jan.vanrossum@wur.nl>
Repository:	CRAN
Date/Publication:	2025-06-24 13:40:02 UTC

statgenSTA: Single Trial Analysis (STA) of Field Trials

Description

Phenotypic analysis of field trials using mixed models with and without spatial components. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (https://vsni.co.uk/software/asreml-r/).

Author(s)

Maintainer: Bart-Jan van Rossum bart-jan.vanrossum@wur.nl (ORCID)

Other contributors:

• Fred van Eeuwijk (ORCID) [contributor]

• Martin Boer (ORCID) [contributor]

• Marcos Malosetti (ORCID) [contributor]

• Daniela Bustos-Korts (ORCID) [contributor]

• Emilie J. Millet (ORCID) [contributor]

• Joao Paulo (ORCID) [contributor]

• Maikel Verouden (ORCID) [contributor]

• Willem Kruijer (ORCID) [contributor]

• Ron Wehrens (ORCID) [contributor]

• Choazhi Zheng (ORCID) [contributor]

See Also

Useful links:

• https://biometris.github.io/statgenSTA/index.html

• https://github.com/Biometris/statgenSTA/

• Report bugs at https://github.com/Biometris/statgenSTA/issues

Convert STA to Cross

Description

Convert an STA object to a cross object from package qtl. Genotypic information should be available in a .csv file.
The only way to create an object of class cross is by importing both the phenotypic and the genotypic data from external files. Therefore the phenotypic data, either the BLUEs or the BLUPs from the fitted model are first written to a temporary file. The genotypic data has to be available in a .csv file in the correct format as well, see genoFile for a description of this format. These phenotypic and genotypic files are then imported into a cross object using the read.cross function in the qtl package.

Usage

STAtoCross(
  STA,
  trial = NULL,
  traits = NULL,
  what = c("BLUEs", "BLUPs"),
  genoFile,
  genotypes = c("A", "H", "B", "D", "C"),
  ...
)

Arguments

See Also

read.cross

Other functions for STA objects: STAtoTD(), plot.STA(), report.STA(), summary.STA()

Examples

## Fit model using SpATS.
modSp <- fitTD(TD = TDHeat05,
               design = "res.rowcol",
               traits = "yield",
               what = "fixed")

## Create cross object with BLUEs from modSp using genotypic information
## from markers.csv in the package.
cross <- STAtoCross(modSp,
                    genoFile = system.file("extdata", "markers.csv",
                                           package = "statgenSTA"))

Convert STA to TD

Description

Convert an STA object to a TD object.
To be able to use the output of a single trial analysis in Genotype-by-Environment (GxE) analysis the output first needs to be converted back to an TD object. This function does exactly that. It extracts BLUEs, BLUPs and their standard errors from the STA object and creates a new TD object using these. Also a column "wt" (weight) may also be added. Weights are then calculated as 1/(SE BLUEs) ^ 2.

Usage

STAtoTD(
  STA,
  what = c("BLUEs", "seBLUEs", "BLUPs", "seBLUPs"),
  traits = NULL,
  keep = NULL,
  addWt = FALSE
)

Arguments

Details

Trial information for the trials in the STA object will be copied from the original TD object on which the modeling was done.

See Also

Other functions for STA objects: STAtoCross(), plot.STA(), report.STA(), summary.STA()

Examples

## Fit model using SpATS.
modSp <- fitTD(TD = TDHeat05,
               design = "res.rowcol",
               traits = "yield",
               what = "fixed")

## Create TD object from the fitted model with BLUEs and standard errors.
TDRes <- STAtoTD(modSp,
                 what = c("BLUEs", "seBLUEs"))

## Add a weight column in the output.
TDResWt <- STAtoTD(modSp,
                   what = c("BLUEs", "seBLUEs"),
                   addWt = TRUE)

S3 class TD

Description

createTD
Function for creating objects of S3 class TD (Trial Data). The function converts a data.frame to an object of class TD in the following steps:

• Check input data

• Rename columns to default column names - default column names: genotype, trial, loc, year, repId, subBlock, rowCoord, colCoord, rowId, colId, checkId

• Convert column types to default column types - rowCoord and colCoord are converted to numeric columns, all other renamed columns to factor columns. Columns other than the default columns, e.g. traits or other covariates will be included in the output unchanged.

