Print method for summary.multisimsum objects
Usage
# S3 method for summary.multisimsum
print(x, digits = 4, mcse = TRUE, ...)Arguments
- x
An object of class
summary.multisimsum.- digits
Number of significant digits used for printing. Defaults to 4.
- mcse
Should Monte Carlo standard errors be reported? If
mcse = FALSE, confidence intervals based on Monte Carlo standard errors will be reported instead, seesummary.multisimsum(). If aNULLvalue is passed, only point estimates are printed regardless of whether Monte Carlo standard errors were computed or not. Defaults toTRUE.- ...
Ignored.
Examples
data(frailty)
ms <- multisimsum(
data = frailty, par = "par", true = c(
trt = -0.50,
fv = 0.75
), estvarname = "b", se = "se", methodvar = "model",
by = "fv_dist"
)
#> 'ref' method was not specified, Cox, Gamma set as the reference
sms <- summary(ms, stats = c("bias", "cover", "mse"))
sms
#> Values are:
#> Point Estimate (Monte Carlo Standard Error)
#>
#>
#> Parameter: fv
#>
#> Bias in point estimate:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma
#> Gamma -0.0124 (0.0045) 0.2299 (0.0076) -0.0179 (0.0044)
#> Log-Normal -0.1064 (0.0043) -0.0175 (0.0049) -0.1066 (0.0041)
#> RP(P), Log-Normal
#> 0.2347 (0.0077)
#> -0.0152 (0.0050)
#>
#> Mean squared error:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.0203 (0.0010) 0.1107 (0.0055) 0.0195 (0.0009) 0.1145 (0.0057)
#> Log-Normal 0.0287 (0.0010) 0.0244 (0.0011) 0.0284 (0.0010) 0.0248 (0.0012)
#>
#> Coverage of nominal 95% confidence interval:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.9201 (0.0087) 0.9220 (0.0085) 0.9300 (0.0082) 0.9030 (0.0094)
#> Log-Normal 0.7503 (0.0140) 0.9020 (0.0094) 0.7683 (0.0134) 0.9280 (0.0082)
#>
#> --------------------------------------------------------------------------------
#>
#> Parameter: trt
#>
#> Bias in point estimate:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma
#> Gamma -0.0006 (0.0016) -0.0013 (0.0016) -0.0003 (0.0016)
#> Log-Normal -0.0006 (0.0015) -0.0014 (0.0015) -0.0006 (0.0015)
#> RP(P), Log-Normal
#> -0.0015 (0.0016)
#> -0.0016 (0.0015)
#>
#> Mean squared error:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.0026 (0.0001) 0.0026 (0.0001) 0.0026 (0.0001) 0.0026 (0.0001)
#> Log-Normal 0.0022 (0.0001) 0.0022 (0.0001) 0.0022 (0.0001) 0.0022 (0.0001)
#>
#> Coverage of nominal 95% confidence interval:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.9500 (0.0069) 0.9490 (0.0070) 0.9506 (0.0070) 0.9500 (0.0069)
#> Log-Normal 0.9410 (0.0075) 0.9420 (0.0074) 0.9428 (0.0074) 0.9430 (0.0073)
# Printing less significant digits:
print(sms, digits = 3)
#> Values are:
#> Point Estimate (Monte Carlo Standard Error)
#>
#>
#> Parameter: fv
#>
#> Bias in point estimate:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma -0.012 (0.005) 0.230 (0.008) -0.018 (0.004) 0.235 (0.008)
#> Log-Normal -0.106 (0.004) -0.017 (0.005) -0.107 (0.004) -0.015 (0.005)
#>
#> Mean squared error:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.020 (0.001) 0.111 (0.005) 0.020 (0.001) 0.114 (0.006)
#> Log-Normal 0.029 (0.001) 0.024 (0.001) 0.028 (0.001) 0.025 (0.001)
#>
#> Coverage of nominal 95% confidence interval:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.920 (0.009) 0.922 (0.008) 0.930 (0.008) 0.903 (0.009)
#> Log-Normal 0.750 (0.014) 0.902 (0.009) 0.768 (0.013) 0.928 (0.008)
#>
#> --------------------------------------------------------------------------------
#>
#> Parameter: trt
#>
#> Bias in point estimate:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma -0.001 (0.002) -0.001 (0.002) -0.000 (0.002) -0.002 (0.002)
#> Log-Normal -0.001 (0.001) -0.001 (0.001) -0.001 (0.001) -0.002 (0.001)
#>
