# fitPoD: PoD curve: fitting function In PoDBAY: Vaccine Efficacy Estimation Package

## Description

Function calculates the root mean squared error (RMSE) between provided PoD values and calculated PoD values. The latter are calculated using for provided titers and provided PoD curve parameters.

By using the input titers `PoDParamPointEstimation` function and median of the estimated set of PoD curve parameters (output of `PoDParamEstimation` function), the point estimate of PoD curve can be obtained (for details see `PoDParamPointEstimation` function).

## Usage

 `1` ```fitPoD(params, TitersInput, CurveTitersMedian) ```

## Arguments

 `params` named data frame ("pmax", "slope", "et50"): provided PoD curve parameters `TitersInput` numeric vector: provided titers `CurveTitersMedian` numeric vector: provided PoD values

## Details

RMSE = sqrt( mean( (PoDmedian(titers) - PoDoptimized(titers))2 ) )

negative RMSE

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```## Data preparation data(estimatedParameters) data(PoDParams) ## Example 1 # grid of titers TitersInput <- seq(from = 0, to = 20, by = 0.01) # for each estimated PoD curve calculate functional values functionValues <- matrix(NA, nrow = nrow(estimatedParameters\$resultsPriorReset), ncol = length(TitersInput)) for (i in 1:nrow(estimatedParameters\$resultsPriorReset)) { functionValues[i,] <- PoD(TitersInput, pmax = estimatedParameters\$resultsPriorReset[i,1], et50 = estimatedParameters\$resultsPriorReset[i,3], slope = estimatedParameters\$resultsPriorReset[i,2], adjustTiters = FALSE) } # functional values corresponding to the median of the estimated PoD curve parameters CurveTitersMedian <- apply(functionValues, 2, median) # squared error of CurveTitersMedian and functional values of "params" curve fitPoD(PoDParams, TitersInput, CurveTitersMedian) ```

PoDBAY documentation built on Sept. 21, 2021, 5:08 p.m.