SE_Agri-vignette
Introduction
This vignette demonstrates how to use simulate situations in which different utility functions apply to diffferent subsets of respondents. We will use a sample dataset and utility functions to generate simulated data and analyze the results. First off it is good practice remove all objects (variables, functions, etc.) from the current workspace and load all R files in the package directory into the current R session.
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.
The manipulation variable allows the user to assign different terms based on the values of column values in the experimental design file
bcoeff <- list(
basc = 4.2, ## very high asc
bprof = 0.3,
bexp = 0.3,
bdomestic = 0.3,
bforeign = 0.3,
bdamage = 0.6,
bprice = 0.2
)
manipulations <- list(
alt1.professional = expr(alt1.initiator == 1),
alt2.professional = expr(alt2.initiator == 1),
alt1.expert = expr(alt1.initiator == 2),
alt2.expert = expr(alt2.initiator == 2),
alt1.domestic = expr(alt1.funding == 1),
alt2.domestic = expr(alt2.funding == 1),
alt1.foreign = expr(alt1.funding == 2),
alt2.foreign = expr(alt2.funding == 2)
)
# place your utility functions here
ul <- list(
u1 =
list(
v1 = V.1 ~ bprof * alt1.professional + bexp * alt1.expert + bdomestic * alt1.domestic + bforeign * alt1.foreign + bdamage * alt1.damage + bprice * alt1.compensation,
v2 = V.2 ~ bprof * alt2.professional + bexp * alt2.expert + bdomestic * alt2.domestic + bforeign * alt2.foreign + bdamage * alt2.damage + bprice * alt2.compensation,
v3 = V.3 ~ basc
)
)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", "SE_AGRI", 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 <- "ngene"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 random 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 beta coefficients. In general these will be printed to the console, but the entire results can also be assigned to an r list object.
seagri <- simulateDCE::sim_all(
nosim = nosim, resps = resps, designtype = destype,
designpath = designpath, u = ul, bcoeff = bcoeff, manipulations = manipulations
)
#> '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 ~ bprof * alt1.professional + bexp * alt1.expert + bdomestic *
#> alt1.domestic + bforeign * alt1.foreign + bdamage * alt1.damage +
#> bprice * alt1.compensation
#> <environment: 0x5588aea7c310>
#>
#> $u1$v2
#> V.2 ~ bprof * alt2.professional + bexp * alt2.expert + bdomestic *
#> alt2.domestic + bforeign * alt2.foreign + bdamage * alt2.damage +
#> bprice * alt2.compensation
#> <environment: 0x5588aeab9e10>
#>
#> $u1$v3
#> V.3 ~ basc
#> <environment: 0x5588aeaf0bd0>
#> New names:
#> 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_initiator
#> alt1_funding alt1_damage alt1_compensation 1 1 6 1 0 1 2 2 1 8 0 1 1 5 3 1 9 2
#> 1 0 15 4 1 13 2 2 1 2 5 1 25 1 2 0 15 6 1 28 0 1 0 15 alt2_initiator
#> alt2_funding alt2_damage alt2_compensation Block group 1 2 2 0 2 1 1 2 1 2 0 5
#> 1 1 3 1 2 1 15 1 1 4 1 0 0 5 1 1 5 0 0 1 15 1 1 6 2 0 1 15 1 1
#> alt1_professional alt2_professional alt1_expert alt2_expert alt1_domestic 1