#> Mean squared error:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.003 (0.000) 0.003 (0.000) 0.003 (0.000) 0.003 (0.000)
#> Log-Normal 0.002 (0.000) 0.002 (0.000) 0.002 (0.000) 0.002 (0.000)
#>
#> Coverage of nominal 95% confidence interval:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.950 (0.007) 0.949 (0.007) 0.951 (0.007) 0.950 (0.007)
#> Log-Normal 0.941 (0.007) 0.942 (0.007) 0.943 (0.007) 0.943 (0.007)
# Printing confidence intervals:
print(sms, digits = 3, mcse = FALSE)
#> Values are:
#> Point Estimate (95% Confidence Interval based on Monte Carlo Standard Errors)
#>
#>
#> Parameter: fv
#>
#> Bias in point estimate:
#> fv_dist Cox, Gamma Cox, Log-Normal
#> Gamma -0.012 (-0.021, -0.003) 0.230 (0.215, 0.245)
#> Log-Normal -0.106 (-0.115, -0.098) -0.017 (-0.027, -0.008)
#> RP(P), Gamma RP(P), Log-Normal
#> -0.018 (-0.027, -0.009) 0.235 (0.220, 0.250)
#> -0.107 (-0.115, -0.098) -0.015 (-0.025, -0.006)
#>
#> Mean squared error:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma
#> Gamma 0.020 (0.018, 0.022) 0.111 (0.100, 0.121) 0.020 (0.018, 0.021)
#> Log-Normal 0.029 (0.027, 0.031) 0.024 (0.022, 0.027) 0.028 (0.026, 0.030)
#> RP(P), Log-Normal
#> 0.114 (0.103, 0.126)
#> 0.025 (0.023, 0.027)
#>
#> Coverage of nominal 95% confidence interval:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma
#> Gamma 0.920 (0.903, 0.937) 0.922 (0.905, 0.939) 0.930 (0.914, 0.946)
#> Log-Normal 0.750 (0.723, 0.778) 0.902 (0.884, 0.920) 0.768 (0.742, 0.794)
#> RP(P), Log-Normal
#> 0.903 (0.885, 0.921)
#> 0.928 (0.912, 0.944)
#>
#> --------------------------------------------------------------------------------
#>
#> Parameter: trt
#>
#> Bias in point estimate:
#> fv_dist Cox, Gamma Cox, Log-Normal
#> Gamma -0.001 (-0.004, 0.003) -0.001 (-0.004, 0.002)
#> Log-Normal -0.001 (-0.004, 0.002) -0.001 (-0.004, 0.002)
#> RP(P), Gamma RP(P), Log-Normal
#> -0.000 (-0.003, 0.003) -0.002 (-0.005, 0.002)
#> -0.001 (-0.004, 0.002) -0.002 (-0.005, 0.001)
#>
#> Mean squared error:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma
#> Gamma 0.003 (0.002, 0.003) 0.003 (0.002, 0.003) 0.003 (0.002, 0.003)
#> Log-Normal 0.002 (0.002, 0.002) 0.002 (0.002, 0.002) 0.002 (0.002, 0.002)
#> RP(P), Log-Normal
#> 0.003 (0.002, 0.003)
#> 0.002 (0.002, 0.002)
#>
#> Coverage of nominal 95% confidence interval:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma
#> Gamma 0.950 (0.936, 0.964) 0.949 (0.935, 0.963) 0.951 (0.937, 0.964)
#> Log-Normal 0.941 (0.926, 0.956) 0.942 (0.928, 0.956) 0.943 (0.928, 0.957)
#> RP(P), Log-Normal
#> 0.950 (0.936, 0.964)
#> 0.943 (0.929, 0.957)
# Printing values only:
print(sms, mcse = NULL)
#> Values are:
#> Point Estimate
#>
#>
#> Parameter: fv
#>
#> Bias in point estimate:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma -0.0124 0.2299 -0.0179 0.2347
#> Log-Normal -0.1064 -0.0175 -0.1066 -0.0152
#>
#> Mean squared error:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.0203 0.1107 0.0195 0.1145
#> Log-Normal 0.0287 0.0244 0.0284 0.0248
#>
#> Coverage of nominal 95% confidence interval:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.9201 0.9220 0.9300 0.9030
#> Log-Normal 0.7503 0.9020 0.7683 0.9280
#>
#> --------------------------------------------------------------------------------
#>
#> Parameter: trt
#>
#> Bias in point estimate:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma -0.0006 -0.0013 -0.0003 -0.0015
#> Log-Normal -0.0006 -0.0014 -0.0006 -0.0016
#>
#> Mean squared error:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.0026 0.0026 0.0026 0.0026
#> Log-Normal 0.0022 0.0022 0.0022 0.0022
#>
#> Coverage of nominal 95% confidence interval:
#> fv_dist Cox, Gamma Cox, Log-Normal RP(P), Gamma RP(P), Log-Normal
#> Gamma 0.9500 0.9490 0.9506 0.9500
#> Log-Normal 0.9410 0.9420 0.9428 0.9430