Returns structured data from an ackwards object in tidy format. The
default (what = "edges") returns the graph edge list that drives diagrams.
Arguments
- x
An
ackwardsobject.- what
What to extract:
"edges"(default) – one row per directed between-level edge:from,to,level_from,level_to,r,is_primary,above_cut. Ifboot_edges()has been run on the object, four bootstrap columns are appended:se,lo,hi(bootstrap standard error and percentile confidence-interval endpoints), andn_boot_ok(usable replicates)."loadings"– one row per item x factor x level:level,factor,item,loading,se,ci_lower,ci_upper.se,ci_lower, andci_upperare populated only forengine = "esem"(which produces rotation-aware loading SEs); they areNAfor PCA and EFA. The confidence level is controlled byconf_level."variance"– one row per factor x level:level,factor,proportion,cumulative. Both are proportions of total item variance on a 0-1 scale (multiply by 100 for a percentage)."fit"– one row per fit statistic x level:level,statistic,value. For PCA objects the statistics are eigenvalues; for EFA objects they arechi,dof,p_value,RMSEA,TLI,BIC– wherechiis the likelihood-ratio chi-square (psych::fa()'sSTATISTIC), sochi,dof,p_value,RMSEA, andTLIall share one statistical framing (psych's residual-based empirical chi-square is a different statistic and is not reported); for ESEM they arechi,dof,p_value,CFI,TLI,RMSEA,SRMR,BIC. For ESEM under a scaled-test estimator ("WLSMV"/"ULSMV"for ordinal data,"MLR"for continuous), the whole row –chi/dof/p_valueandCFI/TLI/RMSEA– reports lavaan's mean-and-variance-adjusted ("scaled") variant, so every quantity shares one scaling. This matters most for WLSMV/ULSMV: the naive chi-square has no valid reference distribution (lavaan's ownsummary()labels its p-value "Unknown"), and the naiveCFI/TLIare badly optimistic for ordinal data (Xia & Yang, 2019)."ML"has no scaled variant, so it reports the naive values (the correct ones for ML).SRMRhas no scaled variant and is reported as-is.BICisNAunder WLSMV/ULSMV (no proper log-likelihood for a limited-information estimator) and populated under ML/MLR. Useformat = "wide"for one row per non-anchor level (k >= 2; the saturated 1-factor anchor is dropped, matchingsummary()andautoplot(what = "fit")), one column per statistic. Conventional fit cutoffs (Hu & Bentler 1999) are shown as reference lines inautoplot(what = "fit")and inline insummary(), but are not returned as a pass/fail column here – they are contested thresholds, report-only, and never gate anything (see those functions' docs).formatis oriented to the EFA/ESEM model-fit statistics; for PCA the "statistics" are per-component eigenvalues."nodes"– Forbes-extension pruning annotations (requiresprune != "none"when the object was created). One row per factor across all levels:id,level,pruned,prune_reason. Returns an empty data frame with the same columns when no pruning was applied."scores"– long-format per-observation factor scores (requireskeep_scores = TRUEat fit time or useaugment.ackwards()for on-the-fly computation). Columns:obs(row index),level,factor,score.
- primary_only
For
what = "edges"only. WhenTRUE, returns just each factor's primary-parent edge (is_primary == TRUE) – the lineage tree that the diagram draws as solid arrows. DefaultFALSE(all edges). Errors for any other value ofwhat.- sort
For
what = "edges"only. One of"none"(default, natural order) or"strength"(descending|r|). Ignored for all other values ofwhat.- format
For
what = "fit"only. One of"long"(default, one row per statistic x level) or"wide"(one row per level, one column per statistic). Errors for all other values ofwhat.- conf_level
For
what = "loadings"only. Confidence level for the loading intervals; default0.95. The intervals are computed asloading ± qnorm((1 + conf_level) / 2) * seand areNAfor engines that carry no SEs (PCA, EFA). Errors for all other values ofwhat.- ...
Ignored.
Factor labels
If factor labels have been attached to the object, the
output gains display-only label columns: factor_label for what = "loadings", "variance", or "scores", and from_label/to_label for
what = "edges". Each carries the label for a labeled factor and NA
otherwise. These columns are absent when no labels are set, so an
unlabeled object's output is unchanged; the ID columns (factor, from,
to) are never altered.
