b'\n_START_ARTICLE_\nConjoint R\n_START_SECTION_\nWymagania\n_START_PARAGRAPH_\nPoprawne dzia\xc5\x82anie pakietu conjoint wymaga zainstalowania wersji bazowej programu GNU R oraz dodatkowych pakiet\xc3\xb3w (m.in. AlgDesign [Wheeler 2015] i innych), kt\xc3\xb3re pocz\xc4\x85wszy od wersji 3.3.2 programu GNU R s\xc4\x85 pobierane i instalowane automatycznie wraz z pakietem conjoint. Pakiet mo\xc5\xbcna pobra\xc4\x87 oraz zainstalowa\xc4\x87 ze strony internetowej repozytorium CRAN R (https://CRAN.R-project.org/package=conjoint) oraz strony WWW Katedry Ekonometrii i Informatyki Uniwersytetu Ekonomicznego we Wroc\xc5\x82awiu (http://keii.ue.wroc.pl/conjoint). \xc5\x81\xc4\x85czna liczba instalacji pakietu conjoint (stan na 21 czerwca 2018) przez u\xc5\xbcytkownik\xc3\xb3w programu RStudio przekroczy\xc5\x82a 35000 pobra\xc5\x84 (statystyka nie uwzgl\xc4\x99dnia u\xc5\xbcytkownik\xc3\xb3w korzystaj\xc4\x85cych z innych wersji program\xc3\xb3w dla R, w tym przede wszystkim oryginalnej wersji \xc5\x9brodowiska R). Miesi\xc4\x99czn\xc4\x85 liczb\xc4\x99 pobra\xc5\x84 pakietu conjoint oszacowan\xc4\x85 z wykorzystaniem pakietu dlstats [Yu 2017] oraz zaprezentowan\xc4\x85 na wykresie za pomoc\xc4\x85 pakietu ggplot2 [Wickham i in. 2018] (odpowiedni kod R poni\xc5\xbcej) przedstawia rys. 1._NEWLINE_library("ggplot2")library("dlstats")_NEWLINE_x<-cran_stats("conjoint")_NEWLINE_ggplot(x,aes(end,downloads,group=package,color=package))+geom_line() +_NEWLINE_geom_point(aes(shape=package))+scale_x_date(date_breaks="1 year",date_labels="%Y")\n_START_SECTION_\nHistoria i wersje\n_START_PARAGRAPH_\nPierwsza wersja pakietu conjoint na serwerze CRAN pojawi\xc5\x82a si\xc4\x99 2 pa\xc5\xbadziernika 2011 roku. Od tego czasu pakiet jest sukcesywnie rozwijany oraz dostosowywany do obowi\xc4\x85zuj\xc4\x85cych standard\xc3\xb3w, w tym r\xc3\xb3\xc5\xbcnych platform sprz\xc4\x99towych. Pakiet mo\xc5\xbce by\xc4\x87 zainstalowany na komputerze z procesorem 32-bitowym lub 64-bitowym. Funkcjonalno\xc5\x9b\xc4\x87 pakietu jest taka sama w obu przypadkach, z wy\xc5\x82\xc4\x85czeniem cz\xc4\x85stkowych uk\xc5\x82ad\xc3\xb3w czynnikowych. W systemach 32-bitowych istnieje mo\xc5\xbcliwo\xc5\x9b\xc4\x87 otrzymania innego cz\xc4\x85stkowego uk\xc5\x82ad czynnikowego ni\xc5\xbc w przypadku system\xc3\xb3w 64-bitowych (wynika to z uwarunkowa\xc5\x84 numerycznych dotycz\xc4\x85cych d\xc5\x82ugo\xc5\x9bci s\xc5\x82owa maszynowego i jego wp\xc5\x82ywu na ziarno generatora liczb pseudolosowych, kt\xc3\xb3re jest wykorzystywane w procedurze generowania uk\xc5\x82ad\xc3\xb3w cz\xc4\x85stkowych). Zaprezentowane w artykule przyk\xc5\x82ady zosta\xc5\x82y opracowane z wykorzystaniem procesor\xc3\xb3w 64-bitowych pracuj\xc4\x85cych pod kontrol\xc4\x85 systemu operacyjnego Windows 10.\n_START_SECTION_\nKonstrukcja badania\n_START_PARAGRAPH_\nDeklaracja zmiennych badania (wraz z odpowiadaj\xc4\x85cymi im poziomami zmiennych): flavour (chocolate, vanilla, strawberry), price ($1.50, $2.00, $2.50), container (cone, cup) oraz topping (yes, no):_NEWLINE_> library(conjoint)> experiment<-expand.grid(+ flavor=c("chocolate","vanilla","strawberry"),+ price=c("$1.50","$2.00","$2.50"),+ container=c("cone","cup"),+ topping=c("yes","no"))_NEWLINE_Wyznaczanie cz\xc4\x85stkowego, ortogonalnego uk\xc5\x82adu czynnikowego z zachowaniem nazw zmiennych oraz ich poziom\xc3\xb3w na potrzeby konstrukcji kwestionariusza ankietowego:_NEWLINE_> factdesign<-caFactorialDesign(data=experiment,type="orthogonal")> print(factdesign)_NEWLINE_       flavor price container topping_NEWLINE_2     vanilla $1.50      cone     