lavaan frequency tables
lavTables.RdFrequency tables for categorical variables and related statistics.
Usage
lavTables(object, dimension = 2L, type = "cells", categorical = NULL,
group = NULL, statistic = "default", g2_min = 3, x2_min = 3,
p_value = FALSE, output = "data.frame", pattern_as_string = TRUE, ...)Arguments
- object
Either a
data.frame, or an object of classlavaan.- dimension
Integer. If 0L, display all response patterns. If 1L, display one-dimensional (one-way) tables; if 2L, display two-dimensional (two-way or pairwise) tables. For the latter, the information shown per row can be changed: if
type = "cells", each row is a cell in a pairwise table; iftype = "table", each row is a table.- type
If
"cells", display information for each cell in the (one-way or two-way) table. If"table", display information per table. If"pattern", display response patterns (implying"dimension = 0L").- categorical
Only used if
objectis adata.frame. Specify variables that need to be treated as categorical.- group
Only used if
objectis adata.frame. Specify a grouping variable.- statistic
Either a character string, or a vector of character strings requesting one or more statistics for each cell, pattern or table. Always available are
X2andG2for the Pearson and LRT based goodness-of-fit statistics. A distinction is made between the unrestricted and restricted model. The statistics based on the former carry the suffix*.un, as inX2.unandG2.un. If object is adata.frame, only the unrestricted versions of the statistics are available. For one-way tables, additional statistics are the thresholds (th.unandth). For two-way tables andtype = "table", the following statistics are available:X2,G2,cor(polychoric correlation),RMSEAand the corresponding unrestricted versions (X2.unetc). Additional statistics areG2.average,G2.nlargeandG2.plargestatistics based on the cell valuesG2:G2.averageis the average of theG2values in each cell of the two-way table;G2.nlargeis the number of cells with aG2value larger thang2_min, andG2.plargeis the proportion of cells with aG2value larger thang2_min. A similar set of statistics based onX2is also available. If"default", the selection of statistics (if any) depends on thedimandtypearguments, and on whether the object is adata.frameor a fitted lavaan object.- g2_min
Numeric. All cells with a G2 statistic larger than this number are considered `large', as reflected in the (optional)
"G2.plarge"and"G2.nlarge"columns.- x2_min
Numeric. All cells with a X2 statistic larger than this number are considered `large', as reflected in the (optional)
"X2.plarge"and"X2.nlarge"columns.- p_value
Logical. If
"TRUE", p-values are computed for requested statistics (eg G2 or X2) if possible.- output
If
"data.frame", the output is presented as a data.frame where each row is either a cell, a table, or a response pattern, depending on the"type"argument. If"table", the output is presented as a table (or matrix) or a list of tables. Only a single statistic can be shown in this case, and if thestatisticis empty, the observed frequencies are shown.- pattern_as_string
Logical. Only used for response patterns (dimension = 0L). If
"TRUE", response patterns are displayed as a compact string. If"FALSE", as many columns as observed variables are displayed.- ...
To support old argument names.
Value
If output = "data.frame", the output is presented as a data.frame
where each row is either a cell, a table, or a response pattern, depending on
the "type" argument.
If output = "table" (only for two-way tables), the output is
a list of tables (if type = "cells") in which each list element
corresponds to a pairwise table, or a single table per group (if
type = "table"). In both cases, the table entries are determined by the
(single) statistic argument.
References
Joreskog, K.G. & Moustaki, I. (2001). Factor analysis of ordinal variables: A comparison of three approaches. Multivariate Behavioral Research, 36, 347-387.
