Package 'phytoclass'

Add weights to the data, bound at a maximum.

Description

Add weights to the data, bound at a maximum.

Usage

Bounded_weights(S, weight.upper.bound = 30)

Arguments

S	Sample data matrix – a matrix of pigment samples
weight.upper.bound	Upper bound for weights (default is 30)

Value

A vector with upper bounds for weights

Examples

Bounded_weights(Sm, weight.upper.bound = 30)

---

Cluster things

Description

Cluster things

Usage

Cluster(
  Data,
  minSamplesPerCluster,
  row_ids = NULL,
  dist_method = "euclidean",
  hclust_method = "ward.D2"
)

Arguments

Data	S (sample) matrix
minSamplesPerCluster	the minimum number of samples required for a cluster
row_ids	A vector of custom row names to be added to dendrogram
dist_method	Distance metric to be used in stats::dist. This should be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski".
hclust_method	Cluster method to be used in stats::hclust. This should be one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).

Value

A named list of length two. The first element "cluster.list" is a list of clusters, and the second element "cluster.plot" the cluster analysis object (dendogram) that can be plotted.

Examples

Cluster.result <- Cluster(Sm, 14)
Cluster.result$cluster.list
plot(Cluster.result$cluster.plot)

---

Convergence Figure

Description

A figure to show the pigment ratios for each phytoplankton group for each iteration.

Usage

convergence_figure(fm_iter, niter = NULL)

Arguments

fm_iter	A data.frame with columns of iter, phyto, pigment and ratio
niter	Optional: the number of iterations on the x axis. If NULL, will extract from the iter column of fm_iter.

Value

A figure with each pigment ratio per iteration per group

Examples

# ADD_EXAMPLES_HERE

---

Fm data

Description

Fm data

Usage

Fm

Format

Fm

A data frame with 9 rows and 15 columns:

chl_c1

XX

Per

XX

X19but

XX

...

Source

XX

---

Fp data

Description

Fp data

Usage

Fp

Format

Fp

A data frame with 9 rows and 15 columns:

chl_c1

XX

Per

XX

X19but

XX

...

Source

XX

---

This function ensures S and F matrices are properly formatted and ordered for the simulated annealing function.

Description

Some checks applied:

• drops columns with 0 values

• drops taxa with missing major pigments, which are indicated with a '2'

• drops pigments with < 1% in samples

Usage

Matrix_checks(S, Fmat)

Arguments

S	Sample data matrix – a matrix of pigment samples
Fmat	Pigment to taxa matrix

Value

Named list with new S and Fmat matrices

Examples

MC <- Matrix_checks(Sm, Fm)  
Snew <- MC$Snew

---

min_max data

Description

min_max data

Usage

min_max

Format

min_max

A data frame with 51 rows and 4 columns:

class

XX

Pig_Abbrev

XX

min

XX

max

max

...

Source

XX

---

Performs the non-negative matrix factorisation for given phytoplankton pigments and pigment ratios, to attain an estimate of phytoplankton class abundances.

Description

Performs the non-negative matrix factorisation for given phytoplankton pigments and pigment ratios, to attain an estimate of phytoplankton class abundances.

Usage

NNLS_MF(Fn, S, S_weights = NULL)

Arguments

Fn	Pigment to Chl a matrix
S	Sample data matrix – a matrix of pigment samples
S_weights	Weights for each column

Value

A list containing

1. The F matrix (pigment: Chl a) ratios

2. The root mean square error (RMSE)

3. The C matrix (class abundances for each group)

Examples

Fmat <- as.matrix(phytoclass::Fm)
 S <- as.matrix(phytoclass::Sm)
 S_weights <- as.numeric(phytoclass:::Bounded_weights(S))
 place <- which(Fmat[, seq(ncol(Fmat) - 2)] > 0)
 num.loops <- 2
 # Run Steepest_Descent
 result <- phytoclass:::Steepest_Descent(Fmat, place, S, S_weights, num.loops)

---

Phytoplankton Class Abundance Figure

Description

This function plots the class abundances as output by simulated_annealing.

Usage

phyto_figure(c_matrix)

Arguments

c_matrix

C matrix of class abundance concentrations

Value

A stacked line plot with sample number on x axis, chl a concentrations on y axis, and phytoplankton groups as colors

Examples

# ADD_EXAMPLES_HERE

---

This is the main phytoclass algorithm. It performs simulated annealing algorithm for S and F matrices. See the examples (Fm, Sm) for how to set up matrices, and the vignette for more detailed instructions. Different pigments and phytoplankton groups may be used.

Description

This is the main phytoclass algorithm. It performs simulated annealing algorithm for S and F matrices. See the examples (Fm, Sm) for how to set up matrices, and the vignette for more detailed instructions. Different pigments and phytoplankton groups may be used.