yes_NEWLINE_6  strawberry $2.00      cone     yes_NEWLINE_10  chocolate $1.50       cup     yes_NEWLINE_13  chocolate $2.00       cup     yes_NEWLINE_17    vanilla $2.50       cup     yes_NEWLINE_18 strawberry $2.50       cup     yes_NEWLINE_25  chocolate $2.50      cone      no_NEWLINE_30 strawberry $1.50       cup      no_NEWLINE_32    vanilla $2.00       cup      no_NEWLINE_Kodowanie poziom\xc3\xb3w zmiennych uk\xc5\x82adu cz\xc4\x85stkowego:_NEWLINE_> prof=caEncodedDesign(design=factdesign)> print(prof)_NEWLINE_   flavor price container topping_NEWLINE_2       2     1         1       16       3     2         1       110      1     1         2       113      1     2         2       117      2     3         2       118      3     3         2       125      1     3         1       230      3     1         2       232      2     2         2       2_NEWLINE_Weryfikacja (z wykorzystaniem macierzy kowariancji i korelacji)  jako\xc5\x9bci uk\xc5\x82adu cz\xc4\x85stkowego:_NEWLINE_> print(round(cov(prof),5))_NEWLINE_          flavor price container topping_NEWLINE_flavor      0.75  0.00      0.00    0.00_NEWLINE_price       0.00  0.75      0.00    0.00_NEWLINE_container   0.00  0.00      0.25    0.00_NEWLINE_topping     0.00  0.00      0.00    0.25> print(round(cor(prof),5))_NEWLINE_          flavor price container topping_NEWLINE_flavor         1     0         0       0_NEWLINE_price          0     1         0       0_NEWLINE_container      0     0         1       0_NEWLINE_topping        0     0         0       1> print(det(cor(prof)))[1] 1\n_START_SECTION_\nWczytanie danych\n_START_PARAGRAPH_\nWczytanie z plik\xc3\xb3w zewn\xc4\x99trznych: danych o preferencjach empirycznych, uk\xc5\x82adu badania, nazw zmiennych i ich poziom\xc3\xb3w:_NEWLINE_> pref=read.csv2("ice_preferences.csv", header=TRUE)> profiles=read.csv2("ice_profiles.csv", header=TRUE)> levelnames=read.csv2("ice_levels.csv", header=TRUE)> print(pref)_NEWLINE_  profile1 profile2 profile3 profile4 profile5 profile6 profile7 profile8 profile9_NEWLINE_1        1        6        2        7        8        4        3        9        52        3        4        9        8        1        5        7        6        23        3        5        1        6        8        9        2        7        44        1        4        2        8        9        5        7        6        35        2        6        3        7        8        1        4        5        96        2        5        9        6        7        8        3        4        1> print(profiles)_NEWLINE_  flavour price container topping_NEWLINE_1       2     1         1       12       3     2         1       13       1     1         2       14       1     2         2       15       2     3         2       16       3     3         2       17       1     3         1       28       3     1         2       29       2     2         2       2> print(levelnames)_NEWLINE_       levels1   chocolate_NEWLINE_2     vanilla_NEWLINE_3  strawberry_NEWLINE_4       $1.505       $2.006       $2.507        cone_NEWLINE_8         cup_NEWLINE_9         yes_NEWLINE_10         no_NEWLINE_Pliki danych w formacie .csv (rozdzielany przecinkami) do pobrania: ice_preferences.csv, ice_profiles.csv, ice_levels.csv_NEWLINE_Zmiana formatu danych o preferencjach z porz\xc4\x85dkowania rangowego (tzw. ranking) na oceny wa\xc5\xbcno\xc5\x9bci (tzw. rating):_NEWLINE_> preferences=caRankToScore(y.rank=pref)> print(preferences)_NEWLINE_  profile1 profile2 profile3 profile4 profile5 profile6 profile7 profile8 profile9_NEWLINE_1        9        4        8        3        2        