• Split input data by trial - each trial in the input data will become a list item in the output.

• Add meta data - the trial meta data are added as attributes to the different output items. The function parameters starting with "tr" provide the meta data. Their values will be recycled if needed, so by setting a single "trDesign", all trials will get the same design. For trLat, trLong, trDesign and trDate a column in data that contains the information can be specified as well. The meta data can be changed later on using getMeta and setMeta

addTD
Function for adding extra trial data to an existing object of class TD. The data for the new trials will be added after the data for existing trials. It is possible to add data for an already existing trial, but this will cause multiple items in the output with identical names, which might cause problems later on in the analysis. Therefore a warning will be issued in this case.

dropTD
Function for removing data for selected trials from an existing object of class TD.

summary.TD and plot.TD methods are available.

Usage

createTD(
  data,
  genotype = NULL,
  trial = NULL,
  loc = NULL,
  year = NULL,
  repId = NULL,
  subBlock = NULL,
  plotId = NULL,
  rowCoord = NULL,
  colCoord = NULL,
  rowId = rowCoord,
  colId = colCoord,
  checkId = NULL,
  trLocation = NULL,
  trDate = NULL,
  trDesign = NULL,
  trLat = NULL,
  trLong = NULL,
  trPlWidth = NULL,
  trPlLength = NULL
)

addTD(
  TD,
  data,
  genotype = NULL,
  trial = NULL,
  loc = NULL,
  year = NULL,
  repId = NULL,
  subBlock = NULL,
  plotId = NULL,
  rowCoord = NULL,
  colCoord = NULL,
  rowId = rowCoord,
  colId = colCoord,
  checkId = NULL,
  trLocation = NULL,
  trDate = NULL,
  trDesign = NULL,
  trLat = NULL,
  trLong = NULL,
  trPlWidth = NULL,
  trPlLength = NULL
)

dropTD(TD, rmTrials)

Arguments

Value

An object of class TD, a list of data.frames with renamed columns and an attribute renamedCols containing an overview of renamed columns. For each unique value of trial, the output has a data.frame in the list with the same name as the trial. These data.frames have attributes containing the metadata for the corresponding trial. If there is no column for trial, the list will contain one item named after the input data.

See Also

Other functions for TD objects: getMeta(), plot.TD(), summary.TD()

Examples

## Create a data.frame to be converted to TD object.
## The data consists of genotype, trial, row and column information and
## two traits, yield and flowering time.
datT1 <- data.frame(geno = paste0("G", 1:10),
                    tr = "T1",
                    row = rep(1:5, each = 2),
                    col = rep(1:2, times = 5),
                    yield = 1:10,
                    flowering = 3:12)

## Convert data.frame to TD object.
TDT1 <- createTD(data = datT1,
                 genotype = "geno",
                 trial = "tr",
                 rowCoord = "row",
                 colCoord = "col")

## Create a second data.frame similar to the first with data for a second trial.
datT2 <- data.frame(geno = paste0("G", 1:10),
                    tr = "T2",
                    row = rep(1:2, each = 5),
                    col = rep(1:5, times = 2),
                    yield = 10:1,
                    flowering = 12:3)

## Add this data to the TD object created above.
TDTot <- addTD(TD = TDT1,
               data = datT2,
               genotype = "geno",
               trial = "tr",
               rowCoord = "row",
               colCoord = "col")

## Drop the data for the first trial from the object.
TDT2 <- dropTD(TD = TDTot,
               rmTrials = "T1")

Field data for a wheat experiment in Mexico

Description

A dataset converted to a TD object containing raw plot data for one trial from a series of wheat trials conducted in Mexico by CIMMYT. The different trials took place under different regimes of irrigation and temperature, there were 4 trials across two years, labelled as DRIP05, HEAT05, HEAT06 and IRRI06. The TD object only contains the data for HEAT05. Within each trial, a set of 167 progeny of a RIL (Recombinant Inbred Line; 8 generations) population were tested alongside the population parents (Seri and Babax). A lattice design with two replicates was used for each trial. In the first replicate the entries were not randomized, as they were considered to be a random selection from a population.

