Storing, Manipulating and Analysis Spectroscopy and Associated Data in R

Description

Stores and eases the manipulation of spectra and associated data.

Author(s)

Pierre Roudier

Constructor for the Spectra class.

Description

Constructor for the Spectra class. Creates a Spectra object from scratch.

Usage

Spectra(wl = numeric(), nir = matrix(), id = as.character(NA), units = "nm")

Arguments

Value

a new "Spectra" object

Author(s)

Pierre Roudier pierre.roudier@gmail.com

See Also

spectra, wl, Spectra-class, SpectraDataFrame

Examples

  wls <- 350:2500
  id <- c("A", "B")
  nir <- matrix(runif(2*length(wls)), nrow = 2)
  s <- Spectra(wl = wls, nir = nir, id = id, units = "nm")

Spectra* classes

Description

The spectacles package provides the user with S4 classes that have been developped to store and manipulate spectroscopy data.

The Spectra class is storing the spectra matrix, along with the wavelengths at which those have been measured, the units in whioch those wavelengths are expressed, and a unique id for each sample in the collection.

The SpectraDataFrame class is extending the Spectra class by giving the opportunity to store attribute data along with the spectra - this is mostly the case when we want to predict physical or chemical properties from the spectra set.

The SpatialSpectra and SpatialSpectraDataFrame classes are extending the Spectra and SpectraDataFrame classes using the SpatialPoints class from package sp. This allows to store spatial information on the dataset: coordinates, coordinate reference system, bounding box, etc.

Common generic methods implemented for these classes include:

summary, show, nrow, length, plot, [, [[, $.

SpatialPoints methods from the sp package can be applied to SpatialSpectra and SpatialSpectraDataFrame objects are they inherit from this class.

Slots

wl

object of class "numeric"; the wavelengths at which the spectra has been measured

nir

object of class "matrix"; the spectra, with as many columns as wavelengths, and as many rows as samples

id

object of class "data.frame" with one attribute; the identification strings for each sample in the collection

units

object of class "character"; units in which the wavelengths are expressed

data

object of class data.frame containing the attribute data

Objects from the Class

Objects can be created by calls of the form new("Spectra", ...), with the constructor functions like Spectra(...), or with the helper functions such as wl and spectra.

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

showClass("Spectra")
showClass("SpectraDataFrame")

Constructor for the SpectraDataFrame class.

Description

Constructor for the SpectraDataFrame class. Creates a SpectraDataFrame object, either from scratch, or from an existing Spectra object.

Usage

SpectraDataFrame(
  ...,
  wl = numeric(),
  nir = matrix(),
  id = as.character(NA),
  units = "nm",
  data = data.frame()
)

Arguments

Value

a new "SpectraDataFrame" object

Author(s)

Pierre Roudier pierre.roudier@gmail.com

See Also

spectra, wl, Spectra-class

Examples

  # Creating a SpectraDataFrame object from scratch
  my.wl <- 350:2500
  my.id <- c("A", "B")
  my.nir <- matrix(runif(2*length(my.wl)), nrow=2)
  my.data <- data.frame(foo = runif(2), bar = LETTERS[1:2])
  my.sdf <- SpectraDataFrame(wl = my.wl, nir = my.nir, id = my.id, data = my.data)

  # Creating a SpectraDataFrame object from an existing Spectra object
  my.s <- Spectra(wl = my.wl, nir = my.nir, id = my.id)
  my.sdf <- SpectraDataFrame(my.s, data = my.data)

Aggregates spectral and data information

Description

Aggregates spectral and data information of a Spectra object using a user-defined function

Usage

## S4 method for signature 'Spectra'
aggregate_spectra(obj, fun = mean, ...)

## S4 method for signature 'SpectraDataFrame'
aggregate_spectra(obj, fun = mean, id = NULL, ...)

Arguments

Details

For SpectraDataFrame objects, associated data is also aggregated using the function provided by the fun option. Additionally, the method for SpectraDataFrame has an id option that allows to specify an attribute which will be used to split the object, apply sequentially the fun function, and recombine the results in an unique object.

Value

An object of the same class as obj

Methods

x=Spectra

aggregate_spectra(obj, fun=mean, ...)

x=SpectraDataFrame

aggregate_spectra(obj, fun=mean, id=NULL, ...)

Author(s)

Pierre Roudier pierre.roudier@gmail.com

See Also

apply_spectra

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Aggregation on the whole collection
m <- aggregate_spectra(australia, fun = mean)
plot(m)

# Aggregation factor-wise

# Generate some kind of factor
australia$fact <- sample(
  LETTERS[1:3], 
  size = nrow(australia), 
  replace = TRUE
)
m <- aggregate_spectra(australia, fun = mean, id = 'fact')
plot(m)

Apply a function on the spectra of a Spectra* object

Description

Aggregates spectral and data information of a Spectra object using a user-defined function.

