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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.
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:
# pass beta coefficients as a list
bcoeff <- list(
bpreis = -0.01,
blade = -0.07,
bwarte = 0.02
)
manipulations <- list(
alt1.x2 = expr(alt1.x2 / 10),
alt1.x3 = expr(alt1.x3 / 10),
alt2.x2 = expr(alt2.x2 / 10),
alt2.x3 = expr(alt2.x3 / 10)
)
# place your utility functions here
ul <- list(
u1 =
list(
v1 = V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3,
v2 = V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
),
u2 = list(
v1 = V.1 ~ bpreis * alt1.x1,
v2 = V.2 ~ bpreis * alt2.x1
)
)
If the utility function set passed to sim all has more than 1 utility function group as is the case above (u1, u2 etc.) an additional variable, ‘decision groups’ must be given to tell the function what portion of respondents should be given each utility function.
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:
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.
sedrive <- sim_all(
nosim = nosim, resps = resps, designtype = destype,
designpath = designpath, u = ul, bcoeff = bcoeff, decisiongroups = decisiongroups
)
#> '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 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
#> <environment: 0x5f74e3f43ca8>
#>
#> $u1$v2
#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
#> <environment: 0x5f74e3f5cc40>
#>
#>
#> $u2
#> $u2$v1
#> V.1 ~ bpreis * alt1.x1
#> <environment: 0x5f74e3f8d0b0>
#>
#> $u2$v2
#> V.2 ~ bpreis * alt2.x1
#> <environment: 0x5f74e3fb7478>
#> New names:
#> • `Choice situation` -> `Choice.situation`
#> • `` -> `...10`
#> assign keys for bcoeff): 0 sec elapsed
#> user entered manipulations: 0.012 sec elapsed
#> split dataframe into groups: 0.006 sec elapsed
#> for each group calculate utility: 0.782 sec elapsed
#> add random component: 0.042 sec elapsed
#> whole simulate choices: 0.853 sec elapsed
#>
#> No preprocess function provided. Proceeding without additional preprocessing.
#>
#>
#> dataset preprossed_data exists: FALSE
#>
#>
#> decisiongroups exists: TRUE
#>
#> Group counts:
#>
#> 1 2
#> 1007 433
#>
#> data has been created
#>
#>
#> First few observations of the dataset
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 7 80 25 100 60 200 100 1
#> 2 1 19 20 25 50 60 25 0 1
#> 3 1 30 20 100 50 80 50 100 1
#> 4 1 32 40 200 25 80 25 0 1
#> 5 1 39 40 200 0 80 100 100 1
#> 6 1 48 60 50 25 20 50 100 1
#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
#> 1 1 -0.55 -12.60 1.4192138 0.2215459 0.8692138 -12.378454 1
#> 2 1 -0.95 -2.35 2.4848661 0.6918230 1.5348661 -1.658177 1
#> 3 1 -6.20 -2.30 1.6265497 0.2191308 -4.5734503 -2.080869 2
#> 4 1 -13.90 -2.55 0.2251272 -0.5299483 -13.6748728 -3.079948 2
#> 5 1 -14.40 -5.80 0.3548944 -0.8771261 -14.0451056 -6.677126 2
#> 6 1 -3.60 -1.70 -1.2371637 -0.1345258 -4.8371637 -1.834526 2
#> assign keys for bcoeff): 0 sec elapsed
#> user entered manipulations: 0.021 sec elapsed
#> split dataframe into groups: 0.003 sec elapsed
#> for each group calculate utility: 0.744 sec elapsed
#> add random component: 0.041 sec elapsed
#> whole simulate choices: 0.819 sec elapsed
#>
#> No preprocess function provided. Proceeding without additional preprocessing.
