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

estimateSplineParameters(
HTPSpline,
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
)

## Arguments

HTPSpline An object of class HTPSpline, the output of the fitSpline function. The P-Spline component for which the estimate should be extracted, the predictions, the first derivatives or the second derivatives ("derivatives2") 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. 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. 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. 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. 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. 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.

## Value

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

Other functions for spline parameter estimation: plot.HTPSplineEst()

## Examples

## 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.
paramVator <- estimateSplineParameters(HTPSpline = fit.spline,
estimate = "predictions",
what = "max",
timeMin = 1527784620,
timeMax = 1528500000,
genotypes = subGeno)
#>   genotype plotId max_predictions max_timeNumber       max_timePoint
#> 1     G151  c13r1       0.6878932              0 2018-05-31 16:37:00
#> 2     G151 c15r48       0.6955162         663200 2018-06-08 08:50:20
#> 3     G151  c1r16       0.6878921         665600 2018-06-08 09:30:20
#> 4     G151 c21r25       0.7757065         168000 2018-06-02 15:17:00
#> 5     G151   c2r7       0.6897501         477600 2018-06-06 05:17:00
#> 6     G151  c6r46       0.6916913         344800 2018-06-04 16:23:40
## Estimate the minimum and maximum value of the predictions.
paramVator2 <- estimateSplineParameters(HTPSpline = fit.spline,
estimate = "predictions",
what = c("min", "max"),
genotypes = subGeno)
#>   genotype plotId min_predictions min_timeNumber       min_timePoint
#> 1     G151  c13r1       0.4103549         792000 2018-06-09 20:37:00
#> 2     G151 c15r48       0.3558782         768000 2018-06-09 13:57:00
#> 3     G151  c1r16       0.3757698         766400 2018-06-09 13:30:20
#> 4     G151 c21r25       0.2351897         786400 2018-06-09 19:03:40
#> 5     G151   c2r7       0.3565921         769600 2018-06-09 14:23:40
#> 6     G151  c6r46       0.3619749         769600 2018-06-09 14:23:40