Data-Airline13: “The Late List”
The train dataset contains detailed flight records and weather condition recorded per hour from January 1st 2013 to November 31th 2013. The data set can be joined by their recorded time.
Importing Data
flight <- read.csv("data/data-train-flight.csv")
weather <- read.csv("data/data-train-weather.csv")
data_test <- read.csv("data/flight-data-test.csv")Flight:
head(flight)## year month day dep_time sched_dep_time dep_delay sched_arr_time arr_delay
## 1 2013 1 1 629 630 -1 833 Not Delay
## 2 2013 1 1 643 645 -2 848 Not Delay
## 3 2013 1 1 811 815 -4 1016 Delay
## 4 2013 1 1 1010 1015 -5 1210 Not Delay
## 5 2013 1 1 1211 1215 -4 1413 Delay
## 6 2013 1 1 1314 1315 -1 1505 Not Delay
## carrier flight tailnum origin dest distance hour minute time_hour
## 1 US 1019 N426US EWR CLT 529 6 30 2013-01-01 06:00:00
## 2 US 926 N178US EWR CLT 529 6 45 2013-01-01 06:00:00
## 3 US 675 N654AW EWR CLT 529 8 15 2013-01-01 08:00:00
## 4 US 1103 N162UW EWR CLT 529 10 15 2013-01-01 10:00:00
## 5 EV 4135 N21537 EWR CLT 529 12 15 2013-01-01 12:00:00
## 6 US 1615 N177US EWR CLT 529 13 15 2013-01-01 13:00:00The flight data consists of the following variables:
year,month,day: Date of departure. dep_time: Actual departure times (format HHMM or HMM), local tz. sched_dep_time,sched_arr_time: Scheduled departure and arrival times (format HHMM or HMM), local tz. dep_delay: Departure delays, in minutes. Negative times represent early departures. arr_delay: Flight arrival status; Delay or Not Delay. carrier: Two letter carrier (airlines) abbreviation. flight: Flight number. tailnum: Plane tail number. origin,dest: Airports of origin and destination. distance: Distance between airports, in miles hour,minute: Time of scheduled departure broken into hour and minutes. time_hour: Scheduled date and hour of the flight (YYYYMMDD HHMMSS).
Weather:
head(weather)## year month day hour temp dewp humid wind_dir wind_speed wind_gust precip
## 1 2013 1 1 1 39.02 26.06 59.37 270 10.35702 NA 0
## 2 2013 1 1 2 39.02 26.96 61.63 250 8.05546 NA 0
## 3 2013 1 1 3 39.02 28.04 64.43 240 11.50780 NA 0
## 4 2013 1 1 4 39.92 28.04 62.21 250 12.65858 NA 0
## 5 2013 1 1 5 39.02 28.04 64.43 260 12.65858 NA 0
## 6 2013 1 1 6 37.94 28.04 67.21 240 11.50780 NA 0
## pressure visib time_hour
## 1 1012.0 10 2013-01-01 01:00:00
## 2 1012.3 10 2013-01-01 02:00:00
## 3 1012.5 10 2013-01-01 03:00:00
## 4 1012.2 10 2013-01-01 04:00:00
## 5 1011.9 10 2013-01-01 05:00:00
## 6 1012.4 10 2013-01-01 06:00:00The weather data consists of the following variables:
year,month,day,hour: Time of recording. temp: Temperature in Fahrenheit. dewp: Dewpoint in Fahrenheit. humid: Relative humidity. wind_dir: Wind direction (in degrees). wind_speed: Wind speed (in mph). wind_gust: Wind gust speed (in mph). precip: Precipitation, in inches. pressure: Sea level pressure in millibars. visib: Visibility in miles. time_hour: Date and hour of the recording (YYYYMMDD HHMMSS).
Data Test:
head(data_test)## id month day dep_delay sched_arr_hour sched_arr_minute sched_dep_hour
## 1 F1 12 Sun -7 6 51 5
## 2 F2 12 Sun -7 8 11 6
## 3 F3 12 Sun 6 8 17 6
## 4 F4 12 Sun -4 8 35 6
## 5 F5 12 Sun -3 10 21 8
## 6 F6 12 Sun -7 12 18 10
## sched_dep_minute temp dewp humid wind_dir wind_speed precip visib
## 1 0 35.06 28.04 75.31 10 4.60312 0 10
## 2 1 33.80 26.60 78.45 40 5.75390 0 10
## 3 15 33.80 26.60 78.45 40 5.75390 0 10
## 4 30 33.80 26.60 78.45 40 5.75390 0 10
## 5 20 39.02 30.92 72.46 30 3.45234 0 10
## 6 23 44.96 32.00 60.20 0 0.00000 0 10
## arr_status
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NAIt can be seen that we need to combine the flight dataset with the weather dataset.
Merging datasets using left_join() function.
dataset <- left_join(weather, flight, by = "time_hour")It can be seen that columns such as year, distance, origin and dest can be deleted because they have the same value in all columns.
# dataset %>% select(year.x,year.y,distance,origin,dest) %>% unique()
# dataset <- dataset %>% select(-c("year.x","year.y","distance","origin","dest"))Explored which airline company might needed this prediction model the most. Do you think airline that has the highest number of delayed flight needed the model the most? Is there any airline that has more delayed flight than non-delayed flight?
Explored the proportion of the target variable. What is the target variable? Is there any class imbalance between the target value? *What should you do if there is a class imbalance?
Demonstrated how to properly do data preprocessing and feature engineering. Do you need to transform the data type of some variables? Do you need to separate time into hour and minute? *Do you need to remove some variables? How?
Demonstrated how to properly handle missing values (includes reasoning for the method applied). Should you check any missing values? Do you need to impute missing values? *Should you use median or mean imputation? Why?
Demonstrated how to prepare cross-validation data for this case. What is your proportion of training-testing dataset? Do you need to use stratified random sampling during the cross-validation?
Demonstrated how to properly do model fitting and evaluation. What model do you use? How do you set the model parameter? *Do you concerned more with precision than accuracy for this case? Why?
Demonstrated how to properly do model selection by comparing models or making adjustment to single model. Which model is better? What kind of adjustment you need to do in order to improve the performance of your chosen model? *Can you adjust the classification threshold to get better model performance?
Interpretation
(3 Points) Use LIME method to interpret the model that you have used There any pre-processing that you need in order to be more interpretable? How many features do you use to explain the model? What is the difference between using LIME compared to interpretable machine learning models such as Decision Tree or metrics such as Variable Importance in Random Forest? (3 Points) Interpret the first 4 observation of the plot What is the difference between interpreting black box model with LIME and using an interpretable machine learning model? How good is the explanation fit? What does it signify?
dataset %>%
select(year.x, month.x, day.x, dep_delay, sched_arr_time,
sched_dep_time, temp, dewp, humid,
wind_dir, wind_speed, precip,
visib, arr_delay) %>%
rename("year"=year.x, "month"= month.x, "day" = day.x) %>%
filter(!is.na(arr_delay)) -> datasetSys.setlocale("LC_TIME", "English")## [1] "English_United States.1252"dataset$day <- paste(dataset$year,
dataset$month,
dataset$day, sep = "-") %>%
as.Date(., "%Y-%m-%d") %>%
lubridate::wday(.,label = T)dataset$arr_delay=as.factor(dataset$arr_delay)
new_data <- missForest(dataset)## missForest iteration 1 in progress...done!
