R 279855661419, 4.82279855661419, 4.82279855661419, 4.90829244292775, 4.90829244292775, 4.90829244292775, SDEV=c（0.013166086761169

279855661419, 4.82279855661419, 4.82279855661419, 4.90829244292775, 4.90829244292775, 4.90829244292775, SDEV=c（0.013166086761169，0.0227885077248578， 0.0385063774922944, 0.0453701832587235, 0.0517023216849306, 0.0540557778054221, 0.0488190684785587, 0.0421655604203246, 0.0325068730742725, 0.0331639448052763, 0.0593717259238862, 0.0586095568412675, 0.0710643594998291, 0.0542769644148696, 0.0622404765975576, 0.0536064244218059, 0.0519102885973204, 0.0493863445475082, 0.0517804569480521, 0.0517211456806851, 0.0511253987894482, 0.0419591547767014, 0.0528999962423706, 0.06419336266174, 0.069237410318895, 0.0713884397366327, 0.0580538193950814, 0.084730114026892, 0.0769335955817974, 0.0855593629362882, 0.0519292899551365, 0.050876773415997, 0.0676511078249483, 0.0732312376684092, 0.0743946723794898, 0.0665503482658373, 0.0555488251322273, 0.0470531380177778, 0.037777592584443, 0.0409234761554936, 0.0345834473195202, 0.0334895212291953, 0.0277260994170984, 0.0265708838575095, 0.0271452389595483, 0.0259084599153654, 0.027959580475224, 0.0306489955753367, 0.0328760720029017, 0.0311483829721427, 0.0413979830020902, 0.047728997714206, 0.0515035254984261, 0.0421878234920752, 0.0418645252465591, 0.0434096272625613, 0.0521579392399781, 0.0514410097895711, 0.0499883145437549, 0.0404397864018975, 0.0425833520016323, 0.0486655254790584, 0.0564389639990789, 0.0556186336854241, 0.0630539085431343, 0.065666079347007, 0.070869476857214, 0.0615096987541091, 0.049069128261053, 0.0487439588352498, 0.0480084448188716, 0.0491650639209262, 0.0436654975228482, 0.0415799139247511, 0.039640484736356, 0.0382358642019319, 0.0345567696383646, 0.054637171859723, 0.0539703286358973, 0.051239025865198, 0.0304741735069425, 0.0276547408764417, 0.0277588467652429, 0.0269259048208619, 0.0304542870148651, 0.0378968781497573, 0.0385792550357231），歪斜=c（0.96728019756724，2.42472216135109， -0.21788331915771, -0.589261798446696, -0.549744301636096, -0.0422495194778549, 0.0412380549826091, 0.35137501000572, 0.682869097532209, 0.782281964174801, -0.450361125171004, -0.488016712280699, 0.943430970115142, 2.61494605790314, 1.67840104658292, 0.0494001791565993, 0.504744823418497, 1.05389215830831, 1.22511769927417, 1.03801906089498, 0.505638715226445, 0.074791465001239, 0.368198217755297, 0.717926021653681, 0.471423996442527, 0.69552417891756, 0.926078921390832, 2.36266799955845, 2.94477649644965, 0.640469883369529, 0.799507060035616, 0.265779764693551, 0.191606636855567, 0.0447592922353591, 0.00169280395714207, -0.13065910539762, -0.213861988500762, -0.184466027518926, 0.272899362222252, 0.392509260677987, 0.780935428471732, 0.681187297374058, 0.445637321564354, 0.477287511166656, 0.406893835763671, 0.345468287910708, 0.391817316521643, 0.605539416003897, 0.479926487469797, 0.369660204550789, -0.605369185234758, -0.352414395767028, -0.240446750178277, -0.090164697152621, 0.0895116184929062, 0.0648045199933538, 0.0417585338552771, -0.0341388270129196, 0.132806642274464, 0.255034672791302, 0.311955438741785, 0.380001462123998, 0.356764315563415, 0.305222472394173, 0.104992327972631, 0.0413874602372328, -0.034391920634866, 0.0559119034906321, -0.0421201193774125, 0.219703537578214, 0.30842629833452, 0.177409422251624, -0.371288947062746, 0.169037654477974, 0.330452704882459, 0.710518808997853, 0.387160218562557, -1.50906163140839, -1.59114639387075, -1.85119669017341, 0.0163547882160698, 0.739007828574639, 0.703121528579507, 0.49400874385351, -0.282583144457125, -0.301598441316929, -0.262435057731434），RET12=c（0.693147180559945，0.7504624694439， 0.67116827384117, 0.559615787935423, 0.356903541493734, 0.278713402469021, 0.228841572428848, 0.356674943938732, 0.162571218446518, 0.100083458556983, -0.321583624127462, -0.405465108108164, -0.416893803931787, -0.944461608840852, -0.548565951748838, -0.259511195485085, 0.0392207131532814, -0.296877373096692, -0.488352767913932, -0.7339691750802, -0.762140052046897, -0.741937344729377, -0.371563556432483, -0.0741079721537219, -0.322773392263051, 0.0233444335897671, -0.367724780125317, 0.259511195485085, 0.352821374622742, -0.385662480811985, -0.318453731118535, -0.405465108108164, -0.287682072451781, -0.0512932943875504, -0.328504066972036, -0.422856850820033, 0.111225635110225, 0.171850256926659, 0.405465108108164, 0.53062825106217, 0.365459773494465, 0.462623521948113, 0.577315365034824, 0.611801541105993, 0.737598943130779, 0.596520344870874, 0.441832752279039, 0.666478933477784, 0.63907995928967, 0.62509371731493, 0.405465108108164, -0.103184236235231, -0.0631789016215316, -0.0571584138399488, -0.214409871345455, -0.248896047416624, -0.139761942375159, -0.263814591045137, -0.196710294246054, -0.49740260343385, -0.587786664902119, -0.573800422927379, -0.405465108108164, -0.191055236762709, 0, 0.0194180858571016, 0.36101334553733, 0.231801614057324, -0.0339015516756813, 0.278203328497237, 0.36101334553733, 0.647684806483188, 1.01160091167848, 1.06471073699243, 1.44036158239017, 1.46228026809781, 1.45962563420544, 0.997516171796741, 0.708185057924486, 0.923163611161917, 0.989412996703118, 0.814099790977608, 0.847297860387204, 0.669616683149751, 0.241162056816888, 0.0826917158451135, -0.23638877806423），RET.Next.12=c（-0.416893803931787，-0.944461608840852， -0.548565951748838, -0.259511195485085, 0.0392207131532814, -0.296877373096692, -0.488352767913932, -0.7339691750802, -0.762140052046897, -0.741937344729377, -0.371563556432483, -0.0741079721537219, -0.322773392263051, 0.0233444335897671, -0.367724780125317, 0.259511195485085, 0.352821374622742, 0.182467021918947, 0.169899036795397, 0.206200830583898, 0.153987401191905, 0.573687740522617, 0.821892136450508, 0.653926467406664, 0.662982480513678, 0.803334139594701, 1.09861228866811, 0.555525802683897, 0.432133355190326, 0.46262352

