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The qgraph package for network visualizations ofpsychometric data in R
Sacha Epskamp
University of AmsterdamDepartment of Psychological Methods
AmstRdam
Outline
IntroductionWhat is qgraph?Graphs
Creating graphsInput modesLayout modes
Visualizing statisticsCorrelation matricesFactor loadingsConfirmatory Factor Analysis
Concluding commentsReferences
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:
I Focus on Weighted GraphsI Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraph
I Simple inputI Summarize a large amount of statistics without needing data
reduction methods.I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:
I Focus on Weighted GraphsI Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraph
I Simple inputI Summarize a large amount of statistics without needing data
reduction methods.I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphs
I Different from other R packages (e.g. igraph Csardi &Nepusz, 2006) in:
I Focus on Weighted GraphsI Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraph
I Simple inputI Summarize a large amount of statistics without needing data
reduction methods.I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:
I Focus on Weighted GraphsI Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraph
I Simple inputI Summarize a large amount of statistics without needing data
reduction methods.I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:I Focus on Weighted Graphs
I Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraph
I Simple inputI Summarize a large amount of statistics without needing data
reduction methods.I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:I Focus on Weighted GraphsI Intended for visualization of data as graphs
I Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraph
I Simple inputI Summarize a large amount of statistics without needing data
reduction methods.I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:I Focus on Weighted GraphsI Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraph
I Simple inputI Summarize a large amount of statistics without needing data
reduction methods.I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:I Focus on Weighted GraphsI Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraph
I Simple inputI Summarize a large amount of statistics without needing data
reduction methods.I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:I Focus on Weighted GraphsI Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraphI Simple input
I Summarize a large amount of statistics without needing datareduction methods.
I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:I Focus on Weighted GraphsI Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraphI Simple inputI Summarize a large amount of statistics without needing data
reduction methods.
I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:I Focus on Weighted GraphsI Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraphI Simple inputI Summarize a large amount of statistics without needing data
reduction methods.I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
qgraph
I A R package (CRAN link)
I Can be used to plot various types of graphsI Different from other R packages (e.g. igraph Csardi &
Nepusz, 2006) in:I Focus on Weighted GraphsI Intended for visualization of data as graphsI Optimized for vector-type image files (e.g. PDF, SVG)
I Aims in qgraphI Simple inputI Summarize a large amount of statistics without needing data
reduction methods.I Visualize relations between variables
I Main idea: Show variables as nodes, relationships as edges
Graphs
I A graph is a network that consists of n nodes (or vertices)that are connected with m edges.
I Each edge has a weight indicating the strength of thatconnection
I An edge can be directed (have an arrow) or undirected
Graphs
I A graph is a network that consists of n nodes (or vertices)that are connected with m edges.
I Each edge has a weight indicating the strength of thatconnection
I An edge can be directed (have an arrow) or undirected
Graphs
I A graph is a network that consists of n nodes (or vertices)that are connected with m edges.
I Each edge has a weight indicating the strength of thatconnection
I An edge can be directed (have an arrow) or undirected
Graphs
I A graph is a network that consists of n nodes (or vertices)that are connected with m edges.
I Each edge has a weight indicating the strength of thatconnection
I An edge can be directed (have an arrow) or undirected
Unweighted graph
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Weighted graph
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Weighted graph
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Directed graph
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Outline
IntroductionWhat is qgraph?Graphs
Creating graphsInput modesLayout modes
Visualizing statisticsCorrelation matricesFactor loadingsConfirmatory Factor Analysis
Concluding commentsReferences
The qgraph() function
I The main function in qgraph is qgraph()
I Most other functions are either wrapping functions usingqgraph() or functions used in qgraph()
I The qgraph() function requires only one argument (adj)
I A lot of other arguments can be specified, but these are alloptional
Usage:
qgraph( adj, ... )
The qgraph() function
I The main function in qgraph is qgraph()I Most other functions are either wrapping functions using
qgraph() or functions used in qgraph()
I The qgraph() function requires only one argument (adj)
I A lot of other arguments can be specified, but these are alloptional
Usage:
qgraph( adj, ... )
The qgraph() function
I The main function in qgraph is qgraph()I Most other functions are either wrapping functions using
qgraph() or functions used in qgraph()
I The qgraph() function requires only one argument (adj)
I A lot of other arguments can be specified, but these are alloptional
Usage:
qgraph( adj, ... )
The qgraph() function
I The main function in qgraph is qgraph()I Most other functions are either wrapping functions using
qgraph() or functions used in qgraph()
I The qgraph() function requires only one argument (adj)
I A lot of other arguments can be specified, but these are alloptional
Usage:
qgraph( adj, ... )
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:
I A 0 indicates no relationshipI Absolute negative values are similar in strength to positive
values
I Examples:
I A 1 indicating a connection (unweighted graphs)I CorrelationsI Regression parametersI Factor loadings
I Adjacency matrices occur naturally in statistics!