6        7        1        52        7        6        1        2        9        5        3        4        83        7        5        9        4        2        1        8        3        64        9        6        8        2        1        5        3        4        75        8        4        7        3        2        9        6        5        16        8        5        1        4        3        2        7        6        9\n_START_SECTION_\nPomiar preferencji na poziomie indywidualnym (dla wybranych respondent\xc3\xb3w)\n_START_PARAGRAPH_\nOczacowanie modelu conjoint analysis dla 1-go respondenta:_NEWLINE_> caModel(preferences[1,],profiles)_NEWLINE__NEWLINE_Call:_NEWLINE_lm(formula = frml)_NEWLINE__NEWLINE_Residuals:_NEWLINE_         1          2          3          4          5          6          7          8          9_NEWLINE_ 6.667e-01 -6.667e-01  1.500e+00 -1.500e+00 -2.833e+00  2.833e+00  2.591e-16 -2.167e+00  2.167e+00_NEWLINE__NEWLINE_Coefficients:_NEWLINE_                     Estimate Std. Error t value Pr(>|t|)(Intercept)            5.2500     1.4633   3.588   0.0697 .factor(x$flavour)1     1.0000     1.8509   0.540   0.6431factor(x$flavour)2     0.3333     1.8509   0.180   0.8737factor(x$price)1       1.0000     1.8509   0.540   0.6431factor(x$price)2      -1.0000     1.8509  -0.540   0.6431factor(x$container)1   1.2500     1.3882   0.900   0.4629factor(x$topping)1     0.5000     1.3882   0.360   0.7532---_NEWLINE_Signif. codes:  0 \xe2\x80\x98***\xe2\x80\x99 0.001 \xe2\x80\x98**\xe2\x80\x99 0.01 \xe2\x80\x98*\xe2\x80\x99 0.05 \xe2\x80\x98.\xe2\x80\x99 0.1 \xe2\x80\x98 \xe2\x80\x99 1_NEWLINE__NEWLINE_Residual standard error: 3.926 on 2 degrees of freedom_NEWLINE_Multiple R-squared:  0.4861,    Adjusted R-squared:  -1.056F-statistic: 0.3153 on 6 and 2 DF,  p-value: 0.8851_NEWLINE_Wyznaczanie relatywnej wa\xc5\xbcno\xc5\x9bci zmiennych (atrybut\xc3\xb3w) dla 1-go respondenta:_NEWLINE_> importance=caImportance(y=preferences[1,],x=profiles)> print(importance)[1] 29.79 25.53 31.91 12.77\n_START_SECTION_\nPomiar preferencji na poziomie zagregowanym (w przekroju respondent\xc3\xb3w)\n_START_PARAGRAPH_\nPomiar u\xc5\xbcyteczno\xc5\x9bci cz\xc4\x85stkowych:_NEWLINE_> partutilities=caPartUtilities(y=preferences,x=profiles,z=levelnames)> print(partutilities)_NEWLINE_     intercept chocolate vanilla strawberry  $1.50  $2.00  $2.50 cone   cup   yes    no_NEWLINE_[1,]     5.250     1.000   0.333     -1.333  1.000 -1.000  0.000 1.25 -1.25  0.50 -0.50[2,]     5.083    -3.000   3.000      0.000 -1.000  0.333  0.667 0.25 -0.25  0.00  0.00[3,]     5.583     2.000   0.000     -2.000  1.333  0.000 -1.333 1.25 -1.25 -0.50  0.50[4,]     5.167    -0.667   0.667      0.000  2.000  0.000 -2.000 0.75 -0.75  0.25 -0.25[5,]     5.000     0.333  -1.333      1.000  1.667 -2.333  0.667 0.75 -0.75  0.75 -0.75[6,]     6.000    -1.000   1.667     -0.667  0.000  1.000 -1.000 1.25 -1.25 -1.75  1.75_NEWLINE_Pomiar u\xc5\xbcyteczno\xc5\x9bci ca\xc5\x82kowitych:_NEWLINE_> totalutilities=caTotalUtilities(y=preferences,x=profiles)> print(totalutilities)_NEWLINE_      [,1]  [,2]  [,3]  [,4]  [,5]  [,6] [,7]  [,8]   [,9][1,] 8.333 4.667 6.500 4.500 4.833 3.167    7 3.167  2.833[2,] 7.333 5.667 0.833 2.167 8.500 5.500    3 3.833  8.167[3,] 7.667 4.333 7.167 5.833 2.500 0.500    8 4.167  4.833[4,] 8.833 6.167 6.000 4.000 3.333 2.667    3 6.167  4.833[5,] 6.833 5.167 7.000 3.000 4.333 6.667    6 6.167 -0.167[6,] 7.167 5.833 2.000 3.000 3.667 1.333    7 5.833  9.167_NEWLINE_Podsumowanie najwa\xc5\xbcniejszych wynik\xc3\xb3w pomiaru preferencji funkcj\xc4\x85 