## missForest iteration 2 in progress...done!
## missForest iteration 3 in progress...done!
## missForest iteration 4 in progress...done!
## missForest iteration 5 in progress...done!new_data <- new_data$ximp
anyNA(new_data)## [1] FALSEupSample(new_data[,-14], y = new_data$arr_delay, yname = "arr_delay") -> new_datasetind <- createDataPartition(new_dataset$arr_delay, p = .7, list = F)
dat_train <- new_dataset[ind,]
dat_test <- new_dataset[-ind,]ctrl = trainControl(method = "cv", number = 5)
mod <- train(arr_delay ~ dep_delay + dewp + temp + humid +
wind_dir + wind_speed + precip +
visib, data = dat_train,
method = "avNNet",
preProcess = "scale",
trControl = ctrl)## Warning: executing %dopar% sequentially: no parallel backend registered## Fitting Repeat 1
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## iter 20 value 970.533515
## iter 30 value 941.798187
## iter 40 value 935.738500
## iter 50 value 930.472755
## iter 60 value 928.714079
## iter 70 value 925.535867
## iter 80 value 920.760597
## iter 90 value 916.410517
## iter 100 value 915.968842
## final value 915.968842
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1646.192429
## iter 10 value 952.609894
## iter 20 value 939.621430
## iter 30 value 931.185370
## iter 40 value 922.641748
## iter 50 value 920.094821
## iter 60 value 919.165680
## iter 70 value 918.783360
## iter 80 value 918.492000
## iter 90 value 918.387287
## iter 100 value 918.378845
## final value 918.378845
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 1761.840808
## iter 10 value 1011.711514
## iter 20 value 939.470334
## iter 30 value 921.347225
## iter 40 value 900.172184
## iter 50 value 893.859159
## iter 60 value 891.227425
## iter 70 value 889.967051
## iter 80 value 888.481678
## iter 90 value 886.683645
## iter 100 value 886.006390
## final value 886.006390
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1779.864270
## iter 10 value 1118.713965
## iter 20 value 981.825545
## iter 30 value 957.334553
## iter 40 value 935.586798
## iter 50 value 927.789976
## iter 60 value 925.017961
## iter 70 value 921.204386
## iter 80 value 917.267779
## iter 90 value 914.733462
## iter 100 value 912.871232
## final value 912.871232
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 1831.894793
## iter 10 value 1196.694216
## iter 20 value 968.703022
## iter 30 value 941.574158
## iter 40 value 925.941734
## iter 50 value 921.011057
## iter 60 value 914.723103
## iter 70 value 907.605029
## iter 80 value 892.820705
## iter 90 value 886.569917
## iter 100 value 882.278092
## final value 882.278092
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1989.155376
## final value 1665.005280
## converged
## Fitting Repeat 5
##
## # weights: 57
## initial value 1922.472707
## iter 10 value 1572.904823
## iter 20 value 1008.672233
## iter 30 value 964.528599
## iter 40 value 946.648644
## iter 50 value 935.251765
## iter 60 value 926.461237
## iter 70 value 917.705299
## iter 80 value 916.133311
## iter 90 value 914.798406
## iter 100 value 911.080992
## final value 911.080992
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1748.499704
## iter 10 value 1552.223262
## iter 20 value 1231.340040
## iter 30 value 1084.980249
## iter 40 value 1050.688571
## iter 50 value 1044.838230
## iter 60 value 1043.348520
## iter 70 value 1041.922068
## iter 80 value 1041.837134
## iter 90 value 1041.827457
## iter 100 value 1041.788472
## final value 1041.788472
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 13
## initial value 1669.507911
## iter 10 value 1475.333891
## iter 20 value 1174.836082
## iter 30 value 1001.960233
## iter 40 value 990.146372
## iter 50 value 982.726911
## iter 60 value 957.844047
## iter 70 value 952.229926
## iter 80 value 950.856464
## iter 90 value 949.530431
## iter 100 value 949.245519
## final value 949.245519
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 13
## initial value 1823.937622
## iter 10 value 1395.344669
## iter 20 value 1038.367366
## iter 30 value 981.363673
## iter 40 value 974.824017
## iter 50 value 954.788992
## iter 60 value 951.052433
## iter 70 value 950.187634
## iter 80 value 949.390137
## iter 90 value 949.224285
## iter 100 value 949.158039
## final value 949.158039
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 13
## initial value 1820.147822
## iter 10 value 1389.487984
## iter 20 value 1009.699041
## iter 30 value 981.104935
## iter 40 value 966.524583
## iter 50 value 956.573931
## iter 60 value 954.120186
## iter 70 value 952.266321
## iter 80 value 950.757458
## iter 90 value 950.501399
## iter 100 value 950.080222
## final value 950.080222
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 13
## initial value 1702.376257
## iter 10 value 1485.105918
## iter 20 value 1144.221396
## iter 30 value 994.213497
## iter 40 value 978.286285
## iter 50 value 972.579586
## iter 60 value 971.012501
## iter 70 value 970.955767
## final value 970.949676
## converged
## Fitting Repeat 1
##
## # weights: 35
## initial value 1880.316175
## iter 10 value 1439.552452
## iter 20 value 1036.217244
## iter 30 value 998.188642
## iter 40 value 977.676795
## iter 50 value 957.234407
## iter 60 value 938.115028
## iter 70 value 928.244780
## iter 80 value 922.992053
## iter 90 value 918.324481
## iter 100 value 915.129485
## final value 915.129485
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1789.576057
## iter 10 value 1594.187733
## iter 20 value 1021.223955
## iter 30 value 1004.768827
## iter 40 value 998.551122
## iter 50 value 979.089567
## iter 60 value 961.005623
## iter 70 value 953.753632
## iter 80 value 952.514915
## iter 90 value 951.136488
## iter 100 value 950.359834
## final value 950.359834
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 1770.971253
## iter 10 value 1205.406675
## iter 20 value 998.133276
## iter 30 value 938.978308
## iter 40 value 930.293691
## iter 50 value 928.479165
## iter 60 value 924.829357
## iter 70 value 921.400457
## iter 80 value 916.940840
## iter 90 value 914.354472
## iter 100 value 913.922263
## final value 913.922263
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1644.415542
## iter 10 value 996.874799
## iter 20 value 970.121509
## iter 30 value 952.457042
## iter 40 value 944.813386
## iter 50 value 941.127708
## iter 60 value 939.996429
## iter 70 value 939.778927
## iter 80 value 939.681090
## iter 90 value 939.502729
## iter 100 value 939.483328
## final value 939.483328
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1698.691290
## iter 10 value 1432.351210
## iter 20 value 982.673324
## iter 30 value 955.819180
## iter 40 value 950.284092
## iter 50 value 940.668896
## iter 60 value 932.565949
## iter 70 value 930.307336
## iter 80 value 921.741695
## iter 90 value 917.017767
## iter 100 value 914.498291
## final value 914.498291
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 1829.333492
## iter 10 value 1418.934577
## iter 20 value 977.876532
## iter 30 value 937.442972
## iter 40 value 926.279177
## iter 50 value 922.051672
## iter 60 value 918.791663
## iter 70 value 916.097691
## iter 80 value 914.569719
## iter 90 value 910.583639
## iter 100 value 908.643872
## final value 908.643872
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1835.895077
## iter 10 value 1570.065820
## iter 20 value 1385.287008
## iter 30 value 1069.583506
## iter 40 value 981.070739
## iter 50 value 954.091201
## iter 60 value 941.843651
## iter 70 value 935.954668
## iter 80 value 924.518189
## iter 90 value 915.835414
## iter 100 value 913.056198
## final value 913.056198
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 1896.324537
## iter 10 value 995.756904
## iter 20 value 948.356284
## iter 30 value 936.936927
## iter 40 value 932.052088
## iter 50 value 925.924278
## iter 60 value 922.764084
## iter 70 value 919.954890
## iter 80 value 919.025185
## iter 90 value 918.386697
## iter 100 value 917.405957
## final value 917.405957
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1876.039689
## iter 10 value 1540.880032
## iter 20 value 1275.529014
## iter 30 value 1026.762422
## iter 40 value 950.519328
## iter 50 value 935.203020
## iter 60 value 932.119941
## iter 70 value 931.013879
## iter 80 value 926.221511
## iter 90 value 924.306102
## iter 100 value 922.745714
## final value 922.745714
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1718.967389
## iter 10 value 1108.346517
## iter 20 value 962.148078
## iter 30 value 943.774318
## iter 40 value 923.634744
## iter 50 value 912.229828
## iter 60 value 907.096008
## iter 70 value 903.425367
## iter 80 value 901.044276
## iter 90 value 899.473466
## iter 100 value 898.544934
## final value 898.544934
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1732.773493