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:
I A 0 indicates no relationshipI Absolute negative values are similar in strength to positive
values
I Examples:
I A 1 indicating a connection (unweighted graphs)I CorrelationsI Regression parametersI Factor loadings
I Adjacency matrices occur naturally in statistics!
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:
I A 0 indicates no relationshipI Absolute negative values are similar in strength to positive
values
I Examples:
I A 1 indicating a connection (unweighted graphs)I CorrelationsI Regression parametersI Factor loadings
I Adjacency matrices occur naturally in statistics!
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:I A 0 indicates no relationship
I Absolute negative values are similar in strength to positivevalues
I Examples:
I A 1 indicating a connection (unweighted graphs)I CorrelationsI Regression parametersI Factor loadings
I Adjacency matrices occur naturally in statistics!
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:I A 0 indicates no relationshipI Absolute negative values are similar in strength to positive
values
I Examples:
I A 1 indicating a connection (unweighted graphs)I CorrelationsI Regression parametersI Factor loadings
I Adjacency matrices occur naturally in statistics!
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:I A 0 indicates no relationshipI Absolute negative values are similar in strength to positive
values
I Examples:
I A 1 indicating a connection (unweighted graphs)I CorrelationsI Regression parametersI Factor loadings
I Adjacency matrices occur naturally in statistics!
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:I A 0 indicates no relationshipI Absolute negative values are similar in strength to positive
values
I Examples:I A 1 indicating a connection (unweighted graphs)
I CorrelationsI Regression parametersI Factor loadings
I Adjacency matrices occur naturally in statistics!
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:I A 0 indicates no relationshipI Absolute negative values are similar in strength to positive
values
I Examples:I A 1 indicating a connection (unweighted graphs)I Correlations
I Regression parametersI Factor loadings
I Adjacency matrices occur naturally in statistics!
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:I A 0 indicates no relationshipI Absolute negative values are similar in strength to positive
values
I Examples:I A 1 indicating a connection (unweighted graphs)I CorrelationsI Regression parameters
I Factor loadings
I Adjacency matrices occur naturally in statistics!
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:I A 0 indicates no relationshipI Absolute negative values are similar in strength to positive
values
I Examples:I A 1 indicating a connection (unweighted graphs)I CorrelationsI Regression parametersI Factor loadings
I Adjacency matrices occur naturally in statistics!
The adjacency matrix
I The adj argument is the input. This can be an adjacencymatrix
I An adjacency matrix is a square n by n matrix in which eachelement indicates the relationship between two variables
I Any relationship can be used as long as:I A 0 indicates no relationshipI Absolute negative values are similar in strength to positive
values
I Examples:I A 1 indicating a connection (unweighted graphs)I CorrelationsI Regression parametersI Factor loadings
I Adjacency matrices occur naturally in statistics!