Apply a function and update the spectra of a Spectra object. This function is particularly interesting for pre-processing, e.g to derive the spectra, or apply pre-processing functions such as snv.

Usage

apply_spectra(obj, fun, ...)

Arguments

Details

The philosophy of this function is to let the user free to use any function to pre-process a spectra collection, using either functions from the stats package, functions from other packages such as signal, or personal functions.

Value

An object of the same class as obj

Author(s)

Pierre Roudier pierre.roudier@gmail.com

See Also

aggregate_spectra, snv, rnv

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Second derivative
r <- apply_spectra(australia, diff, 2)
plot(r)

# Smoothing kernel
k <- kernel("daniell", 20) # define a kernel
r <- apply_spectra(australia, kernapply, k)
plot(r)

## Not run: 
# Savitzky-Golay filter (from the signal package)
library(signal)
r <- apply_spectra(australia, sgolayfilt, n = 33, p = 4)
plot(r)

## End(Not run)

Australia spectra library data set

Description

The australia data set gathers 100 soil spectra, along side with their organic carbon, pH and clay content values. This data set has been collected by CSIRO.The oz dataset is the first 5 samples of australia, and is here to keep examples fast to run.

The data.frame contains the following columns:

• sr_no: a unique identifier for each spectrum

• carbon: soil organic carbon values

• ph: soil pH values

• clay: soil clay values

• X350, X351, ...,X2499, X2500: reflectance in wavelengths 350 to 2500nm

Author(s)

Data kindly contributed by Raphael Viscarra Rossel. To reduce the size of the package, the original dataset has been reduced to 100 spectra using the Kennard-Stone algorithm.

References

Viscarra Rossel, R. and Behrens, T. 2010.

Examples

data(australia)
big.head(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

data(oz)
big.head(oz)
spectra(oz) <- sr_no ~ ... ~ 350:2500

Baseline correction using the baseline package

Description

Estimates baselines for the spectra in the obj object, using the algorithm named in 'method'.

Usage

## S4 method for signature 'Spectra'
base_line(object, ...)

Arguments

Details

The baseline package implements various algorithms for the baseline correction. The following methods are available:

• 'als': Baseline correction by 2nd derivative constrained weighted regression

• 'fillPeaks': An iterative algorithm using suppression of baseline by means in local windows

• 'irls' (default): An algorithm with primary smoothing and repeated baseline suppressions and regressions with 2nd derivative constraint

• 'lowpass': An algorithm for removing baselines based on Fast Fourier Transform filtering

• 'medianWindow': An implementation and extention of Mark S. Friedrichs' model-free algorithm

• 'modpolyfit': An implementation of Chad A. Lieber and Anita Mahadevan-Jansen's algorithm for polynomial fiting

• 'peakDetection': A translation from Kevin R. Coombes et al.'s MATLAB code for detecting peaks and removing baselines

• 'rfbaseline': Wrapper for Andreas F. Ruckstuhl, Matthew P. Jacobson, Robert W. Field, James A. Dodd's algorithm based on LOWESS and weighted regression

• 'rollingBall': Ideas from Rolling Ball algorithm for X-ray spectra by M.A.Kneen and H.J. Annegarn. Variable window width has been left out

See baseline package documentation for more information and references.

Additionally, the baseline package provides a nice GUI that helps choosing the good baseline method and the good parametrisation. This GUI can be used with the inspectr package. This is demonstrate in the Examples section.

Value

An object of the same class as obj with the continuum removed from its spectra.

Author(s)

Interface to the baseline package by Pierre Roudier pierre.roudier@gmail.com, baseline package authored by Kristian Hovde Liland and Bjorn-Helge Mevik

References

Kristian Hovde Liland and Bjorn-Helge Mevik (2011). baseline: Baseline Correction of Spectra. R package version 1.0-1. http://CRAN.R-project.org/package=baseline

See Also

continuum_removal, snv, rnv

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Subsample for demo purposes
australia <- australia[1:10,]

# Correction using the default method (irls)
bl <- base_line(australia)
plot(bl)

# Specifying another method for baseline calculation
bl2 <- base_line(australia, method = "modpolyfit")
plot(bl2)

# Using the baseline package independently
# (useful to plot the corrections)
## Not run: 
library(baseline)
bl3 <- baseline(spectra(australia), method = 'irls')
class(bl3) # this is a baseline object
plot(bl3)
# Affecting the baseline-corrected spectra back
# to the SpectraDataFrame object
spectra(australia) <- getCorrected(bl3)
plot(australia)

