Package 'mixchar'

Accessor function to extract mean weights

Description

Accessor function to extract mean weights

Usage

component_weights(object)

Arguments

object

a decon object

Value

Extract mean fractions of the object

Examples

data(juncus)
tmp <- process(juncus, init_mass = 18.96,
               temp = 'temp_C', mass_loss = 'mass_loss')
output <- deconvolve(tmp)
component_weights(output)

---

Deconvolves Thermogravimetric Data

Description

This function deconvolves thermogravimetric data using a Fraser-Suzuki mixture model

Usage

deconvolve(
  process_object,
  lower_temp = 120,
  upper_temp = 700,
  seed = 1,
  n_peaks = NULL,
  start_vec = NULL,
  lower_vec = NULL,
  upper_vec = NULL
)

Arguments

process_object	process object obtained from process function
lower_temp	lower temperature bound to crop dataset, default to 120
upper_temp	upper temperature bound to crop dataset, default to 700
seed	random seed for nloptr optimiser
n_peaks	number of curves optional specification
start_vec	vector of starting values for nls function. Only specify this vector if you have selected the number of curves in the n_peaks parameter.
lower_vec	vector of lower bound values for nls. Only specify this vector if you have selected the number of curves in the n_peaks parameter.
upper_vec	vector of upper bound values for nls. Only specify this vector if you have selected the number of curves in the n_peaks parameter.

Value

decon list containing amended dataframe, temperature bounds, minpack.lm model fit, the number of curves fit, and estimated component weights

Examples

data(juncus)
tmp <- process(juncus, init_mass = 18.96,
               temp = 'temp_C', mass_loss = 'mass_loss')
output <- deconvolve(tmp)
my_starting_vec <- c(height_1 = 0.003, skew_1 = -0.15, position_1 = 250, width_1 = 50,
                     height_2 = 0.006, skew_2 = -0.15, position_2 = 320, width_2 = 30,
                     height_3 = 0.001, skew_3 = -0.15, position_3 = 390, width_3 = 200)
output <- deconvolve(tmp, n_peaks = 3, start_vec = my_starting_vec)

---

Fraser-Suzuki function for a single curve

Description

This function calculates the Fraser-Suzuki function.

Usage

fs_function(temp, height, skew, position, width)

Arguments

temp	temperature values
height	height value
skew	shape value
position	position value
width	width value

Value

Fraser-Suzuki function

Examples

temp <- 150:600
fs_output <- fs_function(temp, height = 0.004, skew = -.15,
position = 250, width = 50)

---

Fraser-Suzuki mixture model

Description

Fraser-Suzuki mixture model

Usage

fs_mixture(
  temp,
  height_1,
  skew_1,
  position_1,
  width_1,
  height_2,
  skew_2,
  position_2,
  width_2,
  height_3,
  skew_3,
  position_3,
  width_3,
  height_0 = NULL,
  skew_0 = NULL,
  position_0 = NULL,
  width_0 = NULL
)

Arguments

temp	temperature values
height_1	height value for hemicellulose
skew_1	shape value for hemicellulose
position_1	position value for hemicellulose
width_1	width value for hemicellulose
height_2	height value for cellulose
skew_2	shape value for cellulose
position_2	position value for cellulose
width_2	width value for cellulose
height_3	height value for lignin
skew_3	shape value for lignin
position_3	position value for lignin
width_3	width value for lignin
height_0	height value for second hemicellulose curve, if present
skew_0	shape value for second hemicellulose curve, if present
position_0	position value for second hemicellulose curve, if present
width_0	width value for second hemicellulose curve, if present

Value

Fraser-Suzuki model output

Examples

temp <- 150:600
fs_mixture_output <- fs_mixture(temp,
height_1 = 0.003, skew_1 = -0.15, position_1 = 250, width_1 = 50,
height_2 = 0.006, skew_2 = -0.15, position_2 = 320, width_2 = 30,
height_3 = 0.001, skew_3 = -0.15, position_3 = 390, width_3 = 200)

---

Non-linear model using Fraser-Suzuki mixture model

Description

Non-linear model output using optimised parameter values with a three-part mixture model using Fraser-Suzuki equation

Usage

fs_model(dataframe, params, lb, ub)

Arguments

dataframe	dataframe
params	starting parameter values
lb	lower bounds for model
ub	upper bounds for model

Value

model output

---

Calculate weight quantiles

Description

Calculate weight quantiles

Usage

get_weights(param_vec, output)

Arguments

param_vec	parameter estimates from minpack model
output	deconvolve output of model

Value

weights for each component

---

Thermogravimetric data for Juncus amabilis

Description

Raw thermogravimetric data from the wetland rush, J. amabilis

Usage

data(juncus)

Format

An object of class 'cross'

Source

Saras M Windecker

Examples

data(juncus)

---

Thermogravimetric data for Marsilea drumondii

Description

Raw thermogravimetric data from the wetland forb, M. drumondii.

