csa-vignette
Introduction
This vignette demonstrates how to use the simulateDCE
package to simulate discrete choice experiments (DCEs). We will use a
sample dataset and utility functions to generate simulated data and
analyze the results.
Inititalize Variables
sim_all is the highest level function in the package and will run simulations for all designs contained in the specified design folder. Accordingly, this is generally the function the user will want to call. To prepare for using this function, a hypothesized utility function with corresponding beta coefficients representing the weight of each term must be declared in R like so:
Other parameters
Besides these arguments the user must also specify the number of respondents in the simulated survey and the number of times to run the model. The number of respondents (resps) should be selected based on experimental design parameters, while the number of simulations (nosim) should be large enough to glean statistically significant data. It is best to use a small number for this while learning to use the package and a large number (at least 500) once the other parameters have been settled.
The simulation can be ran using spdesign or NGENE design files which will be contained in the design path. The design path and design type, must also be specified as strings:
designpath <- system.file("extdata", "CSA", "linear", package = "simulateDCE")
## can also be specified using relative path eg. designpath<- "Projects/CSA/Designs/"
# notes <- "This design consists of different heuristics. One group did not attend the methan attribute, another group only decided based on the payment"
notes <- "No Heuristics"
resps <- 240 # number of respondents
nosim <- 2 # number of simulations to run (about 500 is minimum)
## design type must be either 'spdesign' or 'ngene'
destype <- "spdesign"Randomness
As several part of the simulation rely on random values within experimentally defined bounds, the output of a given simulation call using sim_all will vary each time it is called. Unless the seed for R’s randome number generator is set like so:
Output
The sim_all function returns a multidimensional R list containing graphs, simulated observations and a dataframe containing sumaries of estimated b coefficients. In general these will be printed to the console, but the entire results can also be assigned to an r list object.
csa <- simulateDCE::sim_all(
nosim = nosim, resps = resps, designtype = destype,
designpath = designpath, u = ul, bcoeff = bcoeff
)
#> 'simple' is deprecated and will be removed in the future. Use 'exact' instead.
#> bcoeff_lookup already exists; skipping modification.
#>
#> Utility function used in simulation (true utility):
#> $u1
#> $u1$v1
#> V.1 ~ bx1 * alt1.x1 + bx2 * alt1.x2 + bx3 * alt1.x3 + bx4 * alt1.x4
#> <environment: 0x64535828f8f0>
#>
#> $u1$v2
#> V.2 ~ bx1 * alt2.x1 + bx2 * alt2.x2 + bx3 * alt2.x3 + bx4 * alt2.x4
#> <environment: 0x6453582d2470>
#>
#> $u1$v3
#> V.3 ~ -5
#> <environment: 0x645358301018>
#>
#> No preprocess function provided. Proceeding without additional preprocessing.
#>
#>
#> dataset preprossed_data exists: FALSE
#>
#> decisiongroups exists: FALSE
#>
#>
#> data has been created
#>
#> First few observations of the dataset
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt1_x4 alt2_x1 alt2_x2 alt2_x3
#> 1 1 2 0 0 0 100 0 1 1
#> 2 1 5 50 1 0 120 50 0 1
#> 3 1 9 0 0 1 90 0 1 0
#> 4 1 10 25 0 0 110 25 1 0
#> 5 1 12 25 0 1 120 25 0 0
#> 6 1 13 0 1 0 80 25 1 1
#> alt2_x4 alt3_sq Block group V_1 V_2 V_3 e_1 e_2
#> 1 120 1 1 1 -2.50 -3.15 -5 -1.3431262 0.4311932
#> 2 80 1 1 1 -8.10 -7.05 -5 1.5177036 -0.7730600
#> 3 130 1 1 1 -2.30 -3.35 -5 0.2870881 0.0786451
#> 4 80 1 1 1 -5.25 -4.60 -5 1.5647928 1.1257428
#> 5 150 1 1 1 -5.55 -6.25 -5 -1.2806816 -0.4102479
#> 6 130 1 1 1 -2.10 -5.90 -5 0.9839135 1.3273417
#> e_3 U_1 U_2 U_3 CHOICE
#> 1 0.618892998 -3.843126 -2.718807 -4.381107 2
#> 2 -0.003069536 -6.582296 -7.823060 -5.003070 3
#> 3 0.329465008 -2.012912 -3.271355 -4.670535 1
#> 4 -0.410028767 -3.685207 -3.474257 -5.410029 2
#> 5 -0.134615085 -6.830682 -6.660248 -5.134615 3
#> 6 1.590954748 -1.116087 -4.572658 -3.409045 1
#>
#> No preprocess function provided. Proceeding without additional preprocessing.