#>
#>
#> dataset preprossed_data exists: FALSE
#>
#>
#> decisiongroups exists: TRUE
#>
#> Group counts:
#>
#> 1 2
#> 1007 433
#>
#> data has been created
#>
#>
#> First few observations of the dataset
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 7 80 25 100 60 200 100 1
#> 2 1 19 20 25 50 60 25 0 1
#> 3 1 30 20 100 50 80 50 100 1
#> 4 1 32 40 200 25 80 25 0 1
#> 5 1 39 40 200 0 80 100 100 1
#> 6 1 48 60 50 25 20 50 100 1
#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
#> 1 1 -0.55 -12.60 0.4151715 -1.2972373 -0.1348285 -13.8972373 1
#> 2 1 -0.95 -2.35 -0.9774608 2.8345422 -1.9274608 0.4845422 2
#> 3 1 -6.20 -2.30 -0.4108713 -0.6769322 -6.6108713 -2.9769322 2
#> 4 1 -13.90 -2.55 0.7479234 -0.2349984 -13.1520766 -2.7849984 2
#> 5 1 -14.40 -5.80 0.1328288 1.0853506 -14.2671712 -4.7146494 2
#> 6 1 -3.60 -1.70 -1.2191798 0.7015295 -4.8191798 -0.9984705 2
#>
#>
#>
#> Transformed utility function (type: simple):
#> [1] "U_1 = @bpreis * $alt1_x1 + @blade * $alt1_x2 + @bwarte * $alt1_x3 ;U_2 = @bpreis * $alt2_x1 + @blade * $alt2_x2 + @bwarte * $alt2_x3 ;"
#> Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
#> 6820 -40550 6225
#> initial value 998.131940
#> iter 2 value 789.382374
#> iter 3 value 777.131356
#> iter 4 value 774.698957
#> iter 5 value 743.983087
#> iter 6 value 735.184026
#> iter 7 value 733.822906
#> iter 8 value 733.788021
#> iter 9 value 733.787950
#> iter 9 value 733.787941
#> iter 9 value 733.787934
#> final value 733.787934
#> converged
#> Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
#> 6440 -39175 6600
#> initial value 998.131940
#> iter 2 value 809.190369
#> iter 3 value 797.131190
#> iter 4 value 795.175818
#> iter 5 value 760.235522
#> iter 6 value 751.601571
#> iter 7 value 750.199349
#> iter 8 value 750.167039
#> iter 9 value 750.166980
#> iter 9 value 750.166970
#> iter 9 value 750.166963
#> final value 750.166963
#> converged
#> start_estimation: 0.056 sec elapsed
#>
#> Summary table:
#>
#>
#> ================ ==== === ===== === ====== ===== ===== ===== ==== ======== ===
#> \ vars n mean sd median min max range skew kurtosis se
#> ================ ==== === ===== === ====== ===== ===== ===== ==== ======== ===
#> est_bpreis 1 2 -0.01 0 -0.01 -0.01 -0.01 0 0 -2.75 0
#> est_blade 2 2 -0.02 0 -0.02 -0.02 -0.01 0 0 -2.75 0
#> est_bwarte 3 2 0.01 0 0.01 0.01 0.01 0 0 -2.75 0
#> rob_pval0_bpreis 4 2 0.01 0 0.01 0.01 0.02 0 0 -2.75 0
#> rob_pval0_blade 5 2 0.00 0 0.00 0.00 0.00 0 0 -2.75 0
#> rob_pval0_bwarte 6 2 0.00 0 0.00 0.00 0.00 0 0 -2.75 0
#> ================ ==== === ===== === ====== ===== ===== ===== ==== ======== ===
#>
#> Power results:
#>
#> TRUE
#> 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 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
#> <environment: 0x5f74df01f7b8>
#>
#> $u1$v2
#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
#> <environment: 0x5f74deff9460>
#>
#>
#> $u2
#> $u2$v1
#> V.1 ~ bpreis * alt1.x1
#> <environment: 0x5f74defcab90>
#>
#> $u2$v2
#> V.2 ~ bpreis * alt2.x1
#> <environment: 0x5f74def9c260>
#>
#>
#> New names:
#> assign keys for bcoeff): 0 sec elapsed
#> user entered manipulations: 0.01 sec elapsed
#> split dataframe into groups: 0.004 sec elapsed
#> for each group calculate utility: 0.804 sec elapsed
#> add random component: 0.044 sec elapsed
#> whole simulate choices: 0.871 sec elapsed
#>
#> No preprocess function provided. Proceeding without additional preprocessing.
#>
#>
#> dataset preprossed_data exists: FALSE
#>
#>
#> decisiongroups exists: TRUE
#>
#> Group counts:
#>
#> 1 2
#> 1007 433
#>
#> data has been created
#>
#>
#> First few observations of the dataset
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 12 60 25 0 20 200 100 1
#> 2 1 16 20 100 50 40 50 0 1
#> 3 1 17 20 200 0 80 100 100 1
#> 4 1 25 60 50 100 20 200 50 1
#> 5 1 29 20 50 100 80 50 0 1
#> 6 1 32 40 100 25 80 25 50 1
#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
#> 1 1 -2.35 -12.20 -0.5779528 -0.8428726 -2.9279528 -13.042873 1
#> 2 1 -6.20 -3.90 0.1718701 2.4305443 -6.0281299 -1.469456 2
#> 3 1 -14.20 -5.80 3.6657341 -0.3602399 -10.5342659 -6.160240 2
#> 4 1 -2.10 -13.20 1.0176273 -0.8641231 -1.0823727 -14.064123 1
#> 5 1 -1.70 -4.30 2.0034992 -1.2099166 0.3034992 -5.509917 1
#> 6 1 -6.90 -1.55 -0.8402340 0.2320075 -7.7402340 -1.317993 2
#> assign keys for bcoeff): 0 sec elapsed
#> user entered manipulations: 0.02 sec elapsed
#> split dataframe into groups: 0.003 sec elapsed
#> for each group calculate utility: 0.776 sec elapsed
#> add random component: 0.044 sec elapsed
#> whole simulate choices: 0.853 sec elapsed
#>
#> No preprocess function provided. Proceeding without additional preprocessing.