#> TRUE FALSE FALSE TRUE FALSE 2 FALSE TRUE FALSE FALSE TRUE 3 FALSE TRUE TRUE
#> FALSE TRUE 4 FALSE TRUE TRUE FALSE FALSE 5 TRUE FALSE FALSE FALSE FALSE 6 FALSE
#> FALSE FALSE TRUE TRUE alt2_domestic alt1_foreign alt2_foreign V_1 V_2 V_3 e_1
#> e_2 1 FALSE FALSE TRUE 1.3 1.0 4.2 -1.3431262 2.63622565 2 FALSE FALSE TRUE 1.9
#> 1.6 4.2 1.5177036 0.43091129 3 FALSE FALSE TRUE 3.6 4.2 4.2 0.2870881
#> -0.06943918 4 FALSE TRUE FALSE 1.6 1.3 4.2 1.5647928 0.58230520 5 FALSE TRUE
#> FALSE 3.6 3.6 4.2 -1.2806816 0.54726968 6 FALSE FALSE FALSE 3.3 3.9 4.2
#> 0.9839135 3.08688131 e_3 U_1 U_2 U_3 CHOICE 1 0.01658856 -0.04312623 3.636226
#> 4.216589 3 2 -0.98983394 3.41770356 2.030911 3.210166 1 3 1.67247387 3.88708806
#> 4.130561 5.872474 3 4 0.09601975 3.16479283 1.882305 4.296020 3 5 -2.08711740
#> 2.31931835 4.147270 2.112883 2 6 -0.49399837 4.28391348 6.986881 3.706002 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_initiator
#> alt1_funding alt1_damage alt1_compensation 1 1 6 1 0 1 2 2 1 8 0 1 1 5 3 1 9 2
#> 1 0 15 4 1 13 2 2 1 2 5 1 25 1 2 0 15 6 1 28 0 1 0 15 alt2_initiator
#> alt2_funding alt2_damage alt2_compensation Block group 1 2 2 0 2 1 1 2 1 2 0 5
#> 1 1 3 1 2 1 15 1 1 4 1 0 0 5 1 1 5 0 0 1 15 1 1 6 2 0 1 15 1 1
#> alt1_professional alt2_professional alt1_expert alt2_expert alt1_domestic 1
#> TRUE FALSE FALSE TRUE FALSE 2 FALSE TRUE FALSE FALSE TRUE 3 FALSE TRUE TRUE
#> FALSE TRUE 4 FALSE TRUE TRUE FALSE FALSE 5 TRUE FALSE FALSE FALSE FALSE 6 FALSE
#> FALSE FALSE TRUE TRUE alt2_domestic alt1_foreign alt2_foreign V_1 V_2 V_3 e_1
#> e_2 1 FALSE FALSE TRUE 1.3 1.0 4.2 3.261831036 -0.5990366 2 FALSE FALSE TRUE
#> 1.9 1.6 4.2 0.002154937 0.5962256 3 FALSE FALSE TRUE 3.6 4.2 4.2 1.795082315
#> 0.4310999 4 FALSE TRUE FALSE 1.6 1.3 4.2 -0.747121351 -1.0736813 5 FALSE TRUE
#> FALSE 3.6 3.6 4.2 -0.265176431 0.3375761 6 FALSE FALSE FALSE 3.3 3.9 4.2
#> 1.083209887 0.2774360 e_3 U_1 U_2 U_3 CHOICE 1 1.9167622 4.5618310 0.4009634
#> 6.116762 3 2 1.2415698 1.9021549 2.1962256 5.441570 3 3 0.8683595 5.3950823
#> 4.6310999 5.068359 1 4 -1.1185588 0.8528786 0.2263187 3.081441 3 5 2.1003382
#> 3.3348236 3.9375761 6.300338 3 6 1.4533109 4.3832099 4.1774360 5.653311 3
#> Transformed utility function (type: simple): [1] "U_1 = @bprof *
#> $alt1_professional + @bexp * $alt1_expert + @bdomestic * $alt1_domestic +
#> @bforeign * $alt1_foreign + @bdamage * $alt1_damage + @bprice *
#> $alt1_compensation ;U_2 = @bprof * $alt2_professional + @bexp * $alt2_expert +
#> @bdomestic * $alt2_domestic + @bforeign * $alt2_foreign + @bdamage *
#> $alt2_damage + @bprice * $alt2_compensation ;U_3 = @basc ;"
#> • `Choice situation` -> `Choice.situation`
#> • `` -> `...12`
#> Initial function value: -2373.003
#> Initial gradient value:
#> bprof bexp bdomestic bforeign bdamage bprice basc
#> -71 -136 -117 -129 -88 976 405
#> initial value 2373.002544
#> iter 2 value 2367.379602
#> iter 3 value 1964.645050
#> iter 4 value 1835.404658
#> iter 5 value 1742.192109
#> iter 6 value 1741.734913
#> iter 7 value 1711.964813
#> iter 8 value 1680.199286