#>
#>
#> dataset preprossed_data exists: FALSE
#>
#> decisiongroups exists: FALSE
#>
#>
#> data has been created
#>
#> First few observations of the dataset
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt1_x4 alt2_x1 alt2_x2 alt2_x3
#> 1 1 2 0 0 0 100 0 1 1
#> 2 1 5 50 1 0 120 50 0 1
#> 3 1 9 0 0 1 90 0 1 0
#> 4 1 10 25 0 0 110 25 1 0
#> 5 1 12 25 0 1 120 25 0 0
#> 6 1 13 0 1 0 80 25 1 1
#> alt2_x4 alt3_sq Block group V_1 V_2 V_3 e_1 e_2
#> 1 120 1 1 1 -2.50 -3.15 -5 3.261831036 -0.2080592
#> 2 80 1 1 1 -8.10 -7.05 -5 0.002154937 -0.8044204
#> 3 130 1 1 1 -2.30 -3.35 -5 1.795082315 -0.1115009
#> 4 80 1 1 1 -5.25 -4.60 -5 -0.747121351 0.6680545
#> 5 150 1 1 1 -5.55 -6.25 -5 -0.265176431 -0.5003314
#> 6 130 1 1 1 -2.10 -5.90 -5 1.083209887 1.3210412
#> e_3 U_1 U_2 U_3 CHOICE
#> 1 0.3793593 0.7618310 -3.358059 -4.620641 1
#> 2 1.3282928 -8.0978451 -7.854420 -3.671707 3
#> 3 1.8405168 -0.5049177 -3.461501 -3.159483 1
#> 4 0.2740223 -5.9971214 -3.931946 -4.725978 2
#> 5 1.1296270 -5.8151764 -6.750331 -3.870373 3
#> 6 -0.7475822 -1.0167901 -4.578959 -5.747582 1
#>
#> Transformed utility function (type: simple):
#> [1] "U_1 = @bx1 * $alt1_x1 + @bx2 * $alt1_x2 + @bx3 * $alt1_x3 + @bx4 * $alt1_x4 ;U_2 = @bx1 * $alt2_x1 + @bx2 * $alt2_x2 + @bx3 * $alt2_x3 + @bx4 * $alt2_x4 ;U_3 = -5 ;"
#> Initial function value: -4011.625
#> Initial gradient value:
#> bx1 bx2 bx3 bx4
#> -24359.1607 -143.1723 -324.7693 -65577.4606
#> initial value 4011.625180
#> iter 2 value 2242.346879
#> iter 3 value 2124.609072
#> iter 4 value 2119.388696
#> iter 5 value 2117.047458
#> iter 6 value 1712.530505
#> iter 7 value 1699.349949
#> iter 8 value 1684.529726
#> iter 9 value 1684.372229
#> iter 10 value 1683.852662
#> iter 11 value 1683.852224
#> iter 12 value 1683.851937
#> iter 13 value 1683.851248
#> iter 14 value 1683.851227
#> iter 15 value 1683.849890
#> iter 16 value 1683.849443
#> iter 16 value 1683.849443
#> iter 16 value 1683.849443
#> final value 1683.849443
#> converged
#> Initial function value: -4231.625
#> Initial gradient value:
#> bx1 bx2 bx3 bx4
#> -25109.1607 -168.1723 -369.7693 -69127.4606
#> initial value 4231.625180
#> iter 2 value 2312.805941
#> iter 3 value 2158.658712
#> iter 4 value 2146.314073
#> iter 5 value 1914.129390
#> iter 6 value 1895.606947
#> iter 7 value 1744.554758
#> iter 8 value 1742.989739
#> iter 9 value 1736.926702
#> iter 10 value 1736.884285
#> iter 11 value 1736.883422
#> iter 12 value 1736.878133
#> iter 13 value 1736.876580
#> iter 14 value 1736.876542
#> iter 15 value 1736.875692
#> iter 16 value 1736.874250
#> iter 16 value 1736.874250
#> iter 16 value 1736.874250
#> final value 1736.874250