# Using the baselineGUI with inspectr
baselineGUI(spectra(australia))
## When happy with a configuration, clik "Apply to all" and 
## save the results under a name, e.g. "corrected.spectra"
spectra(australia) <- getCorrected(corrected.spectra)
plot(australia)

## End(Not run)

Return the First or Last Part of an Object

Description

Return the First or Last Part of an Object

Usage

big.head(x, n = 5, l = 5, r = 5)

Arguments

Details

Returns the first or last rows of a data frame like head() and tail(), but also only returns the first and last columns. This has been implemented to check big data frames.

Value

An object (usually) like 'x' but generally smaller.

Author(s)

Pierre Roudier pierre.roudier@gmail.com

See Also

head, tail

Examples

big.head(mtcars)
big.tail(mtcars)
big.tail(mtcars, 10)
big.head(mtcars, 10, 2, 4)
big.head(mtcars, , , 1)

data(australia)
big.head(australia)

Continuum removal

Description

Operates a continuum removal on a vector.

Usage

continuum_removal(x, wl = as.numeric(names(x)), upper = TRUE)

Arguments

Details

This operation is commonly done to normalize reflectance spectra and allow comparison of individual absorption features from a common baseline. The removal is based on the upper convex hull of the spectra.

This function is working on vectors. It may applied on matrix or data.frames using the apply function, or on Spectra* objects using the apply_spectra function.

Value

A numeric vector with its continuum removed.

Author(s)

Pierre Roudier pierre.roudier@gmail.com, based on code from Raphael Viscarra-Rossel.

References

Clark, R.N., and Roush, T.L. 1984. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research 89, 6329–6340.

See Also

baseline, snv, rnv

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

s <- apply_spectra(australia, continuum_removal)
plot(s)

s <- apply_spectra(australia, continuum_removal, upper = FALSE)
plot(s)

Manipulating the wavelength range of a Spectra object

Description

This methods allows to either select or remove a specific range of wavelengths from a Spectra object.

Usage

## S4 method for signature 'Spectra'
cut(x, ..., wl)

Arguments

Details

The wavelengths are extracted if wl > 0, or removed if wl < 0. You can't mix negative and positive index in wl.

Value

An object of the same class as x.

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Extracting a specific wavelengths range
s <- cut(australia, wl = 450:550)
plot(s)

s <- cut(australia, wl = c(450:550, 1850:2050))
plot(s)

# Removing a specific wavelengths range
s <- cut(australia, wl = -1*450:550)
plot(s)

s <- cut(australia, wl = -1*c(450:550, 1850:2050))
plot(s)

Retrieve dimensions of Spectra* objects

Description

Retrieves the wavelengths units and the spectral resolution from Spectra* objects.

Usage

## S3 method for class 'Spectra'
length(x)

## S4 method for signature 'Spectra'
nrow(x)

## S4 method for signature 'Spectra'
ncol(x)

## S3 method for class 'Spectra'
dim(x)

Arguments

Details

* Methods for Spectra objects

nrow returns the number of individuals in the collection length returns the number of wavelengths in the collection ncol returns NULL dim returns a vector containing (1) the number of individuals and (2) in the number of wavelengths in the collection

* Methods for Spectra objects

nrow returns the number of individuals in the collection length returns the number of wavelengths in the collection ncol returns the number of attributes in the collection dim returns a vector containing (1) the number of individuals, (2) in the number of wavelengths, and (3) the number of attributes in the collection

Value

nrow, ncol, nwl, and length, return an integer, dim returns a vector of length 2 or 3 (see Details section).

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

nrow(australia)
ncol(australia)
length(australia)
dim(australia)

Extracting and replacing parts of Spectra* objects

Description

These methods emulates classic base methods '[', '[[' and '$' to extract or replace parts of Spectra* objects.

Usage

\S4method{[}{Spectra}(x,i,j,...,drop=FALSE)
\S4method{[[}{Spectra}(x,i,j,...)
\S4method{$}{SpectraDataFrame}(x,name)
\S4method{$}{Spectra}(x,name) <- value
\S4method{[[}{Spectra}(x,i,j,...) <- value

Arguments

Value

These methods either return an object of the same class as x, or can promote a Spectra object to a SpectraDataFrame object by adding data ("[[<-" and "$<-" methods).

