In langfob/bdpg: Package For Building and Testing Biodiversity Problem Generators
Function: summarize_and_plot_graph_and_distribution_structure_information
summarize_and_plot_graph_and_distribution_structure_information =
function (PU_spp_pair_indices,
cor_or_app_label,
all_correct_node_IDs,
plot_output_dir,
spp_col_name,
PU_col_name,
presences_col_name)
- Function defined in biodivprobgen_utilities.R.
-
PU_spp_pair_indices
- data frame with one PU/spp pair for every occurrence of every species in the problem
- exactly 2 columns
- column headings are PU_ID and spp_ID
- dimensions are [num PU spp pairs, 2]
- Example:
dim (PU_spp_pair_indices)
1628 2
PU_spp_pair_indices:
PU_ID spp_ID
1 1 1
2 2 1
...
1625 64 813
1626 82 813
1627 54 814
1628 112 814
-
cor_or_app_label
- character string to use in writing labels on plots or messages
- Only 2 values allowed, either "cor" or "app".
- That might change if I allow multiple different apparents.
- Example:
cor_or_app_label:
"cor"
-
all_correct_node_IDs
- Vector listing all node IDs (PU IDs) in the problem, regardless of whether the PU is occupied by any species or not
- length of the vector is just the total number of PUs, empty and not empty
- Example:
length (all_correct_node_IDs)
122
head (all_correct_node_IDs)
1 2 3 4 5 6
-
column names
- character strings labelling columns in various tables
- the names are derived from what is used in the original creation of the tables.
- At the moment, I don't know how to specify the names ahead of time as variables and then use the variables to specify the column names.
- Example:
spp_col_name
"spp_ID"
PU_col_name
"PU_ID"
presences_col_name
"freq"
-
returned value: final_link_counts_for_each_node
- data frame with one column of unique PU IDs (both empty and non-empty) and a second column containing the number of species occurring on the corresponding PU
- column names are "PU_ID" and "freq"
- total number of rows in table is the total number of PUs in the problem, both empty and non-empty PUs
- Example:
final_link_counts_for_each_node:
PU_ID freq
1 1 1
2 2 25
...
Function: compute_network_measures_using_bipartite_package
compute_network_measures_using_bipartite_package = function (bpm)
- Function defined in gscp_11a_network_measures_using_bipartite_package.R
-
bpm
- 2D matrix with one row for every species and one column for every PU
- Created by create_adj_matrix_with_spp_rows_vs_PU_cols() in
- gscp_10c_build_adj_and_cooccurrence_matrices.R
- Will be viewed as a bipartite matrix (bpm) by the bipartite network routines
- Example:
```
dim (bpm)
[1] 814 122
bpm [1:4,1:8]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1 1 0 0 0 0 0 0 ...
[2,] 0 0 1 1 0 0 0 0 ...
[3,] 0 0 0 0 1 1 0 0 ...
[4,] 0 0 0 0 0 0 1 1 ...
...
```
- returned value: bipartite_metrics_from_bipartite_package
- data frame with one column of unique PU IDs (both empty and non-empty) and a second column containing the number of species occurring on the corresponding PU
- column names are "PU_ID" and "freq"
- total number of rows in table is the total number of PUs in the problem, both empty and non-empty PUs
- Example:
```
bipartite_metrics_from_bipartite_package
connectance web_asymmetry links_per_species number_of_compartments
[1,] 0.0169207 -0.744086 1.755914 2
compartment_diversity cluster_coefficient nestedness weighted_nestedness
[1,] 1.015475 0.01726264 4.373023 0.3154829
weighted_NODF interaction_strength_asymmetry specialisation_asymmetry
[1,] 0 0 0.5206311
linkage_density weighted_connectance Fisher_alpha Shannon_diversity
[1,] 12.70423 0.01366046 1.717987e+12 7.398174
interaction_evenness Alatalo_interaction_evenness H2 number.of.species.HL
[1,] 0.6445867 1 0 119
number.of.species.LL mean.number.of.shared.partners.HL
[1,] 811 0.1365902
mean.number.of.shared.partners.LL cluster.coefficient.HL cluster.coefficient.LL
[1,] 0.05527089 0.02864849 0.01827332
weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL niche.overlap.HL
[1,] 0.01722157 0.3061294 0.006840523
niche.overlap.LL togetherness.HL togetherness.LL C.score.HL C.score.LL V.ratio.HL
