Return model implied residuals for linear dependencies between items or at the person level.
If the latent trait density was approximated (e.g., Davidian curves, Empirical histograms, etc)
then passing use_dentype_estimate = TRUE will use the internally saved quadrature and
density components (where applicable).
# S4 method for SingleGroupClass
residuals(
object,
type = "LD",
p.adjust = "none",
df.p = FALSE,
approx.z = FALSE,
full.scores = FALSE,
QMC = FALSE,
printvalue = NULL,
tables = FALSE,
verbose = TRUE,
Theta = NULL,
suppress = NA,
theta_lim = c(-6, 6),
quadpts = NULL,
fold = TRUE,
upper = TRUE,
technical = list(),
...
)an object of class SingleGroupClass or
MultipleGroupClass. Bifactor models are automatically detected and utilized for
better accuracy
type of residuals to be displayed.
Can be either 'LD' or 'LDG2' for a local dependence matrix based on the
X2 or G2 statistics (Chen & Thissen, 1997), 'Q3' for the statistic proposed by
Yen (1984), 'JSI' for the jack-knife statistic proposed Edwards et al. (2018),
'exp' for the expected values for the frequencies of every response pattern,
and 'expfull' for the expected values for every theoretically observable response pattern.
For the 'LD' and 'LDG2' types, the upper diagonal elements represent the standardized
residuals in the form of signed Cramers V coefficients
method to use for adjusting all p-values (see p.adjust
for available options). Default is 'none'
logical; print the degrees of freedom and p-values?
logical; transform \(\chi^2(df)\) information from LD tests into approximate z-ratios instead using the transformation \(z=\sqrt{2 * \chi^2} - \sqrt{2 * df - 1}\)?
logical; compute relevant statistics for each subject in the original data?
logical; use quasi-Monte Carlo integration? If quadpts is omitted the
default number of nodes is 5000
a numeric value to be specified when using the res='exp'
option. Only prints patterns that have standardized residuals greater than
abs(printvalue). The default (NULL) prints all response patterns
logical; for LD type, return the observed, expected, and standardized residual tables for each item combination?
logical; allow information to be printed to the console?
a matrix of factor scores used for statistics that require empirical estimates (i.e., Q3).
If supplied, arguments typically passed to fscores() will be ignored and these values will
be used instead
a numeric value indicating which parameter local dependency combinations to flag as being too high. Absolute values for the standardized estimates greater than this value will be returned, while all values less than this value will be set to NA
range for the integration grid
number of quadrature nodes to use. The default is extracted from model (if available) or generated automatically if not available
logical; apply the sum 'folding' described by Edwards et al. (2018) for the JSI statistic?
logical; which portion of the matrix (upper versus lower triangle)
should the suppress argument be applied to?
list of technical arguments when models are re-estimated (see mirt
for details)
additional arguments to be passed to fscores()
Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06
Chen, W. H. & Thissen, D. (1997). Local dependence indices for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22, 265-289.
Edwards, M. C., Houts, C. R. & Cai, L. (2018). A Diagnostic Procedure to Detect Departures From Local Independence in Item Response Theory Models. Psychological Methods, 23, 138-149.
Yen, W. (1984). Effects of local item dependence on the fit and equating performance of the three parameter logistic model. Applied Psychological Measurement, 8, 125-145.
# \donttest{
x <- mirt(Science, 1)
residuals(x)
#> LD matrix (lower triangle) and standardized values.