Conjoint:_NEWLINE_> Conjoint(y=preferences,x=profiles,z=levelnames)_NEWLINE__NEWLINE_Call:_NEWLINE_lm(formula = frml)_NEWLINE__NEWLINE_Residuals:_NEWLINE_    Min      1Q  Median      3Q     Max_NEWLINE_-3,9444 -1,6944  0,0833  1,3333  5,6944_NEWLINE__NEWLINE_Coefficients:_NEWLINE_                     Estimate Std. Error t value Pr(>|t|)(Intercept)            5,3472     0,3747  14,269   <2e-16 ***factor(x$flavour)1    -0,2222     0,4740  -0,469   0,6414factor(x$flavour)2     0,7222     0,4740   1,524   0,1343factor(x$price)1       0,8333     0,4740   1,758   0,0853 .factor(x$price)2      -0,3333     0,4740  -0,703   0,4854factor(x$container)1   0,9167     0,3555   2,578   0,0131 *factor(x$topping)1    -0,1250     0,3555  -0,352   0,7267---_NEWLINE_Signif. codes:  0 \xe2\x80\x98***\xe2\x80\x99 0,001 \xe2\x80\x98**\xe2\x80\x99 0,01 \xe2\x80\x98*\xe2\x80\x99 0,05 \xe2\x80\x98.\xe2\x80\x99 0,1 \xe2\x80\x98 \xe2\x80\x99 1_NEWLINE__NEWLINE_Residual standard error: 2,463 on 47 degrees of freedom_NEWLINE_Multiple R-squared:  0,2079,    Adjusted R-squared:  0,1068F-statistic: 2,057 on 6 and 47 DF,  p-value: 0,07656[1] "Part worths (utilities) of levels (model parameters for whole sample):"_NEWLINE_       levnms    utls_NEWLINE_1   intercept  5,34722   chocolate -0,22223     vanilla  0,72224  strawberry    -0,55       $1.50  0,83336       $2.00 -0,33337       $2.50    -0,58        cone  0,91679         cup -0,916710        yes  -0,12511         no   0,125[1] "Average importance of factors (attributes):"[1] 35,13 31,39 20,43 13,05[1] Sum of average importance:  100[1] "Chart of average factors importance"\n_START_SECTION_\nKonstrukcja badania\n_START_PARAGRAPH_\nDeklaracja zmiennych badania (wraz z odpowiadaj\xc4\x85cymi im poziomami zmiennych): purpose (cognitive, vacation, health, business), form (organized, own), season (summer, winter), accommodation (1-2-3 star hotel, 4-5 star hotel, guesthouse, hostel):_NEWLINE_> library(conjoint)> journey<-expand.grid(purpose=c("cognitive","vacation","health","business"),+ form=c("own","organized"),+ season=c("summer","winter"),+ accommodation=c("1-2-3 star hotel","4-5 star hotel","guesthouse","hostel"))_NEWLINE_Wyznaczanie cz\xc4\x85stkowego uk\xc5\x82adu czynnikowego z zachowaniem nazw zmiennych oraz ich poziom\xc3\xb3w na potrzeby konstrukcji kwestionariusza ankietowego:_NEWLINE_> journeyfactdesign<-caFactorialDesign(data=journey,type="fractional")> journeyfactdesign_NEWLINE_     purpose      form season    accommodation_NEWLINE_1  cognitive       own summer 1-2-3 star hotel_NEWLINE_8   business organized summer 1-2-3 star hotel_NEWLINE_10  vacation       own winter 1-2-3 star hotel_NEWLINE_15    health organized winter 1-2-3 star hotel_NEWLINE_19    health       own summer   4-5 star hotel_NEWLINE_21 cognitive organized summer   4-5 star hotel_NEWLINE_30  vacation organized winter   4-5 star hotel_NEWLINE_34  vacation       own summer       guesthouse_NEWLINE_39    health organized summer       guesthouse_NEWLINE_41 cognitive       own winter       guesthouse_NEWLINE_48  business organized winter       guesthouse_NEWLINE_54  vacation organized summer           hostel_NEWLINE_60  business       own winter           hostel_NEWLINE_61 cognitive organized winter           hostel_NEWLINE_Kodowanie poziom\xc3\xb3w zmiennych uk\xc5\x82adu cz\xc4\x85stkowego:_NEWLINE_> prof=caEncodedDesign(design=journeyfactdesign)> prof_NEWLINE_   purpose form season accommodation_NEWLINE_1        1    1      1             18        4    2      1             110       2    1      2             115       3    2      2             119       3    