Examples
HS9 <- HolzingerSwineford1939[,c("x1","x2","x3","x4","x5",
"x6","x7","x8","x9")]
HSbinary <- as.data.frame( lapply(HS9, cut, 2, labels=FALSE) )
# using the data only
lavTables(HSbinary, dim = 0L, categorical = names(HSbinary))
#> pattern nobs obs.freq obs.prop
#> 1 111111111 301 19 0.063
#> 2 211121111 301 11 0.037
#> 3 111121111 301 7 0.023
#> 4 211111111 301 7 0.023
#> 5 221111111 301 7 0.023
#> 6 221121111 301 7 0.023
#> 7 222221111 301 7 0.023
#> 8 221111211 301 6 0.020
#> 9 221221211 301 6 0.020
#> 10 221222111 301 6 0.020
#> 11 222121111 301 6 0.020
#> 12 111111211 301 5 0.017
#> 13 111221211 301 5 0.017
#> 14 121221211 301 5 0.017
#> 15 122111111 301 5 0.017
#> 16 221221111 301 5 0.017
#> 17 121111111 301 4 0.013
#> 18 211111211 301 4 0.013
#> 19 212111111 301 4 0.013
#> 20 212221111 301 4 0.013
#> 21 222221211 301 4 0.013
#> 22 222222111 301 4 0.013
#> 23 222222222 301 4 0.013
#> 24 111121212 301 3 0.010
#> 25 111221111 301 3 0.010
#> 26 121121111 301 3 0.010
#> 27 211211211 301 3 0.010
#> 28 211221111 301 3 0.010
#> 29 212111211 301 3 0.010
#> 30 212121111 301 3 0.010
#> 31 212221212 301 3 0.010
#> 32 212222111 301 3 0.010
#> 33 221221212 301 3 0.010
#> 34 222111111 301 3 0.010
#> 35 222221112 301 3 0.010
#> 36 222221212 301 3 0.010
#> 37 111111221 301 2 0.007
#> 38 111121211 301 2 0.007
#> 39 112111111 301 2 0.007
#> 40 121111212 301 2 0.007
#> 41 121121112 301 2 0.007
#> 42 121221111 301 2 0.007
#> 43 122111112 301 2 0.007
#> 44 211111122 301 2 0.007
#> 45 211111212 301 2 0.007
#> 46 211121211 301 2 0.007
#> 47 211211111 301 2 0.007
#> 48 211221112 301 2 0.007
#> 49 211221211 301 2 0.007
#> 50 211222112 301 2 0.007
#> 51 211222212 301 2 0.007
#> 52 221121112 301 2 0.007
#> 53 221222112 301 2 0.007
#> 54 222111112 301 2 0.007
#> 55 222111212 301 2 0.007
#> 56 222121211 301 2 0.007
#> 57 222221222 301 2 0.007
#> 58 222222112 301 2 0.007
#> 59 222222211 301 2 0.007
#> 60 111111112 301 1 0.003
#> 61 111111121 301 1 0.003
#> 62 111111212 301 1 0.003
#> 63 111121112 301 1 0.003
#> 64 111121222 301 1 0.003
#> 65 111211111 301 1 0.003
#> 66 111211211 301 1 0.003
#> 67 111211212 301 1 0.003
#> 68 111221212 301 1 0.003
#> 69 111222111 301 1 0.003
#> 70 112111211 301 1 0.003
#> 71 112111212 301 1 0.003
#> 72 112111222 301 1 0.003
#> 73 112121111 301 1 0.003
#> 74 112221111 301 1 0.003
#> 75 121121211 301 1 0.003
#> 76 121121212 301 1 0.003