#> iter 9 value 1650.610306
#> iter 10 value 1627.971338
#> iter 11 value 1625.622402
#> iter 12 value 1625.224770
#> iter 13 value 1625.204590
#> iter 14 value 1625.203035
#> iter 15 value 1625.202940
#> iter 15 value 1625.202937
#> iter 15 value 1625.202937
#> final value 1625.202937
#> converged
#> Initial function value: -2373.003
#> Initial gradient value:
#> bprof bexp bdomestic bforeign bdamage bprice basc
#> -99 -140 -121 -153 -73 855 401
#> initial value 2373.002544
#> iter 2 value 2367.878241
#> iter 3 value 1948.123268
#> iter 4 value 1856.230890
#> iter 5 value 1784.911121
#> iter 6 value 1783.162573
#> iter 7 value 1748.742585
#> iter 8 value 1716.488849
#> iter 9 value 1714.450080
#> iter 10 value 1674.991878
#> iter 11 value 1674.216624
#> iter 12 value 1673.949505
#> iter 13 value 1673.947172
#> iter 14 value 1673.947128
#> iter 14 value 1673.947127
#> iter 14 value 1673.947127
#> final value 1673.947127
#> converged
#>
#>
#> Summary table:
#>
#>
#> =================== ==== === ==== ==== ====== ==== ==== ===== ==== ======== ====
#> \ vars n mean sd median min max range skew kurtosis se
#> =================== ==== === ==== ==== ====== ==== ==== ===== ==== ======== ====
#> est_bprof 1 2 0.30 0.18 0.30 0.17 0.42 0.25 0 -2.75 0.12
#> est_bexp 2 2 0.21 0.12 0.21 0.12 0.29 0.17 0 -2.75 0.09
#> est_bdomestic 3 2 0.35 0.13 0.35 0.25 0.44 0.18 0 -2.75 0.09
#> est_bforeign 4 2 0.28 0.17 0.28 0.16 0.40 0.25 0 -2.75 0.12
#> est_bdamage 5 2 0.62 0.01 0.62 0.62 0.63 0.02 0 -2.75 0.01
#> est_bprice 6 2 0.20 0.01 0.20 0.19 0.21 0.01 0 -2.75 0.01
#> est_basc 7 2 4.16 0.34 4.16 3.92 4.40 0.47 0 -2.75 0.24
#> rob_pval0_bprof 8 2 0.02 0.03 0.02 0.00 0.04 0.04 0 -2.75 0.02
#> rob_pval0_bexp 9 2 0.08 0.12 0.08 0.00 0.17 0.16 0 -2.75 0.08
#> rob_pval0_bdomestic 10 2 0.00 0.00 0.00 0.00 0.00 0.00 0 -2.75 0.00
#> rob_pval0_bforeign 11 2 0.04 0.06 0.04 0.00 0.09 0.09 0 -2.75 0.04
#> rob_pval0_bdamage 12 2 0.00 0.00 0.00 0.00 0.00 0.00 0 -2.75 0.00
#> rob_pval0_bprice 13 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> rob_pval0_basc 14 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> =================== ==== === ==== ==== ====== ==== ==== ===== ==== ======== ====
#>
#> Power results:
#>
#> FALSE TRUE
#> 50 50
#> '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 ~ bprof * alt1.professional + bexp * alt1.expert + bdomestic *
#> alt1.domestic + bforeign * alt1.foreign + bdamage * alt1.damage +
#> bprice * alt1.compensation
#> <environment: 0x5588b8b7ad50>
#>
#> $u1$v2
#> V.2 ~ bprof * alt2.professional + bexp * alt2.expert + bdomestic *
#> alt2.domestic + bforeign * alt2.foreign + bdamage * alt2.damage +
#> bprice * alt2.compensation
#> <environment: 0x5588b8b41d80>
#>
#> $u1$v3
#> V.3 ~ basc
#> <environment: 0x5588b8b09028>
#>
#>
#> New names:
#> 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_initiator alt1_funding alt1_damage alt1_compensation
#> 1 1 1 2 1 0 2
#> 2 1 12 2 2 0 30
#> 3 1 20 0 1 1 20
#> 4 1 23 2 2 1 2
#> 5 1 25 2 0 0 30
#> 6 1 26 1 2 1 20
#> alt2_initiator alt2_funding alt2_damage alt2_compensation Block group
