Extract data slots from an object of class simsum, summary.simsum, multisimsum, or summary.multisimsum.
Arguments
- x
An object of class
simsum.- stats
Summary statistics to include; can be a scalar value or a vector. Possible choices are:
nsim, the number of replications with non-missing point estimates and standard error.thetamean, average point estimate.thetamedian, median point estimate.se2mean, average standard error.se2median, median standard error.bias, bias in point estimate.empse, empirical standard error.mse, mean squared error.relprec, percentage gain in precision relative to the reference method.modelse, model-based standard error.relerror, relative percentage error in standard error.cover, coverage of a nominallevel\becover, bias-eliminated coverage of a nominallevel\power, power of a (1 -level)\ Defaults toNULL, in which case all summary statistics are returned.
- ...
Ignored.
Examples
data(MIsim)
x <- simsum(
data = MIsim, estvarname = "b", true = 0.5, se = "se",
methodvar = "method"
)
#> 'ref' method was not specified, CC set as the reference
get_data(x)
#> Warning: `get_data()` was deprecated in rsimsum 0.10.0.
#> ℹ Please use `tidy()` instead.
#> stat est mcse method
#> 1 nsim 1.000000e+03 NA CC
#> 2 thetamean 5.167662e-01 NA CC
#> 3 thetamedian 5.069935e-01 NA CC
#> 4 se2mean 2.163731e-02 NA CC
#> 5 se2median 2.114245e-02 NA CC
#> 6 bias 1.676616e-02 4.778676e-03 CC
#> 7 rbias 3.353232e-02 9.557351e-03 CC
#> 8 empse 1.511150e-01 3.380725e-03 CC
#> 9 mse 2.309401e-02 1.133839e-03 CC
#> 10 relprec 0.000000e+00 9.429038e-08 CC
#> 11 modelse 1.470963e-01 5.274099e-04 CC
#> 12 relerror -2.659384e+00 2.205482e+00 CC
#> 13 cover 9.430000e-01 7.331507e-03 CC
#> 14 becover 9.400000e-01 7.509993e-03 CC
#> 15 power 9.460000e-01 7.147307e-03 CC
#> 16 nsim 1.000000e+03 NA MI_LOGT
#> 17 thetamean 5.009231e-01 NA MI_LOGT
#> 18 thetamedian 4.969223e-01 NA MI_LOGT
#> 19 se2mean 1.820915e-02 NA MI_LOGT
#> 20 se2median 1.721574e-02 NA MI_LOGT
#> 21 bias 9.230987e-04 4.174410e-03 MI_LOGT
#> 22 rbias 1.846197e-03 8.348820e-03 MI_LOGT
#> 23 empse 1.320064e-01 2.953231e-03 MI_LOGT
#> 24 mse 1.740913e-02 8.812805e-04 MI_LOGT
#> 25 relprec 3.104634e+01 3.937473e+00 MI_LOGT
#> 26 modelse 1.349413e-01 6.046041e-04 MI_LOGT
#> 27 relerror 2.223259e+00 2.332338e+00 MI_LOGT
#> 28 cover 9.490000e-01 6.956939e-03 MI_LOGT
#> 29 becover 9.490000e-01 6.956939e-03 MI_LOGT
#> 30 power 9.690000e-01 5.480785e-03 MI_LOGT
#> 31 nsim 1.000000e+03 NA MI_T
#> 32 thetamean 4.988092e-01 NA MI_T
#> 33 thetamedian 4.939111e-01 NA MI_T
#> 34 se2mean 1.791169e-02 NA MI_T
#> 35 se2median 1.693191e-02 NA MI_T
#> 36 bias -1.190835e-03 4.250977e-03 MI_T
#> 37 rbias -2.381670e-03 8.501953e-03 MI_T
#> 38 empse 1.344277e-01 3.007399e-03 MI_T
#> 39 mse 1.805415e-02 9.112249e-04 MI_T
#> 40 relprec 2.636816e+01 3.842379e+00 MI_T
#> 41 modelse 1.338346e-01 5.856362e-04 MI_T
#> 42 relerror -4.412233e-01 2.269522e+00 MI_T
#> 43 cover 9.430000e-01 7.331507e-03 MI_T
#> 44 becover 9.430000e-01 7.331507e-03 MI_T
#> 45 power 9.630000e-01 5.969171e-03 MI_T
# Extracting only bias and coverage:
get_data(x, stats = c("bias", "cover"))
