New Features in qgraph Sacha Epskamp

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1 New Features in qgraph Sacha Epskamp Contents New features in version.4. EBICglasso refitting Gaussian graphical model fit measures Palettes Themes and colorblind mode New features in version Shortest path graphs Pie charts New features in version.3.5 Centrality and clustering plot scales Smallworldness index New features in version IMPORTANT: new default EBICglasso Means and SDs Legend modes References 5 New features in version.4. EBICglasso refitting The EBICglasso and qgraph functions now have an argument, refit, that can be used to refit a model estimated using the EBICglasso procedure. If set to TRUE, the optimal graph structure is used, but edge weights are re-estimated without LASSO regularization. This can lead to a different interpretation of the model, and is important when comparing fit of the network to other models that are not regularized. For example: library("qgraph" library("psych" data(bfi # Compute correlation matrix: CorMat <- cor_auto(bfi[,:5] layout(t(: # Original: Original <- qgraph(cormat, graph = "glasso", samplesize = nrow(bfi, layout = "spring", refit = FALSE, title = "Original" # Refitted: qgraph(cormat, graph = "glasso", samplesize = nrow(bfi, layout = Original$layout, refit = TRUE, title = "Refitted"

2 Original Refitted N N C C A A E E O O Gaussian graphical model fit measures The qgraph package now contains the function ggmfit, which can be used to obtain fit measures from a Gaussian graphical model (GGM; the partial correlation network obtained with graph = "glasso". This is made possible due to the recent formulation of the GGM as a psychometric model (Epskamp, Rhemtulla, and Borsboom 06 and makes use of codes inspired by the lavaan package, which are also used in the lvnet package. The input is a qgraph object, a partial correlation matrix or an inverse variance-covariance matrix. The network is refitted by default to obtain fit measures comparable to Lavaan. Important to note is that the fit measures are only appropriate if the input to EBICglasso was a variance covariance matrix of the raw data. That is, the fit measures do not take polychoric correlations into account. This is expected in a future version of qgraph. For example: library("qgraph" library("psych" data(bfi # Compute covariance matrix: CovMat <- cov(bfi[,:5], use="pairwise.complete.obs" # Estimate network:: GGMnetwork <- qgraph(covmat, graph = "glasso", samplesize = nrow(bfi, layout = "spring", refit = FALSE, title = "Original", DoNotPlot = TRUE # Fit measures: fitggm <- ggmfit(ggmnetwork, covmat = CovMat, samplesize = nrow(bfi fitggm ## ## ggmfit object: ## Use plot(object to plot the network structure

3 ## Fit measures stored under object$fitmeasures ## ## Measure Value ## nvar 5 ## nobs 35 ## 3 npar 55 ## 4 df 70 ## 5 fmin 0.05 ## 6 chisq ## 7 pvalue < ## 8 baseline.chisq ## 9 baseline.df 5 ## 0 baseline.pvalue 0 ## nfi.0 ## tli.0 ## 3 rfi.0 ## 4 ifi.0 ## 5 rni.0 ## 6 cfi.0 ## 7 rmsea 0.06 ## 8 rmsea.ci.lower 0.03 ## 9 rmsea.ci.upper 0.09 ## 0 rmsea.pvalue ## rmr 0.07 ## srmr 0.03 ## 3 logl ## 4 unrestricted.logl ## 5 aic ## 6 bic ## 7 bic ## 8 ebic # Estimate factor model in Lavaan: library("lavaan" #' Lavaan model: Mod <- ' A =~ A E =~ E C =~ C N =~ N O =~ O ' #' Fit in Lavaan: fitlavaan <- cfa(mod,sample.cov = CovMat, sample.nobs = nrow(bfi,std.lv=true # Compare BIC: # Network: fitggm$fitmeasures$bic ## []

4 # CFA model: fitmeasures(fitlavaan['bic'] ## bic ## Palettes The palette argument can now be used to change the default palette used for coloring nodes. For example, the colorblind option uses the palette from Groups <- c(rep("a",5,rep("c",5,rep("e",5,rep("n",5,rep("o",5 qgraph(cormat, graph = "glasso", samplesize = nrow(bfi, groups = Groups, legend = FALSE, layout = "spring", palette = "colorblind" N C A E O All options currently built in are: Palette rainbow colorblind pastel gray R ggplot Colors The default, using rainbow( Taken from only up to 7 nodes supported Pastel colors previously obtained with pastel = TRUE Shades of gray The R default colors The same palette as used by default in ggplot 4