[1,] 0.02669537 0.004202092 0.01122122 0.9788591 0.9584458 9.688273
V.ratio.LL discrepancy.HL discrepancy.LL extinction.slope.HL extinction.slope.LL
[1,] 0.1657092 1563 1565 5.53822 1.934873
robustness.HL robustness.LL functional.complementarity.HL
[1,] 0.8232885 0.6583167 461.6131
functional.complementarity.LL partner.diversity.HL partner.diversity.LL
[1,] 1031.04 3.019578 0.7403126
generality.HL vulnerability.LL
[1,] 23.23393 2.174525
```
Function: summarize_and_plot_graph_and_distribution_structure_information
compute_igraph_related_network_measures <-
function (
num_spp,
num_PUs,
PU_spp_pair_indices,
emulatingTzar,
DEBUG_LEVEL,
network_output_dir,
# cor_num_PUs, # app_ ???
# cor_num_spp,
PU_col_name,
spp_col_name
)
- Function defined in gscp_11b_network_measures_using_igraph_package.R
- num_spp
- integer number of correct or apparent species, depending on whether the routine is passed a correct or apparent problem
- num_PUs
- integer number of correct or apparent PUs, depending on whether the routine is passed a correct or apparent problem
- PU_spp_pair_indices
- data frame with one PU/spp pair for every occurrence of every species in the problem
- exactly 2 columns
- column headings are PU_ID and spp_ID
- dimensions are [num PU spp pairs, 2]
- Example:
dim (PU_spp_pair_indices)
1628 2
PU_spp_pair_indices:
PU_ID spp_ID
1 1 1
2 2 1
...
1625 64 813
1626 82 813
1627 54 814
1628 112 814
- emulatingTzar
- boolean flag indicating whether the run is emulating tzar
- DEBUG_LEVEL
- integer number indicating what level of debugging output to do
- 0 means no debugging output
- increasing integers > 0 imply different levels of debugging output
- can't remember whether higher means more or less output, but it doesn't matter much at the moment since I don't think that I have used anything other than 0 and 1 at this point
- network_output_dir
- character string indicating full path where network metric outputs should be written
- is this right, i.e., network metric outputs? or is it for when you draw the network to a file?
- Example:
"/Users/bill/tzar/outputdata/biodivprobgen/default_runset/1165_marxan_simulated_annealing.inprogress/Networks"
- REMOVING THESE: counts that I'm not sure about, i.e., are they supposed to always be cor or should they be cor or app depending on what problem is passed in? Looks like they're only used in constructing the list of vertex names for the network that is being constructed. Having extra (empty) vertices for the PUs seems like it may distort the statistics that are computed. 2016 04 08 - BTL
- cor_num_PUs # app_ ???
- cor_num_spp
- column names
- character strings labelling columns in various tables
- the names are derived from what is used in the original creation of the tables.
- At the moment, I don't know how to specify the names ahead of time as variables and then use the variables to specify the column names.
- Example:
PU_col_name
"PU_ID"
spp_col_name
"spp_ID"
- returned value: bipartite_metrics_from_igraph_package_df
- data frame with one column for each bipartite metric computed by igraph
- there is always only one row in the data frame?
- each column heading is the name of the metric
bipartite_metrics_from_igraph_package_df
ig_top ig_bottom ig_num_edges_m ig_ktop ig_kbottom ig_bidens ig_lcctop ig_lccbottom ig_distop ig_disbottom ig_cctop ig_ccbottom ig_cclowdottop
1 814 122 1633 2.006143 13.38525 0.01644379 810 118 4.342801 4.405056 0.3403602 0.03887111 0.5983846
ig_cclowdotbottom ig_cctopdottop ig_cctopdotbottom ig_mean_bottom_bg_redundancy ig_median_bottom_bg_redundancy ig_mean_top_bg_redundancy
1 0.3004119 0.45096 0.05012125 0.07886978 0 0.01429777
ig_median_top_bg_redundancy
1 0
Function: create_PU_spp_pair_names
HAVEN'T DECODED THIS ONE YET. JUST LEAVING HERE WHAT I HAD CUT OUT OF THE CODE TO TRY TO FIGURE OUT WHETHER THERE SHOULD BE app_num_spp AND app_num_PUs AND WHAT WERE PU_spp_pair_names AND PU_spp_pair_names_triple.
REMEMBER THAT THE KEY IS THE FLAGGING OF top AND bottom. THEY ARE THE THINGS THAT INDICATE THE FACT THAT THIS IS A BIPARTITE NETWORK. THE True AND False VALUES IN THE VERTICES type INDICATE WHETHER A NODE IS PART OF THE top OR bottom HALF OF THE BIPARTITE NETWORK. PUs ARE top? SPECIES ARE bottom?
PU_spp_pair_names_triple = create_PU_spp_pair_names (cor_num_PUs, # app_ ???
cor_num_spp, # app_ ???
PU_spp_pair_indices, # app_ ???
PU_col_name,
spp_col_name
)
PU_spp_pair_names = PU_spp_pair_names_triple$PU_spp_pair_names
PU_vertex_names = PU_spp_pair_names_triple$PU_vertex_names
spp_vertex_names = PU_spp_pair_names_triple$spp_vertex_names
# ...
vertices = data.frame (name=c(spp_vertex_names, PU_vertex_names),
type=c(rep(FALSE, num_spp),
rep(TRUE, num_PUs)))
bg = graph.data.frame (PU_spp_pair_names, directed=FALSE, vertices=vertices)
bgp = bipartite.projection (bg)
bgp_proj1_PUs = bgp$proj1
bgp_proj2_spp = bgp$proj2
head(bg)
6 x 936 sparse Matrix of class "dgCMatrix"
[[ suppressing 936 column names ‘s1’, ‘s2’, ‘s3’ ... ]]
s1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
...
s1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s1 . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s2 . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s3 . . . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . 1 . . . . . . . . .
s4 . . . . . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . . . . .
s5 . . . . . . . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . . .
s6 . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . .
s1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Number of top and bottom nodes
top <- length(V(bg)[type==FALSE])
bottom <- length(V(bg)[type==TRUE])
vertices
name type
1 s1 FALSE
2 s2 FALSE
3 s3 FALSE
4 s4 FALSE
5 s5 FALSE
6 s6 FALSE
7 s7 FALSE
8 s8 FALSE
9 s9 FALSE
10 s10 FALSE
...