#> stat est mcse method
#> 1 bias 0.0167661608 0.004778676 CC
#> 2 cover 0.9430000000 0.007331507 CC
#> 3 bias 0.0009230987 0.004174410 MI_LOGT
#> 4 cover 0.9490000000 0.006956939 MI_LOGT
#> 5 bias -0.0011908351 0.004250977 MI_T
#> 6 cover 0.9430000000 0.007331507 MI_T
xs <- summary(x)
get_data(xs)
#> stat est mcse method lower upper
#> 1 nsim 1.000000e+03 NA CC NA NA
#> 2 thetamean 5.167662e-01 NA CC NA NA
#> 3 thetamedian 5.069935e-01 NA CC NA NA
#> 4 se2mean 2.163731e-02 NA CC NA NA
#> 5 se2median 2.114245e-02 NA CC NA NA
#> 6 bias 1.676616e-02 4.778676e-03 CC 7.400129e-03 2.613219e-02
#> 7 rbias 3.353232e-02 9.557351e-03 CC 1.480026e-02 5.226439e-02
#> 8 empse 1.511150e-01 3.380725e-03 CC 1.444889e-01 1.577411e-01
#> 9 mse 2.309401e-02 1.133839e-03 CC 2.087173e-02 2.531629e-02
#> 10 relprec 0.000000e+00 9.429038e-08 CC -1.848057e-07 1.848057e-07
#> 11 modelse 1.470963e-01 5.274099e-04 CC 1.460626e-01 1.481300e-01
#> 12 relerror -2.659384e+00 2.205482e+00 CC -6.982049e+00 1.663281e+00
#> 13 cover 9.430000e-01 7.331507e-03 CC 9.286305e-01 9.573695e-01
#> 14 becover 9.400000e-01 7.509993e-03 CC 9.252807e-01 9.547193e-01
#> 15 power 9.460000e-01 7.147307e-03 CC 9.319915e-01 9.600085e-01
#> 16 nsim 1.000000e+03 NA MI_LOGT NA NA
#> 17 thetamean 5.009231e-01 NA MI_LOGT NA NA
#> 18 thetamedian 4.969223e-01 NA MI_LOGT NA NA
#> 19 se2mean 1.820915e-02 NA MI_LOGT NA NA
#> 20 se2median 1.721574e-02 NA MI_LOGT NA NA
#> 21 bias 9.230987e-04 4.174410e-03 MI_LOGT -7.258595e-03 9.104792e-03
#> 22 rbias 1.846197e-03 8.348820e-03 MI_LOGT -1.451719e-02 1.820958e-02
#> 23 empse 1.320064e-01 2.953231e-03 MI_LOGT 1.262182e-01 1.377947e-01
#> 24 mse 1.740913e-02 8.812805e-04 MI_LOGT 1.568185e-02 1.913640e-02
#> 25 relprec 3.104634e+01 3.937473e+00 MI_LOGT 2.332904e+01 3.876365e+01
#> 26 modelse 1.349413e-01 6.046041e-04 MI_LOGT 1.337563e-01 1.361263e-01
#> 27 relerror 2.223259e+00 2.332338e+00 MI_LOGT -2.348040e+00 6.794558e+00
#> 28 cover 9.490000e-01 6.956939e-03 MI_LOGT 9.353647e-01 9.626353e-01
#> 29 becover 9.490000e-01 6.956939e-03 MI_LOGT 9.353647e-01 9.626353e-01
#> 30 power 9.690000e-01 5.480785e-03 MI_LOGT 9.582579e-01 9.797421e-01
#> 31 nsim 1.000000e+03 NA MI_T NA NA
#> 32 thetamean 4.988092e-01 NA MI_T NA NA
#> 33 thetamedian 4.939111e-01 NA MI_T NA NA
#> 34 se2mean 1.791169e-02 NA MI_T NA NA
#> 35 se2median 1.693191e-02 NA MI_T NA NA
#> 36 bias -1.190835e-03 4.250977e-03 MI_T -9.522596e-03 7.140926e-03
#> 37 rbias -2.381670e-03 8.501953e-03 MI_T -1.904519e-02 1.428185e-02
#> 38 empse 1.344277e-01 3.007399e-03 MI_T 1.285333e-01 1.403221e-01
#> 39 mse 1.805415e-02 9.112249e-04 MI_T 1.626818e-02 1.984012e-02
#> 40 relprec 2.636816e+01 3.842379e+00 MI_T 1.883724e+01 3.389909e+01
#> 41 modelse 1.338346e-01 5.856362e-04 MI_T 1.326867e-01 1.349824e-01
#> 42 relerror -4.412233e-01 2.269522e+00 MI_T -4.889404e+00 4.006957e+00
#> 43 cover 9.430000e-01 7.331507e-03 MI_T 9.286305e-01 9.573695e-01
#> 44 becover 9.430000e-01 7.331507e-03 MI_T 9.286305e-01 9.573695e-01
#> 45 power 9.630000e-01 5.969171e-03 MI_T 9.513006e-01 9.746994e-01