| Title: | Mixture Model for the Deconvolution of Thermal Decay Curves |
| Version: | 0.1.0 |
| Date: | 2018-08-11 |
| Description: | Deconvolution of thermal decay curves allows you to quantify proportions of biomass components in plant litter. Thermal decay curves derived from thermogravimetric analysis (TGA) are imported, modified, and then modelled in a three- or four- part mixture model using the Fraser-Suzuki function. The output is estimates for weights of pseudo-components corresponding to hemicellulose, cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, H. (2007) <doi:10.1016/j.jaap.2006.12.008>, Órfão, J. J. M. and Figueiredo, J. L. (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and Yan, R. and Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) <doi:10.1021/ef0580117>. |
| Depends: | R (≥ 3.2.0) |
| Imports: | graphics, minpack.lm, nloptr, stats, zoo, tmvtnorm |
| License: | MIT + file LICENSE |
| URL: | http://github.com/smwindecker/mixchar |
| BugReports: | http://github.com/smwindecker/mixchar/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.1.0 |
| Suggests: | knitr, rmarkdown, devtools, testthat, covr |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2018-08-15 15:32:45 UTC; windeckers |
| Author: | Saras Windecker [aut, cre], Nick Golding [aut] |
| Maintainer: | Saras Windecker <saras.windecker@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2018-08-16 11:50:09 UTC |
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
Function to determine x-value of peak < 500 K
Description
Function to determine x-value of peak < 500 K
Usage
inflection(x, y, w = 1, ...)
Arguments
x |
x-values |
y |
y-values |
Value
returns a list of values. $x returns the inflections points within x.
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)
This function evaluates root mean squared error of a model
Description
This function evaluates root mean squared error of a model
Usage
objective(theta, model, temp, obs)
Arguments
theta |
values for each of the parameters |
model |
mathematical function to be used to model data |
temp |
temperature values |
obs |
DTG mass values |
Value
root mean square error of model output
Optimise parameters
Description
This function otimises parameter selection so that the model will ultimately converge.
Usage
param_select(theta, lb, ub, model, temp, obs, seed, restarts = 300)
Arguments
theta |
vector of starting values for each parameter |
lb |
vector of lower bounds on each parameter |
ub |
vector of upper bounds on each parameter |
model |
mathematical function to be applied |
temp |
temperature values |
obs |
DTG mass values |
seed |
random seed for optimiser |
restarts |
number of times for optimiser to restart |
Value
optimised starting parameter values
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)
Calculate room mean squared error
Description
This function calculated root mean squarer error of prediction.
Usage
rmse(pred, obs)
Arguments
pred |
predicted parameter value |
obs |
DTG mass values |
Value
root mean squared error
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)
Function to convert inflection point to logical argument for presence of fourth peak
Description
Function to convert inflection point to logical argument for presence of fourth peak
Usage
three_peaks(x)
Arguments
x |
numeric |
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