810 s810 FALSE
811 s811 FALSE
812 s812 FALSE
813 s813 FALSE
814 s814 FALSE
815 p1 TRUE
816 p2 TRUE
817 p3 TRUE
818 p4 TRUE
819 p5 TRUE
820 p6 TRUE
821 p7 TRUE
822 p8 TRUE
823 p9 TRUE
824 p10 TRUE
...
929 p115 TRUE
930 p116 TRUE
931 p117 TRUE
932 p118 TRUE
933 p119 TRUE
934 p120 TRUE
935 p121 TRUE
936 p122 TRUE
Function: set_up_for_and_run_marxan
marxan_control_values = set_up_for_and_run_marxan (
app_PU_spp_pair_indices,
PU_col_name,
spp_col_name,
current_os,
marxan_input_dir,
marxan_output_dir,
parameters,
marxan_IO_dir
)
)
- Function defined in gscp_13_write_marxan_control_file_and_run_marxan.R
- app_PU_spp_pair_indices
- Should have same form as PU_spp_pair_indices but just be for an apparent problem.
- PU_spp_pair_indices
- data frame with one PU/spp pair for every occurrence of every species in the problem
- exactly 2 columns
- column headings are PU_ID and spp_ID
- dimensions are [num PU spp pairs, 2]
- Example:
dim (PU_spp_pair_indices)
1628 2
PU_spp_pair_indices:
PU_ID spp_ID
1 1 1
2 2 1
...
1625 64 813
1626 82 813
1627 54 814
1628 112 814
- column names
- character strings labelling columns in various tables
- the names are derived from what is used in the original creation of the tables.
- At the moment, I don't know how to specify the names ahead of time as variables and then use the variables to specify the column names.
- Example:
PU_col_name
"PU_ID"
spp_col_name
"spp_ID"
- Character strings from derived_bdpg_parameters
- derived_bdpg_parameters$current_os
- derived_bdpg_parameters$marxan_input_dir
- derived_bdpg_parameters$marxan_output_dir
- parameters
-
derived_bdpg_parameters$marxan_IO_dir
-
cor_or_app_label
- character string to use in writing labels on plots or messages
- Only 2 values allowed, either "cor" or "app".
- That might change if I allow multiple different apparents.
- Example:
cor_or_app_label:
"cor"
- returned value: marxan_control_values = retVal
- list with one name:value entry for each marxan option
- each entry has a name that is the same as the control variable
retVal = list ()
retVal$marxan_PROP = marxan_PROP
retVal$marxan_RANDSEED = marxan_RANDSEED
retVal$marxan_NUMREPS = marxan_NUMREPS
retVal$marxan_NUMITNS = marxan_NUMITNS
retVal$marxan_STARTTEMP = marxan_STARTTEMP
retVal$marxan_NUMTEMP = marxan_NUMTEMP
retVal$marxan_COSTTHRESH = marxan_COSTTHRESH
retVal$marxan_THRESHPEN1 = marxan_THRESHPEN1
retVal$marxan_THRESHPEN2 = marxan_THRESHPEN2
retVal$marxan_RUNMODE = marxan_RUNMODE
retVal$marxan_MISSLEVEL = marxan_MISSLEVEL
retVal$marxan_ITIMPTYPE = marxan_ITIMPTYPE
retVal$marxan_HEURTYPE = marxan_HEURTYPE
retVal$marxan_CLUMPTYPE = marxan_CLUMPTYPE
spf_const = write_network_to_marxan_files (
app_PU_spp_pair_indices,
PU_col_name,
spp_col_name,
parameters,
marxan_input_dir,
marxan_output_dir
)
- Function defined in gscp_13_write_marxan_control_file_and_run_marxan.R
marxan_control_values =
set_marxan_controls_and_run_marxan (current_os,
marxan_input_dir,
marxan_output_dir,
parameters,
marxan_IO_dir)
- Function defined in gscp_13_write_marxan_control_file_and_run_marxan.R
create_nodes_data_structure =
function (tot_num_nodes,
num_nodes_per_group,
n__num_groups,
num_independent_nodes_per_group)
- Function defined in gscp_6_create_data_structures.R
- independent_node_IDs
- integer vector (sorted?) of IDs of nodes in the independent set
- dependent_node_IDs
- integer vector (sorted?) of IDs of nodes in the dependent set
- returned value: nodes
- dataframe with
- 1 row for each node (PU) in the problem
- 3 columns for row (node), i.e.,
- node ID (integer)
- group ID (integer)
- flag indicating whether node is in the dependent set (boolean)