Examples
x <- ackwards(sim16, k_max = 5)
tidy(x) # edges in natural order
#> from to level_from level_to r is_primary above_cut
#> 1 m1f1 m2f1 1 2 0.7072961126 TRUE TRUE
#> 2 m1f1 m2f2 1 2 0.7069173991 TRUE TRUE
#> 3 m2f1 m3f1 2 3 -0.0113423946 FALSE FALSE
#> 4 m2f1 m3f2 2 3 0.6970929809 TRUE TRUE
#> 5 m2f1 m3f3 2 3 0.7168910141 TRUE TRUE
#> 6 m2f2 m3f1 2 3 0.9955932310 TRUE TRUE
#> 7 m2f2 m3f2 2 3 0.0746157674 FALSE FALSE
#> 8 m2f2 m3f3 2 3 -0.0568032178 FALSE FALSE
#> 9 m3f1 m4f1 3 4 -0.0164580578 FALSE FALSE
#> 10 m3f1 m4f2 3 4 -0.0048128349 FALSE FALSE
#> 11 m3f1 m4f3 3 4 0.6639134885 TRUE TRUE
#> 12 m3f1 m4f4 3 4 0.7476127666 TRUE TRUE
#> 13 m3f2 m4f1 3 4 0.9406477359 TRUE TRUE
#> 14 m3f2 m4f2 3 4 0.0317352150 FALSE FALSE
#> 15 m3f2 m4f3 3 4 0.2627656201 FALSE FALSE
#> 16 m3f2 m4f4 3 4 -0.2124357359 FALSE FALSE
#> 17 m3f3 m4f1 3 4 0.0512578921 FALSE FALSE
#> 18 m3f3 m4f2 3 4 0.9707784269 TRUE TRUE
#> 19 m3f3 m4f3 3 4 -0.1715840980 FALSE FALSE
#> 20 m3f3 m4f4 3 4 0.1597522194 FALSE FALSE
#> 21 m4f1 m5f1 4 5 0.9997725525 TRUE TRUE
#> 22 m4f1 m5f2 4 5 0.0020429352 FALSE FALSE
#> 23 m4f1 m5f3 4 5 -0.0043948033 FALSE FALSE
#> 24 m4f1 m5f4 4 5 0.0034107252 FALSE FALSE
#> 25 m4f1 m5f5 4 5 -0.0204871256 FALSE FALSE
#> 26 m4f2 m5f1 4 5 -0.0019639857 FALSE FALSE
#> 27 m4f2 m5f2 4 5 0.9999850115 TRUE TRUE
#> 28 m4f2 m5f3 4 5 -0.0025910717 FALSE FALSE
#> 29 m4f2 m5f4 4 5 -0.0012689323 FALSE FALSE
#> 30 m4f2 m5f5 4 5 0.0042184956 FALSE FALSE
#> 31 m4f3 m5f1 4 5 0.0043781997 FALSE FALSE
#> 32 m4f3 m5f2 4 5 0.0026035592 FALSE FALSE
#> 33 m4f3 m5f3 4 5 0.9999840561 TRUE TRUE
#> 34 m4f3 m5f4 4 5 0.0024297386 FALSE FALSE
#> 35 m4f3 m5f5 4 5 -0.0001914960 FALSE FALSE
#> 36 m4f4 m5f1 4 5 -0.0016399230 FALSE FALSE
#> 37 m4f4 m5f2 4 5 0.0008901375 FALSE FALSE
#> 38 m4f4 m5f3 4 5 -0.0023992589 FALSE FALSE
#> 39 m4f4 m5f4 4 5 0.9962530528 TRUE TRUE
#> 40 m4f4 m5f5 4 5 0.0864327285 TRUE FALSE
tidy(x, sort = "strength") # strongest edges first
#> from to level_from level_to r is_primary above_cut
#> 1 m4f2 m5f2 4 5 0.9999850115 TRUE TRUE
#> 2 m4f3 m5f3 4 5 0.9999840561 TRUE TRUE
#> 3 m4f1 m5f1 4 5 0.9997725525 TRUE TRUE
#> 4 m4f4 m5f4 4 5 0.9962530528 TRUE TRUE
#> 5 m2f2 m3f1 2 3 0.9955932310 TRUE TRUE