Methods

x=Spectra

x[i, j, ..., drop = FALSE]

x$name <- value

x[[name]] <- value

x=SpectraDataFrame

x[i, j, k, ..., drop = FALSE]

x[[name]]

x[[name]] <- value

x$name

x$name <- value

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Getting features information from SpectraDataFrame
australia$carbon
australia[['carbon']]

# Creating new features
australia$foo <- runif(nrow(australia))
australia[['bar']] <- runif(nrow(australia))

# Replacing values
australia$foo <- sample(
  LETTERS[1:5], 
  size = nrow(australia), 
  replace = TRUE
)
australia[['bar']] <- sample(
  c(TRUE, FALSE), 
  size = nrow(australia), 
  replace = TRUE
)

# Promote Spectra to SpectraDataFrame
s <- as(australia, 'Spectra')
class(s)
s$foo <- runif(nrow(s))

Retrieves or sets the data slot of a SpectraDataFrame object.

Description

Either retrieves the attributes values from the data slot of a SpectraDataFrame object, or upgrades a Spectra object to a SpectraDataFrame object by initialising its data slot by a suitable "data.frame" object.

Usage

## S4 method for signature 'SpectraDataFrame'
features(object,exclude_id)
## S4 replacement method for signature 'Spectra'
features(object,safe,exclude_id,key,append) <- value

Arguments

Value

The features methods return a data.frame object, while the "features<-" methods return a SpectraDataFrame object.

Methods

x=Spectra

features(object, safe=TRUE, key=NULL, exclude_id=TRUE) <- value

x=SpectraDataFrame

features(obj, exclude_id=TRUE)

features(obj, safe=TRUE, key=NULL, exclude_id=TRUE, append=TRUE) <- value

Author(s)

Pierre Roudier pierre.roudier@gmail.com

See Also

spectra, wl, SpectraDataFrame-class

Examples

# Loading example data
data(oz)
spectra(oz) <- sr_no ~ ... ~ 350:2500

# Printing available data
features(oz)

# Promoting a Spectra to a SpectraDataFrame object
s <- as(oz, "Spectra")

# Generating dummy data
d <- data.frame(
  id = ids(oz), 
  foo = runif(nrow(oz)), 
  bar = sample(LETTERS[1:5], size = nrow(oz), replace = TRUE)
)
head(d)

# Affecting data to Spectra object
features(s) <- d
features(s)

# Adding data to an existing SpectraDataFrame object

features(oz) <- d
features(oz)

Fill missing wavelengths of a Spectra* object with a given value

Description

Fill missing wavelengths of a Spectra* object with a given value. This is mostly usefull to include NA values in the spectra in order to show missing bits in plots.

Usage

## S4 method for signature 'Spectra'
fill_spectra(obj, ref = NULL, fill = NA, ...)

Arguments

Details

At this stage removing gaps does not work well with irreguarly spaced wavelengths. Results might be odd for binned spectra.

Value

An object of the same class as obj

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Cut wavelengths out of the collection
oz <- cut(australia, wl=-1*c(355:400, 2480:2499))
big.head(spectra(oz), , 7)

# Giving the wavelengths at which I want data
oz_filled <- fill_spectra(oz, ref = 350:2500, fill = NA)
big.head(spectra(oz_filled), , 7)
plot(oz_filled)

# Trying to guess ref values
oz_filled <- fill_spectra(oz, fill = -999)
big.head(spectra(oz_filled), , 7)
plot(oz_filled)

Retrieves or sets the ids of a Spectra* object.

Description

Either retrieves the wavelengths from a Spectra* object, or creates a Spectra* object from a "data.frame" object by setting some of its columns as the wavelengths. The "ids<-" method for SpectraDataFrame objects allows to use a formula interface to use a column in its data slot as the object IDs (see the last example provided in the Examples section).

Usage

ids(object, ...)
ids(object) <- value

## S4 replacement method for signature 'Spectra'
ids(object) <- value

## S4 replacement method for signature 'SpectraDataFrame'
ids(object) <- value

Arguments

Value

The ids methods return a vector if as.vector is TRUE, a data.frame otherwise. The "ids<-" method return a SpectraDataFrame object (or a Spectra object if the column in the data slot that has been used to initiate the IDs was the only attribute).

Methods

• ids(object, ..., as.vector = TRUE)

• ids(object) <- value

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

# Loading example data
data(oz)
spectra(oz) <- sr_no ~ ... ~ 350:2500

# Retrieving ids
ids(oz)

# Setting ids using a vector of values
ids(oz) <- seq_len(nrow(oz))
ids(oz)

# Setting ids using an attribute
oz$new_id <- seq_len(nrow(oz)) + 1000
ids(oz) <- ~ new_id
ids(oz)

Kennard-Stone algorithm for optimal calibration set selection.

