LDAcrop.pro: Linear discriminant analysis based on attributes of weed...

In elizabethastroud/Cropprocessing: Data organisation, classification and visualisation of archaeobotanical data to understand crop processing stage

Linear discriminant analysis based on attributes of weed seeds

Description

This function conducts linear discriminant analysis. The size, aerodynamics and tendency to remain in heads of weed seeds from ethnographic crop processing data are used to create a model against which the archaeobotanical samples are classified. The function provides the classification, posterior probabilities of such classifications, and the discriminant score of the entered samples.

Usage

LDAcrop.pro(x)

Arguments

x	The archaeobotanical dataset

Details

The archaeobotanical dataset needs to have been transformed and organised with columns labelled and in the order of: BHH, BFH, SHH, SHL, SFH, SFL. The first column of the dataframe should be the sample names. Transformation can be done manually following (insert reference) or through the use of crop.dataorg which can transform a raw archaeobotanical dataset.

Value

Results table:(note the * asterisked items appear in console output and are used for interpretation, non-asterisked columns provide addition details regarding standardised and unstandardised components

Samples	the archaeobotanical sample names from the first column of the entered dataset (x)
Class_std*	The standardised classification of the samples as either 1,2,3,or 4. 1= winnowing by-product, 2= coarse sieve by-products, 3= fine sieve by-products and 4= fine sieve product
Prob.1_std*	the standardised posterior probability of the sample being classified as group 1
Prob.2_std*	the standardised posterior probability of the sample being classified as group 2
Prob.3_std*	the standardised posterior probability of the sample being classified as group 3
Prob.4_std*	the standardised posterior probability of the sample being classified as group 4
ld1_std	the standardised linear discriminant score for function 1
ld2_std	the standardised linear discriminant score for function 2
ld3_std	the standardised linear discriminant score for function 3
Class	the unstandardised classification of the samples
Prob.1	the unstandardised posterior probability of the sample being classified as group 1
Prob.2	the unstandardised posterior probability of the sample being classified as group 2
Prob.3	the unstandardised posterior probability of the sample being classified as group 3
Prob.4	the unstandardised posterior probability of the sample being classified as group 4
LD1*	the unstandardised linear discriminant score for function 1
LD2*	the unstandardised linear discriminant score for function 2
LD3*	the unstandardised linear discriminant score for function 3

Classification table: showing the count and percentage of samples classified as one of four crop processing groups - as shown in the Class_std column

winnowing by-product	the count and percentage of samples classified as group 1
Coarse sieve by-product	the count and percentage of samples classified as group 2
Fine sieve by-product	the count and percentage of samples classified as group 3
Fine sieve product	the count and percentage of samples classified as group 4

Author(s)

Elizabeth Stroud

References

PAPER reference

Examples

##Create random dataset for examples

BHH<-runif(20, min=0, max=7)
BFH<-runif(20, min=0, max=24)
SHH<-runif(20, min=1, max=13)
SHL<-runif(20, min=0.5, max=17)
SFH<-runif(20, min=1, max=22)
SFL<-runif(20, min=1, max=8)
Samples<-c("s1","s2","s3","s4","s5","s6","s7","s8","s9","s10","s11","s12","s13",
"s14","s15","s16","s17","s18","s19","s20")
data<-data.frame(Samples,BHH,BFH,SHH,SHL,SFH,SFL)

#Use

LDAcrop.pro(data)

elizabethastroud/Cropprocessing documentation built on Sept. 27, 2024, 3:03 p.m.