#> converged
#>
#>
#> Summary table:
#>
#>
#> ============= ==== === ===== ==== ====== ===== ===== ===== ==== ======== ====
#> \ vars n mean sd median min max range skew kurtosis se
#> ============= ==== === ===== ==== ====== ===== ===== ===== ==== ======== ====
#> est_bx1 1 2 -0.10 0.00 -0.10 -0.10 -0.10 0.00 0 -2.75 0.00
#> est_bx2 2 2 -0.04 0.02 -0.04 -0.05 -0.02 0.03 0 -2.75 0.01
#> est_bx3 3 2 -0.03 0.09 -0.03 -0.10 0.03 0.13 0 -2.75 0.06
#> est_bx4 4 2 -0.02 0.00 -0.02 -0.02 -0.02 0.00 0 -2.75 0.00
#> rob_pval0_bx1 5 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> rob_pval0_bx2 6 2 0.55 0.21 0.55 0.40 0.70 0.30 0 -2.75 0.15
#> rob_pval0_bx3 7 2 0.36 0.34 0.36 0.12 0.60 0.48 0 -2.75 0.24
#> rob_pval0_bx4 8 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> ============= ==== === ===== ==== ====== ===== ===== ===== ==== ======== ====
#>
#> Power results:
#>
#> FALSE
#> 100
#> 'simple' is deprecated and will be removed in the future. Use 'exact' instead.
#> bcoeff_lookup already exists; skipping modification.
#>
#> Utility function used in simulation (true utility):
#> $u1
#> $u1$v1
#> V.1 ~ bx1 * alt1.x1 + bx2 * alt1.x2 + bx3 * alt1.x3 + bx4 * alt1.x4
#> <environment: 0x64535bbc7b78>
#>
#> $u1$v2
#> V.2 ~ bx1 * alt2.x1 + bx2 * alt2.x2 + bx3 * alt2.x3 + bx4 * alt2.x4
#> <environment: 0x64535baa5320>
#>
#> $u1$v3
#> V.3 ~ -5
#> <environment: 0x64535ba80e78>
#>
#> No preprocess function provided. Proceeding without additional preprocessing.
#>
#>
#> dataset preprossed_data exists: FALSE
#>
#> decisiongroups exists: FALSE
#>
#>
#> data has been created
#>
#> First few observations of the dataset
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt1_x4 alt2_x1 alt2_x2 alt2_x3
#> 1 1 5 0 0 0 150 100 0 1
#> 2 1 7 25 0 0 80 0 1 0
#> 3 1 9 25 1 1 90 0 1 0
#> 4 1 10 100 0 1 120 25 1 0
#> 5 1 11 100 0 0 80 100 1 0
#> 6 1 12 50 1 1 150 25 0 0
#> alt2_x4 alt3_sq Block group V_1 V_2 V_3 e_1 e_2
#> 1 80 1 1 1 -3.75 -12.05 -5 0.2283479 0.2114376
#> 2 110 1 1 1 -4.50 -2.85 -5 -0.9572889 -0.3431828
#> 3 140 1 1 1 -4.90 -3.60 -5 0.1610206 -0.4258625
#> 4 80 1 1 1 -13.05 -4.60 -5 0.3103184 -1.4416677
#> 5 90 1 1 1 -12.00 -12.35 -5 1.3348132 -0.1615000
#> 6 150 1 1 1 -8.90 -6.25 -5 0.1725666 3.9096835
#> e_3 U_1 U_2 U_3 CHOICE
#> 1 0.4238020 -3.521652 -11.838562 -4.576198 1
#> 2 1.8055939 -5.457289 -3.193183 -3.194406 2
#> 3 -0.2960403 -4.738979 -4.025862 -5.296040 2
#> 4 0.3262861 -12.739682 -6.041668 -4.673714 3
#> 5 -0.2705518 -10.665187 -12.511500 -5.270552 3
#> 6 -1.5870600 -8.727433 -2.340316 -6.587060 2
#>
#> No preprocess function provided. Proceeding without additional preprocessing.