Description

An implemnentation of the Kennard-Stone algorithm for calibration set selection.

Usage

kenstone(x, size, ...)

Arguments

Value

A vector of length size giving the indices of the selected individuals in x.

Author(s)

Pierre Roudier pierre.roudier@gmail.com

References

Kennard, L.A. Stone, Technometrics 11 (1969) 137.

Load the australia dataset

Description

Loads the australia dataset as a SpectraDataFrame

Usage

load_oz(n = NULL)

Arguments

Value

a SpectraDataFrame

Author(s)

Pierre Roudier

Examples

oz <- load_oz()

Melts the spectra data of a Spectra object and returns it as wide format.

Description

This function is very useful when wanting to plot spectra using the lattice or ggplot2 packages

Usage

melt_spectra(obj, attr=NULL, ...)

## S4 method for signature 'SpectraDataFrame'
melt_spectra(obj, attr = NULL, ...)

Arguments

Methods

x=Spectra

melt_spectra(obj, ...)

x=SpectraDataFrame

melt_spectra(obj, attr=NULL, ...)

Author(s)

Pierre Roudier pierre.roudier@gmail.com

References

Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. URL http://www.jstatsoft.org/v40/i01/.

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Simple melt
r <- melt_spectra(australia)
head(r)

## Not run: 
# Melt against some factor (or continuous data), and plot
# using ggplot2

# Create some factor
australia$fact <- sample(
  LETTERS[1:3], 
  size = nrow(australia), 
  replace = TRUE
)
r <- melt_spectra(australia, attr = 'fact')

# Create plot
library(ggplot2)
p <- ggplot(r) + 
  geom_line(aes(x=wl, y=nir, group=id, colour=fact)) + 
  theme_bw()
print(p)

## End(Not run)

Mutate a Spectra* object by transforming the spectra values, and/or adding new or replacing existing attributes.

Description

This function is very similar to transform but it executes the transformations iteratively so that later transformations can use the columns created by earlier transformations. Like transform, unnamed components are silently dropped.

Either the spectra, and/or the attributes (if the .data inherits from the SpectraDataFrame class) can be affected:

• To affect the spectra, one should use the nir placeholder, eg nir = log(1/nir)

• To affect the attributes of the object, the definitions of new columns are simply given using attributes names, newAttribute = 1/sqrt(attribute)

• Both spectra and attrbutes can be transformed in one command.

Usage

## S4 method for signature 'Spectra'
mutate(.data, ...)

Arguments

Author(s)

Pierre Roudier pierre.roudier@gmail.com, from code from Hadley Wickham

References

Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. URL http://www.jstatsoft.org/v40/i01/.

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Modifying spectra
m <- mutate(australia, nir = log1p(1/nir))
plot(m)

# Modifying and creating attributes
m <- mutate(
  australia, 
  sqrt_carbon = sqrt(carbon), 
  foo = clay + ph, 
  nir = log1p(1/nir)
)
plot(m)

Plots an object inheriting from the Spectra class

Description

The philosophy of this plotting routine is to provide a "quick'n'dirty" way to plot your spectra collection. For advanced visualisations, the use of melt_spectra alongside with ggplot2 or lattice is encouraged.

Usage

## S3 method for class 'Spectra'
plot(x,gg,gaps,attr,...)

## S3 method for class 'Spectra'
plot(x, gg = FALSE, gaps = TRUE, attr = NULL, ...)

Arguments

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Default plotting method
plot(australia[1:5,])

# Default plot using ggplot2
plot(australia[1:5,], gg = TRUE)

## Not run: 

# Managing gaps in the spectra
s <- cut(australia, wl =c(-1*450:500, -1*1800:2050))
plot(s, gaps = TRUE)
plot(s, gaps = FALSE)

# passing various options to matplot
plot(
  australia, 
  lty = 1:5, 
  col = 'blue', 
  xlab = 'foo', ylab = 'bar', 
  ylim = c(0.4,0.6), 
  main = 'my plot'
)

# Using colour ramps
plot(
  australia, 
  lty = 1, 
  col = rainbow(10), 
  main = "It is possible to create really ugly visualisations"
)

# Example using colours given by ColorBrewer (http://colorbrewer2.org/)
library(RColorBrewer)
plot(australia, lty = 1, col = brewer.pal(n = 8, name = "Set2"))

# Using an attribute to group spectra

# Generate some kind of factor
australia$fact <- sample(
  LETTERS[1:3], 
  size = nrow(australia), 
  replace = TRUE
) 

s <- aggregate_spectra(australia, fun = mean, id = 'fact')
plot(s, gg = TRUE, attr = 'fact')