[,1] [,2] [,3]
[1,] 0 1 1
[2,] 0 0 1
[3,] 0 0 0
1
23
The Big 5
Included is a dataset in which the Dutch translation of a commonlyused personality test, the NEO-PI-R (Costa & McCrae, 1992;Hoekstra, Fruyt, & Ormel, 2003), was administered to 500 firstyear psychology students (Dolan, Oort, Stoel, & Wicherts, 2009).The NEO-PI-R consists of 240 items designed to measure the fivecentral personality factors:
I Neuroticism
I Extroversion
I Agreeableness
I Openess to Experience
I Conscientiousness
N1 N6 N11 N16 N21 N26 N31 N36 N41 N46N51
N56N61
N66N71
N76N81
N86N91
N96N101
N106N111
N116
N121
N126
N131
N136
N141
N146
N151
N156
N161
N166
N171
N176
N181
N186
N191
N196
N201
N206
N211
N216
N221
N226
N231
N236
E2
E7
E12
E17
E22
E27
E32
E37
E42
E47
E52
E57
E62
E67
E72
E77
E82
E87
E92
E97
E102
E107
E112
E117
E122
E127
E132
E137
E142
E147
E152
E157
E162
E167
E172
E177
E182
E187
E192
E197
E202
E207
E212
E217
E222
E227
E232
E237
O3O8
O13O18
O23O28
O33O38
O43O48
O53O58
O63O68
O73O78O83O88O93O98O103O108O113O118O123O128O133O138O143O148O153O158O163O168
O173O178
O183O188
O193O198
O203O208
O213O218
O223O228
O233O238
A4A9
A14
A19
A24
A29
A34
A39
A44
A49
A54
A59
A64
A69
A74
A79
A84
A89
A94
A99
A104
A109
A114
A119
A124
A129
A134
A139
A144
A149
A154
A159
A164
A169
A174
A179
A184
A189
A194
A199
A204
A209
A214
A219
A224
A229
A234
A239
C5
C10
C15
C20
C25
C30
C35
C40
C45
C50
C55
C60
C65
C70
C75
C80
C85
C90
C95
C100
C105
C110
C115
C120
C125C130
C135C140
C145C150
C155C160
C165C170
C175C180
C185C190
C195C200C205C210C215C220C225C230C235C240
N
E
O
A
C
The Big 5
> data(big5)
> str(big5)
num [1:500, 1:240] 2 3 4 4 5 2 2 1 4 2 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:240] "N1" "E2" "O3" "A4" ...
The Big 5
> cor(big5)[1:15, 1:3]
N1 E2 O3
N1 1.000000e+00 -0.1855271104 0.095361021
E2 -1.855271e-01 1.0000000000 0.082879486
O3 9.536102e-02 0.0828794862 1.000000000
A4 -1.388302e-01 0.2639947325 -0.077486908
C5 -6.958419e-02 0.0433073349 -0.019451268
N6 1.901235e-01 -0.1144449337 0.052585940
E7 -8.556772e-02 0.1968042360 -0.001360255
O8 3.599480e-02 0.0380351107 0.143715099
A9 3.775956e-02 0.0918642940 -0.147722360
C10 7.113742e-02 -0.1250087306 0.005001674
N11 3.491657e-01 -0.1085540815 0.072827947
E12 -2.680803e-01 0.0456805867 -0.003996781
O13 9.768583e-02 -0.0002757219 0.218802592
A14 4.512596e-05 0.1133985066 0.009208309
C15 5.859827e-03 0.0988472658 -0.051836728
> qgraph(cor(big5), minimum = 0.2)
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353637383940414243444546474849505152535455565758596061626364656667686970717273
7475
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7879
8081
8283
8485
8687
8889
9091
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949596979899100
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103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138
139140
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224225226227228229230231232233234235236237238239240
> data(big5groups)
> qgraph(cor(big5), groups = big5groups, minimum = 0.2)
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89
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4344
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5354
55
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5859
60
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6364
65
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6869
70
71
72
7374
75
76
77
7879
80
81
82
8384
85
86
87
8889
90
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92
9394
95
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9899
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103104
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108109
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118119
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Fruchterman-Reingold layout (20 iterations)
> data(big5groups)