#>
#>
#> dataset preprossed_data exists: FALSE
#>
#> decisiongroups exists: FALSE
#>
#>
#> data has been created
#>
#> First few observations of the dataset
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt1_x4 alt2_x1 alt2_x2 alt2_x3
#> 1 1 5 0 0 0 150 100 0 1
#> 2 1 7 25 0 0 80 0 1 0
#> 3 1 9 25 1 1 90 0 1 0
#> 4 1 10 100 0 1 120 25 1 0
#> 5 1 11 100 0 0 80 100 1 0
#> 6 1 12 50 1 1 150 25 0 0
#> alt2_x4 alt3_sq Block group V_1 V_2 V_3 e_1 e_2
#> 1 80 1 1 1 -3.75 -12.05 -5 -0.3410475 1.0663781
#> 2 110 1 1 1 -4.50 -2.85 -5 1.6900014 1.8647184
#> 3 140 1 1 1 -4.90 -3.60 -5 1.0465233 0.3460339
#> 4 80 1 1 1 -13.05 -4.60 -5 -0.6799246 -1.7138636
#> 5 90 1 1 1 -12.00 -12.35 -5 -0.4481841 1.9378475
#> 6 150 1 1 1 -8.90 -6.25 -5 1.3733862 0.7375437
#> e_3 U_1 U_2 U_3 CHOICE
#> 1 0.2114102 -4.091047 -10.9836219 -4.788590 1
#> 2 -0.4196018 -2.809999 -0.9852816 -5.419602 2
#> 3 -0.3157360 -3.853477 -3.2539661 -5.315736 2
#> 4 1.0692299 -13.729925 -6.3138636 -3.930770 3
#> 5 1.3412840 -12.448184 -10.4121525 -3.658716 3
#> 6 0.3656113 -7.526614 -5.5124563 -4.634389 3
#>
#> Transformed utility function (type: simple):
#> [1] "U_1 = @bx1 * $alt1_x1 + @bx2 * $alt1_x2 + @bx3 * $alt1_x3 + @bx4 * $alt1_x4 ;U_2 = @bx1 * $alt2_x1 + @bx2 * $alt2_x2 + @bx3 * $alt2_x3 + @bx4 * $alt2_x4 ;U_3 = -5 ;"
#> Initial function value: -6756.625
#> Initial gradient value:
#> bx1 bx2 bx3 bx4
#> -92542.5915 -463.7693 -528.3737 -120657.6065
#> initial value 6756.625180
#> iter 2 value 2773.052615
#> iter 3 value 1042.472243
#> iter 4 value 1041.724405
#> iter 5 value 1022.750697
#> iter 6 value 1022.102480
#> iter 7 value 1011.269492
#> iter 8 value 996.476915
#> iter 9 value 994.589346
#> iter 10 value 994.197087
#> iter 11 value 994.196890
#> iter 12 value 994.196419
#> iter 13 value 994.196362
#> iter 14 value 994.195883
#> iter 15 value 994.195873
#> iter 16 value 994.195638
#> iter 17 value 994.195594
#> iter 17 value 994.195594
#> iter 17 value 994.195594
#> final value 994.195594
#> converged
#> Initial function value: -6661.625
#> Initial gradient value:
#> bx1 bx2 bx3 bx4
#> -91292.5915 -453.7693 -518.3737 -119117.6065
#> initial value 6661.625180
#> iter 2 value 2753.828662
#> iter 3 value 1144.340190
#> iter 4 value 1143.185319
#> iter 5 value 1121.618995
#> iter 6 value 1115.681307
#> iter 7 value 1072.725508
#> iter 8 value 1065.177544
#> iter 9 value 1063.828401
#> iter 10 value 1063.793997
#> iter 11 value 1063.793948
#> iter 11 value 1063.793945
#> iter 12 value 1063.793917
#> iter 12 value 1063.793917
#> iter 13 value 1063.793902
#> iter 13 value 1063.793899
#> iter 14 value 1063.793887
#> iter 14 value 1063.793886
#> iter 14 value 1063.793882
