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DPM: Distance Precision Matrix Installation Simulation of data linear data non-linear data Computing DPM and reg-DPM Visualization of the network Get the list of the links Get all the links Get only the top-ranked links Get only the links with a score higher than a threshold
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DPM: Distance Precision Matrix

Installation

First, install the necessary R packages from CRAN:

install.packages(c("corpcor", "devtools"))

Then install DPM using the install_github function in the devtools package.

library(devtools)
devtools::install_github("ghanbari/DPM",host="github.molgen.mpg.de/api/v3")

Simulation of data

linear data

To simulate linear data you need to first install the necessary R packages from CRAN:

install.packages(c("pcalg","mvtnorm","qpgraph"))

Then you can simulate linear data by specifying your desired number of nodes (nnodes) and number of samples (nsamp).

library(DPM)
ldata <- rdata(nnodes=10,nsamp=100)[["data"]]

non-linear data

To simulate non-linear data from the network mentioned in the paper with your desired number of samples (nsamp) and the standard deviation of the noise added to the generated data (sigma) use:

library(DPM)
nldata <- nldata(nsamp=100,sigma=0.2)[["data"]]

Computing DPM and reg-DPM

The following example computes DPM and reg-DPM scores for nonlinear data sampled from the network mentioned in the paper. You can use your own data as a matrix containing samples in rows and variables in columns.

library(DPM)
data <- nldata(nsamp=100,sigma=0.2)[["data"]]
dpmres <- dpm(data)
regdpmres <- reg.dpm(data)

You can plot the distribution of DPM and reg-DPM scores using:

library("ggplot2")
dpmdat <- data.frame(score = abs(dpmres[lower.tri(dpmres)]),name ='DPM')
regdpmdat <-data.frame(score = abs( regdpmres[lower.tri(regdpmres)]), name='reg-DPM')
dat <- rbind(dpmdat,regdpmdat)

ggplot(dat, aes(x=score,color=name))+ theme_bw()  + geom_density(size=1.5)+scale_color_manual(values=c("DPM"="SteelBlue3","reg-DPM"="dodgerblue4"))
ggsave(“test.png”, plot = last_plot(), device = “png”)

Visualization of the network

You can visualize the constructed network using qgraph package. First you need to install it:

install.packages("qgraph")

Then you can visualize the network with different options using following parameters (for more details check the documentation of qgraph package):

layout: this argument controls the layout of the graph. "circle" places all nodes in a single circle and "spring" gives a force embedded layout. Defaults to "circular" in weighted graphs and "spring" in unweighted graphs.

minimum: edges with absolute weights under this value are not shown (but not omitted). Defaults to 0.

threshold: a numeric value that defaults to 0. Edges with absolute weight that are not above this value are REMOVED from the network. This differs from minimum which simply changes the scaling of width and color so that edges with absolute weight under minimum are not plotted/invisible. Setting a threshold influences the spring layout and centrality measures obtained with the graph whereas setting a minimum does not.

cut: in weighted graphs, this argument can be used to cut the scaling of edges in width and color saturation. Edges with absolute weights over this value will have the strongest color intensity and become wider the stronger they are, and edges with absolute weights under this value will have the smallest width and become vaguer the weaker the weight. If this is set to NULL, no cutoff is used and all edges vary in width and color. Defaults to NULL for graphs with less then 20 nodes. For larger graphs the cut value is automatically chosen to be equal to the maximum of the 75th quantile of absolute edge strengths or the edge strength corresponding to 2n-th edge strength (n being the number of nodes.)

library(qgraph)
qgraph(matrix(dpmres,nrow(dpmres),ncol(dpmres)), layout = "spring" , minimum=0.25, cut=0.55, filename="dpm-network", filetype="png")
qgraph(matrix(regdpmres,nrow(regdpmres),ncol(regdpmres)), layout = "spring" , minimum=0.25, cut=0.55, filename="regdpm-network", filetype="png")

Get the list of the links

You need to first install the necessary R packages from CRAN:

install.packages("reshape2")

Get all the links

You can get the list of all links (from the highest to the lowest score) with the following command, where the resulting dpm/reddpm_list contains the ranking of links. Each row corresponds to a link. The first and second columns show the corresponding nodes and the third column indicates the score of the link.

dpm_links <- get_link(dpmres)
dim(dpm_links)
head(dpm_links)

regdpm_links <- get_link(regdpmres)
dim(regdpm_links)
head(regdpm_links)

Get only the top-ranked links

If you are interested in extracting the top links with the highest scores, use the parameter top_ranked and specify the number of top-ranked links to report:

dpm_links <- get_link(dpmres,top_ranked = 10)
dim(dpm_links)
head(dpm_links)

regdpm_links <- get_link(regdpmres,top_ranked = 10)
dim(regdpm_links)
head(regdpm_links)

Get only the links with a score higher than a threshold

If you are interested in extracting the links with scores higher than a threshold use the parameter threshold and specify your threshold:

dpm_links <- get_link(dpmres,threshold = 0.2)
dim(dpm_links)
head(dpm_links)

regdpm_links <- get_link(regdpmres,threshold = 0.2)
dim(regdpm_links)
head(regdpm_links)