Usage

data(marsilea)

Format

An object of class 'cross'

Source

Saras M Windecker

Examples

data(marsilea)

---

Accessor function to extract model fit

Description

Accessor function to extract model fit

Usage

model_fit(object)

Arguments

object

a decon object

Value

$minpack.lm of the object

Examples

data(juncus)
tmp <- process(juncus, init_mass = 18.96,
               temp = 'temp_C', mass_loss = 'mass_loss')
output <- deconvolve(tmp)
model_fit(output)

---

Accessor function to extract model parameters

Description

Accessor function to extract model parameters

Usage

model_parameters(object)

Arguments

object

a decon object

Value

model parameters from minpack.lm::nlsLM fit

Examples

data(juncus)
tmp <- process(juncus, init_mass = 18.96,
               temp = 'temp_C', mass_loss = 'mass_loss')
output <- deconvolve(tmp)
model_parameters(output)

---

Default S3 plot method for decon objects (derived from 'deconvolve()')

Description

This function sets up the default plotting method for outputs from deconvolve function

Usage

## S3 method for class 'decon'
plot(x, bw = TRUE, ...)

Arguments

x	decon object as generated by deconvolve
bw	logical argument indicating whether the plot should be in black and white or colour
...	other options passed to plot

Value

plot

---

Default S3 plot method for process objects (derived from 'process()')

Description

This function sets up the default plotting method for outputs from process function

Usage

## S3 method for class 'process'
plot(x, plot_type = NULL, cex = 1, ...)

Arguments

x	process object as generated by process
plot_type	defaults to both plots. Can specify 'mass' or 'rate' curves by themselves.
cex	size of plots features
...	other options passed to plot

Value

plot

---

Default S3 print method for decon object (derived from 'deconvolve()')

Description

This function sets up the default print method for outputs from deconvolve function

Usage

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

Arguments

x	decon object as generated by deconvolve
...	other options passed to plot

Value

print output

---

Default S3 print method for process object (derived from 'process()')

Description

This function sets up the default print method for outputs from process function

Usage

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

Arguments

x	process object as generated by deconvolve
...	other options passed to plot

Value

print output

---

Calculates the derivative rate of mass loss of thermogravimetric data

Description

This function processes thermogravimetric data by calculating the derivative of mass loss

Usage

process(data, init_mass, temp, mass_loss = NULL, mass = NULL, temp_units = "C")

Arguments

data	dataframe
init_mass	numeric value of initial sample mass in mg
temp	column name containing temperature values
mass_loss	column name containing mass loss values in mg
mass	column name containing mass values in mg
temp_units	specify units of temperature, default = Celsius. Can specify 'K' or 'Kelvin' if in Kelvin

Value

process list containing modified dataframe, initial mass of sample, and maximum and minimum temperature values

Examples

data(juncus)
tmp <- process(juncus, init_mass = 18.96,
               temp = 'temp_C', mass_loss = 'mass_loss')

---

Accessor function to extract processed dataframe

Description

Accessor function to extract processed dataframe

Usage

rate_data(object)

Arguments

object

a process or deconvolve object

Value

Dataframe of the object

Examples

data(juncus)
tmp <- process(juncus, init_mass = 18.96,
               temp = 'temp_C', mass_loss = 'mass_loss')
rate_data(tmp)

---

Accessor function to extract selected temperature bounds

Description

Accessor function to extract selected temperature bounds

Usage

temp_bounds(object)

Arguments

object

the output of either the process or deconvolve functions

Value

Temperature bounds of the data in the object

Examples

data(juncus)
tmp <- process(juncus, init_mass = 18.96,
               temp = 'temp_C', mass_loss = 'mass_loss')
temp_bounds(tmp)

---

Calculate weight quantiles

Description

Calculate weight quantiles

Usage

weight_quantiles(output, seed)

Arguments

output	dataframe
seed	seed

Value

list of means and confidence intervals of weight estimates

---

Calculate weight single component

Description

Calculate weight single component

Usage

wt_component(j, param_vec, lower_temp, upper_temp)

Arguments

j	component
param_vec	vector of parameters
lower_temp	lower temperature bound
upper_temp	upper temperature bound

Value

weight of component

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