5 Themes and colorblind mode The new theme argument can be used to change the defaults of various graphical arguments in one go. This is mostly used to change the edge colors to predefined themes. For example, the colorblind theme sets the above palette in addition to changing the edges: qgraph(cormat, graph = "glasso", samplesize = nrow(bfi, groups = Groups, legend = FALSE, layout = "spring", theme = "colorblind" N C A E O In this theme, the default green color is changed to a similar shade of blue ("#0000D5" for edges under the cutoff and "darkblue" for edges over the cutoff. Other theme options are: Theme Colors classic The classic green-red theme colorblind Sets palette = "colorblind" and changes the classic green to blue Hollywood Uses complementary blue (positive and orange (negative Borkulo The colors used in the JAMA psychiatry paper of van Borkulo et al. (05. Similar to colorblind but does not use different colors for edges under the cutoff TeamFortress Colors inspired by Team Fortress Reddit Orangered and Perliwinkle Fried Black edges, with a dark background and only grey nodes Leuven Does not plot a network. Instead, the weights matrix is returned to the console 5

6 New features in version.4.0 Shortest path graphs Using the same network as used above: Graph <- qgraph(cormat, graph = "glasso", samplesize = nrow(bfi, groups = Groups, legend = FALSE, layout = "spring" Inspired by Isvoranu et al. (06, we might be interested in e.g., only the shortest paths between Neuroticism and Agreeableness We can use the new pathways function for this. It takes a qgraph object as input and two vectors with sets of nodes, either specified numerically by using the node number or specified by using the node labels. For example: pathways(graph, from = c("n", "", "", "", "", to = c("a", "", "", "", "" N C A E O The highlighted pathways are edges in shortest paths between Neuroticism and Agreeableness nodes. Pie charts Inspired by Jonas Hasslbeck, pie charts can now be incorporated in plotting of nodes. This can be useful to visualize the difficulty or categorical distribution of a node. To activate pie charts, the pie argument has to be used, with a value between 0 and for each node: library("qgraph" adj <- matrix(,, qgraph(adj, vsize = 30, mar = rep(0,4, 6

7 pie = c(0.3,0.8 Coloring The color is automatically set to a 5% darker version of the color of the node: qgraph(adj, vsize = 30, mar = rep(0,4, pie = c(0.3,0.8, color = c("orange","green" The color of the first block is defined using the piecolor argument (setting it to NA mimics the above behavior and the color of the added block is set by the piecolor argument: qgraph(adj, vsize = 30, mar = rep(0,4, pie = c(0.3,0.8, color = c("orange","green", piecolor = c(na, "black", piecolor = c("white","gray" 7

8 Border size The size of the border can be set using the pieborder argument. Setting this to makes the whole node a pie chart: qgraph(adj, vsize = 30, mar = rep(0,4, pie = c(0.3,0.8, pieborder = The node-label scales with the border if the border is not larger than 0.5. Shifting the pie chart The piestart argument can be used to shift the pie chart. For example: qgraph(adj, vsize = 30, mar = rep(0,4, pie = c(0.3,0.8, piestart = -c(0.3,0.8/ 8

9 Multiple pie blocks To add more pie blocks, a list can be used instead of a vector: qgraph(adj, vsize = 30, mar = rep(0,4, pie = list( c(0.3,0.,0., 0.8 Notice that each element in the pie list is supplemented with a white block (controlled with piecolor argument indicating the area that is not assigned. Also notice that when more than one block is assigned the color is no longer a darker shade of the node color but rather rainbow colors. If the values sum to no white block is added: qgraph(adj, vsize = 30, mar = rep(0,4, pie = list( c(0.3,0.,0.,0.4, c(0.6,0.,0. 9

10 Using the piepastel argument will choose the pastel rainbow set also used in node labels in qgraph instead: qgraph(adj, vsize = 30, mar = rep(0,4, pie = list( c(0.3,0.,0.,0.4, c(0.6,0.,0., piepastel = TRUE The piecolor argument can again be used to change the colors: qgraph(adj, vsize = 30, mar = rep(0,4, pie = list( c(0.3,0.,0.,0.4, c(0.6,0.,0., piecolor = list( c("red","blue","purple","deeppink", c("orange","yellow","green" 0

11 Other shapes The pie functionality also works with some other node shapes qgraph(matrix(,3,3, vsize = 30, mar = rep(0,4, pie = list( c(0.3,0.,0.,0.4, c(0.6,0.,0., 0.6, piepastel = TRUE, shape = c("star","heart","square" 3 New features in version.3.5 Centrality and clustering plot scales The centralityplot and clusteringplot now gained a scale argument, that makes it easier to set the x-axis scale. Set this argument to raw0 to plot the centrality indices on the original scale including the zero