Usage

TDHeat05

Format

A TD object, a list containing a data.frame with the following columns:

trial

trial, a combination of watering regime, year and nitrogen treatment

genotype

genotype

Plot

plot number in the field

repId

replicate

subBlock

block id

rowId

row within the field (as factor)

colId

column within the field (as factor)

yield

yield in grams per square meter

rowCoord

row within the field (as numerical value)

colCoord

column within the field (as numerical value)

Field data for a maize experiment in Tlaltizapan, Mexico

Description

A dataset converted into a TD object containing data corresponding to an F2 maize reference population from CIMMYT maize drought breeding program, which was derived from the cross of a drought-tolerant line (P1) with a drought susceptible line (P2) as described in detail by Ribaut et al. (1996, 1997).
DNA from 211 F2 plants was extracted to produce information for 132 co-dominant markers on 10 linkage groups. Phenotypic evaluations were performed on 211 F2:3 families, each one derived from an original F2 plant. The families were evaluated under different water and nitrogen regimes during 1992, 1994 and 1996. In the winter of 1992 three water regimes were imposed on the trials: well watered (NS), intermediate stress (IS) and severe stress (SS). In the winter of 1994, only the IS and SS trials were available. Nitrogen availability varied in the 1996 trials, with two low nitrogen treatments (LN, in winter and summer) and one high-nitrogen treatment (HN in summer). In each of the trials, five traits were evaluated but only grain yield is included in the data.

Usage

TDMaize

Format

A TD object, a list containing 8 data.frames, each with the following columns:

trial

trial, a combination of watering regime, year and nitrogen treatment

genotype

genotype

regime

stress level, NS (no water stress), IS (intermediate water stress, SS (severe water stress), LN (low nitrogen) or HN (high nitrogen))

yld

grain yield in tons

Source

https://link.springer.com/article/10.1007/BF00221905

References

Ribaut JM, Hoisington DA, Deutsch JA, Jiang C, Gonzalez de Leon D (1996) Identification of quantitative trait loci under drought conditions in tropical maize.1. Flowering parameters and the anthesis-silking interval. Theor Appl Genet 92:905–914

Ribaut JM, Jiang C, Gonzalez de Leon D, Edmeades GO, Hoisington DA (1997) Identification of quantitative trait loci under drought conditions in tropical maize.2. Yield components and marker-assisted selection strategies. Theor Appl Genet 94:887–896

DROPS data set

Description

This dataset comes from the European Union project DROPS (DROught-tolerant yielding PlantS). A panel of 256 maize hybrids was grown with two water regimes (irrigated or rainfed), in seven fields in 2012 and 2013, respectively, spread along a climatic transect from western to eastern Europe, plus one site in Chile in 2013. This resulted in 28 experiments defined as the combination of one year, one site and one water regime, with two and three repetitions for rainfed and irrigated treatments, respectively. A detailed environmental characterisation was carried out, with hourly records of micrometeorological data and soil water status, and associated with precise measurement of phenology. Grain yield and its components were measured at the end of the experiment.
10 experiments have been selected from the full data set, two for each of the five main environmental scenarios that were identified in the data. The scenarios have been added to the data as well as a classification of the genotypes in four genetic groups.

The data.frame contains the raw phenotypic data per experiment (Location × year × water regime), one value per individual plot and outliers have been removed.

A data.frame with 6499 rows and 25 columns.

Experiment

experiments ID described by the three first letters of the city’s name followed by the year of experiment and the water regime with W for watered and R for rain-fed.

Site

location where the experiment was performed

year

year in which the experiment was performed

plotId

plot identifier (when available)

treatment

targeted water regime

Code_ID, Variety_ID, Accession_ID

identifier of the genotype

Replicate, block

experimental design factors

Row, Column

2D coordinates of each plot

grain.yield

genotypic mean for yield adjusted at 15\ in ton per hectare (t ha^-1)

grain.number

genotypic mean for number of grain per square meter

grain.weight

genotypic mean for individual grain weight in milligram (mg)

anthesis

genotypic mean for male flowering (pollen shed), in thermal time cumulated since emergence (d_20°C)

silking

genotypic mean for female flowering (silking emergence), in thermal time cumulated since emergence (d_20°C)