#------------------------------------------------------------
# Build an overall data frame that shows for each node,
# its node ID and group ID, plus a flag indicating whether
# it's in the dependent set or not. For example, if there
# are 3 nodes per group:
#
# node_ID group_ID dependent_set_member
# 1 1 TRUE
# 2 1 TRUE
# 3 1 FALSE
# 4 2 TRUE
# 5 2 TRUE
# 6 2 FALSE
# ... ... ...
#------------------------------------------------------------
nodes = data.frame (node_ID = node_IDs,
group_ID = group_IDs,
dependent_set_member = dependent_set_members)
gen_single_bdprob = function (parameters, bdpg_error_codes, integerize)
- Function defined in gen_bdprob.R
- parameters
- bdpg_error_codes
- integerize
- returned value: bdprob in mainline
- called retVal inside this routine
- list with
retVal$read_Xu_problem_from_file = read_Xu_problem_from_file
retVal$PU_spp_pair_indices = PU_spp_pair_indices_sextet$PU_spp_pair_indices
retVal$PU_col_name = PU_spp_pair_indices_sextet$PU_col_name
retVal$spp_col_name = PU_spp_pair_indices_sextet$spp_col_name
retVal$num_PUs = PU_spp_pair_indices_sextet$num_PUs
retVal$num_spp = PU_spp_pair_indices_sextet$num_spp
retVal$cor_optimum_cost = PU_spp_pair_indices_sextet$correct_solution_cost
retVal$PU_costs = PU_costs
retVal$derived_Xu_params = derived_Xu_params
retVal$prob_is_ok = TRUE
retVal$bpm = bpm # 0/1 adjacency matrix of spp rows vs PU cols
retVal$nodes = nodes
- derived_Xu_params
num_nodes_per_group = derived_Xu_params$num_nodes_per_group
num_rounds_of_linking_between_groups = derived_Xu_params$num_rounds_of_linking_between_groups
target_num_links_between_2_groups_per_round = derived_Xu_params$target_num_links_between_2_groups_per_round
num_links_within_one_group = derived_Xu_params$num_links_within_one_group
tot_num_links_inside_groups = derived_Xu_params$tot_num_links_inside_groups
max_possible_num_links_between_groups = derived_Xu_params$max_possible_num_links_between_groups
max_possible_tot_num_links = derived_Xu_params$max_possible_tot_num_links
max_possible_tot_num_node_link_pairs = derived_Xu_params$max_possible_tot_num_node_link_pairs
n__num_groups = derived_Xu_params$n__num_groups
alpha__ = derived_Xu_params$alpha__
p__prop_of_links_between_groups = derived_Xu_params$p__prop_of_links_between_groups
r__density = derived_Xu_params$r__density
num_independent_nodes_per_group = derived_Xu_params$num_independent_nodes_per_group
num_independent_set_nodes = derived_Xu_params$num_independent_set_nodes
tot_num_nodes = derived_Xu_params$tot_num_nodes
num_dependent_set_nodes = derived_Xu_params$num_dependent_set_nodes
opt_solution_as_frac_of_tot_num_nodes = derived_Xu_params$opt_solution_as_frac_of_tot_num_nodes
base_for_target_num_links_between_2_groups_per_round = derived_Xu_params$base_for_target_num_links_between_2_groups_per_round
at_least_1_for_target_num_links_between_2_groups_per_round = derived_Xu_params$at_least_1_for_target_num_links_between_2_groups_per_round
- nodes
nodes = create_nodes_data_structure (tot_num_nodes,
num_nodes_per_group,
n__num_groups,
num_independent_nodes_per_group
)
- edge_list
edge_list =
create_Xu_graph (num_nodes_per_group,
n__num_groups,
nodes,
max_possible_tot_num_links,
target_num_links_between_2_groups_per_round,
num_rounds_of_linking_between_groups,
DEBUG_LEVEL
)
- correct_solution_vector_is_known
correct_solution_vector_is_known = TRUE
- dependent_node_IDs
dependent_node_IDs = get_dependent_node_IDs (nodes)
- num_PUs
num_PUs = get_num_nodes (nodes)
- PU_costs
PU_costs = get_PU_costs (num_PUs)
- PU_spp_pair_indices_sextet
#-------------------------------------------------------------
# Combine the information in the nodes and edge_list data
# structures into a single data structure that has one line
# for each species on each planning unit (where species
# are edges and PUs are nodes).
#-------------------------------------------------------------
PU_spp_pair_indices_sextet = create_PU_spp_pair_indices (edge_list,
nodes,
dependent_node_IDs,
PU_costs,
num_PUs)
PU_spp_pair_indices = PU_spp_pair_indices_sextet$PU_spp_pair_indices
PU_col_name = PU_spp_pair_indices_sextet$PU_col_name
spp_col_name = PU_spp_pair_indices_sextet$spp_col_name
num_PUs = PU_spp_pair_indices_sextet$num_PUs
num_spp = PU_spp_pair_indices_sextet$num_spp
cor_optimum_cost = PU_spp_pair_indices_sextet$correct_solution_cost
langfob/bdpg documentation built on Dec. 8, 2022, 5:33 a.m.