#> final value 1063.793882
#> converged
#>
#>
#> Summary table:
#>
#>
#> ============= ==== === ===== ==== ====== ===== ===== ===== ==== ======== ====
#> \ vars n mean sd median min max range skew kurtosis se
#> ============= ==== === ===== ==== ====== ===== ===== ===== ==== ======== ====
#> est_bx1 1 2 -0.10 0.01 -0.10 -0.10 -0.09 0.01 0 -2.75 0.00
#> est_bx2 2 2 0.04 0.02 0.04 0.02 0.05 0.03 0 -2.75 0.02
#> est_bx3 3 2 -0.17 0.00 -0.17 -0.17 -0.16 0.01 0 -2.75 0.00
#> est_bx4 4 2 -0.03 0.00 -0.03 -0.03 -0.03 0.00 0 -2.75 0.00
#> rob_pval0_bx1 5 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> rob_pval0_bx2 6 2 0.70 0.21 0.70 0.54 0.85 0.30 0 -2.75 0.15
#> rob_pval0_bx3 7 2 0.06 0.02 0.06 0.04 0.08 0.03 0 -2.75 0.02
#> rob_pval0_bx4 8 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> ============= ==== === ===== ==== ====== ===== ===== ===== ==== ======== ====
#>
#> Power results:
#>
#> FALSE
#> 100
#> 'simple' is deprecated and will be removed in the future. Use 'exact' instead.
#> bcoeff_lookup already exists; skipping modification.
#>
#> Utility function used in simulation (true utility):
#> $u1
#> $u1$v1
#> V.1 ~ bx1 * alt1.x1 + bx2 * alt1.x2 + bx3 * alt1.x3 + bx4 * alt1.x4
#> <environment: 0x64535a07c870>
#>
#> $u1$v2
#> V.2 ~ bx1 * alt2.x1 + bx2 * alt2.x2 + bx3 * alt2.x3 + bx4 * alt2.x4
#> <environment: 0x645359f40ac0>
#>
#> $u1$v3
#> V.3 ~ -5
#> <environment: 0x645359d7d098>
#>
#> No preprocess function provided. Proceeding without additional preprocessing.
#>
#>
#> dataset preprossed_data exists: FALSE
#>
#> decisiongroups exists: FALSE
#>
#>
#> data has been created
#>
#> First few observations of the dataset
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt1_x4 alt2_x1 alt2_x2 alt2_x3
#> 1 1 1 0 0 0 80 0 1 0
#> 2 1 2 25 1 0 90 0 0 1
#> 3 1 3 75 0 1 140 50 1 0
#> 4 1 5 0 0 0 140 0 1 1
#> 5 1 6 0 0 1 130 0 1 1
#> 6 1 8 0 1 0 80 0 0 0
#> alt2_x4 alt3_sq Block group V_1 V_2 V_3 e_1 e_2
#> 1 120 1 1 1 -2.00 -3.10 -5 1.099838554 0.15880011
#> 2 90 1 1 1 -4.85 -2.30 -5 2.532366583 -1.12394155
#> 3 80 1 1 1 -11.05 -7.10 -5 -0.865335081 -0.98131103
#> 4 90 1 1 1 -3.50 -2.40 -5 -0.458208905 -0.20352704
#> 5 80 1 1 1 -3.30 -2.15 -5 0.420976234 -1.18968314
#> 6 110 1 1 1 -2.10 -2.75 -5 0.007028381 -0.08608272
#> e_3 U_1 U_2 U_3 CHOICE
#> 1 0.09572715 -0.9001614 -2.941200 -4.904273 1
#> 2 1.68952223 -2.3176334 -3.423942 -3.310478 1
#> 3 1.62469324 -11.9153351 -8.081311 -3.375307 3
#> 4 -0.96400436 -3.9582089 -2.603527 -5.964004 2
#> 5 0.11649541 -2.8790238 -3.339683 -4.883505 1
#> 6 0.92257558 -2.0929716 -2.836083 -4.077424 1
#>
#> No preprocess function provided. Proceeding without additional preprocessing.