#> 6 m3f3 m4f2 3 4 0.9707784269 TRUE TRUE
#> 7 m3f2 m4f1 3 4 0.9406477359 TRUE TRUE
#> 8 m3f1 m4f4 3 4 0.7476127666 TRUE TRUE
#> 9 m2f1 m3f3 2 3 0.7168910141 TRUE TRUE
#> 10 m1f1 m2f1 1 2 0.7072961126 TRUE TRUE
#> 11 m1f1 m2f2 1 2 0.7069173991 TRUE TRUE
#> 12 m2f1 m3f2 2 3 0.6970929809 TRUE TRUE
#> 13 m3f1 m4f3 3 4 0.6639134885 TRUE TRUE
#> 14 m3f2 m4f3 3 4 0.2627656201 FALSE FALSE
#> 15 m3f2 m4f4 3 4 -0.2124357359 FALSE FALSE
#> 16 m3f3 m4f3 3 4 -0.1715840980 FALSE FALSE
#> 17 m3f3 m4f4 3 4 0.1597522194 FALSE FALSE
#> 18 m4f4 m5f5 4 5 0.0864327285 TRUE FALSE
#> 19 m2f2 m3f2 2 3 0.0746157674 FALSE FALSE
#> 20 m2f2 m3f3 2 3 -0.0568032178 FALSE FALSE
#> 21 m3f3 m4f1 3 4 0.0512578921 FALSE FALSE
#> 22 m3f2 m4f2 3 4 0.0317352150 FALSE FALSE
#> 23 m4f1 m5f5 4 5 -0.0204871256 FALSE FALSE
#> 24 m3f1 m4f1 3 4 -0.0164580578 FALSE FALSE
#> 25 m2f1 m3f1 2 3 -0.0113423946 FALSE FALSE
#> 26 m3f1 m4f2 3 4 -0.0048128349 FALSE FALSE
#> 27 m4f1 m5f3 4 5 -0.0043948033 FALSE FALSE
#> 28 m4f3 m5f1 4 5 0.0043781997 FALSE FALSE
#> 29 m4f2 m5f5 4 5 0.0042184956 FALSE FALSE
#> 30 m4f1 m5f4 4 5 0.0034107252 FALSE FALSE
#> 31 m4f3 m5f2 4 5 0.0026035592 FALSE FALSE
#> 32 m4f2 m5f3 4 5 -0.0025910717 FALSE FALSE
#> 33 m4f3 m5f4 4 5 0.0024297386 FALSE FALSE
#> 34 m4f4 m5f3 4 5 -0.0023992589 FALSE FALSE
#> 35 m4f1 m5f2 4 5 0.0020429352 FALSE FALSE
#> 36 m4f2 m5f1 4 5 -0.0019639857 FALSE FALSE
#> 37 m4f4 m5f1 4 5 -0.0016399230 FALSE FALSE
#> 38 m4f2 m5f4 4 5 -0.0012689323 FALSE FALSE
#> 39 m4f4 m5f2 4 5 0.0008901375 FALSE FALSE
#> 40 m4f3 m5f5 4 5 -0.0001914960 FALSE FALSE
tidy(x, primary_only = TRUE) # just the primary-parent lineage
#> from to level_from level_to r is_primary above_cut
#> 1 m1f1 m2f1 1 2 0.70729611 TRUE TRUE
#> 2 m1f1 m2f2 1 2 0.70691740 TRUE TRUE
#> 3 m2f1 m3f2 2 3 0.69709298 TRUE TRUE
#> 4 m2f1 m3f3 2 3 0.71689101 TRUE TRUE
#> 5 m2f2 m3f1 2 3 0.99559323 TRUE TRUE
#> 6 m3f1 m4f3 3 4 0.66391349 TRUE TRUE
#> 7 m3f1 m4f4 3 4 0.74761277 TRUE TRUE
#> 8 m3f2 m4f1 3 4 0.94064774 TRUE TRUE
#> 9 m3f3 m4f2 3 4 0.97077843 TRUE TRUE
#> 10 m4f1 m5f1 4 5 0.99977255 TRUE TRUE
#> 11 m4f2 m5f2 4 5 0.99998501 TRUE TRUE
#> 12 m4f3 m5f3 4 5 0.99998406 TRUE TRUE
#> 13 m4f4 m5f4 4 5 0.99625305 TRUE TRUE
