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
#>
#> Item model parameters:
#> mean median sd q10 q90 ess_bulk rhat
#> c[1] -0.412 -0.417 0.12 -0.5646 -0.2672 13.8 1.00
#> c[2] -0.351 -0.367 0.22 -0.5662 -0.2090 8.7 1.03
#> c[3] -0.472 -0.393 0.17 -0.6871 -0.3292 7.2 1.75
#> c[4] -0.207 -0.227 0.14 -0.3543 -0.0058 6.3 1.14
#> c[5] -0.196 -0.157 0.13 -0.3790 -0.0354 15.0 0.99
#> c[6] -0.049 -0.042 0.14 -0.2137 0.1168 13.8 1.09
#> c[7] 0.262 0.216 0.16 0.1163 0.4784 3.2 1.42
#> c[8] 0.243 0.223 0.15 0.0681 0.4398 15.0 0.96
#> c[9] 0.418 0.401 0.17 0.2129 0.7170 11.2 1.03
#> c[10] 0.298 0.318 0.20 0.0456 0.5613 2.1 2.12
#> A[1,1] 0.586 0.631 0.17 0.3654 0.7787 10.2 1.40
#> A[2,1] 0.137 0.122 0.18 -0.0692 0.3631 3.0 1.43
#> A[3,1] 0.170 0.146 0.17 0.0209 0.3968 6.7 1.26
#> A[4,1] -0.012 0.056 0.17 -0.2524 0.1550 12.2 1.00
#> A[5,1] 0.418 0.441 0.28 0.1090 0.7660 2.1 2.12
#> A[6,1] 0.249 0.286 0.18 0.0078 0.4217 3.7 1.28
#> A[7,1] 0.543 0.669 0.40 0.0290 0.9593 2.1 2.12
#> A[8,1] 0.265 0.317 0.18 -0.0184 0.4564 13.0 0.97
#> A[9,1] 0.723 0.684 0.22 0.4761 1.0157 2.1 2.12
#> A[10,1] 0.535 0.491 0.23 0.3210 0.8123 3.6 1.31
#> A[2,2] 0.755 0.719 0.20 0.5549 0.9933 2.5 1.73
#> A[3,2] 0.780 0.808 0.28 0.5300 1.1136 3.0 1.46
#> A[4,2] 0.488 0.532 0.19 0.2440 0.6985 10.5 1.06
#> A[5,2] 0.590 0.574 0.32 0.1792 0.9659 2.3 1.85
#> A[6,2] 0.540 0.574 0.23 0.1854 0.7955 2.2 1.97
#> A[7,2] 0.671 0.744 0.27 0.3736 0.9304 3.3 1.51
#> A[8,2] 0.707 0.677 0.26 0.4148 1.0296 6.6 1.13
#> A[9,2] 0.685 0.780 0.25 0.2649 0.9297 10.7 1.22
#> A[10,2] 1.038 1.090 0.44 0.3948 1.5077 4.1 1.21
#> A[3,3] 0.796 0.787 0.16 0.6303 0.9292 2.4 1.79
#> A[4,3] 0.275 0.258 0.15 0.1224 0.4499 2.5 1.69
#> A[5,3] -0.427 -0.376 0.29 -0.8348 -0.1017 2.4 1.69
#> A[6,3] -0.456 -0.341 0.49 -1.2241 0.0679 2.1 2.12
#> A[7,3] -0.101 -0.108 0.20 -0.3618 0.1212 2.3 1.85
#> A[8,3] 0.623 0.589 0.42 0.0416 1.1096 2.0 2.12
#> A[9,3] -0.031 -0.031 0.17 -0.2308 0.1459 10.4 1.05
#> A[10,3] 0.078 0.067 0.22 -0.1763 0.4005 8.5 1.35
#>
#> 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).