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Function: summarize_and_plot_graph_and_distribution_structure_information
summarize_and_plot_graph_and_distribution_structure_information =
function (PU_spp_pair_indices,
cor_or_app_label,
all_correct_node_IDs,
plot_output_dir,
spp_col_name,
PU_col_name,
presences_col_name)
- Function defined in biodivprobgen_utilities.R.
-
PU_spp_pair_indices
- data frame with one PU/spp pair for every occurrence of every species in the problem
- exactly 2 columns
- column headings are PU_ID and spp_ID
- dimensions are [num PU spp pairs, 2]
- Example:
dim (PU_spp_pair_indices) 1628 2 PU_spp_pair_indices: PU_ID spp_ID 1 1 1 2 2 1 ... 1625 64 813 1626 82 813 1627 54 814 1628 112 814
-
cor_or_app_label
- character string to use in writing labels on plots or messages
- Only 2 values allowed, either "cor" or "app".
- That might change if I allow multiple different apparents.
- Example:
cor_or_app_label: "cor"
-
all_correct_node_IDs
- Vector listing all node IDs (PU IDs) in the problem, regardless of whether the PU is occupied by any species or not
- length of the vector is just the total number of PUs, empty and not empty
- Example:
length (all_correct_node_IDs) 122 head (all_correct_node_IDs) 1 2 3 4 5 6
-
column names
- character strings labelling columns in various tables
- the names are derived from what is used in the original creation of the tables.
- At the moment, I don't know how to specify the names ahead of time as variables and then use the variables to specify the column names.
- Example:
spp_col_name "spp_ID" PU_col_name "PU_ID" presences_col_name "freq"
-
returned value: final_link_counts_for_each_node
- data frame with one column of unique PU IDs (both empty and non-empty) and a second column containing the number of species occurring on the corresponding PU
- column names are "PU_ID" and "freq"
- total number of rows in table is the total number of PUs in the problem, both empty and non-empty PUs
- Example:
final_link_counts_for_each_node: PU_ID freq 1 1 1 2 2 25 ...
Function: compute_network_measures_using_bipartite_package
compute_network_measures_using_bipartite_package = function (bpm)
- Function defined in gscp_11a_network_measures_using_bipartite_package.R
-
bpm
- 2D matrix with one row for every species and one column for every PU
- Created by create_adj_matrix_with_spp_rows_vs_PU_cols() in
- gscp_10c_build_adj_and_cooccurrence_matrices.R
- Will be viewed as a bipartite matrix (bpm) by the bipartite network routines
- Example:
``` dim (bpm) [1] 814 122
bpm [1:4,1:8] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 1 1 0 0 0 0 0 0 ... [2,] 0 0 1 1 0 0 0 0 ... [3,] 0 0 0 0 1 1 0 0 ... [4,] 0 0 0 0 0 0 1 1 ... ... ``` - returned value: bipartite_metrics_from_bipartite_package - data frame with one column of unique PU IDs (both empty and non-empty) and a second column containing the number of species occurring on the corresponding PU - column names are "PU_ID" and "freq" - total number of rows in table is the total number of PUs in the problem, both empty and non-empty PUs - Example:
``` bipartite_metrics_from_bipartite_package connectance web_asymmetry links_per_species number_of_compartments [1,] 0.0169207 -0.744086 1.755914 2
compartment_diversity cluster_coefficient nestedness weighted_nestedness
[1,] 1.015475 0.01726264 4.373023 0.3154829
weighted_NODF interaction_strength_asymmetry specialisation_asymmetry
[1,] 0 0 0.5206311
linkage_density weighted_connectance Fisher_alpha Shannon_diversity
[1,] 12.70423 0.01366046 1.717987e+12 7.398174
interaction_evenness Alatalo_interaction_evenness H2 number.of.species.HL
[1,] 0.6445867 1 0 119
number.of.species.LL mean.number.of.shared.partners.HL
[1,] 811 0.1365902
mean.number.of.shared.partners.LL cluster.coefficient.HL cluster.coefficient.LL
[1,] 0.05527089 0.02864849 0.01827332
weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL niche.overlap.HL
[1,] 0.01722157 0.3061294 0.006840523
niche.overlap.LL togetherness.HL togetherness.LL C.score.HL C.score.LL V.ratio.HL
[1,] 0.02669537 0.004202092 0.01122122 0.9788591 0.9584458 9.688273
V.ratio.LL discrepancy.HL discrepancy.LL extinction.slope.HL extinction.slope.LL
[1,] 0.1657092 1563 1565 5.53822 1.934873
robustness.HL robustness.LL functional.complementarity.HL
[1,] 0.8232885 0.6583167 461.6131
functional.complementarity.LL partner.diversity.HL partner.diversity.LL
[1,] 1031.04 3.019578 0.7403126 generality.HL vulnerability.LL [1,] 23.23393 2.174525
```
Function: summarize_and_plot_graph_and_distribution_structure_information
compute_igraph_related_network_measures <- function ( num_spp, num_PUs, PU_spp_pair_indices, emulatingTzar, DEBUG_LEVEL, network_output_dir, # cor_num_PUs, # app_ ??? # cor_num_spp, PU_col_name, spp_col_name )
- Function defined in gscp_11b_network_measures_using_igraph_package.R
- num_spp
- integer number of correct or apparent species, depending on whether the routine is passed a correct or apparent problem
- num_PUs
- integer number of correct or apparent PUs, depending on whether the routine is passed a correct or apparent problem
- PU_spp_pair_indices
- data frame with one PU/spp pair for every occurrence of every species in the problem
- exactly 2 columns
- column headings are PU_ID and spp_ID
- dimensions are [num PU spp pairs, 2]
- Example:
dim (PU_spp_pair_indices) 1628 2 PU_spp_pair_indices: PU_ID spp_ID 1 1 1 2 2 1 ... 1625 64 813 1626 82 813 1627 54 814 1628 112 814
- emulatingTzar
- boolean flag indicating whether the run is emulating tzar
- DEBUG_LEVEL
- integer number indicating what level of debugging output to do
- 0 means no debugging output
- increasing integers > 0 imply different levels of debugging output
- can't remember whether higher means more or less output, but it doesn't matter much at the moment since I don't think that I have used anything other than 0 and 1 at this point
- integer number indicating what level of debugging output to do
- network_output_dir
- character string indicating full path where network metric outputs should be written
- is this right, i.e., network metric outputs? or is it for when you draw the network to a file?