#> 14 m4f4 m5f5 4 5 0.08643273 TRUE FALSE
tidy(x, what = "loadings")
#> level factor item loading se ci_lower ci_upper
#> 1 1 m1f1 i1 0.461752431 NA NA NA
#> 2 1 m1f1 i2 0.485041500 NA NA NA
#> 3 1 m1f1 i3 0.514249968 NA NA NA
#> 4 1 m1f1 i4 0.515548605 NA NA NA
#> 5 1 m1f1 i5 0.575053347 NA NA NA
#> 6 1 m1f1 i6 0.568480393 NA NA NA
#> 7 1 m1f1 i7 0.589789932 NA NA NA
#> 8 1 m1f1 i8 0.508096441 NA NA NA
#> 9 1 m1f1 i9 0.563485394 NA NA NA
#> 10 1 m1f1 i10 0.551924658 NA NA NA
#> 11 1 m1f1 i11 0.546706411 NA NA NA
#> 12 1 m1f1 i12 0.563394632 NA NA NA
#> 13 1 m1f1 i13 0.519943165 NA NA NA
#> 14 1 m1f1 i14 0.525582317 NA NA NA
#> 15 1 m1f1 i15 0.502500732 NA NA NA
#> 16 1 m1f1 i16 0.481206189 NA NA NA
#> 17 2 m2f1 i1 0.670505125 NA NA NA
#> 18 2 m2f1 i2 0.688261361 NA NA NA
#> 19 2 m2f1 i3 0.666960388 NA NA NA
#> 20 2 m2f1 i4 0.695694541 NA NA NA
#> 21 2 m2f1 i5 0.677994613 NA NA NA
#> 22 2 m2f1 i6 0.671411581 NA NA NA
#> 23 2 m2f1 i7 0.688528565 NA NA NA
#> 24 2 m2f1 i8 0.646831586 NA NA NA
#> 25 2 m2f1 i9 0.142480573 NA NA NA
#> 26 2 m2f1 i10 0.106321144 NA NA NA
#> 27 2 m2f1 i11 0.099785669 NA NA NA
#> 28 2 m2f1 i12 0.132520254 NA NA NA
#> 29 2 m2f1 i13 0.045302307 NA NA NA
#> 30 2 m2f1 i14 0.056844031 NA NA NA
#> 31 2 m2f1 i15 0.027746191 NA NA NA
#> 32 2 m2f1 i16 0.002718709 NA NA NA
#> 33 2 m2f2 i1 -0.017672839 NA NA NA
#> 34 2 m2f2 i2 -0.002494047 NA NA NA
#> 35 2 m2f2 i3 0.060136416 NA NA NA
#> 36 2 m2f2 i4 0.033223911 NA NA NA
#> 37 2 m2f2 i5 0.135108279 NA NA NA
#> 38 2 m2f2 i6 0.132396786 NA NA NA
#> 39 2 m2f2 i7 0.145414946 NA NA NA
#> 40 2 m2f2 i8 0.071571268 NA NA NA
#> 41 2 m2f2 i9 0.654545268 NA NA NA
#> 42 2 m2f2 i10 0.674370338 NA NA NA
#> 43 2 m2f2 i11 0.673527623 NA NA NA
#> 44 2 m2f2 i12 0.664382532 NA NA NA
#> 45 2 m2f2 i13 0.690181088 NA NA NA
#> 46 2 m2f2 i14 0.686610283 NA NA NA
#> 47 2 m2f2 i15 0.683072674 NA NA NA
#> 48 2 m2f2 i16 0.677990466 NA NA NA
#> 49 3 m3f1 i1 0.018282326 NA NA NA
#> 50 3 m3f1 i2 0.030497878 NA NA NA
#> 51 3 m3f1 i3 0.088799850 NA NA NA
#> 52 3 m3f1 i4 0.065163580 NA NA NA
#> 53 3 m3f1 i5 0.088559024 NA NA NA
#> 54 3 m3f1 i6 0.085162586 NA NA NA
#> 55 3 m3f1 i7 0.100894156 NA NA NA
