Extract a tidy dataset with results 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.rbias
, relative (to the true value) 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
tidy(x)
#> 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:
tidy(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)
tidy(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