Extract estimates from fitted splines.

Function for extracting parameter estimates from fitted splines on a specified interval.

Usage

estimateSplineParameters(
  x,
  estimate = c("predictions", "derivatives", "derivatives2"),
  what = c("min", "max", "mean", "AUC", "p"),
  AUCScale = c("min", "hour", "day"),
  timeMin = NULL,
  timeMax = NULL,
  genotypes = NULL,
  plotIds = NULL,
  fitLevel = c("geno", "plot", "genoDev", "plotDev")
)

Arguments

x: An object of class HTPSpline, the output of the fitSpline function, or class splineHDm, the output of the fitSplineHDM function
estimate: The P-Spline component for which the estimate should be extracted, the predictions, the first derivatives or the second derivatives ("derivatives2")
what: The types of estimate that should be extracted. Either minimum ("min"), maximum ("max"), mean, area under the curve ("AUC") or a percentile. Percentiles should be given as p + percentile. E.g. for the 10th percentile specify what = "p10". Multiple types of estimate can be extracted at once.
AUCScale: The area under the curve is dependent on the scale used on the x-axis. By default the area is computed assuming a scale in minutes. This can be changed to either hours or days.
timeMin: The lower bound of the time interval from which the estimates should be extracted. If NULL the smallest time value for which the splines were fitted is used. timeMin should be given as a numerical value that corresponds to the time scale used for fitting the splines. See the examples.
timeMax: The upper bound of the time interval from which the estimates should be extracted. If NULL the largest time value for which the splines were fitted is used. timeMin should be given as a numerical value that corresponds to the time scale used for fitting the splines. See the examples.
genotypes: A character vector indicating the genotypes for which estimates should be extracted. If NULL, estimates will be extracted for all genotypes for which splines where fitted.
plotIds: A character vector indicating the plotIds for which estimates should be extracted. If NULL, estimates will be extracted for all plotIds for which splines where fitted.
fitLevel: A character string indicating at which level of the data the parameter estimates should be made. Only used for splines fitted using fitSplineHDM.

Value

An object of class splineEst, a data.frame containing the estimated parameters.

Examples

### Estimate parameters for fitted P-splines.

## Run the function to fit P-splines on a subset of genotypes.
subGeno <- c("G160", "G151")
fit.spline <- fitSpline(inDat = spatCorrectedVator,
                        trait = "EffpsII_corr",
                        genotypes = subGeno,
                        knots = 50)

## Estimate the maximum value of the predictions at the beginning of the time course.
## The spline was fitted at a timePoints scale, i.e. date-time so
## timeMin and timeMax should be given at this scale as well.
paramVator <- estimateSplineParameters(x = fit.spline,
                                       estimate = "predictions",
                                       what = "max",
                                       timeMin = 1527784620,
                                       timeMax = 1528500000,
                                       genotypes = subGeno)
head(paramVator)
#>   genotype plotId max_predictions max_timeNumber       max_timePoint
#> 1     G151  c13r1       0.6916861         636000 2018-06-08 01:17:00
#> 2     G151 c15r48       0.7030388         651200 2018-06-08 05:30:20
#> 3     G151  c1r16       0.6953883         651200 2018-06-08 05:30:20
#> 4     G151 c21r25       0.7324238         166400 2018-06-02 14:50:20
#> 5     G151   c2r7       0.6942117         651200 2018-06-08 05:30:20
#> 6     G151  c6r46       0.6897347         655200 2018-06-08 06:37:00

## Create a boxplot of the estimates.
plot(paramVator, plotType = "box")


## Estimate the minimum and maximum value of the predictions.
paramVator2 <- estimateSplineParameters(x = fit.spline,
                                        estimate = "predictions",
                                        what = c("min", "max"),
                                        genotypes = subGeno)
head(paramVator2)
#>   genotype plotId min_predictions min_timeNumber       min_timePoint
#> 1     G151  c13r1       0.4256623         795200 2018-06-09 21:30:20
#> 2     G151 c15r48       0.3575513         775200 2018-06-09 15:57:00
#> 3     G151  c1r16       0.3769714         775200 2018-06-09 15:57:00
#> 4     G151 c21r25       0.3013801         787200 2018-06-09 19:17:00
#> 5     G151   c2r7       0.3558154         776800 2018-06-09 16:23:40
#> 6     G151  c6r46       0.3613267         776800 2018-06-09 16:23:40
#>   max_predictions max_timeNumber       max_timePoint
#> 1       0.7400419        1212800 2018-06-14 17:30:20
#> 2       0.7367294        1207200 2018-06-14 15:57:00
#> 3       0.7312961        1214400 2018-06-14 17:57:00
#> 4       0.7337103        1214400 2018-06-14 17:57:00
#> 5       0.7353195        1204000 2018-06-14 15:03:40
#> 6       0.7334287        1214400 2018-06-14 17:57:00