## End(Not run)

Offset plot of a collection of spectra

Description

Creates an offset plot of a collection of Spectra

Usage

plot_offset(x, offset = 1)

Arguments

Author(s)

Pierre Roudier

Examples

 oz <- load_oz(3)
 plot_offset(oz)
 plot_offset(oz, 0.3)
 plot_offset(oz, 2)

Stacked plot of a collection of spectra

Description

Creates a stacked plot of a collection of Spectra

Usage

plot_stack(x)

Arguments

Author(s)

Pierre Roudier

Examples

 oz <- load_oz(3)
 plot_stack(oz)

Summary plot of a collection of spectra

Description

Creates a summary plot of a collection of Spectra

Usage

plot_summary(x, fun = mean, se = TRUE, ...)

Arguments

Author(s)

Pierre Roudier

Examples

 oz <- load_oz()
 plot_summary(oz)

Calculates performance indictors across resamples

Description

Given two numeric vectors of data, the root mean squared error, the R-squared, the bias, the RPD, the RPIQ, the CCC and the standard error are calculated. For two factors, the overall agreement rate and Kappa are determined.

Usage

postResampleSpectro(pred, obs)
spectroSummary(data, lev = NULL, model = NULL)

Arguments

Details

This function extends postResample in the caret package.

Author(s)

Pierre Roudier, adapted from code from Max Kuhn

Examples

predicted <-  matrix(rnorm(50), ncol = 5)
observed <- rnorm(10)
apply(predicted, 2, postResampleSpectro, obs = observed)

Stacking Spectra objects together

Description

This method stacks two or more Spectra* objects together.

Usage

## S3 method for class 'Spectra'
rbind(..., create_new_ids = FALSE, new_ids = NULL)

## S3 method for class 'SpectraDataFrame'
rbind(..., create_new_ids = FALSE, new_ids = NULL)

Arguments

Value

a Spectra* object.

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

s <- rbind(australia, australia, create_new_ids = TRUE)

l <- separate(australia, calibration = 0.6)
s <- rbind(l$validation, l$calibration)

Spectral resolution

Description

Returns the spectral resolution of an object

Usage

## S4 method for signature 'numeric'
res(x)

## S4 method for signature 'integer'
res(x)

## S4 method for signature 'Spectra'
res(x)

Arguments

Value

a vector

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Separates a Spectra* object into a calibration and a validation set.

Description

Separates a Spectra* object into a calibration and a validation set.

Usage

## S4 method for signature 'Spectra'
separate(obj, calibration)

Arguments

Value

An list with two SpectraDataFrame objects, one for the calibration, and the other for the validation.

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

l <- separate(australia, calibration=0.7)
# The result is a list of two Spectra* objects
str(l)
lapply(l, nrow)

Standard and Robust Normal Variate transformations

Description

Standard and Robust Normal Variate transformations are often used in chemometrics to normalise a spectra collection and remove the baseline effect.

The Standard Normal Variate transformation (SNV, Barnes et al., 1989) is a common method to reduce within-class variance.

The Robust Normal Variate transformation (RNV, Guo et al., 1999) is a modification of the SNV to make it more robust to closure problems.

These function are to be used in conjonction with apply_spectra.

Usage

snv(x)

rnv(x, r)

Arguments

Value

A vector of numeric values

Author(s)

Pierre Roudier pierre.roudier@gmail.com

References

• Barnes, R.J., Dhanoa, M.S., Lister, S.J. 1989. Standard normal variate transformation and detrending of near-infra-red diffuse reflectance spectra. Applied Spectroscopy 43, 772–777.

• Guo, Q., Wu, W., Massar, D.L. 1999. The robust normal variate transform for pattern recognition with near-infrared data. Analytica Chimica Acta 382:1–2, 87–103.

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Standard Normal Variate transform
s <- apply_spectra(australia[1:10,], snv)
plot(s)

# The scale function in the base package is actually doing
# the same thing!
s <- apply_spectra(australia[1:10,], scale, center = TRUE, scale = TRUE)
plot(s)

# Robust Normal Variate transform
s <- apply_spectra(australia[1:10,], rnv, r = 0.25)
plot(s)

Retrieves or sets the spectra of a Spectra* objects.

Description

Either retrieves the spectra matrix from a Spectra* object, or creates a Spectra* object from a "data.frame" object different interfaces detailed below.When applied to a Spectra* object, this functions simply returns the spectra it is storing.