#> 77 121122211 301 1 0.003
#> 78 121122221 301 1 0.003
#> 79 121211111 301 1 0.003
#> 80 121211211 301 1 0.003
#> 81 121212211 301 1 0.003
#> 82 121221212 301 1 0.003
#> 83 121222111 301 1 0.003
#> 84 122111211 301 1 0.003
#> 85 122111221 301 1 0.003
#> 86 122111222 301 1 0.003
#> 87 122121211 301 1 0.003
#> 88 122121212 301 1 0.003
#> 89 122221121 301 1 0.003
#> 90 122221222 301 1 0.003
#> 91 122222111 301 1 0.003
#> 92 211111112 301 1 0.003
#> 93 211111121 301 1 0.003
#> 94 211111221 301 1 0.003
#> 95 211121122 301 1 0.003
#> 96 211121221 301 1 0.003
#> 97 211122111 301 1 0.003
#> 98 211222111 301 1 0.003
#> 99 211222211 301 1 0.003
#> 100 212111112 301 1 0.003
#> 101 212111221 301 1 0.003
#> 102 212121211 301 1 0.003
#> 103 212121221 301 1 0.003
#> 104 212121222 301 1 0.003
#> 105 212211221 301 1 0.003
#> 106 212221222 301 1 0.003
#> 107 212222112 301 1 0.003
#> 108 212222211 301 1 0.003
#> 109 221111112 301 1 0.003
#> 110 221111221 301 1 0.003
#> 111 221121212 301 1 0.003
#> 112 221122222 301 1 0.003
#> 113 221211111 301 1 0.003
#> 114 221211212 301 1 0.003
#> 115 221221112 301 1 0.003
#> 116 221222211 301 1 0.003
#> 117 221222212 301 1 0.003
#> 118 222111122 301 1 0.003
#> 119 222111221 301 1 0.003
#> 120 222111222 301 1 0.003
#> 121 222121112 301 1 0.003
#> 122 222121222 301 1 0.003
#> 123 222122112 301 1 0.003
#> 124 222211111 301 1 0.003
#> 125 222211112 301 1 0.003
#> 126 222211121 301 1 0.003
#> 127 222211211 301 1 0.003
#> 128 222221121 301 1 0.003
#> 129 222221221 301 1 0.003
#> 130 222222121 301 1 0.003
#> 131 222222122 301 1 0.003
#> 132 222222212 301 1 0.003
#> 133 222222221 301 1 0.003
lavTables(HSbinary, dim = 1L, categorical = names(HSbinary), stat=c("th.un"))
#> id lhs rhs nobs obs.freq obs.prop th.un
#> 1 1 x1 1 301 105 0.349 -0.388
#> 2 1 x1 2 301 196 0.651 Inf
#> 3 2 x2 1 301 144 0.478 -0.054
#> 4 2 x2 2 301 157 0.522 Inf
#> 5 3 x3 1 301 188 0.625 0.318
#> 6 3 x3 2 301 113 0.375 Inf
#> 7 4 x4 1 301 172 0.571 0.180
#> 8 4 x4 2 301 129 0.429 Inf
#> 9 5 x5 1 301 120 0.399 -0.257
#> 10 5 x5 2 301 181 0.601 Inf
#> 11 6 x6 1 301 255 0.847 1.024
#> 12 6 x6 2 301 46 0.153 Inf
#> 13 7 x7 1 301 178 0.591 0.231
#> 14 7 x7 2 301 123 0.409 Inf
#> 15 8 x8 1 301 262 0.870 1.128
#> 16 8 x8 2 301 39 0.130 Inf
#> 17 9 x9 1 301 221 0.734 0.626
#> 18 9 x9 2 301 80 0.266 Inf
lavTables(HSbinary, dim = 2L, categorical = names(HSbinary), type = "table")
#> lhs rhs nobs