## iter 10 value 1638.566761
## iter 20 value 1558.470030
## iter 30 value 1161.773481
## iter 40 value 994.681564
## iter 50 value 963.801573
## iter 60 value 963.184945
## iter 70 value 963.149645
## iter 70 value 963.149645
## iter 70 value 963.149645
## final value 963.149645
## converged
## Fitting Repeat 2
##
## # weights: 13
## initial value 1725.502644
## iter 10 value 1622.451825
## iter 20 value 1142.853343
## iter 30 value 1014.150796
## iter 40 value 971.415616
## iter 50 value 964.001663
## iter 60 value 963.186661
## iter 70 value 963.149645
## iter 70 value 963.149645
## iter 70 value 963.149645
## final value 963.149645
## converged
## Fitting Repeat 3
##
## # weights: 13
## initial value 1674.234426
## iter 10 value 1375.584485
## iter 20 value 1026.504883
## iter 30 value 980.812736
## iter 40 value 965.716107
## iter 50 value 962.735103
## final value 962.690899
## converged
## Fitting Repeat 4
##
## # weights: 13
## initial value 1699.895252
## iter 10 value 1472.062743
## iter 20 value 1025.244975
## iter 30 value 973.430606
## iter 40 value 964.192122
## iter 50 value 962.694516
## final value 962.690852
## converged
## Fitting Repeat 5
##
## # weights: 13
## initial value 1719.779540
## iter 10 value 1309.027463
## iter 20 value 1082.630095
## iter 30 value 970.579533
## iter 40 value 964.393893
## iter 50 value 963.149652
## iter 50 value 963.149650
## iter 50 value 963.149650
## final value 963.149650
## converged
## Fitting Repeat 1
##
## # weights: 35
## initial value 1831.026756
## iter 10 value 1533.726732
## iter 20 value 1009.412493
## iter 30 value 962.775482
## iter 40 value 962.228441
## iter 50 value 960.360512
## iter 60 value 947.475269
## iter 70 value 940.603288
## iter 80 value 939.993586
## iter 90 value 939.597603
## iter 100 value 938.145408
## final value 938.145408
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1874.372410
## iter 10 value 1252.356454
## iter 20 value 1024.016059
## iter 30 value 981.185094
## iter 40 value 959.158601
## iter 50 value 947.367553
## iter 60 value 943.333613
## iter 70 value 940.448557
## iter 80 value 939.841494
## iter 90 value 939.620460
## iter 100 value 939.612870
## final value 939.612870
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 1796.536300
## iter 10 value 1451.526951
## iter 20 value 1043.204452
## iter 30 value 971.235275
## iter 40 value 959.074285
## iter 50 value 950.581763
## iter 60 value 943.682959
## iter 70 value 940.785127
## iter 80 value 940.087518
## iter 90 value 939.909260
## iter 100 value 939.893532
## final value 939.893532
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1785.430448
## iter 10 value 1474.811854
## iter 20 value 1063.912138
## iter 30 value 985.563209
## iter 40 value 968.681684
## iter 50 value 960.004692
## iter 60 value 946.812733
## iter 70 value 943.586052
## iter 80 value 941.764277
## iter 90 value 939.893454
## iter 100 value 938.045394
## final value 938.045394
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1799.400610
## iter 10 value 1220.685614
## iter 20 value 1063.480516
## iter 30 value 972.342545
## iter 40 value 954.465598
## iter 50 value 941.781220
## iter 60 value 938.637942
## iter 70 value 936.054285
## iter 80 value 935.378852
## iter 90 value 934.506777
## iter 100 value 934.209537
## final value 934.209537
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 1707.702606
## iter 10 value 1076.692548
## iter 20 value 964.762593
## iter 30 value 947.545597
## iter 40 value 939.567587
## iter 50 value 937.284660
## iter 60 value 928.505475
## iter 70 value 926.709083
## iter 80 value 925.918798
## iter 90 value 924.764743
## iter 100 value 924.194574
## final value 924.194574
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1767.283570
## iter 10 value 1202.631145
## iter 20 value 1039.339859
## iter 30 value 974.851814
## iter 40 value 931.770187
## iter 50 value 920.166977
## iter 60 value 914.654914
## iter 70 value 913.014605
## iter 80 value 911.834680
## iter 90 value 910.642140
## iter 100 value 910.205808
## final value 910.205808
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 2220.513151
## iter 10 value 1304.536614
## iter 20 value 1004.338175
## iter 30 value 962.448762
## iter 40 value 939.441603
## iter 50 value 933.995247
## iter 60 value 924.235400
## iter 70 value 920.263860
## iter 80 value 919.065967
## iter 90 value 918.794021
## iter 100 value 918.776448
## final value 918.776448
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1705.067106
## iter 10 value 1098.594651
## iter 20 value 996.562549
## iter 30 value 974.915700
## iter 40 value 955.993340
## iter 50 value 946.989969
## iter 60 value 942.814287
## iter 70 value 937.597422
## iter 80 value 933.161079
## iter 90 value 928.739003
## iter 100 value 927.522904
## final value 927.522904
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1778.969037
## iter 10 value 1298.967168
## iter 20 value 995.849842
## iter 30 value 954.691423
## iter 40 value 949.132300
## iter 50 value 946.324414
## iter 60 value 944.245003
## iter 70 value 940.825642
## iter 80 value 932.236932
## iter 90 value 929.323585
## iter 100 value 927.898996
## final value 927.898996
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1736.303831
## iter 10 value 1289.893842
## iter 20 value 1000.320298
## iter 30 value 965.537360
## iter 40 value 960.009679
## iter 50 value 951.887833
## iter 60 value 949.900462
## iter 70 value 949.585751
## iter 80 value 949.215014
## iter 90 value 949.166217
## iter 100 value 949.161491
## final value 949.161491
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 13
## initial value 1698.816635
## iter 10 value 1606.447586
## iter 20 value 1013.643502
## iter 30 value 978.065075
## iter 40 value 976.317734
## iter 50 value 973.840733
## iter 60 value 972.252407
## iter 70 value 971.817551
## iter 80 value 970.944475
## iter 90 value 970.646394
## iter 100 value 969.742107
## final value 969.742107
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 13
## initial value 1721.145536
## iter 10 value 1177.794465
## iter 20 value 974.821647
## iter 30 value 962.819613
## iter 40 value 956.554791
## iter 50 value 950.674610
## iter 60 value 949.663260
## iter 70 value 949.473566
## iter 80 value 949.204081
## iter 90 value 949.167822
## iter 100 value 949.157168
## final value 949.157168
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 13
## initial value 1704.804230
## iter 10 value 1559.707722
## iter 20 value 1163.778956
## iter 30 value 967.043326
## iter 40 value 954.892094
## iter 50 value 950.980014
## iter 60 value 949.515231
## iter 70 value 949.426834
## iter 80 value 949.207514
## iter 90 value 949.153871
## iter 100 value 949.151906
## final value 949.151906
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 13
## initial value 1830.192799
## iter 10 value 1425.685334
## iter 20 value 1115.196462
## iter 30 value 991.792956
## iter 40 value 959.913956
## iter 50 value 950.210680
## iter 60 value 949.732503
## iter 70 value 949.382580
## iter 80 value 949.180918
## iter 90 value 949.172326
## iter 100 value 949.168089
## final value 949.168089
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 35
## initial value 1864.956430
## iter 10 value 1394.043184
## iter 20 value 1031.908120
## iter 30 value 966.227633
## iter 40 value 950.375701
## iter 50 value 937.043900
## iter 60 value 934.594704
## iter 70 value 933.848852
## iter 80 value 933.362820
## iter 90 value 932.821961
## iter 100 value 932.446770
## final value 932.446770
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1915.507884
## iter 10 value 1259.884647
## iter 20 value 1101.480747
## iter 30 value 971.287153
## iter 40 value 949.295800
## iter 50 value 933.440479
## iter 60 value 930.560335
## iter 70 value 929.967396
## iter 80 value 929.908490
## iter 90 value 929.544218
## iter 100 value 920.753272
## final value 920.753272
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 1773.719159
## iter 10 value 1056.162717
## iter 20 value 959.593846
## iter 30 value 945.951482
## iter 40 value 925.302553
## iter 50 value 920.771276
## iter 60 value 918.029499
## iter 70 value 917.081866
## iter 80 value 917.043594
## iter 90 value 916.920380
## iter 100 value 916.816307
## final value 916.816307
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1777.530316
## iter 10 value 1361.542901
## iter 20 value 983.785794
## iter 30 value 944.751756
## iter 40 value 937.558904
## iter 50 value 933.946933
## iter 60 value 930.697232
## iter 70 value 928.689784
## iter 80 value 927.760702
## iter 90 value 926.919110
## iter 100 value 926.373664
## final value 926.373664
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1835.207335
## iter 10 value 1319.459967
## iter 20 value 1003.129452
## iter 30 value 950.743845
## iter 40 value 937.877797
## iter 50 value 933.286710
## iter 60 value 931.963281
## iter 70 value 931.703742
## iter 80 value 931.629620
## iter 90 value 931.579538
## iter 100 value 930.737312
## final value 930.737312
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 2241.719559