1      1             221       1    2      1             230       2    2      2             234       2    1      1             339       3    2      1             341       1    1      2             348       4    2      2             354       2    2      1             460       4    1      2             461       1    2      2             4\n_START_SECTION_\nWczytanie danych\n_START_PARAGRAPH_\nWczytanie z plik\xc3\xb3w zewn\xc4\x99trznych: danych o preferencjach empirycznych, uk\xc5\x82adu badania, nazw zmiennych i ich poziom\xc3\xb3w oraz profil\xc3\xb3w symulacyjnych:_NEWLINE_> preferences=read.csv2("journey_preferences.csv", header=TRUE)> profiles=read.csv2("journey_profiles.csv", header=TRUE)> levelnames=read.csv2("journey_levels.csv", header=TRUE)> simulations=read.csv2("journey_simulations.csv", header=TRUE)> print(head(preferences))_NEWLINE_  profile01 profile02 profile03 profile04 profile05 profile06 profile07 profile08 profile09 profile10 profile11 profile12 profile13 profile14_NEWLINE_1         0        10         0        10        10         8         4         5        10         2         4         0         0         62        10         0        10         3         7         9         2         7         4         0         8        10         3         73         8         2         6         9         7         9         0         1         8         5         0         0         0         54         8        10         1         6         3         0         3         1         8         4         7         4         1        105         3         4         8        10        10         1        10         4         9         4        10         0         7        106         5         1         8         3        10         0         9         5         3        10        10         4         1         8> print(profiles)_NEWLINE_   purpose form season accommodation_NEWLINE_1        1    1      1             12        4    2      1             13        2    1      2             14        3    2      2             15        3    1      1             26        1    2      1             27        2    2      2             28        2    1      1             39        3    2      1             310       1    1      2             311       4    2      2             312       2    2      1             413       4    1      2             414       1    2      2             4> print(levelnames)_NEWLINE_             levels1         cognitive_NEWLINE_2          vacation_NEWLINE_3            health_NEWLINE_4          business_NEWLINE_5         organized_NEWLINE_6               own_NEWLINE_7            summer_NEWLINE_8            winter_NEWLINE_9  1-2-3 star_hotel_NEWLINE_10   4-5 star_hotel_NEWLINE_11       guesthouse_NEWLINE_12           hostel_NEWLINE_> print(simulations)_NEWLINE_  purpose form season accommodation_NEWLINE_1       2    2      1             12       2    1      1             23       3    2      2             24       1    1      1             45       4    1      2             3_NEWLINE_Pliki danych w formacie .csv (rozdzielany przecinkami) do pobrania: journey_preferences.csv, journey_profiles.csv, journej_levels.csv, journey_simulations.csv\n_START_SECTION_\nPomiar preferencji (na poziomie indywidualnym oraz zagregowanym)\n_START_PARAGRAPH_\nPomiar u\xc5\xbcyteczno\xc5\x9bci cz\xc4\x85stkowych (w przekroju respondent\xc3\xb3w):_NEWLINE_> partutilities=caPartUtilities(y=preferences,x=profiles,z=levelnames)> print(head(partutilities))_NEWLINE_     intercept cognitive vacation health business organized    own