#> 1 x1 x2 301
#> 5 x1 x3 301
#> 9 x1 x4 301
#> 13 x1 x5 301
#> 17 x1 x6 301
#> 21 x1 x7 301
#> 25 x1 x8 301
#> 29 x1 x9 301
#> 33 x2 x3 301
#> 37 x2 x4 301
#> 41 x2 x5 301
#> 45 x2 x6 301
#> 49 x2 x7 301
#> 53 x2 x8 301
#> 57 x2 x9 301
#> 61 x3 x4 301
#> 65 x3 x5 301
#> 69 x3 x6 301
#> 73 x3 x7 301
#> 77 x3 x8 301
#> 81 x3 x9 301
#> 85 x4 x5 301
#> 89 x4 x6 301
#> 93 x4 x7 301
#> 97 x4 x8 301
#> 101 x4 x9 301
#> 105 x5 x6 301
#> 109 x5 x7 301
#> 113 x5 x8 301
#> 117 x5 x9 301
#> 121 x6 x7 301
#> 125 x6 x8 301
#> 129 x6 x9 301
#> 133 x7 x8 301
#> 137 x7 x9 301
#> 141 x8 x9 301
# fit a model
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data=HSbinary, ordered=names(HSbinary))
lavTables(fit, 1L)
#> id lhs rhs nobs obs.freq obs.prop est.prop X2
#> 1 1 x1 1 301 105 0.349 0.349 0
#> 2 1 x1 2 301 196 0.651 0.651 0
#> 3 2 x2 1 301 144 0.478 0.478 0
#> 4 2 x2 2 301 157 0.522 0.522 0
#> 5 3 x3 1 301 188 0.625 0.625 0
#> 6 3 x3 2 301 113 0.375 0.375 0
#> 7 4 x4 1 301 172 0.571 0.571 0
#> 8 4 x4 2 301 129 0.429 0.429 0
#> 9 5 x5 1 301 120 0.399 0.399 0
#> 10 5 x5 2 301 181 0.601 0.601 0
#> 11 6 x6 1 301 255 0.847 0.847 0
#> 12 6 x6 2 301 46 0.153 0.153 0
#> 13 7 x7 1 301 178 0.591 0.591 0
#> 14 7 x7 2 301 123 0.409 0.409 0
#> 15 8 x8 1 301 262 0.870 0.870 0
#> 16 8 x8 2 301 39 0.130 0.130 0
#> 17 9 x9 1 301 221 0.734 0.734 0
#> 18 9 x9 2 301 80 0.266 0.266 0
lavTables(fit, 2L, type="cells")
#> id lhs rhs nobs row col obs.freq obs.prop est.prop X2
#> 1 1 x1 x2 301 1 1 63 0.209 0.222 0.228
#> 2 1 x1 x2 301 2 1 81 0.269 0.256 0.198
#> 3 1 x1 x2 301 1 2 42 0.140 0.127 0.400
#> 4 1 x1 x2 301 2 2 115 0.382 0.395 0.128
#> 5 2 x1 x3 301 1 1 83 0.276 0.271 0.022
#> 6 2 x1 x3 301 2 1 105 0.349 0.353 0.017
#> 7 2 x1 x3 301 1 2 22 0.073 0.078 0.078
#> 8 2 x1 x3 301 2 2 91 0.302 0.298 0.020
#> 9 3 x1 x4 301 1 1 76 0.252 0.243 0.101
#> 10 3 x1 x4 301 2 1 96 0.319 0.328 0.075
#> 11 3 x1 x4 301 1 2 29 0.096 0.105 0.233
#> 12 3 x1 x4 301 2 2 100 0.332 0.323 0.076
#> 13 4 x1 x5 301 1 1 56 0.186 0.183 0.020
#> 14 4 x1 x5 301 2 1 64 0.213 0.216 0.017
#> 15 4 x1 x5 301 1 2 49 0.163 0.166 0.022
#> 16 4 x1 x5 301 2 2 132 0.439 0.435 0.009
#> 17 5 x1 x6 301 1 1 99 0.329 0.322 0.043
#> 18 5 x1 x6 301 2 1 156 0.518 0.525 0.027
#> 19 5 x1 x6 301 1 2 6 0.020 0.027 0.522
#> 20 5 x1 x6 301 2 2 40 0.133 0.126 0.111
#> 21 6 x1 x7 301 1 1 60 0.199 0.225 0.893
#> 22 6 x1 x7 301 2 1 118 0.392 0.366 0.549