## iter 10 value 1285.204680
## iter 20 value 1011.879891
## iter 30 value 956.685681
## iter 40 value 935.988469
## iter 50 value 916.479858
## iter 60 value 911.083046
## iter 70 value 906.247282
## iter 80 value 904.920651
## iter 90 value 903.298102
## iter 100 value 900.775180
## final value 900.775180
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1752.949017
## iter 10 value 1291.754594
## iter 20 value 962.814381
## iter 30 value 943.301074
## iter 40 value 932.194041
## iter 50 value 917.574680
## iter 60 value 913.996941
## iter 70 value 911.971280
## iter 80 value 909.486347
## iter 90 value 907.485939
## iter 100 value 906.189172
## final value 906.189172
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 1897.474072
## iter 10 value 1508.469819
## iter 20 value 1100.123834
## iter 30 value 989.127444
## iter 40 value 950.678676
## iter 50 value 943.256991
## iter 60 value 937.836655
## iter 70 value 927.828461
## iter 80 value 926.804134
## iter 90 value 926.429766
## iter 100 value 926.240155
## final value 926.240155
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1704.996682
## iter 10 value 1019.488254
## iter 20 value 954.278784
## iter 30 value 938.404194
## iter 40 value 931.374385
## iter 50 value 924.025929
## iter 60 value 919.299922
## iter 70 value 916.994502
## iter 80 value 914.890715
## iter 90 value 908.862387
## iter 100 value 905.186772
## final value 905.186772
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 2006.823188
## iter 10 value 1093.713646
## iter 20 value 979.888409
## iter 30 value 956.063561
## iter 40 value 947.276973
## iter 50 value 939.297715
## iter 60 value 926.198285
## iter 70 value 920.197645
## iter 80 value 917.107863
## iter 90 value 915.514255
## iter 100 value 911.591495
## final value 911.591495
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1746.287077
## iter 10 value 1241.088451
## iter 20 value 953.634489
## iter 30 value 946.117543
## iter 40 value 942.327937
## iter 50 value 937.300216
## iter 60 value 936.594948
## iter 70 value 936.484798
## iter 80 value 936.372492
## iter 90 value 936.363202
## iter 100 value 936.350188
## final value 936.350188
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 13
## initial value 1804.026652
## iter 10 value 1257.988918
## iter 20 value 998.597581
## iter 30 value 970.694365
## iter 40 value 946.850829
## iter 50 value 939.301281
## iter 60 value 937.690310
## iter 70 value 937.140362
## iter 80 value 936.572964
## iter 90 value 936.434930
## iter 100 value 936.414858
## final value 936.414858
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 13
## initial value 1666.493694
## iter 10 value 1493.124797
## iter 20 value 994.119209
## iter 30 value 971.687754
## iter 40 value 947.030596
## iter 50 value 939.618675
## iter 60 value 937.966036
## iter 70 value 937.074992
## iter 80 value 936.548723
## iter 90 value 936.507808
## iter 100 value 936.435665
## final value 936.435665
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 13
## initial value 1785.966644
## iter 10 value 1646.916641
## iter 20 value 1530.686308
## iter 30 value 1179.982010
## iter 40 value 953.676108
## iter 50 value 938.357773
## iter 60 value 937.657103
## iter 70 value 937.247039
## iter 80 value 936.948266
## iter 90 value 936.934109
## iter 100 value 936.732163
## final value 936.732163
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 13
## initial value 1690.765375
## iter 10 value 1452.044730
## iter 20 value 960.349493
## iter 30 value 949.280900
## iter 40 value 939.526529
## iter 50 value 937.410576
## iter 60 value 937.160978
## iter 70 value 936.614813
## iter 80 value 936.475347
## iter 90 value 936.441026
## iter 100 value 936.357723
## final value 936.357723
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 35
## initial value 1894.566029
## iter 10 value 1065.386444
## iter 20 value 956.632713
## iter 30 value 944.135311
## iter 40 value 939.188490
## iter 50 value 937.010652
## iter 60 value 936.756130
## iter 70 value 936.491247
## iter 80 value 936.454048
## iter 90 value 936.390434
## iter 100 value 936.333306
## final value 936.333306
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1904.997477
## iter 10 value 1623.624555
## iter 20 value 1093.914049
## iter 30 value 1025.325794
## iter 40 value 1004.655178
## iter 50 value 998.327116
## iter 60 value 990.215887
## iter 70 value 978.968902
## iter 80 value 971.167802
## iter 90 value 938.081147
## iter 100 value 931.430667
## final value 931.430667
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 1674.724150
## iter 10 value 1529.120939
## iter 20 value 968.191915
## iter 30 value 920.112565
## iter 40 value 913.042178
## iter 50 value 905.329169
## iter 60 value 902.641438
## iter 70 value 900.998471
## iter 80 value 899.928432
## iter 90 value 899.681509
## iter 100 value 899.210359
## final value 899.210359
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1955.236571
## iter 10 value 1594.127804
## iter 20 value 1005.641938
## iter 30 value 935.816699
## iter 40 value 927.295285
## iter 50 value 922.013933
## iter 60 value 920.137497
## iter 70 value 919.637438
## iter 80 value 918.338545
## iter 90 value 917.030610
## iter 100 value 916.624074
## final value 916.624074
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1757.636156
## iter 10 value 1366.377225
## iter 20 value 1024.759788
## iter 30 value 977.399912
## iter 40 value 960.563083
## iter 50 value 937.991354
## iter 60 value 925.922459
## iter 70 value 918.768134
## iter 80 value 915.264385
## iter 90 value 913.087681
## iter 100 value 911.821258
## final value 911.821258
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 1759.645448
## iter 10 value 1440.633286
## iter 20 value 1121.544934
## iter 30 value 934.545214
## iter 40 value 918.435285
## iter 50 value 913.414484
## iter 60 value 909.373619
## iter 70 value 907.239797
## iter 80 value 902.933438
## iter 90 value 900.895319
## iter 100 value 899.862944
## final value 899.862944
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1863.526037
## final value 1664.999974
## converged
## Fitting Repeat 3
##
## # weights: 57
## initial value 1688.324236
## iter 10 value 1118.305902
## iter 20 value 959.107757
## iter 30 value 942.497697
## iter 40 value 936.369112
## iter 50 value 935.419415
## iter 60 value 934.646847
## iter 70 value 934.364521
## iter 80 value 933.924145
## iter 90 value 932.559964
## iter 100 value 932.436729
## final value 932.436729
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1756.397323
## iter 10 value 1111.188346
## iter 20 value 988.811725
## iter 30 value 931.029750
## iter 40 value 909.081887
## iter 50 value 899.170146
## iter 60 value 888.714135
## iter 70 value 879.657763
## iter 80 value 873.098036
## iter 90 value 869.203367
## iter 100 value 866.506583
## final value 866.506583
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1699.979739
## iter 10 value 1033.245276
## iter 20 value 940.401703
## iter 30 value 914.565093
## iter 40 value 903.086106
## iter 50 value 895.931804
## iter 60 value 888.090235
## iter 70 value 882.857768
## iter 80 value 878.761744
## iter 90 value 875.943569
## iter 100 value 874.688249
## final value 874.688249
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1745.562756
## iter 10 value 1321.875309
## iter 20 value 1002.958017
## iter 30 value 963.101156
## iter 40 value 952.357793
## iter 50 value 950.669628
## final value 950.669611
## converged
## Fitting Repeat 2
##
## # weights: 13
## initial value 1718.492456
## iter 10 value 1437.402089
## iter 20 value 1056.999116
## iter 30 value 972.629372
## iter 40 value 953.507589
## iter 50 value 950.676262
## final value 950.669636
## converged
## Fitting Repeat 3
##
## # weights: 13
## initial value 1712.803460
## iter 10 value 1542.158986
## iter 20 value 1204.248391
## iter 30 value 987.439673
## iter 40 value 959.367144
## iter 50 value 950.942247
## final value 950.669611
## converged
## Fitting Repeat 4
##
## # weights: 13
## initial value 1691.797031
## iter 10 value 1467.730891
## iter 20 value 1051.465042
## iter 30 value 972.158965
## iter 40 value 957.706494
## iter 50 value 950.753333
## final value 950.669614
## converged
## Fitting Repeat 5
##
## # weights: 13
## initial value 1674.181243
## iter 10 value 1461.420637
## iter 20 value 1028.024410
## iter 30 value 962.107903
## iter 40 value 953.337200
## iter 50 value 950.980811
## final value 950.977025
## converged
## Fitting Repeat 1
##
## # weights: 35
## initial value 1724.421641
## iter 10 value 1554.026770
## iter 20 value 1207.685205
## iter 30 value 1022.657171
## iter 40 value 963.337023
## iter 50 value 944.742477
## iter 60 value 936.405208
## iter 70 value 932.516426
## iter 80 value 929.132717
## iter 90 value 927.774590
## iter 100 value 922.586685
## final value 922.586685
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1857.570337