#> 56 3 m3f1 i8 0.021040215 NA NA NA
#> 57 3 m3f1 i9 0.635370326 NA NA NA
#> 58 3 m3f1 i10 0.654349429 NA NA NA
#> 59 3 m3f1 i11 0.656287051 NA NA NA
#> 60 3 m3f1 i12 0.643589738 NA NA NA
#> 61 3 m3f1 i13 0.702820782 NA NA NA
#> 62 3 m3f1 i14 0.704046359 NA NA NA
#> 63 3 m3f1 i15 0.701468675 NA NA NA
#> 64 3 m3f1 i16 0.697838670 NA NA NA
#> 65 3 m3f2 i1 0.132996558 NA NA NA
#> 66 3 m3f2 i2 0.167151079 NA NA NA
#> 67 3 m3f2 i3 0.189869691 NA NA NA
#> 68 3 m3f2 i4 0.181206856 NA NA NA
#> 69 3 m3f2 i5 0.775819255 NA NA NA
#> 70 3 m3f2 i6 0.776938349 NA NA NA
#> 71 3 m3f2 i7 0.767129561 NA NA NA
#> 72 3 m3f2 i8 0.784709189 NA NA NA
#> 73 3 m3f2 i9 0.260572503 NA NA NA
#> 74 3 m3f2 i10 0.245798186 NA NA NA
#> 75 3 m3f2 i11 0.220477487 NA NA NA
#> 76 3 m3f2 i12 0.267289841 NA NA NA
#> 77 3 m3f2 i13 -0.040980546 NA NA NA
#> 78 3 m3f2 i14 -0.070825420 NA NA NA
#> 79 3 m3f2 i15 -0.096078981 NA NA NA
#> 80 3 m3f2 i16 -0.122682914 NA NA NA
#> 81 3 m3f3 i1 0.806261359 NA NA NA
#> 82 3 m3f3 i2 0.798011727 NA NA NA
#> 83 3 m3f3 i3 0.747129970 NA NA NA
#> 84 3 m3f3 i4 0.795261223 NA NA NA
#> 85 3 m3f3 i5 0.192750257 NA NA NA
#> 86 3 m3f3 i6 0.182425580 NA NA NA
#> 87 3 m3f3 i7 0.216089072 NA NA NA
#> 88 3 m3f3 i8 0.139567888 NA NA NA
#> 89 3 m3f3 i9 -0.044575909 NA NA NA
#> 90 3 m3f3 i10 -0.080348554 NA NA NA
#> 91 3 m3f3 i11 -0.064812882 NA NA NA
#> 92 3 m3f3 i12 -0.064871464 NA NA NA
#> 93 3 m3f3 i13 0.114161326 NA NA NA
#> 94 3 m3f3 i14 0.159301070 NA NA NA
#> 95 3 m3f3 i15 0.143227502 NA NA NA
#> 96 3 m3f3 i16 0.134128155 NA NA NA
#> 97 4 m4f1 i1 0.128686176 NA NA NA
#> 98 4 m4f1 i2 0.149030299 NA NA NA
#> 99 4 m4f1 i3 0.143394315 NA NA NA
#> 100 4 m4f1 i4 0.164542205 NA NA NA
#> 101 4 m4f1 i5 0.802597931 NA NA NA
#> 102 4 m4f1 i6 0.810490299 NA NA NA
#> 103 4 m4f1 i7 0.791631093 NA NA NA
#> 104 4 m4f1 i8 0.832623843 NA NA NA
#> 105 4 m4f1 i9 0.082991593 NA NA NA
#> 106 4 m4f1 i10 0.069135718 NA NA NA
#> 107 4 m4f1 i11 0.049095872 NA NA NA
#> 108 4 m4f1 i12 0.101317088 NA NA NA
#> 109 4 m4f1 i13 0.090878291 NA NA NA
#> 110 4 m4f1 i14 0.058515420 NA NA NA
#> 111 4 m4f1 i15 0.037287536 NA NA NA
#> 112 4 m4f1 i16 0.005030840 NA NA NA
#> 113 4 m4f2 i1 0.813408272 NA NA NA
#> 114 4 m4f2 i2 0.814344929 NA NA NA
