This function condenses the calculated log ratio values into a reduced number of features by grouping log ratio values and selecting or calculating a feature value. By default the selected groups each represents a single dimension, i.e. Length and Width. Only one feature is extracted per group. Currently, two methods are possible: priority (default) or average.

CondenseLogs(
  data,
  grouping = list(Length = c("GL", "GLl", "GLm", "HTC"), Width = c("BT", "Bd", "Bp",
    "SD", "Bfd", "Bfp")),
  method = "priority"
)

Arguments

data

A dataframe with the input measurements.

grouping

A list of named character vectors. The list includes a vector per selected group. Each vector gives the group of measurements in order of priority. By default the groups are Length = c("GL", "GLl", "GLm", "HTC") and Width = c("BT", "Bd", "Bp", "SD", "Bfd", "Bfp"). The order is irrelevant for method = "average".

method

Character string indicating which method to use for extracting the condensed features. Currently accepted methods: "priority" (default) and "average".

Value

A dataframe including the input dataframe and additional columns, one for each extracted condensed feature, with the corresponding name given in grouping.

Details

This operation is motivated by two circumstances. First, not all measurements are available for every bone specimen, which obstructs their direct comparison and statistical analysis. Second, several measurements can be strongly correlated (e.g. SD and Bd both represent bone width). Thus, considering them as independent would produce an over-representation of bone remains with more measurements per axis. Condensing each group of measurements into a single feature (e.g. one measure per axis) palliates both problems.

Observe that an important property of the log-ratios from a reference is that it makes the different measures comparable. For instance, if a bone is scaled with respect to the reference, so that it homogeneously doubles its width, then all width related measures (BT, Bd, Bp, SD, ...) will give the same log-ratio (log(2)). In contrast, the absolute measures are not directly comparable.

The measurement names in the grouping list are given without the logPrefix. But the selection is made from the log-ratios.

The default method is "priority", which selects the first available measure log-ratio in each group. The method "average" extracts the mean per group, ignoring the non-available measures. We provide the following by-default group and prioritization: For lengths, the order of priority is: GL, GLl, GLm, HTC. For widths, the order of priority is: BT, Bd, Bp, SD, Bfd, Bfp. This order maximises the robustness and reliability of the measurements, as priority is given to the most abundant, more replicable, and less age dependent measurements.

This method was first used in: Trentacoste, A., Nieto-Espinet, A., & Valenzuela-Lamas, S. (2018). Pre-Roman improvements to agricultural production: Evidence from livestock husbandry in late prehistoric Italy. PloS one, 13(12), e0208109.

Alternatively, a user-defined method can be provided as a function with a single argument (data.frame) assumed to have as columns the measure log-ratios determined by the grouping.

