tda_estimates returns the coefficient table, reading the column
layout from TDA's header so that it works for the rate models
(SN/Org/Des/MT), qreg (Cat/Term) and fml (Parameter/Value)
alike. A parameter TDA held fixed, printed as ---, comes back
NA. tda_output prints everything TDA wrote.
See also
Other TDA infrastructure:
tda_ccnt(),
tda_help(),
tda_read_table(),
tda_run(),
tda_write_data()
Examples
set.seed(38)
d <- data.frame(x = rnorm(100))
d$y <- rbinom(100, 1, plogis(0.2 + 0.7 * d$x))
f <- tda_qreg(y ~ x, d)
tda_estimates(f)
#> Idx Cat Term Variable Coeff Error C/Error Signif
#> 1 1 1 I Intercept 0.4058624 0.2195944 1.848236 0.9354318
#> 2 2 1 X x 0.9012704 0.2672257 3.372694 0.9992556
tda_output(f) # everything TDA printed, not just the coefficient table
#> TDA. Analysis of Transition Data (6.4q). Sun Sep 20 10:43:44 2026
#> Current memory: 390032 bytes.
#>
#> Reading command file: commands
#> ============================================================================
#> Idx Variable T S PFmt Definition
#> -------------------------------------
#> 1 Vy 3 8 24.16 c1
#> 2 Vx 3 8 24.16 c2
#>
#> Reading a data frame to create internal data matrix.
#> Maximum number of cases: 100
#> Created a data matrix with 2 variables and 100 cases.
#> ----------------------------------------------------------------------------
#> qreg(...)=...
#> Quantal response models. Current memory: 391668 bytes.
#>
#> Model: binary logit.
#>
#> Variables (cross-section)
#> -------------------------
#> Y : Vy
#> X1 : Vx
#>
#> Checking available data (pmin=1)
#> Number of cases with valid data: 100
#>
#> Categories of dependent variable.
#> Maximum number of categories: 100
#>
#> Index 0 1 (Weighted)
#> Category 0 1 Observations
#> ---------------------------------------------
#> Wave 1 N 41.00 59.00 100.00
#> Pct 41.00 59.00
#>
#> Maximum likelihood estimation.
#> Algorithm 5: Newton (I)
#>
#> Number of model parameters: 2
#> Type of covariance matrix: 2
#> Maximum number of iterations: 20
#> Convergence criterion: 1
#> Tolerance for norm of final gradient: 1e-06
#> Mue of Armijo condition: 0.2
#> Minimum of step size value: 1e-10
#> Scaling factor: -1
#>
#>
#> Convergence reached in 5 iterations.
#> Number of function evaluations: 6 (6,6)
#>
#> Maximum of log likelihood: -60.764
#> Norm of final gradient vector: 4.21004e-09
#> Last absolute change of function value: 8.73702e-11
#> Last relative change in parameters: 2.89833e-05
#>
#> Idx Cat Term Variable Coeff Error C/Error Signif
#> ------------------------------------------------------------------------------------------------------------
#> 1 1 I Intercept 0.4058623920111429 0.2195944416257131 1.8482361803260641 0.9354
#> 2 1 X Vx 0.9012703568192907 0.2672256852091420 3.3726935946068162 0.9993
#>
#> Log likelihood (starting values): -69.3147180559945895
#> Log likelihood (final estimates): -60.7639890351186409
#>
#> ----------------------------------------------------------------------------
#> Current memory: 390032 bytes. Max memory used: 394519 bytes.
#> End of program. Sun Sep 20 10:43:44 2026
#>
#> --- stderr ---
#>
#> Iter Function Value Norm of Gradient Par Change FCall
#> 1 6.9314718055995e+01 1.8886024681e+01 -- 1 (1,1)
#> 2 6.0927896207017e+01 2.2170219810e+00 1.0000e+00 2 (2,2)
#> 3 6.0764474063353e+01 1.1593901524e-01 1.4768e-01 3 (3,3)
#> 4 6.0763989040428e+01 3.8218230965e-04 8.6593e-03 4 (4,4)
#> 5 6.0763989035119e+01 4.2100428432e-09 2.8983e-05 5 (5,5)