#> 115 4 m4f2 i3 0.782025429 NA NA NA
#> 116 4 m4f2 i4 0.809823496 NA NA NA
#> 117 4 m4f2 i5 0.165603797 NA NA NA
#> 118 4 m4f2 i6 0.150442425 NA NA NA
#> 119 4 m4f2 i7 0.190612643 NA NA NA
#> 120 4 m4f2 i8 0.098061225 NA NA NA
#> 121 4 m4f2 i9 0.067952773 NA NA NA
#> 122 4 m4f2 i10 0.031112689 NA NA NA
#> 123 4 m4f2 i11 0.043242680 NA NA NA
#> 124 4 m4f2 i12 0.039067952 NA NA NA
#> 125 4 m4f2 i13 0.010228110 NA NA NA
#> 126 4 m4f2 i14 0.057767882 NA NA NA
#> 127 4 m4f2 i15 0.039026995 NA NA NA
#> 128 4 m4f2 i16 0.034210703 NA NA NA
#> 129 4 m4f3 i1 -0.015409920 NA NA NA
#> 130 4 m4f3 i2 0.025695304 NA NA NA
#> 131 4 m4f3 i3 0.126581449 NA NA NA
#> 132 4 m4f3 i4 0.046794854 NA NA NA
#> 133 4 m4f3 i5 0.099123172 NA NA NA
#> 134 4 m4f3 i6 0.084120174 NA NA NA
#> 135 4 m4f3 i7 0.108694221 NA NA NA
#> 136 4 m4f3 i8 0.018603667 NA NA NA
#> 137 4 m4f3 i9 0.800528531 NA NA NA
#> 138 4 m4f3 i10 0.810953636 NA NA NA
#> 139 4 m4f3 i11 0.796816493 NA NA NA
#> 140 4 m4f3 i12 0.784529901 NA NA NA
#> 141 4 m4f3 i13 0.162504347 NA NA NA
#> 142 4 m4f3 i14 0.161065367 NA NA NA
#> 143 4 m4f3 i15 0.148774737 NA NA NA
#> 144 4 m4f3 i16 0.154760740 NA NA NA
#> 145 4 m4f4 i1 0.046208286 NA NA NA
#> 146 4 m4f4 i2 0.026498312 NA NA NA
#> 147 4 m4f4 i3 0.014558967 NA NA NA
#> 148 4 m4f4 i4 0.054441870 NA NA NA
#> 149 4 m4f4 i5 0.049164544 NA NA NA
#> 150 4 m4f4 i6 0.058020972 NA NA NA
#> 151 4 m4f4 i7 0.057083956 NA NA NA
#> 152 4 m4f4 i8 0.030583096 NA NA NA
#> 153 4 m4f4 i9 0.141224932 NA NA NA
#> 154 4 m4f4 i10 0.156811061 NA NA NA
#> 155 4 m4f4 i11 0.171594148 NA NA NA
#> 156 4 m4f4 i12 0.166644111 NA NA NA
#> 157 4 m4f4 i13 0.797841995 NA NA NA
#> 158 4 m4f4 i14 0.800352794 NA NA NA
#> 159 4 m4f4 i15 0.807231575 NA NA NA
#> 160 4 m4f4 i16 0.796319167 NA NA NA
#> 161 5 m5f1 i1 0.125845182 NA NA NA
#> 162 5 m5f1 i2 0.146281625 NA NA NA
#> 163 5 m5f1 i3 0.141457000 NA NA NA
#> 164 5 m5f1 i4 0.166373116 NA NA NA
#> 165 5 m5f1 i5 0.802636385 NA NA NA
#> 166 5 m5f1 i6 0.805601696 NA NA NA
#> 167 5 m5f1 i7 0.794174370 NA NA NA
#> 168 5 m5f1 i8 0.834242419 NA NA NA
#> 169 5 m5f1 i9 0.079330358 NA NA NA
#> 170 5 m5f1 i10 0.071440903 NA NA NA
#> 171 5 m5f1 i11 0.053199270 NA NA NA