Examples

## Read an example dataset: dataFile <- system.file("extdata", "dataValenzuelaLamas2008.csv.gz", package="zoolog") dataExample <- utils::read.csv2(dataFile, na.strings = "", encoding = "UTF-8", stringsAsFactors = TRUE) ## Compute the log-ratios and select the cases with available log ratios: dataExampleWithLogs <- RemoveNACases(LogRatios(dataExample)) ## We can observe the first lines (excluding some columns for visibility): head(dataExampleWithLogs)[, -c(6:20,32:63)]
#> Site N.inv UE Especie Os GL Bp Dp SD DD Bd Dd BT #> 1 ALP 3453 10410 ovar 1fal ant 27.1 9.9 12.3 17.9 9.0 9.0 NA NA #> 2 ALP 3455 10410 ovar 1fal ant 27.6 9.6 12.2 7.6 8.9 8.3 NA NA #> 3 ALP 4245 7036 cahi hum NA 128.3 NA 12.9 NA 27.4 26.6 23.6 #> 4 ALP 4674 10227 cahi hum NA NA NA NA NA 26.0 25.7 22.3 #> 5 ALP 4085 10253 cahi hum NA NA NA NA NA 27.9 27.3 23.2 #> 6 TFC 24 407 ceel mc 262.7 41.3 30.8 25.0 21.2 41.1 27.1 NA #> GLc BFd Dl HmandM3 logGL logBp logDp logSD logBd #> 1 NA NA NA NA -0.10786052 -0.07991177 -0.07265930 0.2629585 -0.08911977 #> 2 NA NA NA NA -0.09992073 -0.09327573 -0.07620458 -0.1090810 -0.12428419 #> 3 NA NA NA NA NA 0.40167955 NA -0.2116296 -0.15130497 #> 4 NA NA NA NA NA NA NA NA -0.17408218 #> 5 NA NA NA NA NA NA NA NA -0.14345133 #> 6 NA NA NA NA NA NA NA NA -0.03354115 #> logDd logBT logGLc logBFd logDl logGB logSLC logGLP logBG logLG logDPA #> 1 NA NA NA NA NA NA NA NA NA NA NA #> 2 NA NA NA NA NA NA NA NA NA NA NA #> 3 -0.06787875 NA NA NA NA NA NA NA NA NA NA #> 4 -0.08282727 NA NA NA NA NA NA NA NA NA NA #> 5 -0.05659774 NA NA NA NA NA NA NA NA NA NA #> 6 NA NA NA NA NA NA NA NA NA NA NA #> logBPC logLA logLAR logSH logSB logL logH #> 1 NA NA NA NA NA NA NA #> 2 NA NA NA NA NA NA NA #> 3 NA NA NA NA NA NA NA #> 4 NA NA NA NA NA NA NA #> 5 NA NA NA NA NA NA NA #> 6 NA NA NA NA NA NA NA
## Extract the default condensed features with the default "priority" method: dataExampleWithSummary <- CondenseLogs(dataExampleWithLogs) head(dataExampleWithSummary)[, -c(6:20,32:63)]
#> Site N.inv UE Especie Os GL Bp Dp SD DD Bd Dd BT #> 1 ALP 3453 10410 ovar 1fal ant 27.1 9.9 12.3 17.9 9.0 9.0 NA NA #> 2 ALP 3455 10410 ovar 1fal ant 27.6 9.6 12.2 7.6 8.9 8.3 NA NA #> 3 ALP 4245 7036 cahi hum NA 128.3 NA 12.9 NA 27.4 26.6 23.6 #> 4 ALP 4674 10227 cahi hum NA NA NA NA NA 26.0 25.7 22.3 #> 5 ALP 4085 10253 cahi hum NA NA NA NA NA 27.9 27.3 23.2 #> 6 TFC 24 407 ceel mc 262.7 41.3 30.8 25.0 21.2 41.1 27.1 NA #> GLc BFd Dl HmandM3 logGL logBp logDp logSD logBd #> 1 NA NA NA NA -0.10786052 -0.07991177 -0.07265930 0.2629585 -0.08911977 #> 2 NA NA NA NA -0.09992073 -0.09327573 -0.07620458 -0.1090810 -0.12428419 #> 3 NA NA NA NA NA 0.40167955 NA -0.2116296 -0.15130497 #> 4 NA NA NA NA NA NA NA NA -0.17408218 #> 5 NA NA NA NA NA NA NA NA -0.14345133 #> 6 NA NA NA NA NA NA NA NA -0.03354115 #> logDd logBT logGLc logBFd logDl logGB logSLC logGLP logBG logLG logDPA #> 1 NA NA NA NA NA NA NA NA NA NA NA #> 2 NA NA NA NA NA NA NA NA NA NA NA #> 3 -0.06787875 NA NA NA NA NA NA NA NA NA NA #> 4 -0.08282727 NA NA NA NA NA NA NA NA NA NA #> 5 -0.05659774 NA NA NA NA NA NA NA NA NA NA #> 6 NA NA NA NA NA NA NA NA NA NA NA #> logBPC logLA logLAR logSH logSB logL logH Length Width #> 1 NA NA NA NA NA NA NA -0.10786052 -0.08911977 #> 2 NA NA NA NA NA NA NA -0.09992073 -0.12428419 #> 3 NA NA NA NA NA NA NA NA -0.15130497 #> 4 NA NA NA NA NA NA NA NA -0.17408218 #> 5 NA NA NA NA NA NA NA NA -0.14345133 #> 6 NA NA NA NA NA NA NA NA -0.03354115
## Extract only width with "average" method: dataExampleWithSummary2 <- CondenseLogs(dataExampleWithLogs, grouping = list(Width = c("BT", "Bd", "Bp", "SD")), method = "average") head(dataExampleWithSummary2)[, -c(6:20,32:63)]
#> Site N.inv UE Especie Os GL Bp Dp SD DD Bd Dd BT #> 1 ALP 3453 10410 ovar 1fal ant 27.1 9.9 12.3 17.9 9.0 9.0 NA NA #> 2 ALP 3455 10410 ovar 1fal ant 27.6 9.6 12.2 7.6 8.9 8.3 NA NA #> 3 ALP 4245 7036 cahi hum NA 128.3 NA 12.9 NA 27.4 26.6 23.6 #> 4 ALP 4674 10227 cahi hum NA NA NA NA NA 26.0 25.7 22.3 #> 5 ALP 4085 10253 cahi hum NA NA NA NA NA 27.9 27.3 23.2 #> 6 TFC 24 407 ceel mc 262.7 41.3 30.8 25.0 21.2 41.1 27.1 NA #> GLc BFd Dl HmandM3 logGL logBp logDp logSD logBd #> 1 NA NA NA NA -0.10786052 -0.07991177 -0.07265930 0.2629585 -0.08911977 #> 2 NA NA NA NA -0.09992073 -0.09327573 -0.07620458 -0.1090810 -0.12428419 #> 3 NA NA NA NA NA 0.40167955 NA -0.2116296 -0.15130497 #> 4 NA NA NA NA NA NA NA NA -0.17408218 #> 5 NA NA NA NA NA NA NA NA -0.14345133 #> 6 NA NA NA NA NA NA NA NA -0.03354115 #> logDd logBT logGLc logBFd logDl logGB logSLC logGLP logBG logLG logDPA #> 1 NA NA NA NA NA NA NA NA NA NA NA #> 2 NA NA NA NA NA NA NA NA NA NA NA #> 3 -0.06787875 NA NA NA NA NA NA NA NA NA NA #> 4 -0.08282727 NA NA NA NA NA NA NA NA NA NA #> 5 -0.05659774 NA NA NA NA NA NA NA NA NA NA #> 6 NA NA NA NA NA NA NA NA NA NA NA #> logBPC logLA logLAR logSH logSB logL logH Width #> 1 NA NA NA NA NA NA NA 0.03130898 #> 2 NA NA NA NA NA NA NA -0.10888030 #> 3 NA NA NA NA NA NA NA 0.01291500 #> 4 NA NA NA NA NA NA NA -0.17408218 #> 5 NA NA NA NA NA NA NA -0.14345133 #> 6 NA NA NA NA NA NA NA -0.03354115