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

Value

A vector with upper bounds for weights

Examples

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

Cluster things

Description

Cluster things

Usage

Cluster(Data, min_cluster_size)

Arguments

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)

Calculate the condition number ...

Description

Calculate the condition number ...

Usage

Condition_test(S, Fn, min.val = NULL, max.val = NULL)

Arguments

Conduit between minimise_elements function and Fac_F_R of steepest descent algorithm.

Description

Conduit between minimise_elements function and Fac_F_R of steepest descent algorithm.

Usage

Conduit_1(Fmat, place, S, cm)

Arguments

Conduit between minimise_elements function and Fac_F_R of steepest descent algorithm.

Description

Conduit between minimise_elements function and Fac_F_R of steepest descent algorithm.

Usage

Conduit_2(Fmat, place, S, cm)

Arguments

Conduit between minimise_elements function and Fac_F_R of steepest descent algorithm.

Description

Conduit between minimise_elements function and Fac_F_R of steepest descent algorithm.

Usage

Conduit_3(Fmat, place, S, cm)

Arguments

Sets the default minimum and maximum values for phytoplankton groups pigment ratios. To use this function, pigment and phytoplankton group names will need to fit the naming criteria of phytoclass.

Description

Sets the default minimum and maximum values for phytoplankton groups pigment ratios. To use this function, pigment and phytoplankton group names will need to fit the naming criteria of phytoclass.

Usage

Default_min_max(min_max, Fmat, place)

Arguments

Part of the steepest descent algorithm and work to reduce error given the S and F matrices.

Description

Part of the steepest descent algorithm and work to reduce error given the S and F matrices.

Usage

Fac_F_RR1(Fmat, vary, S, cm)

Arguments

Part of the steepest descent algorithm and work to reduce error given the S and F matrices

Description

Part of the steepest descent algorithm and work to reduce error given the S and F matrices

Usage

Fac_F_RR2(Fmat, vary, place, S, cm)

Arguments

Part of the steepest descent algorithm and work to reduce error given the S and F matrices

Description

Part of the steepest descent algorithm and work to reduce error given the S and F matrices

Usage

Fac_F_RR3(Fmat, vary, place, S, cm)

Arguments

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

Remove any column values that average 0. Further to this, also remove phytoplankton groups from the F matrix if their diagnostic pigment isn’t present.

Description

Remove any column values that average 0. Further to this, also remove phytoplankton groups from the F matrix if their diagnostic pigment isn’t present.

Usage

Matrix_checks(S, Fmat)

Arguments

Value

Named list with new S and Fmat matrices

Examples

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

Part of the steepest descent algorithm

Description

Part of the steepest descent algorithm

Usage

Minimise_elements(Fmat, place, S, cm)

Arguments

A function that reduces every for every element that didn't reduce in index function

Description

A function that reduces every for every element that didn't reduce in index function

Usage

Minimise_elements1(Fmat, place, S, cm)

Arguments

A function that reduces every for every element that didn't reduce in index function

Description

A function that reduces every for every element that didn't reduce in index function

Usage

Minimise_elements2(Fmat, place, S, cm)

Arguments

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, cm = NULL)

Arguments

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

MC <- Matrix_checks(Sm,Fm)
Snew <- MC$Snew
Fnew <- MC$Fnew
cm <- Bounded_weights(Snew, weight.upper.bound = 30)
NNLS_MF(Fnew, Snew, cm)

Perform matrix factorisation for phytoplankton pigments and pigments ratios

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_Final(Fn, S, S_Chl, cm)

Arguments

Details

Unlike NNLS_ML(), it also removes any weighting and normalisation, and also multiplies relative abundances by chlorophyll values to determine the biomass of phytoplankton groups.

This function normalises each column in F to row sum

Description

This function normalises each column in F to row sum

Usage

Normalise_F(Fmat)

Arguments

Value

A matrix

This function normalises each column in S to row sum

Description

This function normalises each column in S to row sum

Usage

Normalise_S(S)

Arguments

Value

A matrix

Final step for MF with prochlorococcus

Description

Final step for MF with prochlorococcus

Usage

Prochloro_NNLS_MF_Final(Fn, S, S_Chl, cm, S_dvChl)

Arguments

Normalise F for prochloro

Description

Normalise F for prochloro

Usage

Prochloro_Normalise_F(Fmat)

Arguments

Prochloro random neighbour

Description

Prochloro random neighbour

Usage

Prochloro_Random_Neighbour(
  Fn,
  Temp,
  chlv,
  s_c,
  N,
  place,
  S,
  cm,
  min.val,
  max.val
)

Arguments

Selects a random neighbour for the simulated annealing algorithm.

Description

Selects a random neighbour for the simulated annealing algorithm.

Usage

Prochloro_Random_Neighbour_2(
  Fn,
  Temp,
  chlv,
  s_c,
  place,
  S,
  cm,
  min.val,
  max.val,
  chlvp
)

Arguments

Prochloro Wrangling

Description

Prochloro Wrangling

Usage

Prochloro_Wrangling(Fl, min.val, max.val)

Arguments

Select a random neighbour when the previous random neighbour is beyond the minimum or maximum value.

Description

Select a random neighbour when the previous random neighbour is beyond the minimum or maximum value.

Usage

Random_neighbour(Fn, Temp, chlv, s_c, N, place, S, cm, min.val, max.val)

Arguments

Selects a random neighbour for the simulated annealing algorithm.

Description

Selects a random neighbour for the simulated annealing algorithm.