> Q <- qgraph(cor(big5), groups = big5groups,
+ minimum = 0.2, layout = "spring")
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> Q <- qgraph(Q, legend = FALSE)
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> Q <- qgraph(Q, vsize = 2, borders = FALSE,
+ vTrans = 150)
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> Q <- qgraph(Q, overlay = TRUE)
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> qgraph(Q, transparency = T, bg = T, bgcontrol = 5,
+ filetype = "png", filename = "bg", res = 144,
+ width = 7, height = 7)
Outline
IntroductionWhat is qgraph?Graphs
Creating graphsInput modesLayout modes
Visualizing statisticsCorrelation matricesFactor loadingsConfirmatory Factor Analysis
Concluding commentsReferences
Association
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Significance
> qgraph(cor(big5), Q, graph = "sig")
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NeuroticismExtraversionOpennessAgreeablenessConscientiousness
p < 0.05p < 0.01p < 0.001p < 1e−04
Factor loadings
I A factor loadings matrix can be visualized usingqgraph.loadings()
I There are two wrapper functions that perform an analysis andsend the results to qgraph.loadings():
I qgraph.efa() performs an exploratory factor analysis (EFA)using stats:::factanal
I qgraph.pca() performs a principal component analysis (PCA)using psych:::principal (Revelle, 2010)
I These functions use a correlation or covariance matrix as input
Factor loadings
I A factor loadings matrix can be visualized usingqgraph.loadings()
I There are two wrapper functions that perform an analysis andsend the results to qgraph.loadings():
I qgraph.efa() performs an exploratory factor analysis (EFA)using stats:::factanal
I qgraph.pca() performs a principal component analysis (PCA)using psych:::principal (Revelle, 2010)
I These functions use a correlation or covariance matrix as input
Factor loadings
I A factor loadings matrix can be visualized usingqgraph.loadings()
I There are two wrapper functions that perform an analysis andsend the results to qgraph.loadings():
I qgraph.efa() performs an exploratory factor analysis (EFA)using stats:::factanal
I qgraph.pca() performs a principal component analysis (PCA)using psych:::principal (Revelle, 2010)
I These functions use a correlation or covariance matrix as input
Factor loadings
I A factor loadings matrix can be visualized usingqgraph.loadings()
I There are two wrapper functions that perform an analysis andsend the results to qgraph.loadings():
I qgraph.efa() performs an exploratory factor analysis (EFA)using stats:::factanal
I qgraph.pca() performs a principal component analysis (PCA)using psych:::principal (Revelle, 2010)
I These functions use a correlation or covariance matrix as input
Factor loadings
I A factor loadings matrix can be visualized usingqgraph.loadings()
I There are two wrapper functions that perform an analysis andsend the results to qgraph.loadings():
I qgraph.efa() performs an exploratory factor analysis (EFA)using stats:::factanal
I qgraph.pca() performs a principal component analysis (PCA)using psych:::principal (Revelle, 2010)
I These functions use a correlation or covariance matrix as input
Factor loadings: EFA
> qgraph.efa(big5, 5, groups = big5groups,
+ rotation = "promax", minimum = 0.2, cut = 0.4,
+ vsize = c(1, 15), borders = FALSE, asize = 0.07,
+ esize = 4, vTrans = 200)
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236
237
238
239
240
Neuroticism
Extraversion
Conscientiousness
Agreeableness
Openness
Factor loadings: EFA crossloadings
> qgraph.efa(big5, 5, groups = big5groups,
+ rotation = "promax", minimum = 0.2, cut = 0.4,
+ vsize = c(1, 15), borders = FALSE, asize = 0.07,
+ esize = 4, vTrans = 200, crossloadings = TRUE)
1
23
4 5
6
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13
