R: shapeR

shapeR {shapeR}

R Documentation

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

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)

[Package shapeR version 1.0-1 Index]