Usage

simulated_annealing(
  S,
  Fmat = NULL,
  user_defined_min_max = NULL,
  do_matrix_checks = TRUE,
  niter = 500,
  step = 0.009,
  weight.upper.bound = 30,
  verbose = TRUE,
  seed = NULL,
  check_converge = 100,
  alt_pro_name = NULL
)

Arguments

S	Sample data matrix – a matrix of pigment samples
Fmat	Pigment to Chl a matrix
user_defined_min_max	data frame with some format as min_max built-in data
do_matrix_checks	This should only be set to TRUE when using the default values. This will remove pigment columns that have column sums of 0. Set to FALSE if using customised names for pigments and phytoplankton groups
niter	Number of iterations (default is 500)
step	Step ratio used (default is 0.009)
weight.upper.bound	Upper limit of the weights applied (default value is 30).
verbose	Logical value. Output error and temperature at each iteration. Default value of TRUE
seed	Set number to reproduce the same results
check_converge	TRUE/FALSE/integer; set the number of F matrices to for convergence checking
alt_pro_name	Optional: additional alternate versions of divinyl-chlorophyll-a spellings used to detect prochlorococcus (Default: "dvchl", "dvchla", "dv_chla")

Value

A list containing

1. Fmat matrix

2. RMSE (Root Mean Square Error)

3. condition number

4. Class abundances

5. Figure (plot of results)

6. MAE (Mean Absolute Error)

7. Error

8. F_mat_iter

9. converge_plot

Examples

# Using the built-in matrices Sm and Fm
set.seed(5326)
sa.example <- simulated_annealing(Sm, Fm, niter = 5)
sa.example$Figure

---

Perform simulated annealing algorithm for samples with divinyl chlorophyll and prochlorococcus. Chlorophyll must be the final column of both S and F matrices, with Divinyl Chlorophyll a the 2nd to last column. See how the example Sp and Fp matrices are organised.

Description

Perform simulated annealing algorithm for samples with divinyl chlorophyll and prochlorococcus. Chlorophyll must be the final column of both S and F matrices, with Divinyl Chlorophyll a the 2nd to last column. See how the example Sp and Fp matrices are organised.

Usage

simulated_annealing_Prochloro(
  S,
  Fmat = NULL,
  user_defined_min_max = NULL,
  do_matrix_checks = TRUE,
  niter = 500,
  step = 0.009,
  weight.upper.bound = 30,
  verbose = TRUE,
  seed = NULL,
  check_converge = 100
)

Arguments

S	Sample data matrix – a matrix of pigment samples
Fmat	Pigment to Chl a matrix
user_defined_min_max	data frame with some format as min_max built-in data
do_matrix_checks	This should only be set to TRUE when using the default values. This will remove pigment columns that have column sums of 0. Set to FALSE if using customised names for pigments and phytoplankton groups
niter	Number of iterations (default is 500)
step	Step ratio used (default is 0.009)
weight.upper.bound	Upper limit of the weights applied (default value is 30).
verbose	Logical value. Output error and temperature at each iteration. Default value of TRUE
seed	Set seed number to reproduce the same results
check_converge	TRUE/FALSE/integer; set the number of F matrices to for convergence checking

Value

A list containing

1. Fmat matrix

2. RMSE (Root Mean Square Error)

3. condition number

4. Class abundances

5. Figure (plot of results)

6. MAE (Mean Absolute Error)

7. Error

Examples

# Using the built-in matrices Sp and Fp.
set.seed(5326)
sa.example <- simulated_annealing_Prochloro(Sp, Fp, niter = 1)
sa.example$Figure

---

Sm data

Description

Sm data

Usage

Sm

Format

Sm

A data frame with 29 rows and 15 columns:

chl_c1

XX

Per

XX

X19but

XX

...

Source

XX

---

Sp data

Description

Sp data

Usage

Sp

Format

Sp

A data frame with 29 rows and 15 columns:

chl_c1

XX

Per

XX

X19but

XX

...

Source

XX

---

Stand-alone version of steepest descent algorithm. This is similar to the CHEMTAX steepest descent algorithm. It is not required to use this function, and as results are not bound by minimum and maximum, results may be unrealistic.

Description

Stand-alone version of steepest descent algorithm. This is similar to the CHEMTAX steepest descent algorithm. It is not required to use this function, and as results are not bound by minimum and maximum, results may be unrealistic.

Usage

Steepest_Desc(Fmat, S, num.loops)

Arguments

Fmat	Pigment to Chl a matrix
S	Sample data matrix – a matrix of pigment samples
num.loops	Number of loops/iterations to perform (no default)

Value

A list containing

1. The F matrix (pigment: Chl a) ratios

2. RMSE (Root Mean Square Error)

3. Condition number

4. class abundances

5. Figure (plot of results)

6. MAE (Mean Absolute Error)

Examples

MC <- Matrix_checks(Sm,Fm)
Snew <- MC$Snew
Fnew <- MC$Fnew
SDRes <- Steepest_Desc(Fnew,Snew, num.loops = 20)

---