# plot_FilterCombinations

##### Plot filter combinations along with the (optional) net transmission window

The function allows to plot transmission windows for different filters. Missing data for specific wavelenghts are automatically interpolated for the given filter data using the function approx. With that a standardised output is reached and a net transmission window can be shown.

##### Usage

```
plot_FilterCombinations(filters, wavelength_range = 200:1000,
show_net_transmission = TRUE, interactive = FALSE, plot = TRUE,
...)
```

##### Arguments

- filters
list (

**required**): a named list of filter data for each filter to be shown. The filter data itself should be either provided as data.frame or matrix. (for more options s. Details)- wavelength_range
numeric (

*with default*): wavelength range used for the interpolation- show_net_transmission
logical (

*with default*): show net transmission window as polygon.- interactive
logical (

*with default*): enable/disable interactive plot- plot
logical (

*with default*): enables or disables the plot output- ...
further arguments that can be passed to control the plot output. Suppored are

`main`

,`xlab`

,`ylab`

,`xlim`

,`ylim`

,`type`

,`lty`

,`lwd`

. For non common plotting parameters see the details section.

##### Details

**Calculations**

**Net transmission window**

The net transmission window of two filters is approximated by

$$T_{final} = T_{1} * T_{2}$$

**Optical density**

$$OD = -log(T)$$

**Total optical density**

$$OD_{total} = OD_{1} + OD_{2}$$

Please consider using own calculations for more precise values.

**How to provide input data?**

*CASE 1*

The function expects that all filter values are either of type `matrix`

or `data.frame`

with two columns. The first columens contains the wavelength, the second the relative transmission
(but not in percentage, i.e. the maximum transmission can be only become 1).

In this case only the transmission window is show as provided. Changes in filter thickness and relection factor are not considered.

*CASE 2*

The filter data itself are provided as list element containing a `matrix`

or
`data.frame`

and additional information on the thickness of the filter, e.g.,
`list(filter1 = list(filter_matrix, d = 2))`

.
The given filter data are always considered as standard input and the filter thickness value
is taken into account by

$$Transmission = Transmission^(d)$$

with d given in the same dimension as the original filter data.

*CASE 3*

Same as CASE 2 but additionally a reflection factor P is provided, e.g.,
`list(filter1 = list(filter_matrix, d = 2, P = 0.9))`

.
The final transmission becomes:

$$Transmission = Transmission^(d) * P$$

**Advanced plotting parameters**

The following further non-common plotting parameters can be passed to the function:

`Argument` |
`Datatype` |
`Description` |

`legend` |
`logical` |
enable/disable legend |

`legend.pos` |
`character` |
change legend position (graphics::legend) |

`legend.text` |
`character` |
same as the argument `legend` in (graphics::legend) |

`net_transmission.col` |
`col` |
colour of net transmission window polygon |

`net_transmission.col_lines` |
`col` |
colour of net transmission window polygon lines |

`net_transmission.density` |
`numeric` |
specify line density in the transmission polygon |

For further modifications standard additional R plot functions are recommend, e.g., the legend can be fully customised by disabling the standard legend and use the function graphics::legend instead.

##### Value

Returns an S4 object of type '>RLum.Results.

**@data**

`Object` |
`Type` `Description` |
`net_transmission_window` |

`matrix` |
the resulting net transmission window |
`OD_total` |

`matrix` |
the total optical density |
`Object` |

**@info**

Object |
Type Description |
Object |

##### Function version

0.3.1 (2018-01-21 17:22:38)

##### How to cite

Kreutzer, S. (2018). plot_FilterCombinations(): Plot filter combinations along with the (optional) net transmission window. Function version 0.3.1. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J. (2018). Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.8.6. https://CRAN.R-project.org/package=Luminescence

##### See Also

'>RLum.Results, approx

##### Examples

```
# NOT RUN {
## (For legal reasons no real filter data are provided)
## Create filter sets
filter1 <- density(rnorm(100, mean = 450, sd = 20))
filter1 <- matrix(c(filter1$x, filter1$y/max(filter1$y)), ncol = 2)
filter2 <- matrix(c(200:799,rep(c(0,0.8,0),each = 200)), ncol = 2)
## Example 1 (standard)
plot_FilterCombinations(filters = list(filter1, filter2))
## Example 2 (with d and P value and name for filter 2)
results <- plot_FilterCombinations(
filters = list(filter_1 = filter1, Rectangle = list(filter2, d = 2, P = 0.6)))
results
## Example 3 show optical density
plot(results$OD_total)
# }
# NOT RUN {
##Example 4
##show the filters using the interactive mode
plot_FilterCombinations(filters = list(filter1, filter2), interactive = TRUE)
# }
# NOT RUN {
# }
```

*Documentation reproduced from package Luminescence, version 0.8.6, License: GPL-3*