#> 172 5 m5f1 i12 0.111309787 NA NA NA
#> 173 5 m5f1 i13 0.088459037 NA NA NA
#> 174 5 m5f1 i14 0.055792727 NA NA NA
#> 175 5 m5f1 i15 0.032789698 NA NA NA
#> 176 5 m5f1 i16 0.011575744 NA NA NA
#> 177 5 m5f2 i1 0.813880739 NA NA NA
#> 178 5 m5f2 i2 0.814971814 NA NA NA
#> 179 5 m5f2 i3 0.782834508 NA NA NA
#> 180 5 m5f2 i4 0.809628684 NA NA NA
#> 181 5 m5f2 i5 0.167503047 NA NA NA
#> 182 5 m5f2 i6 0.153331666 NA NA NA
#> 183 5 m5f2 i7 0.192001129 NA NA NA
#> 184 5 m5f2 i8 0.099430502 NA NA NA
#> 185 5 m5f2 i9 0.071729201 NA NA NA
#> 186 5 m5f2 i10 0.033692262 NA NA NA
#> 187 5 m5f2 i11 0.045365102 NA NA NA
#> 188 5 m5f2 i12 0.040036629 NA NA NA
#> 189 5 m5f2 i13 0.011914118 NA NA NA
#> 190 5 m5f2 i14 0.059428222 NA NA NA
#> 191 5 m5f2 i15 0.040979436 NA NA NA
#> 192 5 m5f2 i16 0.033839573 NA NA NA
#> 193 5 m5f3 i1 -0.018210287 NA NA NA
#> 194 5 m5f3 i2 0.022847977 NA NA NA
#> 195 5 m5f3 i3 0.123874092 NA NA NA
#> 196 5 m5f3 i4 0.043893857 NA NA NA
#> 197 5 m5f3 i5 0.095050271 NA NA NA
#> 198 5 m5f3 i6 0.079955315 NA NA NA
#> 199 5 m5f3 i7 0.104624657 NA NA NA
#> 200 5 m5f3 i8 0.014647210 NA NA NA
#> 201 5 m5f3 i9 0.799531025 NA NA NA
#> 202 5 m5f3 i10 0.810165899 NA NA NA
#> 203 5 m5f3 i11 0.796079656 NA NA NA
#> 204 5 m5f3 i12 0.783678485 NA NA NA
#> 205 5 m5f3 i13 0.160134023 NA NA NA
#> 206 5 m5f3 i14 0.158704883 NA NA NA
#> 207 5 m5f3 i15 0.146512650 NA NA NA
#> 208 5 m5f3 i16 0.152849170 NA NA NA
#> 209 5 m5f4 i1 0.049869940 NA NA NA
#> 210 5 m5f4 i2 0.030875158 NA NA NA
#> 211 5 m5f4 i3 0.018057780 NA NA NA
#> 212 5 m5f4 i4 0.039945216 NA NA NA
#> 213 5 m5f4 i5 0.050944187 NA NA NA
#> 214 5 m5f4 i6 0.080103321 NA NA NA
#> 215 5 m5f4 i7 0.048270051 NA NA NA
#> 216 5 m5f4 i8 0.025018104 NA NA NA
#> 217 5 m5f4 i9 0.171116784 NA NA NA
#> 218 5 m5f4 i10 0.162190188 NA NA NA
#> 219 5 m5f4 i11 0.168862429 NA NA NA
#> 220 5 m5f4 i12 0.139322561 NA NA NA
#> 221 5 m5f4 i13 0.802972834 NA NA NA
#> 222 5 m5f4 i14 0.806163196 NA NA NA
#> 223 5 m5f4 i15 0.820241598 NA NA NA
#> 224 5 m5f4 i16 0.763490591 NA NA NA
#> 225 5 m5f5 i1 -0.046701926 NA NA NA
#> 226 5 m5f5 i2 -0.054283683 NA NA NA
#> 227 5 m5f5 i3 -0.041636964 NA NA NA
#> 228 5 m5f5 i4 0.165490526 NA NA NA