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127
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133
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135
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153
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230
231
232
233
234
235
236
237
238
239
240
Neuroticism
Extraversion
Conscientiousness
Agreeableness
Openness
Factor loadings: PCA
> qgraph.pca(cor(big5), 5, groups = big5groups,
+ rotation = "promax", minimum = 0.2, cut = 0.4,
+ vsize = c(1, 15), borders = FALSE, asize = 0.07,
+ esize = 4, vTrans = 200)
1
23
4 5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
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70
71
72
73
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76
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78
79
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92
93
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95
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97
98
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101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
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141
142
143
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240
Neuroticism
Extraversion
Conscientiousness
Agreeableness
Openness
Confirmatory Factor Analysis
I qgraph.cfa() can be used to fit a simple confirmatory factormodel
I Each variable loads on only one factorI Factors are correlatedI Scaling by fixing first loading of each factor to 1
I This can be done with the sem (Fox, 2006) or lavaan(Rosseel, 2011) packages
I Returns a "sem" or lavaan object
I Results can be send to qgraph.sem() or qgraph.lavaan()for a full report
Confirmatory Factor Analysis
I qgraph.cfa() can be used to fit a simple confirmatory factormodel
I Each variable loads on only one factor
I Factors are correlatedI Scaling by fixing first loading of each factor to 1
I This can be done with the sem (Fox, 2006) or lavaan(Rosseel, 2011) packages
I Returns a "sem" or lavaan object
I Results can be send to qgraph.sem() or qgraph.lavaan()for a full report
Confirmatory Factor Analysis
I qgraph.cfa() can be used to fit a simple confirmatory factormodel
I Each variable loads on only one factorI Factors are correlated
I Scaling by fixing first loading of each factor to 1
I This can be done with the sem (Fox, 2006) or lavaan(Rosseel, 2011) packages
I Returns a "sem" or lavaan object
I Results can be send to qgraph.sem() or qgraph.lavaan()for a full report
Confirmatory Factor Analysis
I qgraph.cfa() can be used to fit a simple confirmatory factormodel
I Each variable loads on only one factorI Factors are correlatedI Scaling by fixing first loading of each factor to 1
I This can be done with the sem (Fox, 2006) or lavaan(Rosseel, 2011) packages
I Returns a "sem" or lavaan object
I Results can be send to qgraph.sem() or qgraph.lavaan()for a full report
Confirmatory Factor Analysis
I qgraph.cfa() can be used to fit a simple confirmatory factormodel
I Each variable loads on only one factorI Factors are correlatedI Scaling by fixing first loading of each factor to 1
I This can be done with the sem (Fox, 2006) or lavaan(Rosseel, 2011) packages
I Returns a "sem" or lavaan object
I Results can be send to qgraph.sem() or qgraph.lavaan()for a full report
Confirmatory Factor Analysis
I qgraph.cfa() can be used to fit a simple confirmatory factormodel
I Each variable loads on only one factorI Factors are correlatedI Scaling by fixing first loading of each factor to 1
I This can be done with the sem (Fox, 2006) or lavaan(Rosseel, 2011) packages
I Returns a "sem" or lavaan object
I Results can be send to qgraph.sem() or qgraph.lavaan()for a full report
Confirmatory Factor Analysis
I qgraph.cfa() can be used to fit a simple confirmatory factormodel