#>
#>
#> dataset preprossed_data exists: FALSE
#>
#> decisiongroups exists: FALSE
#>
#>
#> data has been created
#>
#> First few observations of the dataset
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt1_x4 alt2_x1 alt2_x2 alt2_x3
#> 1 1 1 0 0 0 80 0 1 0
#> 2 1 2 25 1 0 90 0 0 1
#> 3 1 3 75 0 1 140 50 1 0
#> 4 1 5 0 0 0 140 0 1 1
#> 5 1 6 0 0 1 130 0 1 1
#> 6 1 8 0 1 0 80 0 0 0
#> alt2_x4 alt3_sq Block group V_1 V_2 V_3 e_1 e_2
#> 1 120 1 1 1 -2.00 -3.10 -5 0.18768477 0.6182522
#> 2 90 1 1 1 -4.85 -2.30 -5 -0.33070917 0.9394820
#> 3 80 1 1 1 -11.05 -7.10 -5 3.09376692 -0.8518250
#> 4 90 1 1 1 -3.50 -2.40 -5 1.87119573 0.6142236
#> 5 80 1 1 1 -3.30 -2.15 -5 -0.98089710 0.5232656
#> 6 110 1 1 1 -2.10 -2.75 -5 0.07346766 1.0457122
#> e_3 U_1 U_2 U_3 CHOICE
#> 1 1.34067687 -1.812315 -2.481748 -3.659323 1
#> 2 -0.24989946 -5.180709 -1.360518 -5.249899 2
#> 3 2.09885304 -7.956233 -7.951825 -2.901147 3
#> 4 1.10797049 -1.628804 -1.785776 -3.892030 1
#> 5 -0.03074646 -4.280897 -1.626734 -5.030746 2
#> 6 1.14518851 -2.026532 -1.704288 -3.854811 2
#>
#> Transformed utility function (type: simple):
#> [1] "U_1 = @bx1 * $alt1_x1 + @bx2 * $alt1_x2 + @bx3 * $alt1_x3 + @bx4 * $alt1_x4 ;U_2 = @bx1 * $alt2_x1 + @bx2 * $alt2_x2 + @bx3 * $alt2_x3 + @bx4 * $alt2_x4 ;U_3 = -5 ;"
#> Initial function value: -3326.625
#> Initial gradient value:
#> bx1 bx2 bx3 bx4
#> -27929.12418 -68.97081 -63.17227 -48218.04443
#> initial value 3326.625180
#> iter 2 value 2148.319815
#> iter 3 value 1832.767533
#> iter 4 value 1785.141737
#> iter 5 value 1768.982824
#> iter 6 value 1601.160503
#> iter 7 value 1545.183222
#> iter 8 value 1536.542043
#> iter 9 value 1532.908834
#> iter 10 value 1532.572043
#> iter 11 value 1532.571589
#> iter 12 value 1532.571209
#> iter 13 value 1532.567781
#> iter 14 value 1532.567740
#> iter 15 value 1532.566894
#> iter 16 value 1532.566877
#> iter 16 value 1532.566877
#> iter 16 value 1532.566877
#> final value 1532.566877
#> converged
#> Initial function value: -3321.625
#> Initial gradient value:
#> bx1 bx2 bx3 bx4
#> -28129.12418 -68.97081 -56.17227 -47438.04443
#> initial value 3321.625180
#> iter 2 value 2164.015283
#> iter 3 value 1845.797908
#> iter 4 value 1797.993301
#> iter 5 value 1776.921708
#> iter 6 value 1568.761667
#> iter 7 value 1539.965148
#> iter 8 value 1529.815655
#> iter 9 value 1527.562483
#> iter 10 value 1527.353622
#> iter 11 value 1527.353152
#> iter 12 value 1527.353129
#> iter 13 value 1527.350204
#> iter 14 value 1527.349872
#> iter 15 value 1527.349362
#> iter 16 value 1527.349144
#> iter 16 value 1527.349144
#> iter 16 value 1527.349144
#> final value 1527.349144
#> converged
#>
#>
#> Summary table:
#>
#>
#> ============= ==== === ===== ==== ====== ===== ===== ===== ==== ======== ====
