Prints a fitted spifa object (the output of spifa):
model type, formula, data dimensions, MCMC settings, and a posterior
summary table (via summary.spifa).
Usage
# S3 method for class 'spifa'
print(x, ...)Arguments
- x
An object of class
spifa, as returned byspifa.- ...
Further arguments passed to methods (currently unused).
Examples
data(ipixuna)
samples <- spifa(items ~ 1, data = ipixuna, nfactors = 3, ngp = 0, niter = 20)
samples
#> Item factor analysis model: eifa
#> Formula: items ~ 1
#> Dimensions: 100 respondents, 10 items, 3 latent factors, 0 spatial processes
#> MCMC: 1 chain, iter = 20, thin = 1, samples = 20
#>
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Item model parameters:
#> mean median sd q10 q90 ess_bulk rhat
#> c[1] -0.61827 -0.559 0.198 -0.849 -0.423 11.5 0.96
#> c[2] -0.47915 -0.487 0.113 -0.594 -0.351 16.4 0.97
#> c[3] -0.43951 -0.407 0.152 -0.576 -0.285 8.9 1.34
#> c[4] -0.28090 -0.228 0.148 -0.485 -0.112 11.0 1.03
#> c[5] -0.19411 -0.175 0.094 -0.304 -0.099 18.2 1.00
#> c[6] -0.08573 -0.098 0.127 -0.257 0.074 3.0 1.44
#> c[7] 0.30059 0.311 0.126 0.173 0.465 2.9 1.52
#> c[8] 0.25602 0.282 0.164 0.022 0.462 9.5 1.27
#> c[9] 0.44350 0.507 0.158 0.237 0.607 2.8 1.55
#> c[10] 0.35560 0.247 0.248 0.065 0.667 2.1 2.12
#> A[1,1] 1.33305 1.460 0.272 0.935 1.614 3.2 1.37
#> A[2,1] 0.33478 0.379 0.204 0.071 0.547 2.8 1.50
#> A[3,1] 0.23170 0.271 0.261 -0.114 0.538 2.2 2.12
#> A[4,1] -0.19306 -0.180 0.133 -0.357 -0.047 6.0 1.19
#> A[5,1] -0.12407 -0.111 0.172 -0.352 0.112 6.9 1.14
#> A[6,1] 0.06257 0.060 0.132 -0.143 0.237 26.0 0.99
#> A[7,1] -0.21693 -0.251 0.179 -0.411 0.024 7.0 1.27
#> A[8,1] -0.26832 -0.317 0.157 -0.440 -0.046 9.0 1.04
#> A[9,1] 0.28778 0.297 0.179 0.025 0.515 2.3 1.84
#> A[10,1] -0.00023 0.037 0.162 -0.195 0.170 12.0 0.97
#> A[2,2] 0.70885 0.678 0.187 0.517 0.941 3.6 1.29
#> A[3,2] 0.38025 0.371 0.174 0.161 0.597 8.4 1.09
#> A[4,2] 0.15580 0.128 0.181 -0.050 0.423 14.7 1.01
#> A[5,2] 0.55081 0.626 0.378 -0.056 0.961 2.1 2.12
#> A[6,2] 0.49227 0.566 0.348 0.069 0.870 2.1 2.12
#> A[7,2] 0.68732 0.729 0.267 0.303 0.932 8.3 1.15
#> A[8,2] 0.46554 0.395 0.230 0.279 0.776 5.2 1.19
#> A[9,2] 0.59997 0.615 0.154 0.407 0.761 3.0 1.49
#> A[10,2] 0.70715 0.757 0.377 0.240 1.277 2.1 2.12
#> A[3,3] 0.65682 0.623 0.179 0.432 0.861 8.6 1.19
#> A[4,3] 0.54910 0.434 0.309 0.265 0.952 10.8 1.02
#> A[5,3] 0.17844 0.177 0.304 -0.205 0.529 2.1 2.12
#> A[6,3] -0.03184 0.018 0.287 -0.543 0.356 3.0 1.46
#> A[7,3] 0.30015 0.276 0.135 0.125 0.488 13.0 1.01
#> A[8,3] 0.78520 0.838 0.286 0.442 1.069 2.1 1.97
#> A[9,3] 0.69462 0.740 0.249 0.427 1.000 5.4 1.19
#> A[10,3] 0.61241 0.659 0.167 0.425 0.783 11.5 0.96
#>
#> ess_bulk is the bulk effective sample size; rhat is the potential
#> scale reduction factor on split chains (Rhat = 1 at convergence).
#> Use summary() for the full set of statistics (incl. ess_tail).