DW-NOMINATE dynamic weighted ideal-point estimator — morie_spatial_voting_dw

Gaussian-error NOMINATE variant supporting comparable scores across legislative sessions.

Usage

morie_spatial_voting_dw_nominate(
  votes,
  n_dims = 2L,
  max_iter = 100L,
  tol = 1e-06
)

Arguments

votes: Legislator-by-vote binary matrix.
n_dims: Latent dimensions.
max_iter: Maximum iterations.
tol: Tolerance (unused; kept for API parity).

Value

List with ideal_points, dim_weights, normal_vectors, cutpoints, log_lik, gmp, n_dims.

References

Poole, K. T. and Rosenthal, H. (1997). Congress: A Political- Economic History of Roll Call Voting. Oxford University Press.

Examples

set.seed(1)
v <- matrix(stats::rbinom(20 * 30, 1, 0.5), 20, 30)
morie_spatial_voting_dw_nominate(v, max_iter = 20)
#> $ideal_points
#>            [,1]         [,2]
#>  [1,] 0.4537455 -0.018924111
#>  [2,] 0.2404942 -0.200967769
#>  [3,] 0.1552407 -0.114181151
#>  [4,] 0.3661386  0.130901572
#>  [5,] 0.4132845  0.040973512
#>  [6,] 0.2790701 -0.012366485
#>  [7,] 0.2071679 -0.195088903
#>  [8,] 0.4277600 -0.097057151
#>  [9,] 0.2665432  0.066829947
#> [10,] 0.4583470 -0.257635342
#> [11,] 0.4051705 -0.181237721
#> [12,] 0.2187379  0.067019314
#> [13,] 0.0690283 -0.187653057
#> [14,] 0.3410390 -0.293620469
#> [15,] 0.1952023 -0.202651136
#> [16,] 0.2783036 -0.004212361
#> [17,] 0.2820336 -0.029940699
#> [18,] 0.2178203  0.134208927
#> [19,] 0.2628522 -0.029355626
#> [20,] 0.4202374 -0.201705488
#> 
#> $dim_weights
#>      [,1]
#> [1,]  0.5
#> [2,]  0.5
#> 
#> $normal_vectors
#>              [,1]        [,2]
#>  [1,] -0.91052820  0.41344698
#>  [2,] -0.60241584  0.79818241
#>  [3,]  0.07676569  0.99704916
#>  [4,]  0.95509183  0.29630996
#>  [5,] -0.40992195 -0.91212060
#>  [6,]  0.88208311  0.47109382
#>  [7,]  0.76872502 -0.63957942
#>  [8,]  0.91993661 -0.39206713
#>  [9,] -0.23852354  0.97113672
#> [10,]  0.99956858 -0.02937108
#> [11,] -0.63614434 -0.77157007
#> [12,]  0.58945401  0.80780194
#> [13,]  0.24811657 -0.96873018
#> [14,]  0.81823607  0.57488236
#> [15,] -0.70039694 -0.71375355
#> [16,]  0.50034073 -0.86582859
#> [17,]  0.95017235 -0.31172504
#> [18,] -0.47435355 -0.88033443
#> [19,] -0.52860725  0.84886652
#> [20,] -0.23624507 -0.97169350
#> [21,]  0.68635092 -0.72727052
#> [22,]  0.50885875 -0.86085003
#> [23,]  0.01973469  0.99980525
#> [24,] -0.48713080 -0.87332902
#> [25,] -0.05537721  0.99846550
#> [26,]  0.90661429 -0.42196034
#> [27,]  0.78434332 -0.62032697
#> [28,] -0.15072976 -0.98857500
#> [29,]  0.95292401  0.30320923
#> [30,] -0.03691794 -0.99931830
#> 
#> $cutpoints
#>              [,1]
#>  [1,] -0.29893066
#>  [2,] -0.22736995
#>  [3,] -0.06444229
#>  [4,]  0.25638446
#>  [5,] -0.03867829
#>  [6,]  0.22273257
#>  [7,]  0.26228860
#>  [8,]  0.30018420
#>  [9,] -0.15799033
#> [10,]  0.28150919
#> [11,] -0.12759059
#> [12,]  0.11368253
#> [13,]  0.16989723
#> [14,]  0.22184080
#> [15,] -0.11536862
#> [16,]  0.22391448
#> [17,]  0.28985395
#> [18,] -0.06183441
#> [19,] -0.20644566
#> [20,]  0.01517740
#> [21,]  0.23728800
#> [22,]  0.21495166
#> [23,] -0.06487880
#> [24,] -0.06023408
#> [25,] -0.08575182
#> [26,]  0.27621723
#> [27,]  0.28110440
#> [28,]  0.02883028
#> [29,]  0.24365983
#> [30,]  0.06359641
#> 
#> $log_lik
#> [1] -341.4907
#> 
#> $gmp
#> [1] 0.7033333
#> 
#> $n_dims
#> [1] 2
#>