#> 23 6 x1 x7 301 1 2 45 0.150 0.124 1.627
#> 24 6 x1 x7 301 2 2 78 0.259 0.285 0.706
#> 25 7 x1 x8 301 1 1 95 0.316 0.319 0.011
#> 26 7 x1 x8 301 2 1 167 0.555 0.551 0.006
#> 27 7 x1 x8 301 1 2 10 0.033 0.030 0.118
#> 28 7 x1 x8 301 2 2 29 0.096 0.100 0.035
#> 29 8 x1 x9 301 1 1 83 0.276 0.279 0.010
#> 30 8 x1 x9 301 2 1 138 0.458 0.455 0.006
#> 31 8 x1 x9 301 1 2 22 0.073 0.070 0.042
#> 32 8 x1 x9 301 2 2 58 0.193 0.196 0.015
#> 33 9 x2 x3 301 1 1 108 0.359 0.352 0.043
#> 34 9 x2 x3 301 2 1 80 0.266 0.273 0.056
#> 35 9 x2 x3 301 1 2 36 0.120 0.127 0.120
#> 36 9 x2 x3 301 2 2 77 0.256 0.249 0.061
#> 37 10 x2 x4 301 1 1 98 0.326 0.317 0.078
#> 38 10 x2 x4 301 2 1 74 0.246 0.255 0.097
#> 39 10 x2 x4 301 1 2 46 0.153 0.162 0.152
#> 40 10 x2 x4 301 2 2 83 0.276 0.267 0.093
#> 41 11 x2 x5 301 1 1 70 0.233 0.232 0.000
#> 42 11 x2 x5 301 2 1 50 0.166 0.166 0.000
#> 43 11 x2 x5 301 1 2 74 0.246 0.246 0.000
#> 44 11 x2 x5 301 2 2 107 0.355 0.355 0.000
#> 45 12 x2 x6 301 1 1 131 0.435 0.433 0.004
#> 46 12 x2 x6 301 2 1 124 0.412 0.414 0.005
#> 47 12 x2 x6 301 1 2 13 0.043 0.046 0.042
#> 48 12 x2 x6 301 2 2 33 0.110 0.107 0.018
#> 49 13 x2 x7 301 1 1 88 0.292 0.301 0.080
#> 50 13 x2 x7 301 2 1 90 0.299 0.290 0.083
#> 51 13 x2 x7 301 1 2 56 0.186 0.177 0.136
#> 52 13 x2 x7 301 2 2 67 0.223 0.232 0.104
#> 53 14 x2 x8 301 1 1 128 0.425 0.432 0.032
#> 54 14 x2 x8 301 2 1 134 0.445 0.438 0.031
#> 55 14 x2 x8 301 1 2 16 0.053 0.046 0.293
#> 56 14 x2 x8 301 2 2 23 0.076 0.083 0.164
#> 57 15 x2 x9 301 1 1 114 0.379 0.374 0.020
#> 58 15 x2 x9 301 2 1 107 0.355 0.360 0.021
#> 59 15 x2 x9 301 1 2 30 0.100 0.105 0.072
#> 60 15 x2 x9 301 2 2 50 0.166 0.161 0.047
#> 61 16 x3 x4 301 1 1 118 0.392 0.400 0.050
#> 62 16 x3 x4 301 2 1 54 0.179 0.171 0.118
#> 63 16 x3 x4 301 1 2 70 0.233 0.224 0.090
#> 64 16 x3 x4 301 2 2 59 0.196 0.204 0.099
#> 65 17 x3 x5 301 1 1 81 0.269 0.290 0.440
#> 66 17 x3 x5 301 2 1 39 0.130 0.109 1.170
#> 67 17 x3 x5 301 1 2 107 0.355 0.335 0.381
#> 68 17 x3 x5 301 2 2 74 0.246 0.266 0.479
#> 69 18 x3 x6 301 1 1 165 0.548 0.558 0.052
#> 70 18 x3 x6 301 2 1 90 0.299 0.289 0.101
#> 71 18 x3 x6 301 1 2 23 0.076 0.067 0.440
#> 72 18 x3 x6 301 2 2 23 0.076 0.086 0.340
#> 73 19 x3 x7 301 1 1 113 0.375 0.388 0.118
#> 74 19 x3 x7 301 2 1 65 0.216 0.204 0.225
#> 75 19 x3 x7 301 1 2 75 0.249 0.237 0.193
#> 76 19 x3 x7 301 2 2 48 0.159 0.172 0.266
#> 77 20 x3 x8 301 1 1 175 0.581 0.560 0.252