- Example:
"/Users/bill/tzar/outputdata/biodivprobgen/default_runset/1165_marxan_simulated_annealing.inprogress/Networks"
- character string indicating full path where network metric outputs should be written
- REMOVING THESE: counts that I'm not sure about, i.e., are they supposed to always be cor or should they be cor or app depending on what problem is passed in? Looks like they're only used in constructing the list of vertex names for the network that is being constructed. Having extra (empty) vertices for the PUs seems like it may distort the statistics that are computed. 2016 04 08 - BTL
- cor_num_PUs # app_ ???
- cor_num_spp
- column names
- character strings labelling columns in various tables
- the names are derived from what is used in the original creation of the tables.
- At the moment, I don't know how to specify the names ahead of time as variables and then use the variables to specify the column names.
- Example:
PU_col_name "PU_ID" spp_col_name "spp_ID"
- returned value: bipartite_metrics_from_igraph_package_df
- data frame with one column for each bipartite metric computed by igraph
- there is always only one row in the data frame?
- each column heading is the name of the metric
bipartite_metrics_from_igraph_package_df ig_top ig_bottom ig_num_edges_m ig_ktop ig_kbottom ig_bidens ig_lcctop ig_lccbottom ig_distop ig_disbottom ig_cctop ig_ccbottom ig_cclowdottop 1 814 122 1633 2.006143 13.38525 0.01644379 810 118 4.342801 4.405056 0.3403602 0.03887111 0.5983846 ig_cclowdotbottom ig_cctopdottop ig_cctopdotbottom ig_mean_bottom_bg_redundancy ig_median_bottom_bg_redundancy ig_mean_top_bg_redundancy 1 0.3004119 0.45096 0.05012125 0.07886978 0 0.01429777 ig_median_top_bg_redundancy 1 0
Function: create_PU_spp_pair_names
HAVEN'T DECODED THIS ONE YET. JUST LEAVING HERE WHAT I HAD CUT OUT OF THE CODE TO TRY TO FIGURE OUT WHETHER THERE SHOULD BE app_num_spp AND app_num_PUs AND WHAT WERE PU_spp_pair_names AND PU_spp_pair_names_triple.
REMEMBER THAT THE KEY IS THE FLAGGING OF top AND bottom. THEY ARE THE THINGS THAT INDICATE THE FACT THAT THIS IS A BIPARTITE NETWORK. THE True AND False VALUES IN THE VERTICES type INDICATE WHETHER A NODE IS PART OF THE top OR bottom HALF OF THE BIPARTITE NETWORK. PUs ARE top? SPECIES ARE bottom?
PU_spp_pair_names_triple = create_PU_spp_pair_names (cor_num_PUs, # app_ ???
cor_num_spp, # app_ ???
PU_spp_pair_indices, # app_ ???