#>
#> Upper triangle summary:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.147 -0.136 -0.111 -0.045 0.041 0.152
#>
#> Comfort Work Future Benefit
#> Comfort NA -0.147 -0.101 0.152
#> Work 25.512 NA 0.088 -0.141
#> Future 12.002 9.208 NA -0.122
#> Benefit 27.321 23.235 17.461 NA
residuals(x, tables = TRUE)
#> $Comfort_Work
#> $Comfort_Work$Obs
#>
#> 1 2 3 4
#> 1 2 0 1 2
#> 2 2 11 15 4
#> 3 24 71 148 23
#> 4 5 16 42 26
#>
#> $Comfort_Work$Exp
#> [,1] [,2] [,3] [,4]
#> [1,] 1.046123 1.787854 1.922925 0.262908
#> [2,] 5.722535 11.386923 13.625623 1.972172
#> [3,] 23.322258 69.348472 140.029013 31.718143
#> [4,] 3.487224 14.109434 50.763258 21.495135
#>
#> $Comfort_Work$std_res
#>
#> 1 2 3 4
#> 1 0.9326115 -1.3371066 -0.6655567 3.3878247
#> 2 -1.5561252 -0.1146625 0.3723298 1.4439717
#> 3 0.1403392 0.1983204 0.6736015 -1.5479971
#> 4 0.8100928 0.5033119 -1.2299596 0.9716541
#>
#>
#> $Comfort_Future
#> $Comfort_Future$Obs
#>
#> 1 2 3 4
#> 1 2 2 1 0
#> 2 3 11 11 7
#> 3 8 51 155 52
#> 4 1 8 43 37
#>
#> $Comfort_Future$Exp
#> [,1] [,2] [,3] [,4]
#> [1,] 0.7680477 1.812936 2.091572 0.3472546
#> [2,] 3.6749826 11.241583 15.084907 2.7057791
#> [3,] 9.1976778 52.384929 148.579175 54.2561060
#> [4,] 0.7527106 6.828614 42.541147 39.7325783
#>
#> $Comfort_Future$std_res
#>
#> 1 2 3 4
#> 1 1.40572316 0.13893088 -0.75477217 -0.58928310
#> 2 -0.35209910 -0.07205318 -1.05174604 2.61058722
#> 3 -0.39491253 -0.19134814 0.52675890 -0.30629167
#> 4 0.28503062 0.44826372 0.07035076 -0.43351011
#>
#>
#> $Comfort_Benefit
#> $Comfort_Benefit$Obs
#>
#> 1 2 3 4
#> 1 4 0 1 0
#> 2 5 10 13 4
#> 3 11 81 133 41
#> 4 1 9 46 33
#>
#> $Comfort_Benefit$Exp
#> [,1] [,2] [,3] [,4]
#> [1,] 0.6710457 1.95217 1.959779 0.4368153
#> [2,] 3.6241191 12.16983 13.682082 3.2312240
#> [3,] 14.8454892 71.28246 131.343241 46.9466978
#> [4,] 2.3173267 14.55596 44.803655 28.1781067
#>
#> $Comfort_Benefit$std_res
#>
#> 1 2 3 4
#> 1 4.0637950 -1.3972006 -0.6855953 -0.6609200
#> 2 0.7227359 -0.6219892 -0.1844000 0.4276774
#> 3 -0.9980547 1.1509727 0.1445624 -0.8679073
#> 4 -0.8653661 -1.4562597 0.1787310 0.9083677
#>
#>
#> $Work_Future
#> $Work_Future$Obs
#>
#> 1 2 3 4
#> 1 7 10 14 2
#> 2 3 28 57 10
#> 3 3 31 122 50
#> 4 1 3 17 34
#>
#> $Work_Future$Exp
#> [,1] [,2] [,3] [,4]
#> [1,] 4.6004355 12.483087 14.34426 2.150355
#> [2,] 5.8020446 27.786418 52.26417 10.780046
#> [3,] 3.6903551 28.936581 118.01439 55.699488
#> [4,] 0.3005835 3.061977 23.67397 28.411829
#>
#> $Work_Future$std_res
#>
#> 1 2 3 4
#> 1 1.11874979 -0.70279865 -0.09089724 -0.10253275
#> 2 -1.16328067 0.04051800 0.65507895 -0.23757996