#'

plant.height

genotypic mean for plant height, from ground level to the base of the flag leaf (highest) leaf in centimeter (cm)

tassel.height

genotypic mean for plant height including tassel, from ground level to the highest point of the tassel in centimeter (cm)

ear.height

genotypic mean for ear insertion height, from ground level to ligule of the highest ear leaf in centimeter (cm)

Lat

The latitude of the location where the experiment was performed

Long

The longitude of the location where the experiment was performed

scenarioWater

water scenario for the experiment, well watered (WW) or water deficit (WD)

scenarioTemp

temperature scenario for the experiment, Cool, Hot or Hot(Day)

scenarioFull

the full scenario for the experiment, a combination of scenarioWater and scenarioTemp

geneticGroup

the genetic group to which the genotype belongs

Usage

dropsRaw

Format

An object of class data.frame with 6486 rows and 26 columns.

Source

doi:10.15454/IASSTN

References

Millet, E. J., Pommier, C., et al. (2019). A multi-site experiment in a network of European fields for assessing the maize yield response to environmental scenarios (Data set). doi:10.15454/IASSTN

Extract statistics from fitted models

Description

Extract and calculate various results for fitted models such as BLUEs, BLUPs, unit errors and heritabilities. For a full list of results that can be extracted, see the table below.

The result(s) to extract can be specified in what.
If a single result is extracted, this result is returned as a data.frame, If this is not possible, because the format of the result is incompatible with the data.frame format, the result is returned as a list. E.g. if BLUEs are extracted, the output of the function is a data.frame with BLUEs. However if varCompF is extracted, the output of the function is a list.

Results that are returned as data.frame are marked as such in the asDataFrame column in the table. If the default return value for a result is a data.frame that can be overridden by the user by specifying asDataFrame = FALSE. When doing so the result will be returned as a list of data.frames, one per trial. They other way round is not possible. If a result is returned as a list according to the table, it cannot be returned as a data.frame.
If multiple results are extracted at the same time, these are always returned as a list.

Most results can only be calculated if a model is fitted with genotype as fixed or with genotype as random. E.g. to compute heritabilities a model should be fitted with genotype as random effect. This is indicated in the table in the column model with "F" and "R" respectively.

Possible options for what are:

Usage

extractSTA(
  STA,
  trials = names(STA),
  traits = NULL,
  what = "all",
  asDataFrame = length(what) == 1 && what != "all",
  keep = NULL,
  restoreColNames = FALSE
)

Arguments

Value

Depending on the input either a data.frame or a list with, per trial for which statistics have been extracted, a list of those statistics.

See Also

fitTD

Examples

## Fit model using SpATS.
modSp <- fitTD(TD = TDHeat05,
               design = "res.rowcol",
               traits = "yield")

## Extract all available statistics from the fitted model.
extr <- extractSTA(modSp)

## Extract only the BLUEs from the fitted model.
BLUEs <- extractSTA(modSp,
                    what = "BLUEs")

## Extract only the BLUEs from the fitted model and keep trial as variable in
## the output.
BLUEs2 <- extractSTA(modSp,
                     what = "BLUEs",
                     keep = "trial")

Fit single trial mixed model

Description

Perform REML analysis given a specific experimental design using either SpATS, lme4 or asreml. SpATS is used as a default method when row coordinates (rowCoord) and column coordinates (colCoord) are present, lme4 otherwise. See details for the exact models fitted.

Usage

fitTD(
  TD,
  trials = names(TD),
  design = NULL,
  traits,
  what = c("fixed", "random"),
  covariates = NULL,
  useCheckId = FALSE,
  spatial = FALSE,
  engine = NA,
  control = NULL,
  progress = FALSE,
  ...
)

Arguments

Details

The actual model fitted depends on the design. For the supported designs, the following models are used:

In the models above, fixed effects are indicated in italics whereas random effects are indicated in bold. genotype can be fitted as fixed or as random effect depending on the value of the parameter what. Extra fixed effects may be fitted using the parameter covariates.

If SpATS is used as modeling engine, an extra spatial term is always included in the model. This term is constructed using the function PSANOVA from the SpATS package as
PSANOVA(colCoord, rowCoord, nseg = nSeg, nest.div = 2) where
nSeg = (number of columns / 2, number of rows / 2). nseg and nest.div can be modified using the control parameter.