PU_col_name,
spp_col_name
)
PU_spp_pair_names = PU_spp_pair_names_triple$PU_spp_pair_names PU_vertex_names = PU_spp_pair_names_triple$PU_vertex_names spp_vertex_names = PU_spp_pair_names_triple$spp_vertex_names # ... vertices = data.frame (name=c(spp_vertex_names, PU_vertex_names), type=c(rep(FALSE, num_spp), rep(TRUE, num_PUs))) bg = graph.data.frame (PU_spp_pair_names, directed=FALSE, vertices=vertices) bgp = bipartite.projection (bg) bgp_proj1_PUs = bgp$proj1 bgp_proj2_spp = bgp$proj2 head(bg) 6 x 936 sparse Matrix of class "dgCMatrix" [[ suppressing 936 column names ‘s1’, ‘s2’, ‘s3’ ... ]] s1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... s1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s1 . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s2 . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . s3 . . . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . 1 . . . . . . . . . s4 . . . . . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . . . . . s5 . . . . . . . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . . . s6 . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . s1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . # Number of top and bottom nodes top <- length(V(bg)[type==FALSE]) bottom <- length(V(bg)[type==TRUE]) vertices name type 1 s1 FALSE 2 s2 FALSE 3 s3 FALSE 4 s4 FALSE 5 s5 FALSE 6 s6 FALSE 7 s7 FALSE 8 s8 FALSE 9 s9 FALSE 10 s10 FALSE ... 810 s810 FALSE 811 s811 FALSE 812 s812 FALSE 813 s813 FALSE 814 s814 FALSE 815 p1 TRUE 816 p2 TRUE 817 p3 TRUE 818 p4 TRUE 819 p5 TRUE 820 p6 TRUE 821 p7 TRUE 822 p8 TRUE 823 p9 TRUE 824 p10 TRUE ... 929 p115 TRUE 930 p116 TRUE 931 p117 TRUE 932 p118 TRUE 933 p119 TRUE 934 p120 TRUE 935 p121 TRUE 936 p122 TRUE
Function: set_up_for_and_run_marxan
marxan_control_values = set_up_for_and_run_marxan (
app_PU_spp_pair_indices,
PU_col_name,
spp_col_name,
current_os,
marxan_input_dir,
marxan_output_dir,
parameters,
marxan_IO_dir
)
)
- Function defined in gscp_13_write_marxan_control_file_and_run_marxan.R
- app_PU_spp_pair_indices
- Should have same form as PU_spp_pair_indices but just be for an apparent problem.
- PU_spp_pair_indices
- data frame with one PU/spp pair for every occurrence of every species in the problem
- exactly 2 columns
- column headings are PU_ID and spp_ID
- dimensions are [num PU spp pairs, 2]
- Example:
dim (PU_spp_pair_indices) 1628 2 PU_spp_pair_indices: PU_ID spp_ID 1 1 1 2 2 1 ... 1625 64 813 1626 82 813 1627 54 814 1628 112 814
- column names
- character strings labelling columns in various tables
- the names are derived from what is used in the original creation of the tables.
- At the moment, I don't know how to specify the names ahead of time as variables and then use the variables to specify the column names.
- Example:
PU_col_name "PU_ID" spp_col_name "spp_ID"
- Character strings from derived_bdpg_parameters
- derived_bdpg_parameters$current_os
- derived_bdpg_parameters$marxan_input_dir
- derived_bdpg_parameters$marxan_output_dir
- parameters
-
derived_bdpg_parameters$marxan_IO_dir
-
cor_or_app_label
- character string to use in writing labels on plots or messages
- Only 2 values allowed, either "cor" or "app".
- That might change if I allow multiple different apparents.
- Example:
cor_or_app_label: "cor"
- returned value: marxan_control_values = retVal
- list with one name:value entry for each marxan option
- each entry has a name that is the same as the control variable
retVal = list ()
retVal$marxan_PROP = marxan_PROP
retVal$marxan_RANDSEED = marxan_RANDSEED
retVal$marxan_NUMREPS = marxan_NUMREPS
retVal$marxan_NUMITNS = marxan_NUMITNS
retVal$marxan_STARTTEMP = marxan_STARTTEMP
retVal$marxan_NUMTEMP = marxan_NUMTEMP
retVal$marxan_COSTTHRESH = marxan_COSTTHRESH
retVal$marxan_THRESHPEN1 = marxan_THRESHPEN1
retVal$marxan_THRESHPEN2 = marxan_THRESHPEN2
retVal$marxan_RUNMODE = marxan_RUNMODE
retVal$marxan_MISSLEVEL = marxan_MISSLEVEL
retVal$marxan_ITIMPTYPE = marxan_ITIMPTYPE
retVal$marxan_HEURTYPE = marxan_HEURTYPE
retVal$marxan_CLUMPTYPE = marxan_CLUMPTYPE
spf_const = write_network_to_marxan_files (
app_PU_spp_pair_indices,
PU_col_name,
spp_col_name,
parameters,
marxan_input_dir,
marxan_output_dir
)
- Function defined in gscp_13_write_marxan_control_file_and_run_marxan.R
marxan_control_values =
set_marxan_controls_and_run_marxan (current_os,
marxan_input_dir,
marxan_output_dir,
parameters,
marxan_IO_dir)
- Function defined in gscp_13_write_marxan_control_file_and_run_marxan.R
create_nodes_data_structure =
function (tot_num_nodes,
num_nodes_per_group,
n__num_groups,
num_independent_nodes_per_group)
- Function defined in gscp_6_create_data_structures.R
- independent_node_IDs
- integer vector (sorted?) of IDs of nodes in the independent set
- dependent_node_IDs
- integer vector (sorted?) of IDs of nodes in the dependent set
- returned value: nodes
- dataframe with
- 1 row for each node (PU) in the problem
- 3 columns for row (node), i.e.,
- node ID (integer)
- group ID (integer)
- flag indicating whether node is in the dependent set (boolean)