#> 3 -0.35936723 0.38358699 0.36688237 -0.76367799
#> 4 1.27571400 -0.03541829 -1.37166696 1.04838334
#>
#>
#> $Work_Benefit
#> $Work_Benefit$Obs
#>
#> 1 2 3 4
#> 1 4 8 12 9
#> 2 6 34 47 11
#> 3 8 52 111 35
#> 4 3 6 23 23
#>
#> $Work_Benefit$Exp
#> [,1] [,2] [,3] [,4]
#> [1,] 4.233251 13.170723 13.31610 2.858063
#> [2,] 7.660483 32.196149 44.96112 11.814929
#> [3,] 8.417022 46.931424 106.55195 44.440425
#> [4,] 1.147224 7.662122 26.95958 19.679427
#>
#> $Work_Benefit$std_res
#>
#> 1 2 3 4
#> 1 -0.1133671 -1.4247756 -0.3606627 3.6330344
#> 2 -0.5999380 0.3179060 0.3040694 -0.2370851
#> 3 -0.1437407 0.7398678 0.4309125 -1.4161278
#> 4 1.7298113 -0.6004660 -0.7625934 0.7485258
#>
#>
#> $Future_Benefit
#> $Future_Benefit$Obs
#>
#> 1 2 3 4
#> 1 5 1 6 2
#> 2 5 32 30 5
#> 3 8 53 118 31
#> 4 3 14 39 40
#>
#> $Future_Benefit$Exp
#> [,1] [,2] [,3] [,4]
#> [1,] 2.960508 6.706374 4.143629 0.5829072
#> [2,] 7.760422 29.142832 29.887302 5.4775063
#> [3,] 9.227453 52.779344 109.948223 36.3417805
#> [4,] 1.509597 11.331867 47.809604 36.3906499
#>
#> $Future_Benefit$std_res
#>
#> 1 2 3 4
#> 1 1.18532911 -2.20351681 0.91195681 1.85608815
#> 2 -0.99090687 0.52926095 0.02061456 -0.20402700
#> 3 -0.40407692 0.03037266 0.76788760 -0.88610042
#> 4 1.21303423 0.79260501 -1.27408623 0.59832081
#>
#>
residuals(x, type = 'exp')
#> Comfort Work Future Benefit freq exp std.res
#> 1 1 1 1 1 2 0.124 5.324
#> 2 1 3 2 1 1 0.067 3.605
#> 3 1 4 2 3 1 0.019 7.046
#> 4 1 4 3 1 1 0.006 12.642
#> 5 2 1 1 1 1 0.460 0.796
#> 6 2 1 2 4 1 0.095 2.930
#> 7 2 2 1 1 1 0.351 1.095
#> 8 2 2 2 2 4 2.147 1.264
#> 9 2 2 2 3 2 1.616 0.302
#> 10 2 2 3 1 1 0.377 1.015
#> 11 2 2 3 2 1 1.716 -0.547
#> 12 2 2 3 3 1 2.228 -0.823
#> 13 2 2 4 3 1 0.251 1.497
#> 14 2 3 1 3 1 0.213 1.707
#> 15 2 3 2 2 2 1.545 0.366
#> 16 2 3 2 3 1 1.489 -0.401
#> 17 2 3 3 2 3 2.248 0.502
#> 18 2 3 3 3 3 3.910 -0.460
#> 19 2 3 3 4 2 1.035 0.948
#> 20 2 3 4 1 1 0.041 4.763
#> 21 2 3 4 3 2 0.866 1.218
#> 22 2 4 2 1 1 0.029 5.690
#> 23 2 4 4 3 2 0.259 3.418
#> 24 2 4 4 4 1 0.184 1.902
#> 25 3 1 1 1 1 0.644 0.444
#> 26 3 1 1 3 2 0.638 1.705
#> 27 3 1 2 2 2 3.923 -0.971
#> 28 3 1 2 3 4 3.077 0.526
#> 29 3 1 3 2 5 3.567 0.759
#> 30 3 1 3 3 5 5.097 -0.043
#> 31 3 1 3 4 3 1.160 1.709
#> 32 3 1 4 3 1 0.764 0.269
#> 33 3 1 4 4 1 0.320 1.203
#> 34 3 2 1 2 1 1.781 -0.585
#> 35 3 2 1 4 1 0.154 2.157
#> 36 3 2 2 1 1 2.250 -0.833
#> 37 3 2 2 2 10 8.471 0.525
#> 38 3 2 2 3 7 8.067 -0.376
#> 39 3 2 3 1 1 2.221 -0.819
#> 40 3 2 3 2 16 11.743 1.242
#> 41 3 2 3 3 22 19.584 0.546
#> 42 3 2 3 4 5 4.950 0.023
#> 43 3 2 4 1 1 0.193 1.835