# model data 
model_input
# fit data to run the regression on 
fit_data
# run logit model on model input 
> model <-model_input %>%
+   group_by(LPERMNO)%>%
+   do( model = glm(JACKPOT 
+                   ~ AGE+TANG+SALESGRTH+TURN+SIZE+SDEV+SKEW+RET12,
+                   data=., 
+                   family=binomial))
Warnmeldungen:
1: glm.fit: algorithm did not converge 
2: glm.fit: fitted probabilities numerically 0 or 1 occurred 
3: glm.fit: algorithm did not converge 
4: glm.fit: fitted probabilities numerically 0 or 1 occurred 
> # view model
> library(broom)
> model %>% tidy(model)
# A tibble: 16 x 6
# Groups:   LPERMNO [2]
   LPERMNO term            estimate   std.error  statistic p.value
     <int> <chr>              <dbl>       <dbl>      <dbl>   <dbl>
 1   10011 (Intercept)  -759.        3350570.   -0.000227    1.000
 2   10011 TANG         2673.       10422493.    0.000256    1.000
 3   10011 SALESGRTH     421.        5847097.    0.0000720   1.000
 4   10011 TURN           -0.000556        3.27 -0.000170    1.000
 5   10011 SIZE         -121.        1401422.   -0.0000860   1.000
 6   10011 SDEV        -2718.        6750572.   -0.000403    1.000
 7   10011 SKEW           20.7        141960.    0.000146    1.000
 8   10011 RET12        -106.         395987.   -0.000267    1.000
 9   10032 (Intercept)  2509.        2388459.    0.00105     0.999
10   10032 TANG        -5578.        3747618.   -0.00149     0.999
11   10032 SALESGRTH     324.         249059.    0.00130     0.999
12   10032 TURN           -0.00256         6.43 -0.000398    1.000
13   10032 SIZE         -144.         218359.   -0.000661    0.999
14   10032 SDEV         5982.        3605058.    0.00166     0.999
15   10032 SKEW          244.         127777.    0.00191     0.998
16   10032 RET12        -142.          81402.   -0.00174     0.999
> 
> # fit model to new data 
> fitted.results <- predict(model,newdata= fit_data,type='response')
Fehler in UseMethod("predict") : 
  nicht anwendbare Methode für 'predict' auf Objekt der Klasse "c('rowwise_df', 'tbl_df', 'tbl', 'data.frame')" angewendet
> 
> # define decision boundary
> fitted.results <- ifelse(fitted.results > 0.5,1,0)

source("SO58578522_dat.R")  ## get data
library(tidyverse)
form <- JACKPOT ~ AGE+TANG+SALESGRTH+TURN+SIZE+SDEV+SKEW+RET12
model_results <-(model_data
    ## convert data to a list column
    %>% nest(data=-LPERMNO)
    ## fit model
    %>% mutate(model=map(data,
                         ~glm(form,
                              data=.,
                              family=binomial)),
              ## make predictions
               results=map(model,~predict(.,newdata=fit_data,
                        type="response"))
               )
    %>% unnest(cols=c(LPERMNO,results))
    %>% select(LPERMNO,results)
    %>% mutate(results=as.numeric(results>0.5))
)