# ss.lfa: Loss Function Analysis In SixSigma: Six Sigma Tools for Quality Control and Improvement

## Description

This function performs a Quality Loss Function Analysis, based in the Taguchi Loss Function for "Nominal-the-Best" characteristics.

## Usage

 ```1 2``` ```ss.lfa(lfa.data, lfa.ctq, lfa.Delta, lfa.Y0, lfa.L0, lfa.size = NA, lfa.output = "both", lfa.sub = "Six Sigma Project") ```

## Arguments

 `lfa.data` Data frame with the sample to get the average loss. `lfa.ctq` Name of the field in the data frame containing the data. `lfa.Delta` Tolerance of the process. `lfa.Y0` Target of the process (see note). `lfa.L0` Cost of poor quality at tolerance limit. `lfa.size` Size of the production, batch, etc. to calculate the total loss in a group (span, batch, period, ...) `lfa.output` Type of output (see details). `lfa.sub` Subtitle for the graphic output.

## Details

`lfa.output` can take the values "text", "plot" or "both".

## Value

 `lfa.k ` Constant k for the loss function `lfa,lf ` Expression with the loss function `lfa.MSD` Mean Squared Differences from the target `lfa.avLoss` Average Loss per unit of the process `lfa.Loss` Total Loss of the process (if a size is provided)

## Note

For smaller-the-better characteristics, the target should be zero (`lfa.Y0 = 0`). For larger-the-better characteristics, the target should be infinity (`lfa.Y0 = Inf`).

EL Cano

## References

Taguchi G, Chowdhury S,Wu Y (2005) Taguchi's quality engineering handbook. John Wiley

Cano, Emilio L., Moguerza, Javier M. and Redchuk, Andres. 2012. Six Sigma with R. Statistical Engineering for Process Improvement, Use R!, vol. 36. Springer, New York. http://www.springer.com/statistics/book/978-1-4614-3651-5.

`ss.lf`, `ss.data.bolts`.
 ```1 2 3``` ```ss.lfa(ss.data.bolts, "diameter", 0.5, 10, 0.001, lfa.sub = "10 mm. Bolts Project", lfa.size = 100000, lfa.output = "both") ```