#> 1 1 0 1 30 1 1
#> 2 0 1 1 10 1 1
#> 3 2 2 0 10 1 1
#> 4 0 0 1 10 1 1
#> 5 0 2 0 20 1 1
#> 6 2 1 0 10 1 1
#> alt1_professional alt2_professional alt1_expert alt2_expert alt1_domestic
#> 1 FALSE TRUE TRUE FALSE TRUE
#> 2 FALSE FALSE TRUE FALSE FALSE
#> 3 FALSE FALSE FALSE TRUE TRUE
#> 4 FALSE FALSE TRUE FALSE FALSE
#> 5 FALSE FALSE TRUE FALSE FALSE
#> 6 TRUE FALSE FALSE TRUE FALSE
#> alt2_domestic alt1_foreign alt2_foreign V_1 V_2 V_3 e_1 e_2
#> 1 FALSE FALSE FALSE 1.0 6.9 4.2 0.2283479 2.28650158
#> 2 TRUE TRUE FALSE 6.6 2.9 4.2 -0.9572889 0.68756630
#> 3 FALSE FALSE TRUE 4.9 2.6 4.2 0.1610206 1.26127583
#> 4 FALSE TRUE FALSE 1.6 2.6 4.2 0.3103184 1.65458741
#> 5 FALSE FALSE TRUE 6.3 4.3 4.2 1.3348132 -0.02035208
#> 6 TRUE TRUE FALSE 5.2 2.6 4.2 0.1725666 3.00758115
#> e_3 U_1 U_2 U_3 CHOICE
#> 1 0.01814608 1.228348 9.186502 4.218146 2
#> 2 1.71728688 5.642711 3.587566 5.917287 3
#> 3 2.93101929 5.061021 3.861276 7.131019 3
#> 4 -0.09398200 1.910318 4.254587 4.106018 2
#> 5 0.84045996 7.634813 4.279648 5.040460 1
#> 6 0.95409867 5.372567 5.607581 5.154099 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_initiator alt1_funding alt1_damage alt1_compensation
#> 1 1 1 2 1 0 2
#> 2 1 12 2 2 0 30
#> 3 1 20 0 1 1 20
#> 4 1 23 2 2 1 2
#> 5 1 25 2 0 0 30
#> 6 1 26 1 2 1 20
#> alt2_initiator alt2_funding alt2_damage alt2_compensation Block group
#> 1 1 0 1 30 1 1
#> 2 0 1 1 10 1 1
#> 3 2 2 0 10 1 1
#> 4 0 0 1 10 1 1
#> 5 0 2 0 20 1 1
#> 6 2 1 0 10 1 1
#> alt1_professional alt2_professional alt1_expert alt2_expert alt1_domestic
#> 1 FALSE TRUE TRUE FALSE TRUE
#> 2 FALSE FALSE TRUE FALSE FALSE
#> 3 FALSE FALSE FALSE TRUE TRUE
#> 4 FALSE FALSE TRUE FALSE FALSE
#> 5 FALSE FALSE TRUE FALSE FALSE
#> 6 TRUE FALSE FALSE TRUE FALSE
#> alt2_domestic alt1_foreign alt2_foreign V_1 V_2 V_3 e_1 e_2
#> 1 FALSE FALSE FALSE 1.0 6.9 4.2 -0.3410475 -0.23105142
#> 2 TRUE TRUE FALSE 6.6 2.9 4.2 1.6900014 -0.29490327
#> 3 FALSE FALSE TRUE 4.9 2.6 4.2 1.0465233 1.04675908
#> 4 FALSE TRUE FALSE 1.6 2.6 4.2 -0.6799246 0.35365800
#> 5 FALSE FALSE TRUE 6.3 4.3 4.2 -0.4481841 -0.51702784
#> 6 TRUE TRUE FALSE 5.2 2.6 4.2 1.3733862 -0.04140872
#> e_3 U_1 U_2 U_3 CHOICE
#> 1 0.8742507 0.6589525 6.668949 5.074251 2
#> 2 0.3086977 8.2900014 2.605097 4.508698 1
#> 3 0.3649179 5.9465233 3.646759 4.564918 1
#> 4 1.3958206 0.9200754 2.953658 5.595821 3
#> 5 -0.5476639 5.8518159 3.782972 3.652336 1
#> 6 0.1657436 6.5733862 2.558591 4.365744 1
#>
#>
#>
#> Transformed utility function (type: simple):
#> [1] "U_1 = @bprof * $alt1_professional + @bexp * $alt1_expert + @bdomestic * $alt1_domestic + @bforeign * $alt1_foreign + @bdamage * $alt1_damage + @bprice * $alt1_compensation ;U_2 = @bprof * $alt2_professional + @bexp * $alt2_expert + @bdomestic * $alt2_domestic + @bforeign * $alt2_foreign + @bdamage * $alt2_damage + @bprice * $alt2_compensation ;U_3 = @basc ;"
#> Initial function value: -2373.003
#> Initial gradient value:
#> bprof bexp bdomestic bforeign bdamage bprice
#> -119.000000 -5.999999 -32.999999 57.000000 183.000001 9763.000000