summer winter 1-2-3 star_hotel 4-5 star_hotel guesthouse hostel_NEWLINE_[1,]     4.938    -0.937   -2.687  3.639   -0.014    -1.562  1.562  0.692 -0.692            0.063          1.639      0.313 -2.014[2,]     5.625     0.875    1.625 -0.827   -1.673     0.250 -0.250  1.058 -1.058            0.125         -0.452     -0.875  1.202[3,]     4.187     2.563   -2.437  3.341   -3.466     0.063 -0.063  0.135 -0.135            2.062         -0.034     -0.688 -1.341[4,]     4.375     1.125   -2.125  0.788    0.212    -1.625  1.625  0.346 -0.346            1.875         -2.962      0.625  0.462[5,]     6.688    -2.187   -1.187  3.534   -0.159    -0.062  0.062 -2.385  2.385           -0.437          1.034      0.062 -0.659[6,]     5.500     0.250    1.000  0.202   -1.452     0.750 -0.750 -1.808  1.808           -1.250          1.202      1.500 -1.452_NEWLINE_Pomiar u\xc5\xbcyteczno\xc5\x9bci ca\xc5\x82kowitych (w przekroju respondent\xc3\xb3w):_NEWLINE_> totalutilities=caTotalUtilities(y=preferences,x=profiles)> print(head(totalutilities))_NEWLINE_      [,1]  [,2]  [,3]   [,4]  [,5]  [,6]  [,7]  [,8]   [,9] [,10]  [,11] [,12]  [,13] [,14][1,] 3.192 7.240 0.058  9.510 9.346 7.894 4.760 1.692 11.144 2.058  6.106 2.490  0.654 2.856[2,] 7.933 4.885 6.567  3.615 5.654 6.856 5.490 7.683  4.731 4.817  1.769 9.260  4.346 6.394[3,] 9.010 2.856 3.740  9.394 7.692 6.788 1.519 1.260  6.913 5.990 -0.163 0.481 -0.692 5.212[4,] 6.096 8.433 2.154  8.317 0.923 4.510 0.567 1.596  7.760 4.154  6.490 4.683  3.077 7.240[5,] 1.615 3.769 7.385 12.231 8.808 3.212 8.981 3.115  7.962 6.885  9.038 2.519  8.192 6.288[6,] 3.442 0.240 7.808  5.510 5.846 4.394 8.760 6.942  4.644 9.808  6.606 2.490  5.154 5.356_NEWLINE_Wyznaczanie relatywnej wa\xc5\xbcno\xc5\x9bci cech (dla respondenta nr 306):_NEWLINE_> importance=caImportance(y=preferences[306,],x=profiles)> print(importance)[1] 41.97 18.11 13.37 26.56_NEWLINE_Podsumowanie najwa\xc5\xbcniejszych wynik\xc3\xb3w pomiaru preferencji poleceniem Conjoint (dla respondenta nr 306):_NEWLINE_> Conjoint(preferences[306,],profiles,levelnames)_NEWLINE__NEWLINE_Call:_NEWLINE_lm(formula = frml)_NEWLINE__NEWLINE_Residuals:_NEWLINE_        1         2         3         4         5         6         7         8         9        10        11        12        13        14_NEWLINE_ 2,192308 -2,009615  2,557692 -2,740385  0,346154 -0,355769  0,009615 -3,307692  2,394231 -1,442308  2,355769  0,740385 -0,346154 -0,394231_NEWLINE__NEWLINE_Coefficients:_NEWLINE_                         Estimate Std. Error t value Pr(>|t|)(Intercept)                4,9375     0,8685   5,685  0,00235 **factor(x$purpose)1         1,3125     1,4003   0,937  0,39165factor(x$purpose)2        -0,4375     1,4003  -0,312  0,76733factor(x$purpose)3         1,7356     1,6158   1,074  0,33184factor(x$form)1            0,9375     0,8685   1,080  0,32966factor(x$season)1         -0,6923     0,8617  -0,803  0,45823factor(x$accommodation)1   1,3125     1,4003   0,937  0,39165factor(x$accommodation)2   0,7356     1,6158   0,455  0,66802factor(x$accommodation)3  -1,4375     1,4003  -1,027  0,35171---_NEWLINE_Signif. codes:  0 \xe2\x80\x98***\xe2\x80\x99 0,001 \xe2\x80\x98**\xe2\x80\x99 0,01 \xe2\x80\x98*\xe2\x80\x99 0,05 \xe2\x80\x98.