I Each variable loads on only one factorI Factors are correlatedI Scaling by fixing first loading of each factor to 1
I This can be done with the sem (Fox, 2006) or lavaan(Rosseel, 2011) packages
I Returns a "sem" or lavaan object
I Results can be send to qgraph.sem() or qgraph.lavaan()for a full report
Confirmatory Factor Analysis
> names(big5groups) <- strtrim(names(big5groups),
+ 1)
> names(big5) <- 1:ncol(big5)
> fit <- qgraph.cfa(cov(big5), nrow(big5),
+ big5groups, pkg = "lavaan", opts = list(se = "none"),
+ vsize.man = 1, vsize.lat = 6, edge.label.cex = 0.5)
> print(fit)
Lavaan (0.4-8) converged normally after 161 iterations
Number of observations 500
Estimator ML
Minimum Function Chi-square 60838.192
Degrees of freedom 28430
P-value 0.000
Confirmatory Factor Analysis> pdf("big5cfaModel%03d.pdf", width = 7, height = 7,
+ onefile = FALSE)
> qgraph.lavaan(fit, filetype = "", include = 1:7,
+ vsize.man = 1, vsize.lat = 6, edge.label.cex = 0.5,
+ residSize = 0.1, groups = big5groups,
+ titles = FALSE)
> dev.off()
2
> pdf("big5cfaRes%03d.pdf", width = 14, height = 7,
+ onefile = FALSE)
> qgraph.lavaan(fit, filetype = "", include = 8:12,
+ vsize.man = 1, vsize.lat = 6, edge.label.cex = 0.5,
+ residSize = 0.1, groups = big5groups,
+ titles = FALSE, minimum = 0.15)
> dev.off()
2
Model
N1 N6 N11 N16 N21 N26 N31 N36 N41 N46N51
N56N61
N66N71
N76N81
N86N91
N96N101
N106N111
N116
N121
N126
N131
N136
N141
N146
N151
N156
N161
N166
N171
N176
N181
N186
N191
N196
N201
N206
N211
N216
N221
N226
N231
N236
E2
E7
E12
E17
E22
E27
E32
E37
E42
E47
E52
E57
E62
E67
E72
E77
E82
E87
E92
E97
E102
E107
E112
E117
E122
E127
E132
E137
E142
E147
E152
E157
E162
E167
E172
E177
E182
E187
E192
E197
E202
E207
E212
E217
E222
E227
E232
E237
O3O8
O13O18
O23O28
O33O38
O43O48
O53O58
O63O68
O73O78O83O88O93O98O103O108O113O118O123O128O133O138O143O148O153O158O163O168
O173O178
O183O188
O193O198
O203O208
O213O218
O223O228
O233O238
A4A9
A14
A19
A24
A29
A34
A39
A44
A49
A54
A59
A64
A69
A74
A79
A84
A89
A94
A99
A104
A109
A114
A119
A124
A129
A134
A139
A144
A149
A154
A159
A164
A169
A174
A179
A184
A189
A194
A199
A204
A209
A214
A219
A224
A229
A234
A239
C5
C10
C15
C20
C25
C30
C35
C40
C45
C50
C55
C60
C65
C70
C75
C80
C85
C90
C95
C100
C105
C110
C115
C120
C125C130
C135C140
C145C150
C155C160
C165C170
C175C180
C185C190
C195C200C205C210C215C220C225C230C235C240
N
E
O
A
C
Results
10.651.140.830.021.040.90.541.33−0.060.350.451.080.31.220.810.221.031.010.360.50.710.180.530.970.410.580.83−0.110.751.090.450.910.310.40.60.630.651.350.640.240.431.050.691.030.40.661.04
11.571.52−0.990.451.581.441.631.221.180.7510.811.071.070.780.681.861.270.640.991.950.391.441.180.950.751.891.091.921.770.360.670.130.671.71.181.341.010.570.851.340.861.651.221.590.721.29
11.470.760.270.780.520.780.840.680.780.90.381.040.980.340.270.520.170.941.310.620.710.550.350.991.690.620.310.440.330.410.960.410.440.740.40.671.670.40.290.520.10.610.790.980.460.770.33
11.161.260.870.940.340.831.510.490.450.450.680.860.870.950.540.950.540.440.970.580.710.640.650.580.460.540.410.680.480.61.410.260.721.040.860.861.380.710.610.660.550.610.90.870.690.740.03
10.540.781.271.540.94−0.41.481.111.681.631.190.981.491.331.31.180.670.971.170.591.671.791.010.671.120.80.761.340.80.990.740.531.161.561.210.630.141.291.070.950.621.721.251.080.962.130.88
0.950.640.740.780.970.620.610.720.710.780.950.420.830.80.731.290.960.780.510.91.080.910.950.610.70.560.730.550.520.83
0.610.97
0.620.77
1.50.420.781.110.560.870.890.340.840.99
0.60.870.760.470.660.910.810.590.90.970.660.631.10.641.450.520.411.02
10.951.060.9
0.760.6
0.920.7
1.280.750.31
1.020.65
0.460.340.5
0.451.4
0.831.05
1.450.26
0.550.52
0.740.75
1.161.28
0.450.79
0.930.411.060.410.580.980.680.621.030.730.760.680.490.630.760.70.691.410.40.671.011.10.70.880.740.991.280.560.750.80.390.771.220.820.890.660.390.761.010.350.950.760.470.850.610.860.470.960.860.980.570.790.690.810.690.60.80.77