#> 78 20 x3 x8 301 2 1 87 0.289 0.311 0.454
#> 79 20 x3 x8 301 1 2 13 0.043 0.065 2.174
#> 80 20 x3 x8 301 2 2 26 0.086 0.065 2.176
#> 81 21 x3 x9 301 1 1 148 0.492 0.481 0.066
#> 82 21 x3 x9 301 2 1 73 0.243 0.253 0.126
#> 83 21 x3 x9 301 1 2 40 0.133 0.143 0.222
#> 84 21 x3 x9 301 2 2 40 0.133 0.123 0.260
#> 85 22 x4 x5 301 1 1 102 0.339 0.337 0.002
#> 86 22 x4 x5 301 2 1 18 0.060 0.061 0.012
#> 87 22 x4 x5 301 1 2 70 0.233 0.234 0.003
#> 88 22 x4 x5 301 2 2 111 0.369 0.367 0.002
#> 89 23 x4 x6 301 1 1 167 0.555 0.558 0.005
#> 90 23 x4 x6 301 2 1 88 0.292 0.289 0.009
#> 91 23 x4 x6 301 1 2 5 0.017 0.014 0.193
#> 92 23 x4 x6 301 2 2 41 0.136 0.139 0.019
#> 93 24 x4 x7 301 1 1 111 0.369 0.349 0.321
#> 94 24 x4 x7 301 2 1 67 0.223 0.242 0.464
#> 95 24 x4 x7 301 1 2 61 0.203 0.222 0.506
#> 96 24 x4 x7 301 2 2 62 0.206 0.187 0.601
#> 97 25 x4 x8 301 1 1 149 0.495 0.507 0.090
#> 98 25 x4 x8 301 2 1 113 0.375 0.363 0.125
#> 99 25 x4 x8 301 1 2 23 0.076 0.064 0.711
#> 100 25 x4 x8 301 2 2 16 0.053 0.065 0.696
#> 101 26 x4 x9 301 1 1 132 0.439 0.434 0.016
#> 102 26 x4 x9 301 2 1 89 0.296 0.300 0.023
#> 103 26 x4 x9 301 1 2 40 0.133 0.138 0.050
#> 104 26 x4 x9 301 2 2 40 0.133 0.128 0.054
#> 105 27 x5 x6 301 1 1 119 0.395 0.394 0.001
#> 106 27 x5 x6 301 2 1 136 0.452 0.453 0.001
#> 107 27 x5 x6 301 1 2 1 0.003 0.005 0.100
#> 108 27 x5 x6 301 2 2 45 0.150 0.148 0.003
#> 109 28 x5 x7 301 1 1 72 0.239 0.247 0.070
#> 110 28 x5 x7 301 2 1 106 0.352 0.345 0.050
#> 111 28 x5 x7 301 1 2 48 0.159 0.152 0.114
#> 112 28 x5 x7 301 2 2 75 0.249 0.257 0.068
#> 113 29 x5 x8 301 1 1 103 0.342 0.356 0.168
#> 114 29 x5 x8 301 2 1 159 0.528 0.514 0.116
#> 115 29 x5 x8 301 1 2 17 0.056 0.042 1.408
#> 116 29 x5 x8 301 2 2 22 0.073 0.087 0.685
#> 117 30 x5 x9 301 1 1 95 0.316 0.306 0.088
#> 118 30 x5 x9 301 2 1 126 0.419 0.428 0.063
#> 119 30 x5 x9 301 1 2 25 0.083 0.093 0.292
#> 120 30 x5 x9 301 2 2 55 0.183 0.173 0.156
#> 121 31 x6 x7 301 1 1 150 0.498 0.509 0.061
#> 122 31 x6 x7 301 2 1 28 0.093 0.083 0.377
#> 123 31 x6 x7 301 1 2 105 0.349 0.339 0.092
#> 124 31 x6 x7 301 2 2 18 0.060 0.070 0.446
#> 125 32 x6 x8 301 1 1 225 0.748 0.744 0.004
#> 126 32 x6 x8 301 2 1 37 0.123 0.126 0.025
#> 127 32 x6 x8 301 1 2 30 0.100 0.103 0.031
#> 128 32 x6 x8 301 2 2 9 0.030 0.027 0.120
#> 129 33 x6 x9 301 1 1 193 0.641 0.631 0.045
#> 130 33 x6 x9 301 2 1 28 0.093 0.103 0.276