Usage

Random_neighbour2(Fn, Temp, chlv, s_c, place, S, cm, min.val, max.val)

Arguments

Randomise individual elements in the F matrix.

Description

Randomise individual elements in the F matrix.

Usage

Randomise_elements(x, min.scaler, max.scaler)

Arguments

Value

numeric

Select the new F matrix element with lowest error in the steepest descent algorithm.

Description

Select the new F matrix element with lowest error in the steepest descent algorithm.

Usage

Replace_Rand(Fmat, i, S, cm, min.scaler, max.scaler)

Arguments

Apply the steepest descent algorithm

Description

Apply the steepest descent algorithm

Usage

SAALS(Ft, min.value, max.value, place, S, cm, num.loops)

Arguments

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

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)

Performs the steepest descent algorithm for a set number of iterations

Description

Performs the steepest descent algorithm for a set number of iterations

Usage

Steepest_Descent(Fmat, place, S, cm, num.loops)

Arguments

Apply weights to F/S matrices

Description

Apply weights to F/S matrices

Usage

Weight_error(S, cm)

Arguments

Value

A matrix

Converts data-types and selects data for randomisation in the simulated annealing algorithm

Description

Converts data-types and selects data for randomisation in the simulated annealing algorithm

Usage

Wrangling(Fl, min.val, max.val)

Arguments

min_max data

Description

min_max data

Usage

min_max

Format

min_max

A data frame with 76 rows and 4 columns:

class

XX

Pig_Abbrev

XX

min

XX

max

max

...

Source

XX

Phytoclass - simualted annealing

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
)

Arguments

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 Sm and Fm
set.seed(5326)
sa.example <- simulated_annealing(Sm, Fm, niter = 5)
sa.example$Figure

#Using non-default data:
# Set up a new F matrix
Fu <- data.frame(
  Per = c(0, 0, 0, 0, 1, 0, 0, 0),
  X19but = c(0, 0, 0, 0, 0, 1, 1, 0),
  Fuco = c(0, 0, 0, 1, 0, 1, 1, 0),
  Pra = c(1, 0, 0, 0, 0, 0, 0, 0),
  X19hex = c(0, 0, 0, 0, 0, 1, 0, 0),
  Allo = c(0, 0, 1, 0, 0, 0, 0, 0),
  Zea = c(1, 1, 0, 0, 0, 0, 0, 1),
  Chl_b = c(1, 1, 0, 0, 0, 0, 0, 0),
  Tchla = c(1, 1, 1, 1, 1, 1, 1, 1)
)

rownames(Fu) <- c(
  "Prasinophytes", "Chlorophytes", "Cryptophytes"
  , "Diatoms-2", "Dinoflagellates-1",
  "Haptophytes", "Pelagophytes", "Syn"
)

#Set up a new Min_max file
Min_max <- data.frame(
  Class = c(
    "Syn", "Chlorophytes", "Chlorophytes", "Prasinophytes", "Prasinophytes",
    "Prasinophytes", "Cryptophytes", "Diatoms-2", "Diatoms-2", "Pelagophytes",
    "Pelagophytes", "Pelagophytes", "Dinoflagellates-1", "Haptophytes",
    "Haptophytes", "Haptophytes", "Haptophytes", "Diatoms-2", "Cryptophytes",
    "Prasinophytes", "Chlorophytes", "Syn", "Dinoflagellates-1", "Pelagophytes"
  ),
  Pig_Abbrev = c(
    "Zea", "Zea", "Chl_b", "Pra", "Zea", "Chl_b", "Allo", "Chl_c3",
    "Fuco", "Chl_c3", "X19but", "Fuco", "Per", "X19but", "X19hex",
    "Fuco", "Tchla", "Tchla", "Tchla", "Tchla", "Tchla", "Tchla", "Tchla",
    "Tchla"
  ),
  min = as.numeric(c(
    0.0800, 0.0063, 0.1666, 0.0642, 0.0151, 0.4993, 0.2118, 0.0189,
    0.3315, 0.1471, 0.2457, 0.3092, 0.3421, 0.0819, 0.2107, 0.0090,
    1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000
  )),
  max = as.numeric(c(
    1.2123, 0.0722, 0.9254, 0.4369, 0.1396, 0.9072, 0.5479, 0.1840,
    0.9332, 0.2967, 1.0339, 1.2366, 0.8650, 0.2872, 1.3766, 0.4689,
    1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000
  ))
)
#Run the new file with your own set up (make sure all names between your data (S),
#F-marix, and min_max are correct)
Results <- simulated_annealing(
  S = Sm, 
  F = Fu,
  user_defined_min_max = Min_max,
  do_matrix_checks = TRUE, 
  #You may want to change this to faults if your naming conventions are different.
  niter = 1,
  step = 0.01,
  weight.upper.bound = 30)

Perform simulated annealing algorithm for S and F matrices

Description

Perform simulated annealing algorithm for S and F matrices

Perform simulated annealing algorithm for samples with divinyl chlorophyll and prochlorococcus. Divinyl chlorophyll must be the final column of both S and F matrices, with 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
)

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
)

Arguments

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

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 = 5)
sa.example$Figure
# Using the built-in matrices Sp and Fp.
set.seed(5326)
sa.example <- simulated_annealing_Prochloro(Sp, Fp, niter = 1)
sa.example$Figure

# To use with non-defauæy values, see the 'simulated_annealing' example-

Title

Description

Title

Usage

target(x)

Arguments

Turn each non-zero element of the F-matrix into a vector

Description

Turn each non-zero element of the F-matrix into a vector

Usage

vectorise(Fmat)

Arguments