MGraph: parents(adj_mat, i) - File Exchange

MGraph.m MGraph Toolbox

MGraph_gauss.m bellow is object handle

MGraph_logline.m bellow is object handle

MGraph_properties.m

marray_debug.m

G=MGraph_add_best_edge_to_pat...

G=MGraph_remove_worst_edge_in... Input

MGraph_BGscore(d,G)

MGraph_export2Pjart(filename,... %export data to Pajrt

MGraph_model2Graph(selected_m... make graph from G-logline model

V_n=MGraph_GaussEstimate_vari... Input: sample_var is the original sample variance matrix

[G,max_score]=MGraph_add_best...

[G,max_score]=MGraph_remove_w... Input: G is a DAG, isordered==1 means input variables are ordered

[a,mtc]=wang_nexksb(n,k,a,mtc... generate subsets of a sets n with k elements

[color,time,d,f,phi,back_edge... Depth-first search of the graph

[color,time,d,f,phi,idx,back_... it is for directed graph

[cond_density,T0,TL]=gnt_scor... Input: u0 is estimated mean, v is estimated variance, b is the regression coefficient,

[data,varnames,casenames]=mgr... deblank

[diffV ,adjecentV,record_idx]... find adjecent edge of add_edge1 in new_model

[diffV ,adjecentV]=test_adjec... find adjecent edge of add_edge1 in new_model

[marked, marked_data]=dnr_mak... Categorical assessments,

[old_parcorrf,delet_edge,reco... Graphical GAussian model for continues data

[old_parcorrf,delet_edge,reco... P/181, Graphical models in applied multivariate statistics

[old_parcorrf,delet_edge,reco... Input: old_var is the covariance of the data

[old_parcorrf,delet_edge,reco... P/181, Graphical models in applied multivariate statistics

[old_parcorrf,pdag ,GG, maxSc... check whether have edges go from high order to lower order and remove it

[old_parcorrf,pdag ,GG, maxSc... Input: d is the data, G is the initial pattern graph, it should use pattern at here

[old_parcorrf,pdag ,GG, maxSc...

[pdag ,GG, maxScore,ischanged...

[removeEG, needtestEG]=MGraph... because in the out results, the device is arranged as ABCDEF.. order in the matrise

arrow(varargin) ARROW Draw a line with an arrowhead.

b=gnt_graph_to_coeffiecent_b(... Input: adj_matrix of the graph

c_n_alpha=gnt_C(n,alpha) compute the normalization coefficent C(n,alpha)

children(adj_mat, i, t) CHILDREN Return the indices of a node's children in sorted order

condP=gnt_scoring_uncompleteG... %Input: c is the network structure, sigma and alpha is effective sample size of mean and the variance,

cond_density=gnt_conditional_... compute the conditional density based on the network structure

cond_indep_fisher_z(X, Y, S, ... COND_INDEP_FISHER_Z Test if X indep Y given Z using Fisher's Z test

device=MGraph_loglineMakeDevi... make device for both row or both column data

device=MGraph_loglineMakeDevi... make device for one row or one column data

distchck(nparms,arg1,arg2,arg... DISTCHCK Checks the argument list for the probability functions.

draw_graph(adj, labels, node_... DRAW_LAYOUT Draws a layout for a graph

dsep(X, Y, S, G)

family(A,i,t) FAMILY Return the indices of parents and self in sorted order

find_equiv_posns(vsmall, vlar... FIND_EQUIV_POSNS p[i] = the place where vsmall[i] occurs in vlarge.

gama=gnt_gama_function(x) compute the gama function

gp2_idxgp=MGraph_loglineSortI...

graph_separated(G, X, Y, S)

isDecomposable=isDecomposable... Input adj_mat of Graph

isSim=isSimplical_node(G,i) test node i whether it is simplical node in graph G

is_SDordering=MGraph_isSDorde... test function of SD-ordering

isundirected=MGraph_isundirec... check whether the input G is undirected graph

ksubset=wang_kSubset(nb,k) Generate the subsets of a sets nb with k elements

layout_dag(adj) MAKE_LAYOUT Creates a layout from an adjacency matrix

marray_debuge(str)

moralize(G) MORALIZE Ensure that for every child, all its parents are married, and drop directionality of edges.

mysetdiff(A,B) MYSETDIFF Set difference of two sets of positive integers (much faster than built-in setdiff)

myunion(A,B) MYUNION Union of two sets of positive integers (much faster than built-in union)

needtestEG=MGraph_loglineOutp... above is the initial values

neighbors(adj_mat, i) NEIGHBORS Find the parents and children of a node in a graph.

new_model3=MGraph_makeSubGrap... INPUT: oldmodel cliques, delet_edge

new_out=MGraph_loglineMergeEd... make new partiation of raw data based on new model of the data

normcdf(x,mu,sigma) NORMCDF Normal cumulative distribution function (cdf).

norminv(p,mu,sigma); NORMINV Inverse of the normal cumulative distribution function (cdf).

numberVector=MGraph_string2nu...

outdata=MGraph_loadDataforGau... make a function for input data in logline

outdata=MGraph_loadDataforLog... make a function for input data in logline

parents(adj_mat, i) PARENTS Return the list of parents of node i

partial_corr_coef(S, i, j, Y) PARTIAL_CORR_COEF Compute a partial correlation coefficient

pdag=mgraph_meek4ruls(G,pdag) Input G: is undirected adjecent matrix, pdag is the pattern

reachability_graph(G)

result=Chisq(x,n)

result=MGraph_MainGaussloglin... Graphical logline modle for discrete data

result=MGraph_logloneMakeTabl... Input: numofGin_X: number of subtypes in each group

selected_model=MGraph_logline... For current version there is something wrong

setdiag(M, v) SETDIAG Set the diagonal of a matrix to a specified scalar/vector.

som_denormalize(sD,varargin) SOM_DENORMALIZE Denormalize data.

som_norm_variable(x, method, ... SOM_NORM_VARIABLE Normalize or denormalize a scalar variable.

som_normalize(sD,method,comps... SOM_NORMALIZE (Re)normalize data or add new normalizations.

som_set(sS, varargin) SOM_SET Create and check SOM Toolbox structs, give values to their fields.

stringVector=MGraph_num2strin... input number of edge location

w=gnt_make_precision_matrix(v... estimated the precision matrix cov^-1 for gnt

wang_learn_struct_pdag_pc(con... LEARN_STRUCT_PDAG_PC Learn a partially oriented DAG (pattern) using the PC algorithm

wang_learn_struct_pdag_pc_bak... LEARN_STRUCT_PDAG_PC Learn a partially oriented DAG (pattern) using the PC algorithm

wang_learn_struct_undirect2pd... LEARN_STRUCT_PDAG_PC Learn a partially oriented DAG (pattern) using the PC algorithm

Contents.m

leaveOneOut_testGnet_fil.m

leaveOneOut_testGnet_hog.m

leaveOneOut_testGnet_phe.m

leaveOneOut_testGnet_pkc.m

test_baysein_network.m

test_decomposable.m

test_samplesGauss.m

test_samplesLogline.m

Highlights from MGraph

Highlights from
MGraph