Usage

## S4 method for signature 'Spectra'
spectra(object)

## S4 replacement method for signature 'data.frame'
spectra(object, ...) <- value

## S4 replacement method for signature 'Spectra'
spectra(object) <- value

Arguments

Details

If applied on a "data.frame" object, it is an helper function to create a Spectra* object. Two kind of interfaces are then available. value can be:

a vector:

Similarly to wl, the wavelengths of the spectra can be passed by a "numeric" vector. Alternatively, the names of the columns that contain the spectra information can be passed as a "character" vector.

a formula:

This interface is specific to inspectr. It follows a scheme where differents parts can be observed, the id column, the attributes columns, and the spectra columns, described by the wavelengths at which it has been measured:

• Placeholders:

  • ... placeholder for all the columns of your data.frame object except those that have been already used in other parts of the formula. This can lead to errors. E.g. if object has data one every wavelength between 350 and 2500 nm, spectra(object) <- id_field ~ ... ~ 500:2500 will stores the columns corresponding to the wavelengths 350-499 nm in its data slot!

  • id For the creation of a SpectraDataFrame, it is important to always specify an id field in the formula. If no id column is present, the id placeholder will create one for you.

• spectra(object) <- ~ 350:2500 will build a Spectra object from the wavelengths between 350 and 2500, based on the column names.

• spectra(object) <- ~ 350:2:2500 will build a Spectra object from the wavelengths in seq(350, 2500, by = 2)

• spectra(object) <- ~ 500:2350 will build a Spectra object from the wavelengths between 500 and 2350, even though other wavelengths are present (they will be dropped)

  In the three later cases, the id field has been dropped (it will be automatically created). If you want to use a column of "data.frame" as an id filed, you can still use the first part of the formula:

• spectra(object) <- id_field ~ 350:2500

• spectra(object) <- id_field ~ 350:5:2500

  Some data can also be added to the object, which will then be of SpectraDataFrame class:

• spectra(object) <- id_field ~ property1 ~ 500:2300 will create a SpectraDataFrame with ids from the id_field column, data from the property1 column, and spectral information between 500 and 2300 nm. That means that data property2, and all spectral information from bands < 500 nm or > 2300 nm will be dropped

  You can also combine the placeholders:

• spectra(object) <- id_field ~ ... ~ 350:2500 will create a SpectraDataFrame object with ids from the id_field column, all spectral bands between 350 and 2500 nm. The data slot is given all the remaining columns.

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

# Loading example data
data(oz)
class(oz) # this is a simple data.frame
# structure of the data.frame: it is rowwise-formatted
big.head(oz) 

## CREATING Spectra OBJECTS
##

# Using spectra() to initiate a Spectra from 
# the data.frame
spectra(oz) <- sr_no ~ 350:2500

# It is possible to select wavelengths using the formula interface
data(oz)
spectra(oz) <- sr_no ~ 350:5:2500

data(oz)
spectra(oz) <- sr_no ~ 500:1800

## CREATING SpectraDataFrame OBJECTS
##

# Using spectra() to initiate a SpectraDataFrame from 
# the data.frame
data(oz)
spectra(oz) <- sr_no ~ carbon + ph + clay ~ 350:2500

# Selecting data to be included in the SpectradataFrame object
data(oz)
spectra(oz) <- sr_no ~ carbon ~ 350:2500

# Forcing the creation of new ids using the id keyword in the 
# formula interface
data(oz)
spectra(oz) <- id ~ carbon ~ 350:2500
ids(oz, as.vector = TRUE)

# Using the "..." short-hand to select all the remaining columns
data(oz)
spectra(oz) <- sr_no ~ ... ~ 350:2500

## CREATING Spectra OBJECTS FROM
## BY-COLS-FORMATTED DATA
##

# For data formatted in the colwise format, 
# use the "colwise" mode

# Transforming data into colwise format
# for demonstration's sake
#
m <- melt_spectra(oz)
oz_by_col <- reshape2::acast(m, ... ~ sr_no)
oz_by_col <- data.frame(
  wl = rownames(oz_by_col), 
  oz_by_col, 
  check.names = FALSE)

# Here's colwise-formatted data 
big.head(oz_by_col)

# Convert it into Spectra object
spectra(oz_by_col, mode = "colwise") <- wl ~ ...

# Then data can be added to promote it as a SpectraDataFrame
my.data <- features(oz)
features(oz_by_col) <- my.data

Splice correction of a spectra collected using ASD hardware

Description

This is the correction method available in the ViewSpec Pro software from ASD, which aims at correcting steps in the data (see details).