\xe2\x80\x99 0,1 \xe2\x80\x98 \xe2\x80\x99 1_NEWLINE__NEWLINE_Residual standard error: 3,107 on 5 degrees of freedom_NEWLINE_Multiple R-squared:  0,6034,    Adjusted R-squared:  -0,0311F-statistic: 0,951 on 8 and 5 DF,  p-value: 0,549[1] "Part worths (utilities) of levels (model parameters for whole sample):"_NEWLINE_             levnms    utls_NEWLINE_1         intercept  4,93752         cognitive  1,31253          vacation -0,43754            health  1,73565          business -2,61066         organized  0,93757               own -0,93758            summer -0,69239            winter  0,692310 1-2-3 star_hotel  1,312511   4-5 star_hotel  0,735612       guesthouse -1,437513           hostel -0,6106[1] "Average importance of factors (attributes):"[1] 41,97 18,11 13,37 26,56[1] Sum of average importance:  100,01[1] "Chart of average factors importance"_NEWLINE_Podsumowanie najwa\xc5\xbcniejszych wynik\xc3\xb3w pomiaru preferencji poleceniem Conjoint (w przekroju respondent\xc3\xb3w):_NEWLINE_> Conjoint(y=preferences,x=profiles,z=levelnames)_NEWLINE__NEWLINE_Call:_NEWLINE_lm(formula = frml)_NEWLINE__NEWLINE_Residuals:_NEWLINE_    Min      1Q  Median      3Q     Max_NEWLINE_-5,4460 -3,0144 -0,0949  2,7758  5,9051_NEWLINE__NEWLINE_Coefficients:_NEWLINE_                          Estimate Std. Error t value Pr(>|t|)(Intercept)               4,979371   0,052578  94,704  < 2e-16 ***factor(x$purpose)1        0,139093   0,084780   1,641   0,1009factor(x$purpose)2        0,146446   0,084780   1,727   0,0842 .factor(x$purpose)3        0,437924   0,097823   4,477 7,78e-06 ***factor(x$form)1          -0,070057   0,052578  -1,332   0,1828factor(x$season)1        -0,094834   0,052172  -1,818   0,0692 .factor(x$accommodation)1 -0,136234   0,084780  -1,607   0,1081factor(x$accommodation)2 -0,028171   0,097823  -0,288   0,7734factor(x$accommodation)3  0,005923   0,084780   0,070   0,9443---_NEWLINE_Signif. codes:  0 \xe2\x80\x98***\xe2\x80\x99 0,001 \xe2\x80\x98**\xe2\x80\x99 0,01 \xe2\x80\x98*\xe2\x80\x99 0,05 \xe2\x80\x98.\xe2\x80\x99 0,1 \xe2\x80\x98 \xe2\x80\x99 1_NEWLINE__NEWLINE_Residual standard error: 3,291 on 4275 degrees of freedom_NEWLINE_Multiple R-squared:  0,01474,   Adjusted R-squared:  0,0129F-statistic: 7,994 on 8 and 4275 DF,  p-value: 9,444e-11[1] "Part worths (utilities) of levels (model parameters for whole sample):"_NEWLINE_             levnms    utls_NEWLINE_1         intercept  4,97942         cognitive  0,13913          vacation  0,14644            health  0,43795          business -0,72356         organized -0,07017               own  0,07018            summer -0,09489            winter  0,094810 1-2-3 star_hotel -0,136211   4-5 star_hotel -0,028212       guesthouse  0,005913           hostel  0,1585[1] "Average importance of factors (attributes):"[1] 38,62 13,30 13,97 34,11[1] Sum of average importance:  100[1] "Chart of average factors importance"\n_START_SECTION_\nSegmentacja respondent\xc3\xb3w\n_START_PARAGRAPH_\nSegmentacja metod\xc4\x85 k-\xc5\x9brednich \xe2\x80\x93 domy\xc5\x9blny podzia\xc5\x82 na 2 segmenty:_NEWLINE_> segments<-caSegmentation(preferences,profiles)> print(segments$seg)_NEWLINE_K-means clustering with 2 clusters of sizes 149, 157_NEWLINE__NEWLINE_Cluster means:_NEWLINE_      [,1]     [,2]     [,3]    [,4]     [,5]     [,6]     [,7]     [,8]     [,9]    [,10]    [,11]    [,12]    [,13]    [,14]1 6.025658 3.686060 5.200852 5.08743 4.808973 5.088503 4.263604 4.948477 4.835148 6.630383 4.290691 3.291765 3.721228 4.9735772 3.670554 4.482898 4.837408 5.78621 5.618357 5.043720 6.210573 4.984248 5.933051 3.743459 4.555803 7.127006 5.120497 5.886217_NEWLINE__NEWLINE_Clustering vector:_NEWLINE_  [1] 2 2 1 1 2 1 1 1 2 1 2 1 2 1 1 2 1 1 1 2 1 1 2 1 2 2 1 2 1 2 1 2 1 1 1 1 2 1 1 1 1 1 2 2 2 1 1 2 2 1 2 1 1 1 1 1 1 2 2 1 2 1 1 2 2 1 1 2 2 1 1 2 1_NEWLINE_ [74] 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 1 1 2 2 1 2 2 1 1 1 2 2 1 1 2 1 1 2 1 1 2 2 2 1 1 1 2 1 2 2 1 2 2[147] 2 2 2 