#> \ vars n mean sd median min max range skew kurtosis se
#> ============= ==== === ===== ==== ====== ===== ===== ===== ==== ======== ====
#> est_bx1 1 2 -0.10 0.00 -0.10 -0.10 -0.10 0.00 0 -2.75 0.00
#> est_bx2 2 2 -0.14 0.00 -0.14 -0.14 -0.13 0.00 0 -2.75 0.00
#> est_bx3 3 2 0.01 0.02 0.01 0.00 0.02 0.02 0 -2.75 0.01
#> est_bx4 4 2 -0.02 0.00 -0.02 -0.03 -0.02 0.00 0 -2.75 0.00
#> rob_pval0_bx1 5 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> rob_pval0_bx2 6 2 0.02 0.00 0.02 0.02 0.02 0.00 0 -2.75 0.00
#> rob_pval0_bx3 7 2 0.86 0.19 0.86 0.73 0.99 0.26 0 -2.75 0.13
#> rob_pval0_bx4 8 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> ============= ==== === ===== ==== ====== ===== ===== ===== ==== ======== ====
#>
#> Power results:
#>
#> FALSE
#> 100
#> total time for simulation and estimation: 6.896 sec elapsed
#> $tic
#> elapsed
#> 0.644
#>
#> $toc
#> elapsed
#> 7.54
#>
#> $msg
#> [1] "total time for simulation and estimation"
#>
#> $callback_msg
#> [1] "total time for simulation and estimation: 6.896 sec elapsed"
#> Trying tidyr::pivot_longer for reshaping...
Accessing the Output in R
The beta cofficients for each simulation are contained in a dataframe called coeffs within within a nested list structure output. A summary table showing the beta coefficient statistics is also made within each experimental design.
You can also save the results to disk using saveRDS(csa,file = “tests/manual-tests/csa.RDS”)
topLevelResults <- names(csa[sapply(csa, is.list)])
print(topLevelResults)
#> [1] "BLIbay" "BLIeff" "BLIIbay" "time" "arguements"
#> [6] "summaryall" "graphs" "powa"
## saves and prints the key results of the first expreimental design
simulationCoeff <- csa[[1]]$coefs
coeffSummary <- csa[[1]]$summary
print(simulationCoeff)
#> # A tibble: 2 × 9
#> run est_bx1 est_bx2 est_bx3 est_bx4 rob_pval0_bx1 rob_pval0_bx2
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 -0.100 -0.0241 0.0308 -0.0248 5.57e-193 0.698
#> 2 2 -0.104 -0.0524 -0.0973 -0.0247 8.67e-218 0.396
#> # ℹ 2 more variables: rob_pval0_bx3 <dbl>, rob_pval0_bx4 <dbl>
print(coeffSummary)
#> vars n mean sd median min max range skew kurtosis se
#> est_bx1 1 2 -0.10 0.00 -0.10 -0.10 -0.10 0.00 0 -2.75 0.00
#> est_bx2 2 2 -0.04 0.02 -0.04 -0.05 -0.02 0.03 0 -2.75 0.01
#> est_bx3 3 2 -0.03 0.09 -0.03 -0.10 0.03 0.13 0 -2.75 0.06
#> est_bx4 4 2 -0.02 0.00 -0.02 -0.02 -0.02 0.00 0 -2.75 0.00
#> rob_pval0_bx1 5 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> rob_pval0_bx2 6 2 0.55 0.21 0.55 0.40 0.70 0.30 0 -2.75 0.15
#> rob_pval0_bx3 7 2 0.36 0.34 0.36 0.12 0.60 0.48 0 -2.75 0.24
#> rob_pval0_bx4 8 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
## saveRDS(csa,file = "tests/manual-tests/csa.RDS")