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 0.0047786757 CC
#> 7 rbias 3.353232e-02 0.0095573514 CC
#> 8 empse 1.511150e-01 0.0033807248 CC
#> 9 mse 2.309401e-02 0.0011338389 CC
#> 10 relprec 0.000000e+00 0.0000000000 CC
#> 11 modelse 1.470963e-01 0.0005274099 CC
#> 12 relerror -2.659384e+00 2.2054817330 CC
#> 13 cover 9.430000e-01 0.0073315073 CC
#> 14 becover 9.400000e-01 0.0075099933 CC
#> 15 power 9.460000e-01 0.0071473072 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 0.0041744101 MI_LOGT
#> 22 rbias 1.846197e-03 0.0083488201 MI_LOGT
#> 23 empse 1.320064e-01 0.0029532306 MI_LOGT
#> 24 mse 1.740913e-02 0.0008812805 MI_LOGT
#> 25 relprec 3.104634e+01 3.9374726448 MI_LOGT
#> 26 modelse 1.349413e-01 0.0006046041 MI_LOGT
#> 27 relerror 2.223259e+00 2.3323382138 MI_LOGT
#> 28 cover 9.490000e-01 0.0069569390 MI_LOGT
#> 29 becover 9.490000e-01 0.0069569390 MI_LOGT
#> 30 power 9.690000e-01 0.0054807846 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 0.0042509767 MI_T
#> 37 rbias -2.381670e-03 0.0085019534 MI_T
#> 38 empse 1.344277e-01 0.0030073985 MI_T
#> 39 mse 1.805415e-02 0.0009112249 MI_T
#> 40 relprec 2.636816e+01 3.8423791135 MI_T
#> 41 modelse 1.338346e-01 0.0005856362 MI_T
#> 42 relerror -4.412233e-01 2.2695215740 MI_T
#> 43 cover 9.430000e-01 0.0073315073 MI_T
#> 44 becover 9.430000e-01 0.0073315073 MI_T
#> 45 power 9.630000e-01 0.0059691708 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 0.0047786757 CC 0.007400129 0.026132193
#> 7 rbias 3.353232e-02 0.0095573514 CC 0.014800257 0.052264386
#> 8 empse 1.511150e-01 0.0033807248 CC 0.144488895 0.157741093
#> 9 mse 2.309401e-02 0.0011338389 CC 0.020871727 0.025316293
#> 10 relprec 0.000000e+00 0.0000000000 CC 0.000000000 0.000000000
#> 11 modelse 1.470963e-01 0.0005274099 CC 0.146062561 0.148129970
#> 12 relerror -2.659384e+00 2.2054817330 CC -6.982048962 1.663280569
#> 13 cover 9.430000e-01 0.0073315073 CC 0.928630510 0.957369490
#> 14 becover 9.400000e-01 0.0075099933 CC 0.925280684 0.954719316
#> 15 power 9.460000e-01 0.0071473072 CC 0.931991535 0.960008465
#> 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 0.0041744101 MI_LOGT -0.007258595 0.009104792
#> 22 rbias 1.846197e-03 0.0083488201 MI_LOGT -0.014517189 0.018209584
#> 23 empse 1.320064e-01 0.0029532306 MI_LOGT 0.126218211 0.137794663
#> 24 mse 1.740913e-02 0.0008812805 MI_LOGT 0.015681848 0.019136404
#> 25 relprec 3.104634e+01 3.9374726448 MI_LOGT 23.329036439 38.763645587
#> 26 modelse 1.349413e-01 0.0006046041 MI_LOGT 0.133756280 0.136126285
#> 27 relerror 2.223259e+00 2.3323382138 MI_LOGT -2.348039558 6.794558240
#> 28 cover 9.490000e-01 0.0069569390 MI_LOGT 0.935364650 0.962635350
#> 29 becover 9.490000e-01 0.0069569390 MI_LOGT 0.935364650 0.962635350
#> 30 power 9.690000e-01 0.0054807846 MI_LOGT 0.958257860 0.979742140
#> 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 0.0042509767 MI_T -0.009522596 0.007140926
#> 37 rbias -2.381670e-03 0.0085019534 MI_T -0.019045193 0.014281852
#> 38 empse 1.344277e-01 0.0030073985 MI_T 0.128533294 0.140322080
#> 39 mse 1.805415e-02 0.0009112249 MI_T 0.016268182 0.019840118
#> 40 relprec 2.636816e+01 3.8423791135 MI_T 18.837236583 33.899085938
#> 41 modelse 1.338346e-01 0.0005856362 MI_T 0.132686735 0.134982387
#> 42 relerror -4.412233e-01 2.2695215740 MI_T -4.889403808 4.006957286
#> 43 cover 9.430000e-01 0.0073315073 MI_T 0.928630510 0.957369490
#> 44 becover 9.430000e-01 0.0073315073 MI_T 0.928630510 0.957369490
#> 45 power 9.630000e-01 0.0059691708 MI_T 0.951300640 0.974699360