#> basc
#> 155.000001
#> initial value 2373.002544
#> iter 2 value 2319.313011
#> iter 3 value 1692.211150
#> iter 4 value 1588.982590
#> iter 5 value 1562.931840
#> iter 6 value 1538.682344
#> iter 7 value 1523.349994
#> iter 8 value 1434.571809
#> iter 9 value 1342.247691
#> iter 10 value 1316.292968
#> iter 11 value 1308.797776
#> iter 12 value 1307.131698
#> iter 13 value 1307.030565
#> iter 14 value 1307.009247
#> iter 15 value 1307.007733
#> iter 16 value 1307.007675
#> iter 16 value 1307.007675
#> final value 1307.007675
#> converged
#> Initial function value: -2373.003
#> Initial gradient value:
#> bprof bexp bdomestic bforeign bdamage bprice basc
#> -111 -21 -12 32 163 9254 169
#> initial value 2373.002544
#> iter 2 value 2340.169861
#> iter 3 value 1777.102289
#> iter 4 value 1675.618160
#> iter 5 value 1647.440889
#> iter 6 value 1625.245579
#> iter 7 value 1617.690498
#> iter 8 value 1553.900970
#> iter 9 value 1458.371264
#> iter 10 value 1390.258494
#> iter 11 value 1371.211992
#> iter 12 value 1365.633621
#> iter 13 value 1365.014537
#> iter 14 value 1364.999975
#> iter 15 value 1364.997013
#> iter 16 value 1364.996949
#> iter 16 value 1364.996948
#> final value 1364.996948
#> converged
#>
#>
#> Summary table:
#>
#>
#> =================== ==== === ==== ==== ====== ==== ==== ===== ==== ======== ====
#> \ vars n mean sd median min max range skew kurtosis se
#> =================== ==== === ==== ==== ====== ==== ==== ===== ==== ======== ====
#> est_bprof 1 2 0.20 0.05 0.20 0.16 0.23 0.07 0 -2.75 0.04
#> est_bexp 2 2 0.34 0.03 0.34 0.32 0.36 0.04 0 -2.75 0.02
#> est_bdomestic 3 2 0.39 0.13 0.39 0.30 0.49 0.19 0 -2.75 0.10
#> est_bforeign 4 2 0.37 0.03 0.37 0.35 0.39 0.04 0 -2.75 0.02
#> est_bdamage 5 2 0.69 0.07 0.69 0.64 0.74 0.10 0 -2.75 0.05
#> est_bprice 6 2 0.20 0.01 0.20 0.19 0.20 0.01 0 -2.75 0.00
#> est_basc 7 2 4.27 0.08 4.27 4.21 4.33 0.12 0 -2.75 0.06
#> rob_pval0_bprof 8 2 0.12 0.12 0.12 0.04 0.20 0.16 0 -2.75 0.08
#> rob_pval0_bexp 9 2 0.00 0.00 0.00 0.00 0.00 0.00 0 -2.75 0.00
#> rob_pval0_bdomestic 10 2 0.00 0.01 0.00 0.00 0.01 0.01 0 -2.75 0.00
#> rob_pval0_bforeign 11 2 0.00 0.00 0.00 0.00 0.00 0.00 0 -2.75 0.00
#> rob_pval0_bdamage 12 2 0.00 0.00 0.00 0.00 0.00 0.00 0 -2.75 0.00
#> rob_pval0_bprice 13 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> rob_pval0_basc 14 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> =================== ==== === ==== ==== ====== ==== ==== ===== ==== ======== ====
#>
#> Power results:
#>
#> FALSE TRUE
#> 50 50
#> '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 ~ bprof * alt1.professional + bexp * alt1.expert + bdomestic *
#> alt1.domestic + bforeign * alt1.foreign + bdamage * alt1.damage +
#> bprice * alt1.compensation
#> <environment: 0x5588b2cd45d0>
#>
#> $u1$v2
#> V.2 ~ bprof * alt2.professional + bexp * alt2.expert + bdomestic *
#> alt2.domestic + bforeign * alt2.foreign + bdamage * alt2.damage +
#> bprice * alt2.compensation
#> <environment: 0x5588b384f880>
#>
#> $u1$v3
#> V.3 ~ basc
#> <environment: 0x5588b33a0e60>