#> 44 3 2 4 2 1 1.293 -0.258
#> 45 3 2 4 3 3 3.778 -0.400
#> 46 3 2 4 4 2 1.716 0.217
#> 47 3 3 1 3 2 0.971 1.044
#> 48 3 3 2 1 1 1.764 -0.575
#> 49 3 3 2 2 13 7.852 1.837
#> 50 3 3 2 3 10 9.780 0.070
#> 51 3 3 2 4 2 1.975 0.018
#> 52 3 3 3 1 5 3.363 0.893
#> 53 3 3 3 2 23 20.447 0.565
#> 54 3 3 3 3 52 45.243 1.005
#> 55 3 3 3 4 8 14.757 -1.759
#> 56 3 3 4 2 7 4.389 1.246
#> 57 3 3 4 3 13 16.979 -0.966
#> 58 3 3 4 4 12 10.323 0.522
#> 59 3 4 1 3 1 0.090 3.030
#> 60 3 4 2 3 1 1.098 -0.093
#> 61 3 4 3 2 2 3.043 -0.598
#> 62 3 4 3 3 4 8.562 -1.559
#> 63 3 4 3 4 4 3.637 0.191
#> 64 3 4 4 1 1 0.160 2.100
#> 65 3 4 4 2 1 1.287 -0.253
#> 66 3 4 4 3 6 6.466 -0.183
#> 67 3 4 4 4 3 5.659 -1.118
#> 68 4 1 1 4 1 0.006 12.587
#> 69 4 1 2 2 1 0.325 1.183
#> 70 4 1 2 4 2 0.057 8.158
#> 71 4 1 3 4 1 0.300 1.278
#> 72 4 2 2 1 1 0.193 1.836
#> 73 4 2 2 3 3 1.017 1.967
#> 74 4 2 3 3 8 4.462 1.675
#> 75 4 2 3 4 2 1.448 0.459
#> 76 4 2 4 2 1 0.433 0.862
#> 77 4 2 4 4 1 1.080 -0.077
#> 78 4 3 2 3 1 1.665 -0.516
#> 79 4 3 3 2 2 4.874 -1.302
#> 80 4 3 3 3 21 14.002 1.870
#> 81 4 3 3 4 3 5.965 -1.214
#> 82 4 3 4 2 2 2.097 -0.067
#> 83 4 3 4 3 5 10.419 -1.679
#> 84 4 3 4 4 8 8.830 -0.279
#> 85 4 4 3 2 1 0.966 0.034
#> 86 4 4 3 3 2 3.571 -0.831
#> 87 4 4 3 4 3 2.063 0.652
#> 88 4 4 4 2 2 0.906 1.149
#> 89 4 4 4 3 6 5.780 0.092
#> 90 4 4 4 4 12 7.470 1.657
residuals(x, suppress = .15)
#> LD matrix (lower triangle) and standardized values.
#>
#> Upper triangle summary:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.147 -0.136 -0.111 -0.045 0.041 0.152
#>
#> Comfort Work Future Benefit
#> Comfort NA NA NA 0.152
#> Work NA NA NA NA
#> Future NA NA NA NA
#> Benefit 27.321 NA NA NA
residuals(x, df.p = TRUE)
#> Degrees of freedom (lower triangle) and p-values:
#>
#> Comfort Work Future Benefit
#> Comfort NA 0.002 0.213 0.001
#> Work 9 NA 0.418 0.006
#> Future 9 9.000 NA 0.042
#> Benefit 9 9.000 9.000 NA
#>
#> LD matrix (lower triangle) and standardized values.
#>
#> Upper triangle summary:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.147 -0.136 -0.111 -0.045 0.041 0.152
#>
#> Comfort Work Future Benefit
#> Comfort NA -0.147 -0.101 0.152
#> Work 25.512 NA 0.088 -0.141
#> Future 12.002 9.208 NA -0.122
#> Benefit 27.321 23.235 17.461 NA
residuals(x, df.p = TRUE, p.adjust = 'fdr') # apply FWE control
#> Degrees of freedom (lower triangle) and p-values:
#>
#> Comfort Work Future Benefit
#> Comfort NA 0.007 0.256 0.007
#> Work 9 NA 0.418 0.011
#> Future 9 9.000 NA 0.063
#> Benefit 9 9.000 9.000 NA
#>
#> LD matrix (lower triangle) and standardized values.