#>
#>
#> dataset preprossed_data exists: FALSE
#>
#>
#> decisiongroups exists: TRUE
#>
#> Group counts:
#>
#> 1 2
#> 1007 433
#>
#> data has been created
#>
#>
#> First few observations of the dataset
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 12 60 25 0 20 200 100 1
#> 2 1 16 20 100 50 40 50 0 1
#> 3 1 17 20 200 0 80 100 100 1
#> 4 1 25 60 50 100 20 200 50 1
#> 5 1 29 20 50 100 80 50 0 1
#> 6 1 32 40 100 25 80 25 50 1
#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
#> 1 1 -2.35 -12.20 2.72395272 -0.46401377 0.3739527 -12.664014 1
#> 2 1 -6.20 -3.90 0.07465059 0.30152772 -6.1253494 -3.598472 2
#> 3 1 -14.20 -5.80 -0.26590307 -1.03298376 -14.4659031 -6.832984 2
#> 4 1 -2.10 -13.20 -0.38975123 0.49288244 -2.4897512 -12.707118 1
#> 5 1 -1.70 -4.30 -0.54471014 0.08824802 -2.2447101 -4.211752 1
#> 6 1 -6.90 -1.55 0.81615270 -0.59339607 -6.0838473 -2.143396 2
#>
#>
#>
#> Transformed utility function (type: simple):
#> [1] "U_1 = @bpreis * $alt1_x1 + @blade * $alt1_x2 + @bwarte * $alt1_x3 ;U_2 = @bpreis * $alt2_x1 + @blade * $alt2_x2 + @bwarte * $alt2_x3 ;"
#> Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
#> 4840 -38625 8450
#> initial value 998.131940
#> iter 2 value 817.475918
#> iter 3 value 815.431642
#> iter 4 value 813.052538
#> iter 5 value 764.476070
#> iter 6 value 754.717940
#> iter 7 value 753.223793
#> iter 8 value 753.200553
#> iter 9 value 753.200421
#> iter 10 value 753.200013
#> iter 10 value 753.200008
#> iter 11 value 753.199983
#> iter 12 value 753.199955
#> iter 12 value 753.199955
#> iter 12 value 753.199949
#> final value 753.199949
#> converged
#> Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
#> 4800 -37925 8400
#> initial value 998.131940
#> iter 2 value 827.821924
#> iter 3 value 827.231563
#> iter 4 value 825.298772
#> iter 5 value 774.246520
#> iter 6 value 764.599998
#> iter 7 value 763.084991
#> iter 8 value 763.061068
#> iter 9 value 763.060955
#> iter 10 value 763.060676
#> iter 10 value 763.060671
#> iter 10 value 763.060665
#> final value 763.060665
#> converged
#> start_estimation: 0.06 sec elapsed
#>
#> Summary table:
#>
#>
#> ================ ==== === ===== === ====== ===== ===== ===== ==== ======== ===
#> \ vars n mean sd median min max range skew kurtosis se
#> ================ ==== === ===== === ====== ===== ===== ===== ==== ======== ===
#> est_bpreis 1 2 -0.01 0 -0.01 -0.01 -0.01 0 0 -2.75 0
#> est_blade 2 2 -0.01 0 -0.01 -0.01 -0.01 0 0 -2.75 0
#> est_bwarte 3 2 0.01 0 0.01 0.01 0.01 0 0 -2.75 0
#> rob_pval0_bpreis 4 2 0.00 0 0.00 0.00 0.00 0 0 -2.75 0
#> rob_pval0_blade 5 2 0.00 0 0.00 0.00 0.00 0 0 -2.75 0
#> rob_pval0_bwarte 6 2 0.00 0 0.00 0.00 0.00 0 0 -2.75 0
#> ================ ==== === ===== === ====== ===== ===== ===== ==== ======== ===
#>
#> Power results:
#>
#> TRUE
#> 100
#> total time for simulation and estimation: 10.45 sec elapsed
#> $tic
#> elapsed
#> 24.077
#>
#> $toc
#> elapsed
#> 34.527
#>
#> $msg
#> [1] "total time for simulation and estimation"
#>
#> $callback_msg
#> [1] "total time for simulation and estimation: 10.45 sec elapsed"
#>
#> Trying tidyr::pivot_longer for reshaping...
The beta cofficients for each simulation are contained in a dataframe called coeffs located within {result}->olddesign->coefs. and a summary table is contained in ->olddesign->summary
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.