#>
#>
#> New names:
#> 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_initiator alt1_funding alt1_damage alt1_compensation
#> 1 1 4 2 1 1 15
#> 2 1 5 2 2 1 10
#> 3 1 10 2 2 1 15
#> 4 1 13 0 2 0 30
#> 5 1 14 2 2 0 2
#> 6 1 15 1 0 1 2
#> alt2_initiator alt2_funding alt2_damage alt2_compensation Block group
#> 1 1 2 0 15 1 1
#> 2 0 1 0 10 1 1
#> 3 1 0 0 15 1 1
#> 4 1 0 1 30 1 1
#> 5 1 0 1 5 1 1
#> 6 0 1 0 2 1 1
#> alt1_professional alt2_professional alt1_expert alt2_expert alt1_domestic
#> 1 FALSE TRUE TRUE FALSE TRUE
#> 2 FALSE FALSE TRUE FALSE FALSE
#> 3 FALSE TRUE TRUE FALSE FALSE
#> 4 FALSE TRUE FALSE FALSE FALSE
#> 5 FALSE TRUE TRUE FALSE FALSE
#> 6 TRUE FALSE FALSE FALSE FALSE
#> alt2_domestic alt1_foreign alt2_foreign V_1 V_2 V_3 e_1 e_2
#> 1 FALSE FALSE TRUE 4.2 3.6 4.2 1.099838554 1.65516593
#> 2 TRUE TRUE FALSE 3.2 2.3 4.2 2.532366583 -0.45522161
#> 3 FALSE TRUE FALSE 4.2 3.3 4.2 -0.865335081 -0.09076180
#> 4 FALSE TRUE FALSE 6.3 6.9 4.2 -0.458208905 0.03336685
#> 5 FALSE TRUE FALSE 1.0 1.9 4.2 0.420976234 0.24476851
#> 6 TRUE FALSE FALSE 1.3 0.7 4.2 0.007028381 -0.31923689
#> e_3 U_1 U_2 U_3 CHOICE
#> 1 2.3599458 5.299839 5.2551659 6.559946 3
#> 2 0.3645423 5.732367 1.8447784 4.564542 1
#> 3 0.6176394 3.334665 3.2092382 4.817639 3
#> 4 -1.4446060 5.841791 6.9333668 2.755394 2
#> 5 0.4793832 1.420976 2.1447685 4.679383 3
#> 6 -0.7543455 1.307028 0.3807631 3.445655 3
#>
#>
#>
#> 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_initiator alt1_funding alt1_damage alt1_compensation
#> 1 1 4 2 1 1 15
#> 2 1 5 2 2 1 10
#> 3 1 10 2 2 1 15
#> 4 1 13 0 2 0 30
#> 5 1 14 2 2 0 2
#> 6 1 15 1 0 1 2
#> alt2_initiator alt2_funding alt2_damage alt2_compensation Block group
#> 1 1 2 0 15 1 1
#> 2 0 1 0 10 1 1
#> 3 1 0 0 15 1 1
#> 4 1 0 1 30 1 1
#> 5 1 0 1 5 1 1
#> 6 0 1 0 2 1 1
#> alt1_professional alt2_professional alt1_expert alt2_expert alt1_domestic
#> 1 FALSE TRUE TRUE FALSE TRUE
#> 2 FALSE FALSE TRUE FALSE FALSE
#> 3 FALSE TRUE TRUE FALSE FALSE
#> 4 FALSE TRUE FALSE FALSE FALSE
#> 5 FALSE TRUE TRUE FALSE FALSE
#> 6 TRUE FALSE FALSE FALSE FALSE
#> alt2_domestic alt1_foreign alt2_foreign V_1 V_2 V_3 e_1 e_2
#> 1 FALSE FALSE TRUE 4.2 3.6 4.2 0.18768477 0.4588249
#> 2 TRUE TRUE FALSE 3.2 2.3 4.2 -0.33070917 -0.1240796
#> 3 FALSE TRUE FALSE 4.2 3.3 4.2 3.09376692 0.9105486
#> 4 FALSE TRUE FALSE 6.3 6.9 4.2 1.87119573 -0.1629678
#> 5 FALSE TRUE FALSE 1.0 1.9 4.2 -0.98089710 -0.2788252
#> 6 TRUE FALSE FALSE 1.3 0.7 4.2 0.07346766 3.7600494
#> e_3 U_1 U_2 U_3 CHOICE
#> 1 -0.198025826 4.3876848 4.058825 4.001974 1
#> 2 0.001823033 2.8692908 2.175920 4.201823 3
#> 3 0.543891110 7.2937669 4.210549 4.743891 1
#> 4 -0.695615761 8.1711957 6.737032 3.504384 1
#> 5 0.250409343 0.0191029 1.621175 4.450409 3
#> 6 -0.550718725 1.3734677 4.460049 3.649281 2
#>
#>
#>
#> Transformed utility function (type: simple):
#> [1] "U_1 = @bprof * $alt1_professional + @bexp * $alt1_expert + @bdomestic * $alt1_domestic + @bforeign * $alt1_foreign + @bdamage * $alt1_damage + @bprice * $alt1_compensation ;U_2 = @bprof * $alt2_professional + @bexp * $alt2_expert + @bdomestic * $alt2_domestic + @bforeign * $alt2_foreign + @bdamage * $alt2_damage + @bprice * $alt2_compensation ;U_3 = @basc ;"