When asreml is used for modeling and spatial is TRUE seven models are fitted with different random terms and covariance structure. The best model is determined based on a goodness-of-fit criterion, either AIC or BIC. This can be set using the control parameter criterion, default is AIC. The fitted random terms depend on the structure of the data. If the trial has a regular structure, i.e. all replicates appear the same amount of times in the trial, the following combinations of random and spatial terms are fitted:

If the design is not regular the following combinations of random and spatial terms are fitted:

Value

An object of class STA, a list containing, per trial that has been analyzed, a list of:

References

Maria Xose Rodriguez-Alvarez, Martin P. Boer, Fred A. van Eeuwijk, Paul H.C. Eilers (2017). Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spatial Statistics doi:10.1016/j.spasta.2017.10.003

Butler, D. G., et al. (2010). Analysis of Mixed Models for S language environments: ASReml-R reference manual. Brisbane, DPI Publications

Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. https://www.jstatsoft.org/article/view/v067i01/0.

Examples

## Fit model using lme4.
lmeMod <- fitTD(TD = TDHeat05,
                design = "ibd",
                traits = "yield",
                what = "fixed",
                engine = "lme4")

## Summarize results.
summary(lmeMod)

## Create base plots of the results.
plot(lmeMod)

## Create a pdf report summarizing results.
report(lmeMod,
       outfile = tempfile(fileext = ".pdf"),
       what = "fixed")

## Fit model using SpATS.
spaMod <- fitTD(TD = TDHeat05,
                design = "res.rowcol",
                traits = "yield",
                what = "fixed")
summary(spaMod)

## Create spatial plots of the results.
plot(spaMod, plotType = "spatial")

## Fit model using SpATS.
## Specify the number of segments to use in PSANOVA
spaMod2 <- fitTD(TD = TDHeat05,
                 design = "res.rowcol",
                 traits = "yield",
                 what = "fixed",
                 control = list(nSeg = c(13, 26)))

## Create a pdf report summarizing results.
report(spaMod, outfile = tempfile(fileext = ".pdf"), what = "fixed")

## Fit model using asreml.
if (requireNamespace("asreml", quietly = TRUE)) {
  asrMod <- fitTD(TD = TDHeat05,
                  design = "res.rowcol",
                  traits = "yield",
                  what = "fixed",
                  engine = "asreml")
  summary(asrMod)

  report(asrMod,
         outfile = tempfile(fileext = ".pdf"),
         what = "fixed")
}

Get and set metadata for TD objects

Description

Functions for extracting and adding metadata for objects of class TD.

getMeta extracts a data.frame with location, date, design, latitude, longitude, plot width and plot length for all trials in TD.

setMeta adds metadata from a data.frame to an object of class TD. See details for the specifications of the data.frame.

The most common use case is extracting metadata from a TD object, modifying the content and then adding it back to the TD object.
Information in the metadata of a TD object is used in plotting functions (e.g. latitude and longitude for a map plot) and when fitting models on the data (the trial design).

Usage

getMeta(TD)

setMeta(TD, meta)

Arguments

Details

When setting metadata, metadata has to be a data.frame with rownames corresponding to the trials in TD. The data.frame should contain one or more of the following columns:

trLocation

The location of the trial. Used as default name when creating plots and summaries.

trDate

The date of the trial.

trDesign

The design of the trial. One of "none" (no (known) design), "ibd" (incomplete-block design), "res.ibd" (resolvable incomplete-block design), "rcbd" (randomized complete block design), "rowcol" (row-column design) or "res.rowcol" (resolvable row-column design). Used when fitting models.

trLat

The latitude of the trial on a scale of -90 to 90. Used when plotting the trials on a map.

trLong

The longitude of the trial on a scale of -180 to 180. Used when plotting the trials on a map.

trPlWidth

The width of the plot. Used in combination with trPlLength to determine the size of the plots in a layout plot of a trial.

trPlLength

The length of the plot. Used in combination with trPlWidth to determine the size of the plots in a layout plot of a trial.

The values of the metadata of TD will be set to the values in the corresponding column in meta. Existing values will be overwritten, but NA will be ignored so setting a value to NA won't result in accidentally removing it.