## iter 10 value 1477.421058
## iter 20 value 972.992409
## iter 30 value 939.889341
## iter 40 value 935.372084
## iter 50 value 924.936903
## iter 60 value 921.999616
## iter 70 value 918.476280
## iter 80 value 917.231425
## iter 90 value 917.214268
## iter 100 value 917.203056
## final value 917.203056
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 2008.134339
## iter 10 value 1397.290448
## iter 20 value 997.892569
## iter 30 value 959.399509
## iter 40 value 948.554066
## iter 50 value 939.030503
## iter 60 value 930.259795
## iter 70 value 924.700783
## iter 80 value 922.698423
## iter 90 value 921.274365
## iter 100 value 920.943392
## final value 920.943392
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1758.645969
## iter 10 value 1502.568847
## iter 20 value 1054.433292
## iter 30 value 964.068321
## iter 40 value 942.471480
## iter 50 value 931.106864
## iter 60 value 922.661695
## iter 70 value 919.391663
## iter 80 value 918.684335
## iter 90 value 918.509906
## iter 100 value 918.457094
## final value 918.457094
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1947.732872
## iter 10 value 1293.323498
## iter 20 value 1079.153143
## iter 30 value 983.924475
## iter 40 value 945.649082
## iter 50 value 935.429591
## iter 60 value 926.324596
## iter 70 value 924.551992
## iter 80 value 922.248012
## iter 90 value 921.706058
## final value 921.701811
## converged
## Fitting Repeat 1
##
## # weights: 57
## initial value 1756.357463
## iter 10 value 1133.206645
## iter 20 value 1013.905845
## iter 30 value 958.841145
## iter 40 value 947.281191
## iter 50 value 938.154292
## iter 60 value 930.474010
## iter 70 value 926.507073
## iter 80 value 921.840601
## iter 90 value 918.302216
## iter 100 value 911.886208
## final value 911.886208
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1935.314563
## iter 10 value 1267.664109
## iter 20 value 1040.204588
## iter 30 value 969.643632
## iter 40 value 949.739446
## iter 50 value 937.421906
## iter 60 value 921.154916
## iter 70 value 912.676869
## iter 80 value 908.399581
## iter 90 value 904.430003
## iter 100 value 901.930951
## final value 901.930951
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 1763.125623
## iter 10 value 1000.109026
## iter 20 value 939.253350
## iter 30 value 931.518710
## iter 40 value 926.216184
## iter 50 value 921.349173
## iter 60 value 916.700972
## iter 70 value 915.158485
## iter 80 value 910.114025
## iter 90 value 907.237462
## iter 100 value 905.088405
## final value 905.088405
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1975.840960
## iter 10 value 1352.620243
## iter 20 value 1048.081136
## iter 30 value 981.418075
## iter 40 value 952.245552
## iter 50 value 935.576251
## iter 60 value 918.053834
## iter 70 value 908.191853
## iter 80 value 905.666949
## iter 90 value 903.502969
## iter 100 value 901.582579
## final value 901.582579
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1682.007647
## iter 10 value 1152.522033
## iter 20 value 1013.942294
## iter 30 value 951.174434
## iter 40 value 933.259167
## iter 50 value 924.920592
## iter 60 value 919.454548
## iter 70 value 918.370189
## iter 80 value 918.266114
## iter 90 value 918.247726
## iter 100 value 918.154860
## final value 918.154860
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1750.238787
## iter 10 value 1358.171318
## iter 20 value 1056.970920
## iter 30 value 1038.546529
## iter 40 value 977.136824
## iter 50 value 960.490817
## iter 60 value 944.801817
## iter 70 value 939.871863
## iter 80 value 939.058259
## iter 90 value 937.464126
## iter 100 value 937.216439
## final value 937.216439
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 13
## initial value 1689.740232
## iter 10 value 1103.977485
## iter 20 value 997.523066
## iter 30 value 967.262727
## iter 40 value 950.561018
## iter 50 value 940.368340
## iter 60 value 938.304091
## iter 70 value 937.108415
## iter 80 value 936.634351
## iter 90 value 936.587661
## iter 100 value 936.489177
## final value 936.489177
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 13
## initial value 1691.046067
## iter 10 value 1408.425210
## iter 20 value 1273.172442
## iter 30 value 1154.968465
## iter 40 value 1062.870498
## iter 50 value 1056.401460
## iter 60 value 1037.651435
## iter 70 value 1032.806928
## iter 80 value 1029.103507
## iter 90 value 1018.674038
## iter 100 value 1014.906877
## final value 1014.906877
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 13
## initial value 1685.042660
## iter 10 value 1238.272132
## iter 20 value 1028.098877
## iter 30 value 961.044514
## iter 40 value 957.231779
## iter 50 value 953.641755
## iter 60 value 939.597324
## iter 70 value 937.417627
## iter 80 value 937.047055
## iter 90 value 936.656406
## iter 100 value 936.488465
## final value 936.488465
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 13
## initial value 1840.303723
## iter 10 value 1286.061766
## iter 20 value 1002.539611
## iter 30 value 982.388260
## iter 40 value 969.648101
## iter 50 value 958.849669
## iter 60 value 957.141429
## iter 70 value 955.079892
## iter 80 value 948.602023
## iter 90 value 937.334040
## iter 100 value 937.102220
## final value 937.102220
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 35
## initial value 1715.197695
## iter 10 value 1226.757220
## iter 20 value 962.977280
## iter 30 value 936.177170
## iter 40 value 927.277612
## iter 50 value 923.586482
## iter 60 value 920.329622
## iter 70 value 919.659485
## iter 80 value 915.027239
## iter 90 value 911.121313
## iter 100 value 909.888629
## final value 909.888629
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1709.736997
## iter 10 value 1602.418250
## iter 20 value 1303.793085
## iter 30 value 1079.366993
## iter 40 value 949.398790
## iter 50 value 922.262000
## iter 60 value 916.646940
## iter 70 value 914.279965
## iter 80 value 912.997302
## iter 90 value 912.782055
## iter 100 value 912.391324
## final value 912.391324
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 1760.166703
## iter 10 value 1186.972983
## iter 20 value 1000.560423
## iter 30 value 976.548804
## iter 40 value 958.487156
## iter 50 value 942.999307
## iter 60 value 937.134798
## iter 70 value 934.398832
## iter 80 value 933.707171
## iter 90 value 930.741150
## iter 100 value 927.452208
## final value 927.452208
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1848.254943
## iter 10 value 1512.071819
## iter 20 value 1170.589128
## iter 30 value 1055.615381
## iter 40 value 971.042665
## iter 50 value 943.826066
## iter 60 value 938.561112
## iter 70 value 937.242490
## iter 80 value 936.813943
## iter 90 value 936.578997
## iter 100 value 936.496015
## final value 936.496015
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1764.528928
## iter 10 value 1231.741601
## iter 20 value 1058.201151
## iter 30 value 945.722896
## iter 40 value 926.401401
## iter 50 value 915.954723
## iter 60 value 912.598264
## iter 70 value 911.018314
## iter 80 value 910.250518
## iter 90 value 907.739004
## iter 100 value 906.482892
## final value 906.482892
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 1972.262402
## iter 10 value 1175.334485
## iter 20 value 981.723294
## iter 30 value 938.658259
## iter 40 value 925.392131
## iter 50 value 918.200302
## iter 60 value 908.801400
## iter 70 value 901.219600
## iter 80 value 892.169924
## iter 90 value 887.622609
## iter 100 value 884.550616
## final value 884.550616
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1885.950231
## iter 10 value 1477.351892
## iter 20 value 1176.245364
## iter 30 value 989.839520
## iter 40 value 944.899318
## iter 50 value 922.631565
## iter 60 value 913.133790
## iter 70 value 909.756537
## iter 80 value 907.466313
## iter 90 value 906.249118
## iter 100 value 905.509039
## final value 905.509039
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 1845.322538
## iter 10 value 1338.072674
## iter 20 value 1036.338597
## iter 30 value 928.345385
## iter 40 value 911.050368
## iter 50 value 903.579626
## iter 60 value 897.913067
## iter 70 value 894.609436
## iter 80 value 891.938488
## iter 90 value 890.897729
## iter 100 value 889.655515
## final value 889.655515
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1837.218937
## iter 10 value 1292.804168
## iter 20 value 945.208854
## iter 30 value 918.028015
## iter 40 value 907.100697
## iter 50 value 902.902103
## iter 60 value 900.447592
## iter 70 value 899.521028
## iter 80 value 898.729240
## iter 90 value 898.235204
## iter 100 value 898.080770
## final value 898.080770
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1843.496604
## iter 10 value 1254.111996
## iter 20 value 975.563331
## iter 30 value 946.393298
## iter 40 value 917.059322
## iter 50 value 900.367896
## iter 60 value 898.112148
## iter 70 value 892.509822
## iter 80 value 887.495744
## iter 90 value 882.944612
## iter 100 value 878.113335
## final value 878.113335
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1745.111654
