fitTD  R Documentation 
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.
fitTD( TD, trials = names(TD), design = NULL, traits, what = c("fixed", "random"), covariates = NULL, useCheckId = FALSE, spatial = FALSE, engine = NA, control = NULL, progress = FALSE, ... )
TD 
An object of class 
trials 
A character vector specifying the trials for which the models should be fitted. 
design 
A character string specifying the experimental design. Either
"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). Can be ignored when
the trial design is specified in the meta data (see 
traits 
A character vector specifying the traits for which the models should be fitted. 
what 
A character vector specifying whether "genotype" should be fitted as fixed or random effect. If not specified, both models are fitted. 
covariates 
A character vector specifying covariates to be fitted as extra fixed effects in the model. 
useCheckId 
Should checkId be used as a fixed effect in the model? 
spatial 
Should spatial models be tried? Spatial models can
only be fitted with SpATS and asreml. If SpATS is used for modeling, only
spatial models can be fitted and spatial is always set to 
engine 
A character string specifying the name of the mixed modeling engine to use, either "SpATS", "lme4" or "asreml." For spatial models, "SpaTS" is used as default, for other models "lme4". 
control 
An optional list with control parameters to be passed to the
actual fitting functions. Currently 
progress 
Should the progress of the modeling be printed. If

... 
Further arguments to be passed to 
The actual model fitted depends on the design. For the supported designs, the following models are used:
Design  Code  Model fitted 
incomplete block design  ibd  trait = subBlock + genotype + ε 
resolvable incomplete block design  res.ibd  trait = repId + repId:subBlock + genotype + ε 
randomized complete block design  rcbd  trait = repId + genotype + ε 
row column design  rowcol  trait = rowId + colId + genotype + ε 
resolvable row column design  res.rowcol  trait = repId + repId:rowId + repId:colId + genotype + ε 
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 goodnessoffit 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:
Random part  Spatial part 
random effects based on design  none 
random effects based on design  AR1(rowId):colId 
random effects based on design  rowId:AR1(colId) 
random effects based on design  AR1(rowId):ar1(colId) 
random effects based on design + nugget  AR1(rowId):colId 
random effects based on design + nugget  rowId:AR1(colId) 
random effects based on design + nugget  AR1(rowId):AR1(colId) 
If the design is not regular the following combinations of random and spatial terms are fitted:
Random part  Spatial part 
random effects based on design  none 
random effects based on design  exp(rowCoord):colCoord 
random effects based on design  rowCoord:exp(colCoord) 
random effects based on design  iexp(rowCoord, colCoord) 
random effects based on design + nugget  exp(rowCoord):colCoord 
random effects based on design + nugget  rowCoord:exp(colCoord) 
random effects based on design + nugget  iexp(rowCoord,colCoord) 
An object of class STA
, a list containing, per trial
that has been analyzed, a list of:
mRand 
A list of models with fitted with genotype as random effect. 
mFix 
A list of models fitted with genotype as fixed effect. 
TD 
An object of class 
traits 
A character vector indicating the traits for which the models are fitted. 
design 
A character string containing the design of the trial.
(see 
spatial 
A character string indicating the spatial part of the model.

engine 
A character string containing the engine used for the analysis. 
predicted 
A character string indicating the variable that has been predicted. 
sumTab 
A data.frame with a summary table for the spatial models tried
when 
Maria Xose RodriguezAlvarez, Martin P. Boer, Fred A. van Eeuwijk, Paul H.C. Eilers (2017). Correcting for spatial heterogeneity in plant breeding experiments with Psplines. Spatial Statistics doi: 10.1016/j.spasta.2017.10.003
Butler, D. G., et al. (2010). Analysis of Mixed Models for S language environments: ASRemlR reference manual. Brisbane, DPI Publications
Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear MixedEffects Models Using lme4. Journal of Statistical Software, 67(1), 148. https://www.jstatsoft.org/article/view/v067i01/0.
## 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") }
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