See Also

Other functions for TD objects: TD, plot.TD(), summary.TD()

Examples

data("dropsRaw")

## Create a TD object.
dropsTD <- createTD(data = dropsRaw[dropsRaw$year == 2012, ],
                    genotype = "Variety_ID",
                    trial = "Experiment",
                    loc = "Site",
                    repId = "Replicate",
                    subBlock = "block",
                    rowCoord = "Row",
                    colCoord = "Column",
                    trLat = "Lat",
                    trLong = "Long")

## Get meta data from dropsTD.
(dropsMeta <- getMeta(dropsTD))

## Add trial date to meta data.
dropsMeta$trDate <- as.Date(rep("010112", times = 5), "%d%m%y")

## Add back meta data to wheatTD.
dropsTD <- setMeta(dropsTD,
                   dropsMeta)

Identifying outliers in objects of class STA

Description

Function to identify observations with standardized residuals exceeding rLimit. If not provided rLimit is computed as qnorm(1 - 0.5 / rDf) where rDf is the residual degrees of freedom for the model. This value is then restricted to the interval 2..4. Alternatively a custom limit may be provided.
If verbose = TRUE a summary is printed of outliers and observations that have the same value for commonFactors. The column outlier in the output can be used to distinguish real outliers from observations included because of their commonFactors.

Usage

outlierSTA(
  STA,
  trials = NULL,
  traits = NULL,
  what = NULL,
  rLimit = NULL,
  commonFactors = NULL,
  verbose = TRUE
)

Arguments

Value

A list with two components:

• indicator - a list of numeric vectors indicating the location of the outliers in the data

• outliers - a data.frame containing the outliers and observations similar to the outliers as defined by commonFactors

Examples

## Fit a model using lme4.
modLme <- fitTD(TD = TDHeat05,
                traits = "yield",
                design = "res.rowcol",
                engine = "lme4")

## Detect outliers in the standardized residuals of the fitted model.
outliers <- outlierSTA(STA = modLme,
                       traits = "yield")

Plot function for class STA

Description

This function draws either four base plots:

• A histogram of the residuals

• A normal Q-Q plot

• A residuals vs fitted values plot

• An absolute residuals vs fitted values plot

or five or six spatial plots:

• A spatial plot of the raw data

• A spatial plot of the fitted data

• A spatial plot of the residuals

• A spatial plot of the estimated spatial trend (SpATS only)

• A spatial plot of the BLUEs or BLUPs

• A histogram of the BLUEs or BLUPs

Spatial plots can only be made if the data contains both row and column information.

Usage

## S3 method for class 'STA'
plot(
  x,
  ...,
  trials = NULL,
  traits = NULL,
  what = NULL,
  plotType = c("base", "spatial"),
  spaTrend = c("raw", "percentage"),
  outCols = ifelse(plotType == "base", 2, 3),
  title = NULL,
  output = TRUE
)

Arguments

Value

A list containing ggplot objects for the selected plots.

See Also

Other functions for STA objects: STAtoCross(), STAtoTD(), report.STA(), summary.STA()

Examples

## Run a single trait analysis using SpATS.
modSp <- fitTD(TD = TDHeat05,
               design = "res.rowcol",
               traits = "yield")

## Create base plots.
plot(modSp,
     what = "fixed",
     plotType = "base")

## Create spatial plots.
plot(modSp,
     what = "fixed",
     plotType = "spatial")

## Create spatial plots showing the spatial trend as percentage.
plot(modSp,
     what = "fixed",
     plotType = "spatial",
     spaTrend = "percentage")

Plot function for class TD

Description

Plotting function for objects of class TD. Plots either the layout of the different trials within the TD object or locates the trials on a map. Also a boxplot can be made for selected traits and trials, a plot of correlations between trials and a scatter plot matrix. A detailed description and optional extra parameters of the different plots is given in the sections below.