#> Initial function value: -2373.003
#> Initial gradient value:
#> bprof bexp bdomestic bforeign bdamage bprice basc
#> -56 -84 -169 -143 -192 2460 369
#> initial value 2373.002544
#> iter 2 value 2358.147830
#> iter 3 value 1941.507343
#> iter 4 value 1770.350816
#> iter 5 value 1690.220053
#> iter 6 value 1689.200729
#> iter 7 value 1683.035142
#> iter 8 value 1614.557655
#> iter 9 value 1614.516741
#> iter 10 value 1589.552101
#> iter 11 value 1588.545445
#> iter 12 value 1588.314836
#> iter 13 value 1588.294244
#> iter 14 value 1588.293821
#> iter 14 value 1588.293814
#> iter 14 value 1588.293814
#> final value 1588.293814
#> converged
#> Initial function value: -2373.003
#> Initial gradient value:
#> bprof bexp bdomestic bforeign bdamage bprice basc
#> -41 -68 -145 -136 -172 2626 324
#> initial value 2373.002544
#> iter 2 value 2357.017387
#> iter 3 value 2007.129201
#> iter 4 value 1860.769729
#> iter 5 value 1789.564116
#> iter 6 value 1789.025827
#> iter 7 value 1788.416074
#> iter 8 value 1777.343683
#> iter 9 value 1739.014997
#> iter 10 value 1712.293866
#> iter 11 value 1711.325024
#> iter 12 value 1711.163059
#> iter 13 value 1711.156368
#> iter 14 value 1711.155875
#> iter 14 value 1711.155861
#> iter 14 value 1711.155861
#> final value 1711.155861
#> converged
#>
#>
#> Summary table:
#>
#>
#> =================== ==== === ==== ==== ====== ==== ==== ===== ==== ======== ====
#> \ vars n mean sd median min max range skew kurtosis se
#> =================== ==== === ==== ==== ====== ==== ==== ===== ==== ======== ====
#> est_bprof 1 2 0.33 0.00 0.33 0.33 0.33 0.00 0 -2.75 0.00
#> est_bexp 2 2 0.29 0.01 0.29 0.28 0.29 0.01 0 -2.75 0.00
#> est_bdomestic 3 2 0.24 0.01 0.24 0.23 0.25 0.02 0 -2.75 0.01
#> est_bforeign 4 2 0.23 0.06 0.23 0.18 0.27 0.09 0 -2.75 0.04
#> est_bdamage 5 2 0.60 0.05 0.60 0.56 0.63 0.08 0 -2.75 0.04
#> est_bprice 6 2 0.20 0.02 0.20 0.19 0.22 0.03 0 -2.75 0.01
#> est_basc 7 2 4.12 0.41 4.12 3.83 4.42 0.58 0 -2.75 0.29
#> rob_pval0_bprof 8 2 0.00 0.00 0.00 0.00 0.00 0.00 0 -2.75 0.00
#> rob_pval0_bexp 9 2 0.00 0.00 0.00 0.00 0.00 0.00 0 -2.75 0.00
#> rob_pval0_bdomestic 10 2 0.01 0.00 0.01 0.00 0.01 0.01 0 -2.75 0.00
#> rob_pval0_bforeign 11 2 0.02 0.02 0.02 0.00 0.04 0.03 0 -2.75 0.02
#> rob_pval0_bdamage 12 2 0.00 0.00 0.00 0.00 0.00 0.00 0 -2.75 0.00
#> rob_pval0_bprice 13 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> rob_pval0_basc 14 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> =================== ==== === ==== ==== ====== ==== ==== ===== ==== ======== ====
#>
#> Power results:
#>
#> TRUE
#> 100
#> total time for simulation and estimation: 8.251 sec elapsed
#> $tic
#> elapsed
#> 11.95
#>
#> $toc
#> elapsed
#> 20.201
#>
#> $msg
#> [1] "total time for simulation and estimation"
#>
#> $callback_msg
#> [1] "total time for simulation and estimation: 8.251 sec elapsed"
#>
#> Trying tidyr::pivot_longer for reshaping...
