blois_quality: Blois quality index for palynological samples
In dinilu/EPDr: Interact with the European Pollen Database (EPD)

Description Usage Arguments Details Value References Examples

This function apply the quality index described in Blois et al. (2013). From the Ecography 2013 paper, Appendix 3: "For each site at a particular 1 kyr time period, site data-quality was calculated as the mean normalized distance of the nearest pollen sample and the nearest chronological control. We calculated the distance in years of the nearest pollen sample and the nearest chronological control to each 1 kyr time period. We eliminated sites where the nearest pollen sample was over 2000 years away or the nearest chronological control was over 5000 years away. For the remaining sites in each 1 kyr period, we created a summary measure of site data-quality by rescaling the two distances in years to a 0 - 1 scale and calculating the mean. For example, if the nearest sample to the 1 kyr BP time period at a given site was at 1.050 kyr BP and the nearest chronological control was at 1.100 kyr BP, the raw distances would be 50 years and 100 years, respectively. These equate to scaled values of 0.975 (i.e., 1 - 50/2000) and 0.98 (i.e., 1 - 100/5000) for sample and chronological quality, respectively, with a mean data-quality for this site at the 1 kyr BP time period of 0.9775." To replicate the calculation the function allows to specify different maximum distances as parameters of the function.

blois_quality(x, chronology = NULL, max_sample_dist = 2000,
  max_control_dist = 5000, overwrite = FALSE)

## S4 method for signature 'epd.entity.df'
blois_quality(x, chronology = NULL,
  max_sample_dist = 2000, max_control_dist = 5000, overwrite = FALSE)

## S4 method for signature 'epd.entity'
blois_quality(x, chronology = NULL,
  max_sample_dist = 2000, max_control_dist = 5000, overwrite = FALSE)

`x`	epd.entity `epd.entity.df-class` or a `epd.entity-class` object.
`chronology`	numeric Chronology number to look for samples and control points ages. This value become the default chronology in the new object. If not specified the function check the default chronology in `x`. It can be any of the chronologies in the EPD for that particular entity, or the one from Giesecke et al. (2013).
`max_sample_dist`	numeric Maximum numeric distance in years to be considered to the palynological samples for interpolated or ranged data.
`max_control_dist`	numeric Maximum numeric distance in years to be considered to the control points (e.g., C14, top, bottom, etc.).
`overwrite`	logical TRUE or FALSE indicating whether to overwrite blois index in @agesdf@dataquality if it already has a 'blois' column.

When x is an epd.entity-class object, it is first transformed into a epd.entity.df-class object by entity_to_matrices function.

epd.entity.df-class object with no empty @agesdf@dataquality slot. The default chronology in x@defaultchron is changed to the one specified in chronology.

Blois, J.L., Williams, J.W., Fitzpatrick, M.C., Ferrier, S., Veloz, S.D., He, F., Liu, Z., Manion, G., and Otto-Bliesner, B. (2013). Modeling the climatic drivers of spatial patterns in vegetation composition since the Last Glacial Maximum. Ecography, 36, 460-473.

Giesecke, T., Davis, B., Brewer, S., Finsinger, W., Wolters, S., Blaaw, M., de Beaulieu, J.L., Binney, H., Fyfe, R.M., Gaillard, M.J., Gil-Romera, G., van der Knaap, W.O. Kunes, P., Kuhl, N., van Leeuwen, J.F.N, Leydet, M., Lotter, A.F., Ortu, E., Semmler, M., and Bradshaw, R.H.W (2013). Towards mapping the late Quaternary vegetation change of Europe. Vegetation History and Archaeobotany, 23, 75-86.

## Not run: 
epd.connection <- connect_to_epd(host="localhost", database="epd",
                                 user="epdr", password="epdrpw")
epd.1 <- get_entity(1, epd.connection)
epd.1.qi <- blois_quality(epd.1)
epd.1.qi@agesdf@dataquality

epd.1.ran <- intervals_counts(epd.1, tmin = seq(0, 21000, by = 1000),
                              tmax = seq(999, 21999, by = 1000))
epd.1.ran.qi <- blois_quality(epd.1.ran)
epd.1.ran.qi@agesdf@dataquality

t <- c(seq(0, 21000, by = 500))
epd.1.int <- interpolate_counts(epd.1, t)
epd.1.int.qi <- blois_quality(epd.1.int)
epd.1.int.qi@agesdf@dataquality

## End(Not run)