Usage

## S3 method for class 'TD'
plot(
  x,
  ...,
  plotType = c("layout", "map", "box", "cor", "scatter"),
  trials = names(x),
  traits = NULL,
  title = NULL,
  output = TRUE
)

Arguments

Layout Plot

Plots the layout of the selected trials. This plot can only be made for trials that contain both row (rowCoord) and column (colCoord) information. If either one of those is missing the trial is skipped with a warning. If blocks (subBlock) are available for a trial these can be colored in different colors per block by setting colorSubBlock = TRUE. If replicates (repId) are available a black line is plotted between different replicates. Missing plots are indicated in white. These can either be single plots in a trial or complete missing columns or rows.
Extra parameter options:

showGeno

Should individual genotypes be indicated as text in the plot? Defaults to FALSE

sizeGeno

The text size for indicating individual genotypes. Defaults to 2. Ignored if showGeno = FALSE.

highlight

A character vector of genotypes to be highlighted in the plot.

colHighlight

A character vector specifying colors to use for the highlighted genotypes. If not specified, default ggplot colors are used.

colorSubBlock

Should blocks be colored with a different color per subBlock? Defaults to FALSE. colorSubBlock is ignored when highlight is used to highlight genotypes.

colSubBlock

A character vector specifying colors to use for the subBlocks. If not specified, default ggplot colors are used.

Map Plot

A map is plotted with the locations of the trials in the TD object. Mapping the trials is done based on latitude and longitude that can be added when creating an object of class TD. Trials for which either latitude or longitude is not available are skipped with a warning message. The countries in which the trials are located will be plotted on a single map and the location of the trials will be indicated on this map. The actual plot is made using ggplot, but for getting the data for the borders of the countries the maps package is needed.
Extra parameter options:

colorTrialBy

A character string indicating a column in TD by which the trials on the map are colored.

colTrial

A character vector with plot colors for the trials. A single color when colorTrialBy = NULL, a vector of colors otherwise.

printTrialNames

Should trial names be printed. Defaults to TRUE. Setting this to FALSE can be useful if there are many trials.

minLatRange

A positive numerical value indicating the minimum range (in degrees) for the latitude on the plotted map. Defaults to 10.

minLongRange

A positive numerical value indicating the minimum range (in degrees) for the longitude on the plotted map. Defaults to 5.

Box Plot

Creates a boxplot per selected trait grouped by trial. Extra parameter options:

groupBy

A character string indicating a column in TD by which the boxes in the plot should be grouped. By default the boxes are grouped per trial.

colorTrialBy

A character string indicating a column in TD by which the boxes are colored. Coloring will be done within the groups indicated by the groupBy parameter.

colTrial

A character vector with plot colors for the trials. A single color when colorTrialBy = NULL, a vector of colors otherwise.

orderBy

A character string indicating the way the boxes should be ordered. Either "alphabetic" for alphabetical ordering of the groups, "ascending" for ordering by ascending mean, or "descending" for ordering by descending mean. Default boxes are ordered alphabetically.

Correlation Plot

Draws a heat map of correlations between trials per selected trait. If genotypes are replicated within trials genotypic means are taken before computing correlations. The order of the trials in the heat map is determined by clustering them. Closely related trials will be plotted close to each other.

Scatter Plot

Draws a scatter plot matrix per selected trait. If genotypes are replicated within trials genotypic means are taken before plotting. The lower left of the matrix contains scatter plots between trials. The diagonal contains histograms of the data per trial.
Extra parameter options:

colorGenoBy

A character string indicating a column in TD by which the genotypes in the scatter plots are colored.

colGeno

A character vector with plot colors for the genotypes. A single color when colorGenoBy = NULL, a vector of colors otherwise.

colorTrialBy

A character string indicating a column in TD by which the trials in the histograms are colored.

colTrial

A character vector with plot colors for the trials. A single color when colorTrialBy = NULL, a vector of colors otherwise.

trialOrder

A character vector indicating the order of the trials in the plot matrix (left to right and top to bottom). This vector should be a permutation of all trials plotted.

addCorr

A character string indicating the position of the correlation between trials displayed in each plot, either "tl" for top left, "tr", for top right, "bl" for bottom left or "br" for bottom right. If NULL, the default, then no correlation is added to the plot.