0.70.83
0.620.72
0.940.75
0.761.1
0.81.3
0.90.93
0.421.01
0.260.8
0.510.790.4
0.720.680.920.760.610.590.680.280.870.760.510.450.720.480.750.750.630.570.840.570.850.780.90.540.890.490.650.561.090.910.821.040.580.770.620.370.721.02
0.810.47
0.840.73
1.071.480.410.680.770.440.640.40.780.380.760.920.910.670.540.740.730.370.860.470.770.8510.371.121.150.980.590.82
0.41
0.09
0.21
0.2
0.12
−0.11
0.01
−0.04
−0.08
0.04
0.03
0.02
0.04
0
0.03
N1 N6 N11 N16 N21 N26 N31 N36 N41 N46N51
N56N61
N66N71
N76N81
N86N91
N96N101
N106N111
N116
N121
N126
N131
N136
N141
N146
N151
N156
N161
N166
N171
N176
N181
N186
N191
N196
N201
N206
N211
N216
N221
N226
N231
N236
E2
E7
E12
E17
E22
E27
E32
E37
E42
E47
E52
E57
E62
E67
E72
E77
E82
E87
E92
E97
E102
E107
E112
E117
E122
E127
E132
E137
E142
E147
E152
E157
E162
E167
E172
E177
E182
E187
E192
E197
E202
E207
E212
E217
E222
E227
E232
E237
O3O8
O13O18
O23O28
O33O38
O43O48
O53O58
O63O68
O73O78O83O88O93O98O103O108O113O118O123O128O133O138O143O148O153O158O163O168
O173O178
O183O188
O193O198
O203O208
O213O218
O223O228
O233O238
A4A9
A14
A19
A24
A29
A34
A39
A44
A49
A54
A59
A64
A69
A74
A79
A84
A89
A94
A99
A104
A109
A114
A119
A124
A129
A134
A139
A144
A149
A154
A159
A164
A169
A174
A179
A184
A189
A194
A199
A204
A209
A214
A219
A224
A229
A234
A239
C5
C10
C15
C20
C25
C30
C35
C40
C45
C50
C55
C60
C65
C70
C75
C80
C85
C90
C95
C100
C105
C110
C115
C120
C125C130
C135C140
C145C150
C155C160
C165C170
C175C180
C185C190
C195C200C205C210C215C220C225C230C235C240
N
E
O
A
C
Results
N1 N6 N11 N16 N21 N26 N31 N36 N41 N46N51
N56N61
N66N71
N76N81
N86N91
N96N101
N106N111
N116
N121
N126
N131
N136
N141
N146
N151
N156
N161
N166
N171
N176
N181
N186
N191
N196
N201
N206
N211
N216
N221
N226
N231
N236
E2
E7
E12
E17
E22
E27
E32
E37
E42
E47
E52
E57
E62
E67
E72
E77
E82
E87
E92
E97
E102
E107
E112
E117
E122
E127
E132
E137
E142
E147
E152
E157
E162
E167
E172
E177
E182
E187
E192
E197
E202
E207
E212
E217
E222
E227
E232
E237
O3O8
O13O18
O23O28
O33O38
O43O48
O53O58
O63O68
O73O78O83O88O93O98O103O108O113O118O123O128O133O138O143O148O153O158O163O168
O173O178
O183O188
O193O198
O203O208
O213O218
O223O228
O233O238
A4A9
A14
A19
A24
A29
A34
A39
A44
A49
A54
A59
A64
A69
A74
A79
A84
A89
A94
A99
A104
A109
A114
A119
A124
A129
A134
A139
A144
A149
A154
A159
A164
A169
A174
A179
A184
A189
A194
A199
A204
A209
A214
A219
A224
A229
A234
A239
C5
C10
C15
C20
C25
C30
C35
C40
C45
C50
C55
C60
C65
C70
C75
C80
C85
C90
C95
C100
C105
C110
C115
C120
C125C130
C135C140
C145C150
C155C160
C165C170
C175C180
C185C190
C195C200C205C210C215C220C225C230C235C240
N
E
O
A
C
Correlations
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●
●●●●●●
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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●
●●●●●●
1 2 3 45
67
89101112131415
1617
1819
2021
2223242526272829
3031
3233
34353637383940
4142
4344
4546 47 48
49 50 51 5253
5455
5657
585960616263
6465
6667
6869
7071727374757677
7879
8081
82838485868788
8990
9192
9394 95 96
97 98 99 100101
102103
104
105
106
107
108
109
110
111
112
113
114
115116
117118
119120121122123124
125126
127
128
129
130
131
132
133
134
135
136
137
138
139140
141142
143 144145 146 147148
149150
151
152
153
154
155
156
157
158
159
160
161
162
163164
165166
167168169170171172
173174
175
176
177
178
179
180
181
182
183
184
185
186
187188
189190
191 192
193 194 195196
197198
199
200
201
202
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Outline
IntroductionWhat is qgraph?Graphs
Creating graphsInput modesLayout modes
Visualizing statisticsCorrelation matricesFactor loadingsConfirmatory Factor Analysis
Concluding commentsReferences
Concluding comments
I qgraph is still work in progress
I Plans for the future:
I More wrapper functions for different statistics (e.g. IRT)I More layout modesI Estimating and fitting causal models
I Some things I couldn’t describe...