Accessing the Output in R
The beta cofficients for each simulation are contained in a dataframe called coeffs located within {result}->{design}->coefs. and a summary table, which displays statistics of these b coefficients across all simulations is contained in {results}->{design}->summary. You can access these by either searching for a known design name or by browsing design names contained in the output list.
You can also save the results to disk using saveRDS(csa,file = “tests/manual-tests/csa.RDS”)
designs <- names(seagri[sapply(seagri, is.list)])
print(designs)
#> [1] "bayeffnewpriors1" "bayeff3newpriorsdummy" "bayrevisednewpriors1"
#> [4] "time" "arguements" "summaryall"
#> [7] "graphs" "powa"
simulationCoeff <- seagri[[1]]$coefs
coeffSummary <- seagri[[1]]$summary
print(simulationCoeff)
#> # A tibble: 2 × 15
#> run est_bprof est_bexp est_bdomestic est_bforeign est_bdamage est_bprice
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0.420 0.293 0.437 0.404 0.615 0.208
#> 2 2 0.171 0.122 0.254 0.158 0.632 0.194
#> # ℹ 8 more variables: est_basc <dbl>, rob_pval0_bprof <dbl>,
#> # rob_pval0_bexp <dbl>, rob_pval0_bdomestic <dbl>, rob_pval0_bforeign <dbl>,
#> # rob_pval0_bdamage <dbl>, rob_pval0_bprice <dbl>, rob_pval0_basc <dbl>
print(coeffSummary)
#> vars n mean sd median min max range skew kurtosis se
#> est_bprof 1 2 0.30 0.18 0.30 0.17 0.42 0.25 0 -2.75 0.12
#> est_bexp 2 2 0.21 0.12 0.21 0.12 0.29 0.17 0 -2.75 0.09
#> est_bdomestic 3 2 0.35 0.13 0.35 0.25 0.44 0.18 0 -2.75 0.09
#> est_bforeign 4 2 0.28 0.17 0.28 0.16 0.40 0.25 0 -2.75 0.12
#> est_bdamage 5 2 0.62 0.01 0.62 0.62 0.63 0.02 0 -2.75 0.01
#> est_bprice 6 2 0.20 0.01 0.20 0.19 0.21 0.01 0 -2.75 0.01
#> est_basc 7 2 4.16 0.34 4.16 3.92 4.40 0.47 0 -2.75 0.24
#> rob_pval0_bprof 8 2 0.02 0.03 0.02 0.00 0.04 0.04 0 -2.75 0.02
#> rob_pval0_bexp 9 2 0.08 0.12 0.08 0.00 0.17 0.16 0 -2.75 0.08
#> rob_pval0_bdomestic 10 2 0.00 0.00 0.00 0.00 0.00 0.00 0 -2.75 0.00
#> rob_pval0_bforeign 11 2 0.04 0.06 0.04 0.00 0.09 0.09 0 -2.75 0.04
#> rob_pval0_bdamage 12 2 0.00 0.00 0.00 0.00 0.00 0.00 0 -2.75 0.00
#> rob_pval0_bprice 13 2 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> rob_pval0_basc 14 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")