#> 131 33 x6 x9 301 1 2 62 0.206 0.216 0.131
#> 132 33 x6 x9 301 2 2 18 0.060 0.050 0.566
#> 133 34 x7 x8 301 1 1 167 0.555 0.544 0.059
#> 134 34 x7 x8 301 2 1 95 0.316 0.326 0.098
#> 135 34 x7 x8 301 1 2 11 0.037 0.047 0.684
#> 136 34 x7 x8 301 2 2 28 0.093 0.083 0.388
#> 137 35 x7 x9 301 1 1 144 0.478 0.477 0.002
#> 138 35 x7 x9 301 2 1 77 0.256 0.258 0.004
#> 139 35 x7 x9 301 1 2 34 0.113 0.115 0.008
#> 140 35 x7 x9 301 2 2 46 0.153 0.151 0.006
#> 141 36 x8 x9 301 1 1 202 0.671 0.681 0.040
#> 142 36 x8 x9 301 2 1 19 0.063 0.054 0.512
#> 143 36 x8 x9 301 1 2 60 0.199 0.190 0.144
#> 144 36 x8 x9 301 2 2 20 0.066 0.076 0.361
lavTables(fit, 2L, type="table", stat=c("cor.un", "G2", "cor"))
#> lhs rhs nobs df cor cor.un G2
#> 1 x1 x2 301 0 0.367 0.284 0.944
#> 5 x1 x3 301 0 0.383 0.415 0.139
#> 9 x1 x4 301 0 0.303 0.364 0.491
#> 13 x1 x5 301 0 0.296 0.319 0.069
#> 17 x1 x6 301 0 0.327 0.422 0.752
#> 21 x1 x7 301 0 0.132 -0.048 3.718
#> 25 x1 x8 301 0 0.207 0.159 0.167
#> 29 x1 x9 301 0 0.191 0.165 0.073
#> 33 x2 x3 301 0 0.345 0.389 0.283
#> 37 x2 x4 301 0 0.273 0.328 0.422
#> 41 x2 x5 301 0 0.266 0.268 0.001
#> 45 x2 x6 301 0 0.294 0.322 0.069
#> 49 x2 x7 301 0 0.119 0.061 0.403
#> 53 x2 x8 301 0 0.186 0.105 0.511
#> 57 x2 x9 301 0 0.172 0.210 0.160
#> 61 x3 x4 301 0 0.285 0.232 0.355
#> 65 x3 x5 301 0 0.278 0.138 2.418
#> 69 x3 x6 301 0 0.307 0.206 0.926
#> 73 x3 x7 301 0 0.124 0.041 0.802
#> 77 x3 x8 301 0 0.195 0.439 5.147
#> 81 x3 x9 301 0 0.180 0.258 0.674
#> 85 x4 x5 301 0 0.680 0.688 0.019
#> 89 x4 x6 301 0 0.751 0.720 0.214
#> 93 x4 x7 301 0 0.076 0.200 1.894
#> 97 x4 x8 301 0 0.119 -0.029 1.628
#> 101 x4 x9 301 0 0.109 0.146 0.143
#> 105 x5 x6 301 0 0.733 0.761 0.115
#> 109 x5 x7 301 0 0.074 0.023 0.302
#> 113 x5 x8 301 0 0.116 -0.059 2.284
#> 117 x5 x9 301 0 0.107 0.183 0.607
#> 121 x6 x7 301 0 0.081 -0.029 0.985
#> 125 x6 x8 301 0 0.128 0.183 0.177
#> 129 x6 x9 301 0 0.118 0.230 0.995
#> 133 x7 x8 301 0 0.348 0.464 1.271
#> 137 x7 x9 301 0 0.322 0.335 0.020
#> 141 x8 x9 301 0 0.505 0.403 1.043
lavTables(fit, 2L, type="table", output="table", stat="X2")
#> x1 x2 x3 x4 x5 x6 x7 x8 x9
#> x1 .
#> x2 0.954 .
#> x3 0.138 0.281 .
#> x4 0.485 0.420 0.356 .
#> x5 0.069 0.001 2.469 0.019 .
#> x6 0.703 0.069 0.934 0.226 0.105 .
#> x7 3.775 0.404 0.802 1.892 0.302 0.977 .
#> x8 0.171 0.520 5.055 1.622 2.377 0.181 1.229 .
#> x9 0.073 0.159 0.675 0.143 0.600 1.018 0.020 1.057 .