## iter 10 value 1465.932516
## iter 20 value 1132.029147
## iter 30 value 1026.994132
## iter 40 value 978.452910
## iter 50 value 971.276422
## iter 60 value 970.942047
## iter 70 value 970.867066
## final value 970.866682
## converged
## Fitting Repeat 2
##
## # weights: 13
## initial value 1871.950628
## iter 10 value 1484.158981
## iter 20 value 1031.194762
## iter 30 value 968.686647
## iter 40 value 956.028403
## iter 50 value 950.081890
## iter 60 value 949.075669
## iter 70 value 948.352673
## iter 80 value 948.067769
## iter 90 value 948.017631
## iter 100 value 947.956523
## final value 947.956523
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 13
## initial value 1774.505549
## iter 10 value 1307.829728
## iter 20 value 1001.065408
## iter 30 value 952.369075
## iter 40 value 949.289065
## iter 50 value 948.355527
## iter 60 value 948.075895
## iter 70 value 948.071234
## iter 80 value 947.971534
## iter 90 value 947.932174
## final value 947.932040
## converged
## Fitting Repeat 4
##
## # weights: 13
## initial value 1758.779745
## iter 10 value 1614.248074
## iter 20 value 1366.060260
## iter 30 value 1328.038747
## iter 40 value 1286.521991
## iter 50 value 1189.521933
## iter 60 value 1162.428429
## iter 70 value 1139.594138
## iter 80 value 1135.525742
## iter 90 value 1129.968582
## iter 100 value 1127.034463
## final value 1127.034463
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 13
## initial value 1747.874979
## iter 10 value 1590.607592
## iter 20 value 1461.083183
## iter 30 value 1387.376294
## iter 40 value 1328.584861
## iter 50 value 1161.624848
## iter 60 value 1126.698818
## iter 70 value 1070.011180
## iter 80 value 1056.255721
## iter 90 value 1051.092334
## iter 100 value 1051.026921
## final value 1051.026921
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 35
## initial value 1761.824762
## iter 10 value 1642.091159
## iter 20 value 1114.563252
## iter 30 value 962.571420
## iter 40 value 953.144377
## iter 50 value 949.178115
## iter 60 value 948.259457
## iter 70 value 947.822761
## iter 80 value 947.689305
## final value 947.680081
## converged
## Fitting Repeat 2
##
## # weights: 35
## initial value 1725.001759
## iter 10 value 1166.719846
## iter 20 value 998.354272
## iter 30 value 951.661614
## iter 40 value 944.443182
## iter 50 value 939.794882
## iter 60 value 933.617671
## iter 70 value 932.498685
## iter 80 value 932.040016
## iter 90 value 930.595763
## iter 100 value 929.359034
## final value 929.359034
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 1716.922245
## iter 10 value 1108.338090
## iter 20 value 974.356807
## iter 30 value 951.295735
## iter 40 value 946.935743
## iter 50 value 943.849209
## iter 60 value 942.945210
## iter 70 value 942.556297
## iter 80 value 942.309620
## iter 90 value 941.785458
## iter 100 value 940.941190
## final value 940.941190
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1839.321725
## iter 10 value 1514.672343
## iter 20 value 1095.523627
## iter 30 value 990.936178
## iter 40 value 965.644095
## iter 50 value 954.210280
## iter 60 value 950.320122
## iter 70 value 949.081889
## iter 80 value 948.688351
## iter 90 value 948.139106
## iter 100 value 948.003658
## final value 948.003658
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1849.827042
## iter 10 value 1077.853578
## iter 20 value 966.009792
## iter 30 value 945.078125
## iter 40 value 928.577086
## iter 50 value 924.300489
## iter 60 value 920.706262
## iter 70 value 915.800994
## iter 80 value 914.751064
## iter 90 value 913.153760
## iter 100 value 910.152266
## final value 910.152266
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 1692.380815
## iter 10 value 1104.341160
## iter 20 value 970.889070
## iter 30 value 950.980470
## iter 40 value 945.670516
## iter 50 value 944.347618
## iter 60 value 944.016651
## iter 70 value 941.550488
## iter 80 value 939.698220
## iter 90 value 937.592604
## iter 100 value 935.095872
## final value 935.095872
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1809.215462
## iter 10 value 997.681978
## iter 20 value 966.975834
## iter 30 value 948.201933
## iter 40 value 932.597003
## iter 50 value 922.045472
## iter 60 value 916.626807
## iter 70 value 912.717184
## iter 80 value 909.845203
## iter 90 value 906.367612
## iter 100 value 904.214291
## final value 904.214291
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 2084.393636
## iter 10 value 1058.643829
## iter 20 value 972.013014
## iter 30 value 948.128980
## iter 40 value 930.640383
## iter 50 value 924.886575
## iter 60 value 922.290008
## iter 70 value 921.236353
## iter 80 value 920.534643
## iter 90 value 919.195235
## iter 100 value 918.532016
## final value 918.532016
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1691.218624
## iter 10 value 1486.750318
## iter 20 value 1121.756142
## iter 30 value 991.127937
## iter 40 value 945.581249
## iter 50 value 929.403377
## iter 60 value 922.415078
## iter 70 value 919.630939
## iter 80 value 913.990592
## iter 90 value 911.967914
## iter 100 value 909.036137
## final value 909.036137
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1723.382846
## iter 10 value 1248.734041
## iter 20 value 988.212923
## iter 30 value 976.588272
## iter 40 value 973.707552
## iter 50 value 965.055847
## iter 60 value 962.795534
## iter 70 value 958.244811
## iter 80 value 945.417908
## iter 90 value 943.347317
## iter 100 value 939.732998
## final value 939.732998
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1704.132284
## iter 10 value 1529.975713
## iter 20 value 1009.371656
## iter 30 value 967.943613
## iter 40 value 962.979545
## iter 50 value 962.212915
## final value 962.212867
## converged
## Fitting Repeat 2
##
## # weights: 13
## initial value 1797.830155
## iter 10 value 1290.582868
## iter 20 value 1028.406712
## iter 30 value 966.919295
## iter 40 value 965.146050
## iter 50 value 962.218186
## final value 962.212868
## converged
## Fitting Repeat 3
##
## # weights: 13
## initial value 1765.706060
## iter 10 value 1345.419809
## iter 20 value 1000.886791
## iter 30 value 966.764099
## iter 40 value 962.394635
## iter 50 value 961.603844
## iter 50 value 961.603834
## iter 50 value 961.603834
## final value 961.603834
## converged
## Fitting Repeat 4
##
## # weights: 13
## initial value 1731.934857
## iter 10 value 1383.702247
## iter 20 value 974.587344
## iter 30 value 965.139841
## iter 40 value 961.953828
## final value 961.603834
## converged
## Fitting Repeat 5
##
## # weights: 13
## initial value 1798.700522
## iter 10 value 1357.728374
## iter 20 value 985.473713
## iter 30 value 967.906917
## iter 40 value 963.198378
## iter 50 value 962.212868
## iter 50 value 962.212868
## iter 50 value 962.212868
## final value 962.212868
## converged
## Fitting Repeat 1
##
## # weights: 35
## initial value 1810.869975
## iter 10 value 1221.806592
## iter 20 value 1009.552317
## iter 30 value 971.929176
## iter 40 value 964.678609
## iter 50 value 962.473529
## iter 60 value 961.940840
## iter 70 value 960.202690
## iter 80 value 948.655071
## iter 90 value 944.719649
## iter 100 value 943.941156
## final value 943.941156
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1748.719524
## iter 10 value 1064.033623
## iter 20 value 1017.686253
## iter 30 value 980.466004
## iter 40 value 950.864877
## iter 50 value 943.218459
## iter 60 value 936.513148
## iter 70 value 933.498292
## iter 80 value 931.860608
## iter 90 value 931.050133
## iter 100 value 930.945683
## final value 930.945683
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 1908.311441
## iter 10 value 1094.922586
## iter 20 value 975.971324
## iter 30 value 963.131390
## iter 40 value 950.793946
## iter 50 value 948.778712
## iter 60 value 948.008724
## iter 70 value 946.685434
## iter 80 value 944.224791
## iter 90 value 940.847030
## iter 100 value 939.746229
## final value 939.746229
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1811.294648
## iter 10 value 1006.304041
## iter 20 value 970.857106
## iter 30 value 957.731565
## iter 40 value 954.202370
## iter 50 value 951.473760
## iter 60 value 951.111254
## iter 70 value 949.840257
## iter 80 value 949.246051
## iter 90 value 944.533026
## iter 100 value 940.634044
## final value 940.634044
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1748.925124
## iter 10 value 1225.202480
## iter 20 value 1006.777008
## iter 30 value 973.947333
## iter 40 value 961.850926
## iter 50 value 952.800558
## iter 60 value 949.831627
## iter 70 value 947.432542
## iter 80 value 946.491650
## iter 90 value 945.623823
## iter 100 value 945.497147
## final value 945.497147
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 1985.489437