### Estimate parameters for fitted HDM-splines.

## The data from the Phenovator platform have been corrected for spatial
## trends and outliers for single observations have been removed.

## We need to specify the genotype-by-treatment interaction.
## Treatment: water regime (WW, WD).
spatCorrectedArch[["treat"]] <- substr(spatCorrectedArch[["geno.decomp"]],
                                      start = 1, stop = 2)
spatCorrectedArch[["genoTreat"]] <-
  interaction(spatCorrectedArch[["genotype"]],
             spatCorrectedArch[["treat"]], sep = "_")

## Fit P-Splines Hierarchical Curve Data Model for selection of genotypes.
fit.psHDM  <- fitSplineHDM(inDat = spatCorrectedArch,
                          trait = "LeafArea_corr",
                          genotypes = c("GenoA14_WD", "GenoA51_WD",
                                       "GenoB11_WW", "GenoB02_WD",
                                       "GenoB02_WW"),
                          time = "timeNumber",
                          pop = "geno.decomp",
                          genotype = "genoTreat",
                          plotId = "plotId",
                          difVar = list(geno = FALSE, plot = FALSE),
                          smoothPop = list(nseg = 4, bdeg = 3, pord = 2),
                          smoothGeno = list(nseg = 4, bdeg = 3, pord = 2),
                          smoothPlot = list(nseg = 4, bdeg = 3, pord = 2),
                          weights = "wt",
                          trace = FALSE)

## Estimate minimum, maximum, and mean for predictions at the genotype level.
## The spline was fitted at the timeNumber scale, so timeMax
## should be given at that scale as well.
paramArch <- estimateSplineParameters(x = fit.psHDM,
                                     what = c("min", "max", "mean"),
                                     fitLevel = "geno",
                                     estimate = "predictions",
                                     timeMax = 28)
head(paramArch)
#>         pop   genotype min_predictions min_timeNumber min_timePoint
#> 1 WD_Panel1 GenoA14_WD     0.002037944              0    2017-04-13
#> 2 WD_Panel1 GenoA51_WD     0.002994956              0    2017-04-13
#> 3 WD_Panel2 GenoB02_WD     0.002496701              0    2017-04-13
#> 4 WW_Panel2 GenoB02_WW     0.002307846              0    2017-04-13
#> 5 WW_Panel2 GenoB11_WW     0.002291758              0    2017-04-13
#>   max_predictions max_timeNumber max_timePoint mean_predictions
#> 1     0.002037944              0    2017-04-13      0.002037944
#> 2     0.002994956              0    2017-04-13      0.002994956
#> 3     0.002496701              0    2017-04-13      0.002496701
#> 4     0.002307846              0    2017-04-13      0.002307846
#> 5     0.002291758              0    2017-04-13      0.002291758

## Create a boxplot of the estimates.
plot(paramArch, plotType = "box")




## Estimate area under the curve for predictions at the plot level.
paramArch2 <- estimateSplineParameters(x = fit.psHDM,
                                      what = "AUC",
                                      fitLevel = "plot",
                                      estimate = "predictions")
head(paramArch2)
#>         pop   genotype plotId AUC_predictions
#> 1 WD_Panel1 GenoA51_WD c12r17        389.9525
#> 2 WD_Panel1 GenoA14_WD c12r19        308.4269
#> 3 WD_Panel1 GenoA51_WD c13r13        362.0956
#> 4 WD_Panel1 GenoA14_WD c13r33        362.2137
#> 5 WD_Panel1 GenoA51_WD c16r23        339.7837
#> 6 WD_Panel1 GenoA14_WD c16r35        354.3651

Usage

Arguments

Value

See also

Examples