12 value. This will potentially make it more clear when centralities are somewhat equal (although to test this please see the bootnet package; Epskamp, Borsboom, and Fried (06. The functions now give a message when the default scale is used and warns the user that these are z-scores. centralityplot(graph, scale = "raw0" O N E C A Betweenness Closeness Strength Smallworldness index As smallworldness was often very slow, I have added the smallworldindex as well. This function makes the graph unweighted, and computes the usual smallworldness index: smallworldindex(graph ## Warning in smallworldindex(graph: Edge weights removed ## $transitivity ## [] ## ## $transitivity_random ## [] ## ## $APL ## [] ## ## $APL_random ## [] ## ## $index ## [].3684

13 New features in version.3.3 IMPORTANT: new default EBICglasso From the NEWS file: The lambda.min.ratio of EBICglasso now defaults to 0.0 instead of 0., similar to earlier versions of qgraph. This will cause EBICglasso and qgraph(..., graph = "glasso" to potentially return less sparse models! Means and SDs Three new arguments, means, SDs and meanrange, can now be used to gain insight in the distribution of continuous nodes, as long as these are on the same scale. For example: qgraph(cormat, graph = "glasso", samplesize = nrow(bfi, groups = Groups, legend = FALSE, layout = "spring", means = colmeans(bfi[,:5],na.rm=true, SDs = apply(bfi[,:5],,sd,na.rm=true, meanrange = c(,5 N C A E O Legend modes The legend.mode argument now has two new options: style (now the default and style, in which both information from groups and from nodenames are used: Names <- scan(" = "character", sep = "\n" Groups <- rep(c( 'Agreeableness', 'Conscientiousness', 3

14 'Extraversion', 'Neuroticism', 'Opennness',each=5 # Style : qgraph(cormat, graph = "glasso", samplesize = nrow(bfi, groups = Groups, nodenames = Names, layout = "spring", legend.mode = "style", legend.cex = 0.5 E N O C A Agreeableness A: Am indifferent to the feelings of others. : Inquire about others' well being. : Know how to comfort others. : Love children. : Make people feel at ease. Conscientiousness C: Am exacting in my work. : Continue until everything is perfect. : Do things according to a plan. : Do things in a half way manner. : Waste my time. Extraversion E: Don't talk a lot. : Find it difficult to approach others. : Know how to captivate people. : Make friends easily. : Take charge. Neuroticism N: Get angry easily. : Get irritated easily. : Have frequent mood swings. : Often feel blue. : Panic easily. Opennness O: Am full of ideas. : Avoid difficult reading material. : Carry the conversation to a higher level. : Spend time reflecting on things. : Will not probe deeply into a subject. # Style : qgraph(cormat, graph = "glasso", samplesize = nrow(bfi, groups = Groups, nodenames = Names, layout = "spring", legend.mode = "style", legend.cex = 0.5 4

15 E N C A Agreeableness A: Am indifferent to the feelings of others. : Inquire about others' well being. : Know how to comfort others. : Love children. : Make people feel at ease. Conscientiousness C: Am exacting in my work. : Continue until everything is perfect. : Do things according to a plan. : Do things in a half way manner. : Waste my time. Extraversion E: Don't talk a lot. : Find it difficult to approach others. : Know how to captivate people. : Make friends easily. : Take charge. Neuroticism N: Get angry easily. : Get irritated easily. : Have frequent mood swings. : Often feel blue. : Panic easily. Opennness O: Am full of ideas. : Avoid difficult reading material. : Carry the conversation to a higher level. : Spend time reflecting on things. : Will not probe deeply into a subject. O References Borkulo, Claudia D. van, Lynn Boschloo, Denny Borsboom, Brenda W J H Penninx, Lourens J Waldorp, and Robert A Schoevers. 05. Association of Symptom Network Structure With the Course of Depression. JAMA Psychiatry 7 (. American Medical Association: 9 6. Epskamp, Sacha, Denny Borsboom, and Eiko I. Fried. 06. Estimating psychological networks and their accuracy: a tutorial paper. ArXiv Preprint, arxiv: Epskamp, Sacha, M.T. Rhemtulla, and D. Borsboom. 06. Generalized Network Psychometrics: Combining Network and Latent Variable Models. ArXiv Preprint, arxiv: Isvoranu, A. M., Claudia D. van Borkulo, L. Boyette, J. T. W. Wigman, C. H. Vinkers, D. Borsboom, and GROUP Investigators. 06. A Network Approach to Psychosis: Pathways between Childhood Trauma and Psychotic Symptoms. Schizophrenia Bulletin. 5