## iter 10 value 1425.720236
## iter 20 value 1103.841244
## iter 30 value 1013.749872
## iter 40 value 971.688101
## iter 50 value 952.123834
## iter 60 value 938.895431
## iter 70 value 926.593873
## iter 80 value 919.306711
## iter 90 value 916.338825
## iter 100 value 915.645156
## final value 915.645156
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1849.485128
## iter 10 value 1421.129957
## iter 20 value 1054.943765
## iter 30 value 973.938044
## iter 40 value 953.569384
## iter 50 value 946.537102
## iter 60 value 938.760817
## iter 70 value 929.933136
## iter 80 value 922.874892
## iter 90 value 919.906687
## iter 100 value 918.284385
## final value 918.284385
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 1658.868104
## iter 10 value 1000.493307
## iter 20 value 973.282406
## iter 30 value 956.164450
## iter 40 value 951.740810
## iter 50 value 948.173429
## iter 60 value 944.802490
## iter 70 value 939.133450
## iter 80 value 932.865720
## iter 90 value 932.022614
## iter 100 value 929.921225
## final value 929.921225
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1854.290468
## iter 10 value 1180.161996
## iter 20 value 1044.179990
## iter 30 value 975.404029
## iter 40 value 962.960428
## iter 50 value 957.963982
## iter 60 value 949.921320
## iter 70 value 940.208356
## iter 80 value 933.232977
## iter 90 value 924.644125
## iter 100 value 923.379024
## final value 923.379024
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1764.535727
## iter 10 value 1235.771747
## iter 20 value 1032.493002
## iter 30 value 971.632341
## iter 40 value 953.628573
## iter 50 value 945.732783
## iter 60 value 943.679635
## iter 70 value 942.241922
## iter 80 value 939.803781
## iter 90 value 937.780976
## iter 100 value 935.785223
## final value 935.785223
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1711.881309
## iter 10 value 1572.888634
## iter 20 value 1438.314335
## iter 30 value 1061.170913
## iter 40 value 964.397721
## iter 50 value 953.345607
## iter 60 value 950.090944
## iter 70 value 948.464611
## iter 80 value 948.283483
## iter 90 value 948.116610
## iter 100 value 948.010708
## final value 948.010708
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 13
## initial value 1711.047098
## iter 10 value 1545.834423
## iter 20 value 1097.880362
## iter 30 value 988.586974
## iter 40 value 967.652655
## iter 50 value 956.932289
## iter 60 value 950.327059
## iter 70 value 948.732225
## iter 80 value 948.564054
## iter 90 value 948.104017
## iter 100 value 948.030179
## final value 948.030179
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 13
## initial value 1680.103787
## iter 10 value 1196.822983
## iter 20 value 956.600971
## iter 30 value 950.214450
## iter 40 value 949.913709
## iter 50 value 948.475307
## iter 60 value 948.139794
## iter 70 value 948.099765
## iter 80 value 948.023755
## iter 90 value 947.991716
## iter 90 value 947.991711
## final value 947.991580
## converged
## Fitting Repeat 4
##
## # weights: 13
## initial value 1717.899709
## iter 10 value 1507.983570
## iter 20 value 1055.455932
## iter 30 value 1024.259724
## iter 40 value 1023.778627
## iter 50 value 1018.564940
## iter 60 value 989.840060
## iter 70 value 981.318399
## iter 80 value 978.256491
## iter 90 value 975.294514
## iter 100 value 974.863602
## final value 974.863602
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 13
## initial value 1760.055416
## iter 10 value 1498.862717
## iter 20 value 1066.429356
## iter 30 value 970.080060
## iter 40 value 959.992689
## iter 50 value 951.423048
## iter 60 value 949.268762
## iter 70 value 948.867401
## iter 80 value 948.234554
## final value 948.052322
## converged
## Fitting Repeat 1
##
## # weights: 35
## initial value 1732.270072
## iter 10 value 1321.142435
## iter 20 value 1035.251989
## iter 30 value 969.183291
## iter 40 value 952.205225
## iter 50 value 945.610595
## iter 60 value 941.878139
## iter 70 value 937.231936
## iter 80 value 936.233796
## iter 90 value 932.175543
## iter 100 value 928.385460
## final value 928.385460
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1868.669844
## iter 10 value 1030.874156
## iter 20 value 952.568751
## iter 30 value 942.769980
## iter 40 value 938.807138
## iter 50 value 937.868170
## iter 60 value 933.114487
## iter 70 value 923.668407
## iter 80 value 919.537071
## iter 90 value 918.050784
## iter 100 value 915.720931
## final value 915.720931
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 1786.346912
## iter 10 value 1200.442009
## iter 20 value 1026.557464
## iter 30 value 957.659618
## iter 40 value 945.420874
## iter 50 value 934.957953
## iter 60 value 927.092323
## iter 70 value 925.187838
## iter 80 value 924.551838
## iter 90 value 924.110604
## iter 100 value 922.780682
## final value 922.780682
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1737.461562
## iter 10 value 1659.733730
## iter 20 value 1278.796634
## iter 30 value 974.031679
## iter 40 value 950.399128
## iter 50 value 944.893507
## iter 60 value 942.750670
## iter 70 value 941.640337
## iter 80 value 940.701151
## iter 90 value 940.418663
## iter 100 value 940.373543
## final value 940.373543
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1781.154584
## iter 10 value 1060.167639
## iter 20 value 953.679401
## iter 30 value 946.605851
## iter 40 value 942.728443
## iter 50 value 940.786562
## iter 60 value 937.216037
## iter 70 value 934.152693
## iter 80 value 933.379969
## iter 90 value 932.626735
## iter 100 value 931.541343
## final value 931.541343
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 1779.185787
## iter 10 value 1387.992480
## iter 20 value 985.504301
## iter 30 value 946.734940
## iter 40 value 929.450107
## iter 50 value 920.527254
## iter 60 value 918.080904
## iter 70 value 914.746582
## iter 80 value 910.353320
## iter 90 value 909.401933
## iter 100 value 906.689640
## final value 906.689640
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1953.271378
## iter 10 value 1406.075347
## iter 20 value 1142.286361
## iter 30 value 970.906578
## iter 40 value 943.257565
## iter 50 value 929.149515
## iter 60 value 921.246388
## iter 70 value 918.561231
## iter 80 value 916.711023
## iter 90 value 913.816288
## iter 100 value 902.960453
## final value 902.960453
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 1812.520107
## iter 10 value 1124.687340
## iter 20 value 1024.111584
## iter 30 value 984.722427
## iter 40 value 973.230683
## iter 50 value 954.625908
## iter 60 value 946.386511
## iter 70 value 937.324675
## iter 80 value 935.241526
## iter 90 value 931.859595
## iter 100 value 930.860162
## final value 930.860162
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1780.056451
## iter 10 value 1388.754165
## iter 20 value 970.194254
## iter 30 value 959.265530
## iter 40 value 944.619406
## iter 50 value 937.883946
## iter 60 value 920.871626
## iter 70 value 909.160999
## iter 80 value 904.609242
## iter 90 value 899.479336
## iter 100 value 897.179713
## final value 897.179713
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1861.009430
## iter 10 value 1604.209880
## iter 20 value 1388.301043
## iter 30 value 1303.651976
## iter 40 value 1293.418477
## iter 50 value 1076.141037
## iter 60 value 994.479481
## iter 70 value 963.085780
## iter 80 value 951.929225
## iter 90 value 948.534820
## iter 100 value 945.455455
## final value 945.455455
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1708.143165
## iter 10 value 1450.776063
## iter 20 value 1045.995888
## iter 30 value 1021.679551
## iter 40 value 1021.323015
## iter 50 value 1020.818275
## iter 60 value 1014.027826
## iter 70 value 1009.774246
## iter 80 value 1007.581961
## iter 90 value 1004.879670
## iter 100 value 1000.004397
## final value 1000.004397
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 13
## initial value 1691.613959
## iter 10 value 1373.325418
## iter 20 value 1219.237847
## iter 30 value 1191.344156
## iter 40 value 1168.062454
## iter 50 value 1120.494383
## iter 60 value 1113.588375
## iter 70 value 1072.225114
## iter 80 value 1047.733184
## iter 90 value 1045.100368
## iter 100 value 1044.649721
## final value 1044.649721
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 13
## initial value 1758.245386
## iter 10 value 1508.181624
## iter 20 value 1140.587818
## iter 30 value 1064.382857
## iter 40 value 1054.965429
## iter 50 value 1046.809520
## iter 60 value 1024.415824
## iter 70 value 1020.329666
## iter 80 value 1019.387567
## iter 90 value 1015.087043
## iter 100 value 1013.611363
## final value 1013.611363
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 13
## initial value 1699.156897
## iter 10 value 1384.366889
## iter 20 value 1029.167203
## iter 30 value 988.389116
## iter 40 value 970.910428
## iter 50 value 955.911067
## iter 60 value 952.480535
## iter 70 value 947.725229
## iter 80 value 947.564115
## iter 90 value 947.473585
## iter 100 value 947.237264
## final value 947.237264
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 13
