shapeR: shapeR

In shapeR: Collection and Analysis of Otolith Shape Data

shapeR

Description

Collection and analysis of otolith shape data

a shapeR class

Usage

shapeR(project.path, info.file, ...)

Arguments

project.path	The base project path where the images are stored
info.file	The information file which store the information on the fish and otoliths. This is the base for the master.list
...	Additional parameters to be passed to 'read.csv' for reading the info.file

Value

a shapeR object

Slots

project.path

Path to the project where the images are stored

info.file

Info file containing fish and otolith information

master.list.org

The contents of the info.file

master.list

The contents of the info.file with added shape parameters and descriptors

outline.list.org

A list of all the original otolith outlines

outline.list

A list of all the otolith outlines. It returns a list of smoothed if contour smoothing (usingsmoothout) has been conducted.

filter

A logical vector selecting the otoliths used for analysis

wavelet.coef.raw

The wavelet coefficients for all the otolith outlines

wavelet.coef

The wavelet coefficients after aligning with the info.file. The data is generated when enrich.master.list is run

wavelet.coef.std

The standardized wavelet coefficients. The data is generated when stdCoefs is run

wavelet.coef.std.removed

The index of the removed wavelet coefficients after standardization. The data is generated when stdCoefs is run

fourier.coef.raw

The Fourier coefficients for all the otolith outlines

fourier.coef

The Fourier coefficients for after aligning with the info file. The data is generated when enrich.master.list is run

fourier.coef.std

The standardized Fourier coefficients. The data is generated when stdCoefs is run

fourier.coef.std.removed

The index of the removed Fourier coefficents after standardization. The data is generated when stdCoefs is run

shape.coef.raw

The uncalibrated shape measurements for all the otoliths. The shape parameters are: otolith.area, otolith.length, otolith.width, otolith.perimeter

shape.coef

The shape measurements for after aligning with the info file. The shape parameters have been calibrated using the calibration parameter as registered in the datafile as the column 'cal'.

shape.std

The standardized shape measurements. The data is generated when stdCoefs is run

shape.std.removed

The index of the removed shape measurements after standardization. The data is generated when stdCoefs is run

Author(s)

Lisa Anne Libungan & Snaebjorn Palsson

References

Libungan LA and Palsson S (2015) ShapeR: An R Package to Study Otolith Shape Variation among Fish Populations. PLoS ONE 10(3): e0121102. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121102

See Also

https://github.com/lisalibungan/shapeR

plotWavelet

plotFourier

plotWaveletShape

plotFourierShape

capscale

cluster.plot

setFilter

lda

detect.outline

generateShapeCoefficients

enrich.master.list

Examples

## Not run: 

# This example has two sections: (1) Demonstration of how a shapeR object
# is analyzed and (2) How to create a shapeR object from an archive of
# image files.

#-----------------------------------------
# Section 1: Analyzing a shapeR object

data(shape)

#Standardize coefficients
shape = stdCoefs(shape,"pop","length_cm")

#Visualize Wavelet and Fourier coefficients
plotWavelet(shape,level=5,class.name= "pop",useStdcoef=TRUE)
plotFourier(shape,class.name= "pop",useStdcoef=TRUE)

#Examine the mean shapes
plotWaveletShape(shape, "pop",show.angle = TRUE,lwd=2,lty=1)
plotFourierShape(shape, "pop",show.angle = TRUE,lwd=2,lty=1)

#Canonical analysis
library(vegan)
cap.res = capscale(getStdWavelet(shape) ~ getMasterlist(shape)$pop)
anova(cap.res)

#Visualize the canonical scores
eig=eigenvals(cap.res,constrained=TRUE)
eig.ratio = eig/sum(eig)

cluster.plot(scores(cap.res)$sites[,1:2],getMasterlist(shape)$pop
,plotCI=TRUE
,xlab=paste("CAP1 (",round(eig.ratio[1]*100,1),"%)",sep="")
,ylab=paste("CAP2 (",round(eig.ratio[2]*100,1),"%)",sep="")
,main="Canonical clustering"
)

#Only analyze Icelandic and Norwegian samples
shape = setFilter(shape, getMasterlist(shape, useFilter = FALSE)$pop %in% c("NO","IC"))

#Classifier on standardized wavelet
lda.res.w = lda(getStdWavelet(shape),getMasterlist(shape)$pop,CV=TRUE)
ct.w = table(getMasterlist(shape)$pop,lda.res.w$class)

diag(prop.table(ct.w, 1))

# Total percent correct
sum(diag(prop.table(ct.w)))

cap.res = capscale(getStdWavelet(shape) ~ getMasterlist(shape)$pop)
anova(cap.res)

#Classifier on canoncial values
lda.res.w = lda(scores(cap.res)$sites,getMasterlist(shape)$pop,CV=TRUE)
ct.w = table(getMasterlist(shape)$pop,lda.res.w$class)

diag(prop.table(ct.w, 1))

# Total percent correct
sum(diag(prop.table(ct.w)))

#-----------------------------------------
# Section 2: Creating a shapeR object from image files

# The following example requires the user to download an archive of JPEG
# files from https://github.com/lisalibungan/shapeR/
# place the ShapeAnalysis directory inside the working directory.

shape = shapeR("~/ShapeAnalysis/","FISH.csv")
shape = detect.outline(shape,write.outline.w.org = TRUE)

shape = generateShapeCoefficients(shape)
shape = enrich.master.list(shape)

## End(Not run)

shapeR documentation built on Nov. 22, 2022, 1:07 a.m.