Printing method for optimal single-stage goldstandard designs
Source:R/design_display_functions.R
print.OneStageGoldStandardDesign.RdPrinting method for optimal single-stage goldstandard designs
Usage
# S3 method for OneStageGoldStandardDesign
print(x, ...)Value
returns the input x invisibly. This functions is used for its side effects, i.e. printing design characteristics to the screen.
Examples
# Should take about 2 second with the chosen accuracy
optimize_design_onestage(
alpha = .025,
beta = .2,
alternative_TP = .4,
alternative_TC = 0,
Delta = .2,
mvnorm_algorithm = mvtnorm::Miwa(steps = 512, checkCorr = FALSE, maxval = 1000),
nloptr_opts = list(algorithm = "NLOPT_LN_SBPLX", ftol_rel = 1e-03, xtol_abs = 1e-08,
xtol_rel = 1e-07, maxeval = 1000, print_level = 0)
)
#>
iteration: 1/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.25, 1.00) f(x) = 946.9105
iteration: 2/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.25, 1.00) f(x) = 946.9105
iteration: 3/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.25, 1.00) f(x) = 946.9105
iteration: 4/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.428125, 1.000000) f(x) = 961.2598
iteration: 5/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2500, 1.7125) f(x) = 1056.666
iteration: 6/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.428125, 0.287500) f(x) = 1507.581
iteration: 7/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2945313, 1.3562500) f(x) = 968.3002
iteration: 8/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3835937, 0.6437500) f(x) = 1018.988
iteration: 9/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3167969, 1.1781250) f(x) = 945.7473
iteration: 10/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.1386719, 1.1781250) f(x) = 1142.114
iteration: 11/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3557617, 1.0445312) f(x) = 942.7458
iteration: 12/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.400293, 1.044531) f(x) = 951.7809
iteration: 13/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3557617, 1.2226562) f(x) = 948.9537
iteration: 14/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3112305, 1.2226562) f(x) = 949.9056
iteration: 15/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3334961, 1.1781250) f(x) = 945.294
iteration: 16/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3334961, 1.0000000) f(x) = 940.0275
iteration: 17/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3223633, 0.8886719) f(x) = 943.0804
iteration: 18/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3557617, 0.8664062) f(x) = 952.3089
iteration: 19/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3390625, 1.1001953) f(x) = 941.7243
iteration: 20/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3167969, 1.0556641) f(x) = 939.3978
iteration: 21/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2973145, 1.0612305) f(x) = 940.1934
iteration: 22/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3112305, 0.9554688) f(x) = 938.7124
iteration: 23/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2973145, 0.8831055) f(x) = 940.6196
iteration: 24/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2945313, 1.0111328) f(x) = 938.7033
iteration: 25/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2750488, 1.0166992) f(x) = 941.122
iteration: 26/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2889648, 0.9109375) f(x) = 939.1165
iteration: 27/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2959229, 0.9471191) f(x) = 938.3272
iteration: 28/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2792236, 1.0027832) f(x) = 939.952
iteration: 29/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3032288, 0.9672974) f(x) = 938.202
iteration: 30/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3046204, 0.9032837) f(x) = 940.0546
iteration: 31/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2970535, 0.9841705) f(x) = 938.2006
iteration: 32/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3043594, 1.0043488) f(x) = 938.299
iteration: 33/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3022503, 0.9900414) f(x) = 938.1766
iteration: 34/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2960751, 1.0069145) f(x) = 938.5352
iteration: 35/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3014403, 0.9772017) f(x) = 938.1401
iteration: 36/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.3066371, 0.9830725) f(x) = 938.2095
iteration: 37/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2994494, 0.9838960) f(x) = 938.1602
iteration: 38/1000 cP1 <- x[1L] cC1 <- x[2L] x = c(0.2986395, 0.9710563) f(x) = 938.1456
#> Optimization finished. Calculating final design with greater accuracy...
#> Sample sizes (stage 1): T: 412, P: 125, C: 403
#> Efficacy boundaries (stage 1): Z_TP_e: 1.95996, Z_TC_e: 1.95996
#> Maximum overall sample size: 940
#> Placebo penalty at optimum (kappa * nP): 0.0
#> Objective function value: 940.0
#> Type I error for TP testing: 2.5%
#> Type I error for TC testing: 2.5%
#> Power: 80.1%