See Also

Other functions for TD objects: TD, getMeta(), summary.TD()

Examples

data("dropsRaw")

## Create a TD object.
dropsTD <- createTD(data = dropsRaw[dropsRaw$year == 2012, ],
                    genotype = "Variety_ID",
                    trial = "Experiment",
                    loc = "Site",
                    repId = "Replicate",
                    subBlock = "block",
                    rowCoord = "Row",
                    colCoord = "Column",
                    trLat = "Lat",
                    trLong = "Long")

### Layout plot.


## Plot the layout of one of the trials.
plot(dropsTD,
    trials = "Kar12W")

## Highlight some of the genotypes in the layout.
plot(dropsTD,
     trials = "Kar12W",
     highlight = c("A3", "11430"))

### Map plot.

## Plot the location of the trials on the map.
plot(dropsTD,
     plotType = "map")

### Box plot.

## Create a box plot for grain.yield.
plot(dropsTD,
     plotType = "box",
     traits = "grain.yield")

## Add coloring by scenarioFull to the boxes.
plot(dropsTD,
     plotType = "box",
     traits = "grain.yield",
     colorTrialBy = "scenarioFull")

## Sort the boxes in descending order.
plot(dropsTD,
     plotType = "box",
     traits = "grain.yield",
     orderBy = "descending")

### Correlation plot.

## Plot the correlations between trials for grain.yield.
plot(dropsTD,
     plotType = "cor",
     traits = "grain.yield")

### Scatter plot

## Plot scatter plot for grain.yield.
plot(dropsTD,
    plotType = "scatter",
    traits = "grain.yield")

## Create a scatter plot matrix for grain yield.
## Color trials by scenario and genotypes by family.
plot(dropsTD,
     plotType = "scatter",
     traits = "grain.yield",
     colorTrialBy = "scenarioFull",
     colorGenoBy = "geneticGroup")

Base method for creating a report

Description

Base method for creating a .pdf and .tex report from an R object.

Usage

report(x, ...)

Arguments

See Also

report.STA

Report method for class STA

Description

pdf reports will be created containing a summary of the results of the fitted model(s). For all selected trails and traits a separate pdf file will be generated. Also a .tex file and a folder containing figures will be created for each report to enable easy modifying of the report.

Usage

## S3 method for class 'STA'
report(
  x,
  ...,
  trials = NULL,
  traits = NULL,
  descending = TRUE,
  outfile = NULL,
  what = c("fixed", "random")
)

Arguments

Details

This function uses pdflatex to create a pdf report. For it to run correctly an installation of LaTeX is required. Checking for this is done with Sys.which("pdflatex").

Value

A pdf report and the .tex file and figures folder that can be used to recreate the report.

See Also

Other functions for STA objects: STAtoCross(), STAtoTD(), plot.STA(), summary.STA()

Examples

## Fit model using lme4.
modLme <- fitTD(TD = TDHeat05,
                design = "ibd",
                traits = "yield",
                engine = "lme4")

## Create a pdf report summarizing the results for the model with genotype
## as fixed factor.

report(modLme,
       outfile = tempfile(fileext = ".pdf"),
       what = "fixed")


## Create two pdf report summarizing the results for the model with genotype
## as fixed factor and for the model with genotype as random factor. Order
## the results in ascending order.

report(modLme,
       outfile = tempfile(fileext = ".pdf"),
       descending = FALSE)

Summarizing objects of class STA

Description

summary method for class STA.

Usage

## S3 method for class 'STA'
summary(
  object,
  trials = NULL,
  trait = NULL,
  nBest = 20,
  sortBy = NULL,
  naLast = TRUE,
  decreasing = TRUE,
  ...
)

Arguments

See Also

Other functions for STA objects: STAtoCross(), STAtoTD(), plot.STA(), report.STA()

Examples

## Run a single trait analysis using SpATS.
modSp <- fitTD(TD = TDHeat05,
               design = "res.rowcol",
               traits = "yield")

## Print a summary of the fitted model.
summary(modSp)

Summarizing objects of class TD

Description

summary method for class TD.

Usage

## S3 method for class 'TD'
summary(
  object,
  ...,
  trial = names(object),
  traits,
  groupBy = NULL,
  what = if (!is.null(groupBy)) {
     c("nObs", "mean", "sd")
 } else {
     c("nObs",
    "nMiss", "mean", "median", "min", "max", "firstQ", "thirdQ", "var")
 }
)

Arguments

Value

A table containing the selected summary statistics.

See Also

Other functions for TD objects: TD, getMeta(), plot.TD()

Examples

## Summarize TDHeat05.
summary(TDHeat05,
        traits = "yield")

## Summarize TDHeat05 grouping by repId.
summary(TDHeat05,
        traits = "yield",
        groupBy = "repId")