Usage

## S4 method for signature 'Spectra'
splice(x, locations = list(c(750, 1000), c(1830, 1950)))

Arguments

Details

The SWIR1 part of the spectrum (1000-1800 nm) is taken as a reference for corrections as it is stable to the instrument sensitivity drift (Beal and Eamon, 2010)

This is based on a description of the splice correction algorithm described in:

Beal, D. and Eamon, M., 1996. Dynamic, Parabolic Linear Transformations of 'Stepped' Radiometric Data. Analytical Spectral Devices Inc., Boulder, CO.

Value

an object of same class as x

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500
oz_spliced <- splice(australia)
plot(oz_spliced)

Split a Spectra* object using factors

Description

Splits a Spectra* object into groups using a factor, either a provided as a vector or as an attribute in the features of the object.

Usage

## S4 method for signature 'Spectra'
split(x, f, drop = FALSE, ...)

Arguments

Details

This is an adaptation of the split function in the base package.

Value

A list of objects of same class as x.

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# On a Spectra object, we need to provide a vector of factors
# to split the object
s <- as(australia, 'Spectra')
# We make up some kind of factor to split the data. 
idx <- sample(letters[1:5], replace = TRUE, size = nrow(s)) # This is a vector
r <- split(s, idx)
str(r)

# On a SpectradataFrame object, we can also provide the name or index 
# of an attribute

# Generate some kind of factor
australia$fact <- sample(LETTERS[1:3], size = nrow(australia), replace = TRUE) 
r <- split(australia, 'fact')
str(r)

# A list is returned, and is thus ready for use with lapply, or any
# of the l*ply functions from the plyr package
lapply(r, nrow)

Subset SpectraDataFrame object

Description

Returns subsets of a SpectraDataFrame object.

Usage

subset(x, ...)

Arguments

Details

Additional parameters:

subset

logical expression indicating elements or rows to keep: missing values are taken as false

select

expression, indicating columns to select from the data slot

drop

passed on to "[" indexing operator

...

Additional arguments

Value

SpectraDataFrame object

Author(s)

Pierre Roudier pierre.roudier@gmail.com

See Also

mutate

Examples

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Subset on attributes
s <- subset(australia, carbon < 5)

# Subset and selection of attributes
s <- subset(australia, carbon < 5, select = 1)

Summary

Description

Summarize a Spectra* object.

Usage

\method{summary}{Spectra}(object, ...)

## S3 method for class 'summary.Spectra'
print(x, ...)

Arguments

Value

A "summary.Spectra" object

NULL

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

data(oz)
spectra(oz) <- sr_no ~ ... ~ 350:2500
summary(oz)

Retrieves or sets the wavelengths of a Spectra* object.

Description

Either retrieves the wavelengths from a Spectra* object, or creates a Spectra* object from a "data.frame" object by setting some of its columns as the wavelengths.

When applied to a Spectra* object, this functions simply returns the wavelengths of the spectra it is storing.

If applied on a "data.frame" object, it is an helper function to create a Spectra* object. It then needs to be indicated the wavelengths at which the spectra values are measured. The assumption is that each row of the "data.frame" is a spectra, and the column names of the "data.frame" contain the wavelengths values.

If all the columns are used to create the Spectra* object, a Spectra object is created. If some attributes are left, they will be used to generate a SpectraDataFrame object.

Usage

## S4 method for signature 'Spectra'
wl(object)

## S4 replacement method for signature 'data.frame'
wl(object) <- value

## S4 replacement method for signature 'Spectra'
wl(object) <- value

Arguments

Value

If applied on a "data.frame", either a Spectra or a SpectraDataFrame object. If applied on a Spectra* object, a vector.

Author(s)

Pierre Roudier pierre.roudier@gmail.com

See Also

spectra, Spectra-class, SpectraDataFrame-class

Examples

# Loading example data
data(oz)
spectra(oz) <- sr_no ~ ... ~ 350:2500

# Retrieving wavelengths from Spectra* object
wl(oz)

# Replacing wavelength values - USE WITH CAUTION!
wl(oz) <- 1:length(oz)
wl(oz)

# Use to initiate a Spectra* object from a data.frame
data(oz)
wl(oz) <- 350:2500
ids(oz) <- ~ sr_no

Wavelengths of Spectra* objects

Description

Retrieves the wavelengths units from Spectra* object

Usage

wl_units(object)

## S4 replacement method for signature 'Spectra'
wl_units(object) <- value

Arguments

Value

A vector

Author(s)

Pierre Roudier pierre.roudier@gmail.com

Examples

# Loading example data
data(oz)
spectra(oz) <- sr_no ~ ... ~ 350:2500

# Print wavelength information
wl(oz)
range(wl(oz))

# Manipulate wavelength information
wl(oz) <- wl(oz) + 1000
wl(oz)