Concluding comments
I qgraph is still work in progressI Plans for the future:
I More wrapper functions for different statistics (e.g. IRT)I More layout modesI Estimating and fitting causal models
I Some things I couldn’t describe...
Concluding comments
I qgraph is still work in progressI Plans for the future:
I More wrapper functions for different statistics (e.g. IRT)
I More layout modesI Estimating and fitting causal models
I Some things I couldn’t describe...
Concluding comments
I qgraph is still work in progressI Plans for the future:
I More wrapper functions for different statistics (e.g. IRT)I More layout modes
I Estimating and fitting causal models
I Some things I couldn’t describe...
Concluding comments
I qgraph is still work in progressI Plans for the future:
I More wrapper functions for different statistics (e.g. IRT)I More layout modesI Estimating and fitting causal models
I Some things I couldn’t describe...
Concluding comments
I qgraph is still work in progressI Plans for the future:
I More wrapper functions for different statistics (e.g. IRT)I More layout modesI Estimating and fitting causal models
I Some things I couldn’t describe...
Layout constraints
Grayscale colors
> Q <- qgraph(cor(big5), minimum = 0.25, cut = 0.4,
+ vsize = 2, groups = big5groups, legend = T,
+ borders = F, vTrans = 200, gray = TRUE)
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NEOAC
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NEOAC
Tooltips
Link
Modelling
ξ
η1 η2 η3
y1 y2 y3 y4 y5 y6 y7
x1 x2 x3
Concluding comments
Thank you for your attention!
ReferencesCosta, P. T., & McCrae, R. R. (1992). Neo personality inventory
revised (neo pi-r). Psychological Assessment Resources.Csardi, G., & Nepusz, T. (2006). The extttigraph software package
for complex network research. InterJournal, ComplexSystems, 1695. Available from http://igraph.sf.net
Dolan, C., Oort, F., Stoel, R., & Wicherts, J. (2009). TestingMeasurement Invariance in the Target Rotated MultigroupExploratory Factor Model. Structural Equation Modeling: AMultidisciplinary Journal, 16(2), 20.
Fox, J. (2006). Structural Equation Modeling With the extttsemPackage in R. Structural Equation Modeling, 13(3),465–486.
Hoekstra, H., Fruyt, F. de, & Ormel, J. (2003).Neo-Persoonlijkheidsvragenlijsten: NEO-PI-R, NEO-FFI [Neopersonality questionnaires: NEO-PI-R, NEO-FFI]. Lisse:Swets Test Services.
Revelle, W. (2010). extttpsych: Procedures for psychological,psychometric, and personality research [Computer softwaremanual]. Evanston, Illinois. Available from http://
personality-project.org/r/psych.manual.pdf (Rpackage version 1.0-93)
Rosseel, Y. (2011). extttlavaan: Latent variable analysis[Computer software manual]. Available fromhttp://CRAN.R-project.org/package=lavaan (Rpackage version 0.4-7)