2 2 2 1 2 2 1 1 1 2 1 2 2 2 1 2 2 2 2 1 2 1 2 1 2 2 1 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2 1 1 1 2 1 1 2 1 2 2 1 2 2 2 2 2 1 1 1 2 1 1 2 1[220] 1 2 2 2 2 2 2 1 2 1 1 2 1 1 1 2 2 2 1 1 1 2 1 1 1 2 2 1 1 1 1 2 2 2 1 1 2 2 1 2 1 2 1 2 1 1 2 1 1 1 1 1 2 2 1 1 1 2 2 2 1 2 1 1 1 1 2 1 1 1 2 1 1[293] 2 1 1 1 1 2 2 1 1 2 1 1 1 1_NEWLINE__NEWLINE_Within cluster sum of squares by cluster:[1] 12885.85 11758.15_NEWLINE_ (between_SS / total_SS =  10.6%)_NEWLINE__NEWLINE_Available components:[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss" "betweenss"    "size"         "iter"         "ifault"_NEWLINE_Segmentacja metod\xc4\x85 k-\xc5\x9brednich \xe2\x80\x93 podzia\xc5\x82 na 3 segmenty:_NEWLINE_> segments<-caSegmentation(preferences,profiles,c=3)> print(segments$seg)_NEWLINE_K-means clustering with 3 clusters of sizes 104, 97, 105_NEWLINE__NEWLINE_Cluster means:_NEWLINE_      [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]     [,9]    [,10]    [,11]    [,12]    [,13]    [,14]1 5.263000 3.860952 4.155269 7.124625 7.068404 4.630298 3.522462 3.895212 6.864673 5.561519 4.159365 3.494365 4.614288 5.1605672 5.602402 3.695979 6.044505 3.409691 3.393330 5.303907 5.746031 6.161680 3.526845 6.583165 4.676763 4.284897 3.513887 4.7064023 3.650619 4.695133 4.913667 5.664390 5.089067 5.276390 6.539390 4.924429 5.675200 3.416048 4.460514 7.908229 5.120457 6.399800_NEWLINE__NEWLINE_Clustering vector:_NEWLINE_  [1] 1 3 1 1 1 2 2 2 2 1 3 1 1 2 2 3 1 1 2 1 1 2 1 2 3 3 2 2 1 3 2 3 2 2 2 2 3 2 1 2 2 1 2 3 3 2 2 3 3 1 3 2 2 1 2 2 1 3 1 2 3 1 2 3 2 1 1 3 1 2 2 1 2_NEWLINE_ [74] 2 1 1 1 1 3 3 3 3 3 3 3 1 3 2 2 1 3 3 3 3 1 3 2 3 3 3 3 2 2 3 3 2 3 1 3 3 3 1 2 2 2 3 3 2 3 3 2 1 2 3 3 2 2 1 1 1 3 1 1 3 1 1 2 3 2 3 2 3 3 1 3 1[147] 1 1 3 3 1 3 2 2 1 2 1 2 3 1 3 3 1 2 1 3 3 3 1 3 2 3 1 3 3 1 3 2 2 1 3 1 3 2 3 3 3 3 2 3 3 3 2 1 3 2 2 2 1 1 1 3 1 3 3 1 2 3 3 1 2 1 1 2 3 2 2 3 1[220] 2 3 3 3 3 3 2 2 2 2 1 2 2 2 1 1 1 3 2 1 1 3 1 1 2 3 3 1 2 1 1 1 1 1 1 2 3 1 1 3 2 2 1 1 1 1 1 2 2 1 2 1 3 3 1 1 1 3 1 3 2 3 2 1 2 1 3 2 1 1 1 2 2[293] 3 2 1 2 2 3 3 2 1 3 1 2 1 1_NEWLINE__NEWLINE_Within cluster sum of squares by cluster:[1] 8321.434 7030.496 7021.380_NEWLINE_ (between_SS / total_SS =  18.9%)_NEWLINE__NEWLINE_Available components:[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss" "betweenss"    "size"         "iter"         "ifault"_NEWLINE_Wizualizacja podzia\xc5\x82u na 3 segmenty:_NEWLINE_> summary(segments)_NEWLINE_     Length Class  Mode_NEWLINE_segm    9   kmeans list_NEWLINE_util 4284   -none- numeric_NEWLINE_sclu  306   -none- numeric> require(fpc)> plotcluster(segments$util,segments$sclu)> require(fpc)> require(broom)> require(ggplot2)> dcf<-discrcoord(segments$util,segments$sclu)> assignments<-augment(segments$segm,dcf$proj[,1:2])> ggplot(assignments)+geom_point(aes(x=X1,y=X2,color= .cluster))+labs(color="Cluster Assignment",title="K-Means Clustering Results")\n_START_SECTION_\nAnaliza udzia\xc5\x82u w rynku profil\xc3\xb3w symulacyjnych\n_START_PARAGRAPH_\nAnaliza udzia\xc5\x82u w rynku profil\xc3\xb3w symulacyjnych za pomoc\xc4\x85 modelu maksymalnej u\xc5\xbcyteczno\xc5\x9bci, modelu probabilistycznego BTL (Bradley\xe2\x80\x99a-Terry\xe2\x80\x99ego-Luce\xe2\x80\x99a) oraz modelu logitowego:_NEWLINE_> ShowAllSimulations(sym=simulations,y=preferences,x=profiles)_NEWLINE_  TotalUtility MaxUtility BTLmodel LogitModel_NEWLINE_1         4,96      20,26    19,31      17,512         4,93      11,44    20,01      15,723         5,55      31,05    22,32      29,024         5,11      24,84    20,77      23,075         4,29      12,42    17,59      14,68'