#>
#> Upper triangle summary:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.147 -0.136 -0.111 -0.045 0.041 0.152
#>
#> Comfort Work Future Benefit
#> Comfort NA -0.147 -0.101 0.152
#> Work 25.512 NA 0.088 -0.141
#> Future 12.002 9.208 NA -0.122
#> Benefit 27.321 23.235 17.461 NA
# Pearson's X2 estimate for goodness-of-fit
full_table <- residuals(x, type = 'expfull')
head(full_table)
#> Comfort Work Future Benefit freq exp res
#> 1 1 1 1 1 2 0.124 5.324
#> 2 1 1 1 2 0 0.160 -0.400
#> 3 1 1 1 3 0 0.054 -0.233
#> 4 1 1 1 4 0 0.005 -0.073
#> 5 1 1 2 1 0 0.092 -0.303
#> 6 1 1 2 2 0 0.219 -0.468
X2 <- with(full_table, sum((freq - exp)^2 / exp))
df <- nrow(full_table) - extract.mirt(x, 'nest') - 1
p <- pchisq(X2, df = df, lower.tail=FALSE)
data.frame(X2, df, p, row.names='Pearson-X2')
#> X2 df p
#> Pearson-X2 689.3347 239 2.942933e-45
# above FOG test as a function
PearsonX2 <- function(x){
full_table <- residuals(x, type = 'expfull')
X2 <- with(full_table, sum((freq - exp)^2 / exp))
df <- nrow(full_table) - extract.mirt(x, 'nest') - 1
p <- pchisq(X2, df = df, lower.tail=FALSE)
data.frame(X2, df, p, row.names='Pearson-X2')
}
PearsonX2(x)
#> X2 df p
#> Pearson-X2 689.3347 239 2.942933e-45
# extract results manually
out <- residuals(x, df.p = TRUE, verbose=FALSE)
str(out)
#> List of 2
#> $ df.p: 'mirt_matrix' num [1:4, 1:4] NA 9 9 9 0.00245 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:4] "Comfort" "Work" "Future" "Benefit"
#> .. ..$ : chr [1:4] "Comfort" "Work" "Future" "Benefit"
#> $ LD : 'mirt_matrix' num [1:4, 1:4] NA 25.512 12.002 27.321 -0.147 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:4] "Comfort" "Work" "Future" "Benefit"
#> .. ..$ : chr [1:4] "Comfort" "Work" "Future" "Benefit"
out$df.p[1,2]
#> [1] 0.002454207
# with and without supplied factor scores
Theta <- fscores(x)
residuals(x, type = 'Q3', Theta=Theta)
#> Q3 summary statistics:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.320 -0.249 -0.225 -0.190 -0.205 0.085
#>
#> Comfort Work Future Benefit
#> Comfort 1.000 -0.203 -0.252 0.085
#> Work -0.203 1.000 -0.208 -0.242
#> Future -0.252 -0.208 1.000 -0.320
#> Benefit 0.085 -0.242 -0.320 1.000
residuals(x, type = 'Q3', method = 'ML')
#> Q3 summary statistics:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.514 -0.426 -0.357 -0.311 -0.270 0.053
#>
#> Comfort Work Future Benefit
#> Comfort 1.000 -0.262 -0.419 0.053
#> Work -0.262 1.000 -0.428 -0.295
#> Future -0.419 -0.428 1.000 -0.514
#> Benefit 0.053 -0.295 -0.514 1.000
# Edwards et al. (2018) JSI statistic
N <- 250
a <- rnorm(10, 1.7, 0.3)
d <- rnorm(10)
dat <- simdata(a, d, N=250, itemtype = '2PL')
mod <- mirt(dat, 1)
residuals(mod, type = 'JSI')
#> JSI summary statistics:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -1.046 -0.175 0.011 0.012 0.219 1.356
#>
#> Item_1 Item_2 Item_3 Item_4 Item_5 Item_6 Item_7 Item_8 Item_9 Item_10