- dataframe with
#------------------------------------------------------------
# Build an overall data frame that shows for each node,
# its node ID and group ID, plus a flag indicating whether
# it's in the dependent set or not. For example, if there
# are 3 nodes per group:
#
# node_ID group_ID dependent_set_member
# 1 1 TRUE
# 2 1 TRUE
# 3 1 FALSE
# 4 2 TRUE
# 5 2 TRUE
# 6 2 FALSE
# ... ... ...
#------------------------------------------------------------
nodes = data.frame (node_ID = node_IDs,
group_ID = group_IDs,
dependent_set_member = dependent_set_members)
gen_single_bdprob = function (parameters, bdpg_error_codes, integerize)
- Function defined in gen_bdprob.R
- parameters
- bdpg_error_codes
- integerize
- returned value: bdprob in mainline
- called retVal inside this routine
- list with
retVal$read_Xu_problem_from_file = read_Xu_problem_from_file retVal$PU_spp_pair_indices = PU_spp_pair_indices_sextet$PU_spp_pair_indices retVal$PU_col_name = PU_spp_pair_indices_sextet$PU_col_name retVal$spp_col_name = PU_spp_pair_indices_sextet$spp_col_name retVal$num_PUs = PU_spp_pair_indices_sextet$num_PUs retVal$num_spp = PU_spp_pair_indices_sextet$num_spp retVal$cor_optimum_cost = PU_spp_pair_indices_sextet$correct_solution_cost retVal$PU_costs = PU_costs retVal$derived_Xu_params = derived_Xu_params retVal$prob_is_ok = TRUE retVal$bpm = bpm # 0/1 adjacency matrix of spp rows vs PU cols retVal$nodes = nodes
- derived_Xu_params
num_nodes_per_group = derived_Xu_params$num_nodes_per_group
num_rounds_of_linking_between_groups = derived_Xu_params$num_rounds_of_linking_between_groups
target_num_links_between_2_groups_per_round = derived_Xu_params$target_num_links_between_2_groups_per_round
num_links_within_one_group = derived_Xu_params$num_links_within_one_group
tot_num_links_inside_groups = derived_Xu_params$tot_num_links_inside_groups
max_possible_num_links_between_groups = derived_Xu_params$max_possible_num_links_between_groups
max_possible_tot_num_links = derived_Xu_params$max_possible_tot_num_links
max_possible_tot_num_node_link_pairs = derived_Xu_params$max_possible_tot_num_node_link_pairs
n__num_groups = derived_Xu_params$n__num_groups
alpha__ = derived_Xu_params$alpha__
p__prop_of_links_between_groups = derived_Xu_params$p__prop_of_links_between_groups
r__density = derived_Xu_params$r__density
num_independent_nodes_per_group = derived_Xu_params$num_independent_nodes_per_group
num_independent_set_nodes = derived_Xu_params$num_independent_set_nodes
tot_num_nodes = derived_Xu_params$tot_num_nodes
num_dependent_set_nodes = derived_Xu_params$num_dependent_set_nodes
opt_solution_as_frac_of_tot_num_nodes = derived_Xu_params$opt_solution_as_frac_of_tot_num_nodes
base_for_target_num_links_between_2_groups_per_round = derived_Xu_params$base_for_target_num_links_between_2_groups_per_round
at_least_1_for_target_num_links_between_2_groups_per_round = derived_Xu_params$at_least_1_for_target_num_links_between_2_groups_per_round
- nodes
nodes = create_nodes_data_structure (tot_num_nodes,
num_nodes_per_group,
n__num_groups,
num_independent_nodes_per_group
)
- edge_list
edge_list =
create_Xu_graph (num_nodes_per_group,
n__num_groups,
nodes,
max_possible_tot_num_links,
target_num_links_between_2_groups_per_round,
num_rounds_of_linking_between_groups,
DEBUG_LEVEL
)
- correct_solution_vector_is_known
correct_solution_vector_is_known = TRUE
- dependent_node_IDs
dependent_node_IDs = get_dependent_node_IDs (nodes)
- num_PUs
num_PUs = get_num_nodes (nodes)
- PU_costs
PU_costs = get_PU_costs (num_PUs)
- PU_spp_pair_indices_sextet
#-------------------------------------------------------------
# Combine the information in the nodes and edge_list data
# structures into a single data structure that has one line
# for each species on each planning unit (where species
# are edges and PUs are nodes).
#-------------------------------------------------------------
PU_spp_pair_indices_sextet = create_PU_spp_pair_indices (edge_list,
nodes,
dependent_node_IDs,
PU_costs,
num_PUs)
PU_spp_pair_indices = PU_spp_pair_indices_sextet$PU_spp_pair_indices
PU_col_name = PU_spp_pair_indices_sextet$PU_col_name
spp_col_name = PU_spp_pair_indices_sextet$spp_col_name
num_PUs = PU_spp_pair_indices_sextet$num_PUs
num_spp = PU_spp_pair_indices_sextet$num_spp
cor_optimum_cost = PU_spp_pair_indices_sextet$correct_solution_cost
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