#> 229 5 m5f5 i5 -0.002239278 NA NA NA
#> 230 5 m5f5 i6 -0.236088047 NA NA NA
#> 231 5 m5f5 i7 0.120061811 NA NA NA
#> 232 5 m5f5 i8 0.080680811 NA NA NA
#> 233 5 m5f5 i9 -0.315461030 NA NA NA
#> 234 5 m5f5 i10 -0.031706091 NA NA NA
#> 235 5 m5f5 i11 0.061565807 NA NA NA
#> 236 5 m5f5 i12 0.345595171 NA NA NA
#> 237 5 m5f5 i13 -0.018551750 NA NA NA
#> 238 5 m5f5 i14 -0.027424572 NA NA NA
#> 239 5 m5f5 i15 -0.110696540 NA NA NA
#> 240 5 m5f5 i16 0.417028618 NA NA NA
tidy(x, what = "variance")
#> level factor proportion cumulative
#> 1 1 m1f1 0.28172978 0.2817298
#> 2 2 m2f1 0.23251408 0.2325141
#> 3 2 m2f2 0.23246133 0.4649754
#> 4 3 m3f1 0.23028614 0.2302861
#> 5 3 m3f2 0.17522787 0.4055140
#> 6 3 m3f3 0.16924345 0.5747575
#> 7 4 m4f1 0.17155300 0.1715530
#> 8 4 m4f2 0.16895577 0.3405088
#> 9 4 m4f3 0.16846890 0.5089777
#> 10 4 m4f4 0.16753770 0.6765154
#> 11 5 m5f1 0.17147388 0.1714739
#> 12 5 m5f2 0.16935421 0.3408281
#> 13 5 m5f3 0.16774286 0.5085710
#> 14 5 m5f4 0.16695127 0.6755222
#> 15 5 m5f5 0.03262035 0.7081426
tidy(x, what = "fit")
#> level statistic value
#> 1 1 eigenvalue.m1f1 4.5076765
#> 2 2 eigenvalue.m2f1 4.5076765
#> 3 2 eigenvalue.m2f2 2.9319301
#> 4 3 eigenvalue.m3f1 4.5076765
#> 5 3 eigenvalue.m3f2 2.9319301
#> 6 3 eigenvalue.m3f3 1.7565127
#> 7 4 eigenvalue.m4f1 4.5076765
#> 8 4 eigenvalue.m4f2 2.9319301
#> 9 4 eigenvalue.m4f3 1.7565127
#> 10 4 eigenvalue.m4f4 1.6281266
#> 11 5 eigenvalue.m5f1 4.5076765
#> 12 5 eigenvalue.m5f2 2.9319301
#> 13 5 eigenvalue.m5f3 1.7565127
#> 14 5 eigenvalue.m5f4 1.6281266
#> 15 5 eigenvalue.m5f5 0.5060352
tidy(x, what = "fit", format = "wide")
#> level eigenvalue.m2f1 eigenvalue.m2f2 eigenvalue.m3f1 eigenvalue.m3f2
#> 1 2 4.507677 2.93193 NA NA
#> 2 3 NA NA 4.507677 2.93193
#> 3 4 NA NA NA NA
#> 4 5 NA NA NA NA
#> eigenvalue.m3f3 eigenvalue.m4f1 eigenvalue.m4f2 eigenvalue.m4f3
#> 1 NA NA NA NA
#> 2 1.756513 NA NA NA
#> 3 NA 4.507677 2.93193 1.756513
#> 4 NA NA NA NA
#> eigenvalue.m4f4 eigenvalue.m5f1 eigenvalue.m5f2 eigenvalue.m5f3
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 1.628127 NA NA NA
#> 4 NA 4.507677 2.93193 1.756513
#> eigenvalue.m5f4 eigenvalue.m5f5
#> 1 NA NA
#> 2 NA NA
#> 3 NA NA
#> 4 1.628127 0.5060352