fitSpATS.Rda function to model curves using a spatial P-Spline modelling using SpATS library
fitSpATS( datain, trait, genotypeId, rowId, colId, typeModel = "anova", genotype.as.random = FALSE, nseg = c(14, 30), verbose )
| datain | input dataframe of parameters |
|---|---|
| trait | character, parameter of interest (ex: Biomass24) |
| genotypeId | character, column name of genotype alias |
| rowId | character, column name of the row id in the lattice (greenhouse or field) |
| colId | character, column name of the column id in the lattice (greenhouse or field) |
| typeModel | character, choice of the spatial P-Spline model, |
| genotype.as.random | logical. If TRUE, the genotype is included as random effect in the model. The default is FALSE. |
| nseg | numerical vector of length 2 containing the number of segments for each marginal (strictly nseg - 1 is the number of internal knots in the domain of the covariate). Atomic values are also valid, being recycled. Default set to c(14,30) |
| verbose | logical FALSE by default, if TRUE display information about the progress |
a SpATS object
the input dataset must contain Position,Line,Ref,scenario,genotypeAlias columns
SpATS, PSANOVA and SAP
# \donttest{ mydata<-plant4 test<-fitSpATS(datain=mydata,trait="Biomass24",genotypeId="genotypeAlias",rowId="Line", colId="Position",typeModel="anova",genotype.as.random=FALSE,nseg=c(14,30),verbose)#> Effective dimensions #> ------------------------- #> It. Deviance C R f(Line) f(Position)f(Line):PositionLine:f(Position)f(Line):f(Position) #> 1 123559.752371 8.846 6.592 4.115 10.640 4.641 10.043 54.308 #> 2 8348.931485 9.316 8.333 5.826 10.002 3.290 7.291 39.324 #> 3 8331.017434 10.113 8.812 6.076 9.170 2.824 5.333 28.497 #> 4 8318.901739 11.224 8.983 6.117 8.111 2.610 3.671 21.197 #> 5 8311.216526 12.649 9.053 6.114 6.826 2.483 2.565 16.448 #> 6 8306.340008 14.292 9.090 6.104 5.397 2.400 2.318 13.370 #> 7 8302.852951 15.912 9.116 6.095 4.071 2.343 2.317 11.392 #> 8 8301.334144 17.214 9.136 6.088 3.239 2.306 2.336 10.072 #> 9 8300.961308 18.105 9.152 6.083 2.969 2.281 2.352 9.157 #> 10 8300.809391 18.704 9.163 6.079 2.919 2.265 2.366 8.485 #> 11 8300.720106 19.111 9.169 6.076 2.909 2.254 2.378 7.966 #> 12 8300.663881 19.386 9.172 6.075 2.906 2.246 2.387 7.551 #> 13 8300.626712 19.569 9.174 6.073 2.905 2.240 2.395 7.213 #> 14 8300.601056 19.690 9.174 6.072 2.905 2.236 2.402 6.932 #> 15 8300.582688 19.769 9.175 6.072 2.906 2.232 2.407 6.695 #> 16 8300.569147 19.821 9.175 6.071 2.907 2.229 2.412 6.493 #> 17 8300.558925 19.855 9.174 6.071 2.909 2.227 2.416 6.319 #> 18 8300.551062 19.877 9.174 6.070 2.910 2.225 2.420 6.167 #> 19 8300.544919 19.891 9.174 6.070 2.912 2.223 2.423 6.034 #> 20 8300.540059 19.900 9.173 6.070 2.913 2.221 2.426 5.917 #> 21 8300.536171 19.906 9.173 6.070 2.915 2.220 2.429 5.813 #> 22 8300.533032 19.909 9.173 6.070 2.916 2.219 2.431 5.720 #> 23 8300.530476 19.912 9.173 6.069 2.917 2.218 2.433 5.636 #> 24 8300.528381 19.913 9.172 6.069 2.918 2.217 2.435 5.561 #> 25 8300.526651 19.914 9.172 6.069 2.919 2.216 2.436 5.493 #> 26 8300.525215 19.914 9.172 6.069 2.920 2.215 2.438 5.432 #> 27 8300.524017 19.914 9.172 6.069 2.921 2.215 2.439 5.376 #> 28 8300.523012 19.914 9.172 6.069 2.922 2.214 2.440 5.325 #> 29 8300.522166 19.914 9.171 6.069 2.922 2.213 2.442 5.278 #> Timings: #> SpATS 5.19 seconds #> All process 6.25 seconds# }