#> Item_1 NA -0.161 0.054 0.229 0.332 -0.330 0.307 -0.619 0.176 -0.028
#> Item_2 -0.161 NA -0.471 -0.160 0.319 0.317 -0.180 -0.016 0.412 0.018
#> Item_3 0.054 -0.471 NA -0.256 -0.506 0.056 -0.178 1.356 0.104 -0.079
#> Item_4 0.229 -0.160 -0.256 NA 0.362 -0.175 -0.142 0.512 0.044 -0.377
#> Item_5 0.332 0.319 -0.506 0.362 NA 0.135 -0.066 -1.046 -0.516 0.866
#> Item_6 -0.330 0.317 0.056 -0.175 0.135 NA 0.047 0.219 -0.007 0.011
#> Item_7 0.307 -0.180 -0.178 -0.142 -0.066 0.047 NA 0.020 0.139 -0.081
#> Item_8 -0.619 -0.016 1.356 0.512 -1.046 0.219 0.020 NA 0.333 -0.051
#> Item_9 0.176 0.412 0.104 0.044 -0.516 -0.007 0.139 0.333 NA -0.393
#> Item_10 -0.028 0.018 -0.079 -0.377 0.866 0.011 -0.081 -0.051 -0.393 NA
residuals(mod, type = 'JSI', fold=FALSE) # unfolded
#> JSI summary statistics:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.551 -0.089 -0.003 0.006 0.105 0.690
#>
#> Item_1 Item_2 Item_3 Item_4 Item_5 Item_6 Item_7 Item_8 Item_9 Item_10
#> Item_1 NA -0.102 0.108 0.147 0.128 -0.152 0.223 -0.339 0.079 -0.016
#> Item_2 -0.059 NA -0.184 -0.084 0.151 0.166 -0.091 -0.010 0.200 0.003
#> Item_3 -0.054 -0.288 NA -0.089 -0.276 -0.002 -0.147 0.690 0.067 -0.013
#> Item_4 0.082 -0.076 -0.167 NA 0.161 -0.102 -0.100 0.273 0.039 -0.187
#> Item_5 0.203 0.169 -0.230 0.201 NA 0.072 -0.017 -0.551 -0.278 0.491
#> Item_6 -0.178 0.151 0.058 -0.073 0.062 NA 0.026 0.108 -0.004 0.011
#> Item_7 0.084 -0.088 -0.030 -0.042 -0.049 0.022 NA 0.004 0.041 -0.041
#> Item_8 -0.279 -0.007 0.666 0.239 -0.495 0.111 0.017 NA 0.183 -0.045
#> Item_9 0.097 0.212 0.037 0.004 -0.238 -0.003 0.098 0.150 NA -0.213
#> Item_10 -0.012 0.015 -0.066 -0.190 0.374 0.000 -0.040 -0.006 -0.180 NA
# LD between items 1-2
aLD <- numeric(10)
aLD[1:2] <- rnorm(2, 2.55, 0.15)
a2 <- cbind(a, aLD)
dat <- simdata(a2, d, N=250, itemtype = '2PL')
mod <- mirt(dat, 1)
# JSI executed in parallel over multiple cores
if(interactive()) mirtCluster()
residuals(mod, type = 'JSI')
#> JSI summary statistics:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -1.676 -0.550 -0.006 0.019 0.489 4.804
#>
#> Item_1 Item_2 Item_3 Item_4 Item_5 Item_6 Item_7 Item_8 Item_9 Item_10
#> Item_1 NA 4.804 -1.676 -0.731 -0.550 -0.546 -1.542 -1.020 -0.100 -0.514
#> Item_2 4.804 NA -1.289 -0.629 -1.082 -1.025 -0.948 -0.326 -0.427 -0.610
#> Item_3 -1.676 -1.289 NA 0.802 -0.117 0.837 0.820 1.140 0.734 0.039
#> Item_4 -0.731 -0.629 0.802 NA 0.847 -0.006 0.226 0.367 0.470 -0.317
#> Item_5 -0.550 -1.082 -0.117 0.847 NA 0.195 0.467 0.201 -0.055 0.680
#> Item_6 -0.546 -1.025 0.837 -0.006 0.195 NA 0.306 -0.420 0.489 0.978
#> Item_7 -1.542 -0.948 0.820 0.226 0.467 0.306 NA 0.514 0.211 0.086
#> Item_8 -1.020 -0.326 1.140 0.367 0.201 -0.420 0.514 NA -0.475 0.672
#> Item_9 -0.100 -0.427 0.734 0.470 -0.055 0.489 0.211 -0.475 NA -0.629
#> Item_10 -0.514 -0.610 0.039 -0.317 0.680 0.978 0.086 0.672 -0.629 NA
# }