## initial value 1831.269399
## iter 10 value 1133.008419
## iter 20 value 983.216231
## iter 30 value 964.620288
## iter 40 value 955.924621
## iter 50 value 949.906520
## iter 60 value 948.478514
## iter 70 value 947.962395
## iter 80 value 947.486519
## iter 90 value 947.404725
## iter 100 value 947.102552
## final value 947.102552
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 35
## initial value 1714.779939
## iter 10 value 1503.441387
## iter 20 value 1105.097492
## iter 30 value 952.977809
## iter 40 value 943.973976
## iter 50 value 937.618271
## iter 60 value 934.106873
## iter 70 value 930.916527
## iter 80 value 927.928585
## iter 90 value 927.094334
## iter 100 value 925.234129
## final value 925.234129
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1693.598173
## iter 10 value 1233.543001
## iter 20 value 981.886821
## iter 30 value 937.754567
## iter 40 value 926.216087
## iter 50 value 923.397529
## iter 60 value 921.671974
## iter 70 value 920.918809
## iter 80 value 920.313196
## iter 90 value 918.802660
## iter 100 value 913.746106
## final value 913.746106
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 1936.274658
## iter 10 value 1030.231904
## iter 20 value 968.733446
## iter 30 value 958.467446
## iter 40 value 950.250917
## iter 50 value 947.962375
## iter 60 value 944.851724
## iter 70 value 934.664597
## iter 80 value 933.279551
## iter 90 value 932.095880
## iter 100 value 931.574217
## final value 931.574217
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1951.585602
## iter 10 value 1259.501005
## iter 20 value 998.783335
## iter 30 value 947.433208
## iter 40 value 933.061384
## iter 50 value 923.083637
## iter 60 value 918.785454
## iter 70 value 916.733783
## iter 80 value 916.257936
## iter 90 value 915.259946
## iter 100 value 913.207582
## final value 913.207582
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1968.995728
## iter 10 value 1105.826296
## iter 20 value 967.911349
## iter 30 value 934.240570
## iter 40 value 920.308771
## iter 50 value 916.423077
## iter 60 value 914.179112
## iter 70 value 912.931799
## iter 80 value 910.949162
## iter 90 value 908.446112
## iter 100 value 907.212981
## final value 907.212981
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 2006.849026
## iter 10 value 1177.246961
## iter 20 value 970.221962
## iter 30 value 953.677182
## iter 40 value 931.704503
## iter 50 value 922.933556
## iter 60 value 918.299823
## iter 70 value 913.822908
## iter 80 value 912.151562
## iter 90 value 911.293542
## iter 100 value 910.794456
## final value 910.794456
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1743.414075
## iter 10 value 1234.347755
## iter 20 value 958.035108
## iter 30 value 943.509129
## iter 40 value 917.696138
## iter 50 value 907.507408
## iter 60 value 901.879182
## iter 70 value 894.901036
## iter 80 value 892.511722
## iter 90 value 891.750088
## iter 100 value 890.819039
## final value 890.819039
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 1829.306041
## iter 10 value 1093.667380
## iter 20 value 952.917986
## iter 30 value 929.290078
## iter 40 value 914.434919
## iter 50 value 905.794270
## iter 60 value 900.905235
## iter 70 value 898.485220
## iter 80 value 894.995320
## iter 90 value 893.564667
## iter 100 value 891.325813
## final value 891.325813
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1799.302517
## iter 10 value 1495.683020
## iter 20 value 1270.770009
## iter 30 value 955.054053
## iter 40 value 945.110964
## iter 50 value 941.714288
## iter 60 value 940.203627
## iter 70 value 939.274212
## iter 80 value 930.059714
## iter 90 value 928.827642
## iter 100 value 927.770403
## final value 927.770403
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1840.650387
## iter 10 value 1537.495119
## iter 20 value 1244.910028
## iter 30 value 1016.901062
## iter 40 value 958.659410
## iter 50 value 936.637937
## iter 60 value 923.811552
## iter 70 value 918.692648
## iter 80 value 912.922911
## iter 90 value 907.479961
## iter 100 value 894.892419
## final value 894.892419
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1748.929322
## iter 10 value 1560.143394
## iter 20 value 1294.748311
## iter 30 value 1005.259088
## iter 40 value 971.842742
## iter 50 value 962.679550
## iter 60 value 962.299414
## iter 60 value 962.299414
## final value 962.299414
## converged
## Fitting Repeat 2
##
## # weights: 13
## initial value 1662.055524
## iter 10 value 1201.983935
## iter 20 value 993.827989
## iter 30 value 967.537019
## iter 40 value 963.504399
## iter 50 value 962.481003
## final value 962.299411
## converged
## Fitting Repeat 3
##
## # weights: 13
## initial value 1702.655148
## iter 10 value 1359.560845
## iter 20 value 992.613598
## iter 30 value 966.429552
## iter 40 value 962.304079
## final value 962.299410
## converged
## Fitting Repeat 4
##
## # weights: 13
## initial value 1678.860811
## iter 10 value 1296.377907
## iter 20 value 1024.394644
## iter 30 value 966.224714
## iter 40 value 963.620623
## final value 963.278397
## converged
## Fitting Repeat 5
##
## # weights: 13
## initial value 1692.877747
## iter 10 value 1634.569760
## iter 20 value 1504.502182
## iter 30 value 1056.831618
## iter 40 value 981.777686
## iter 50 value 966.516893
## iter 60 value 962.979460
## final value 962.784202
## converged
## Fitting Repeat 1
##
## # weights: 35
## initial value 2016.404851
## iter 10 value 1306.160465
## iter 20 value 1034.057925
## iter 30 value 979.321430
## iter 40 value 960.291167
## iter 50 value 954.492620
## iter 60 value 950.600422
## iter 70 value 945.639755
## iter 80 value 943.477126
## iter 90 value 942.242323
## iter 100 value 941.609399
## final value 941.609399
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1696.402025
## iter 10 value 1193.115238
## iter 20 value 1000.289993
## iter 30 value 967.224160
## iter 40 value 954.358611
## iter 50 value 945.966952
## iter 60 value 941.848691
## iter 70 value 940.557986
## iter 80 value 939.745013
## iter 90 value 936.968492
## iter 100 value 934.550025
## final value 934.550025
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 2048.343774
## iter 10 value 1441.544875
## iter 20 value 1048.947365
## iter 30 value 972.180943
## iter 40 value 960.883504
## iter 50 value 955.235082
## iter 60 value 944.072031
## iter 70 value 938.701822
## iter 80 value 937.327784
## iter 90 value 936.591630
## iter 100 value 935.836875
## final value 935.836875
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1702.718776
## iter 10 value 1157.856390
## iter 20 value 1005.384466
## iter 30 value 962.713459
## iter 40 value 944.427815
## iter 50 value 934.537383
## iter 60 value 931.973013
## iter 70 value 931.522119
## iter 80 value 929.448884
## iter 90 value 927.751951
## iter 100 value 927.549898
## final value 927.549898
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1730.178443
## iter 10 value 1146.478566
## iter 20 value 1016.260381
## iter 30 value 981.679516
## iter 40 value 969.323033
## iter 50 value 952.657396
## iter 60 value 943.814427
## iter 70 value 939.134897
## iter 80 value 935.464322
## iter 90 value 926.346968
## iter 100 value 921.234845
## final value 921.234845
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 1885.087719
## iter 10 value 1124.187971
## iter 20 value 1038.605566
## iter 30 value 974.742922
## iter 40 value 966.073269
## iter 50 value 945.886354
## iter 60 value 939.673810
## iter 70 value 928.450258
## iter 80 value 923.315574
## iter 90 value 920.199505
## iter 100 value 917.879676
## final value 917.879676
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1978.151080
## iter 10 value 1182.288251
## iter 20 value 1042.891741
## iter 30 value 976.652816
## iter 40 value 950.476477
## iter 50 value 943.412613
## iter 60 value 941.513220
## iter 70 value 939.089185
## iter 80 value 937.089228
## iter 90 value 935.588536
## iter 100 value 929.966257
## final value 929.966257
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 1689.951876
## iter 10 value 1011.380737
## iter 20 value 966.003661
## iter 30 value 954.865199
## iter 40 value 946.412727
## iter 50 value 938.669532
## iter 60 value 937.351665
## iter 70 value 929.672521
## iter 80 value 922.841472
## iter 90 value 920.902921
## iter 100 value 919.026334
## final value 919.026334
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1764.669525
## iter 10 value 1180.051300
## iter 20 value 1040.681992
## iter 30 value 972.497828
## iter 40 value 956.715071
## iter 50 value 953.368074
## iter 60 value 944.031003
## iter 70 value 937.925167
## iter 80 value 933.151365
## iter 90 value 932.133955
## iter 100 value 930.356237
## final value 930.356237
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1780.901536
## iter 10 value 1203.214097
## iter 20 value 1063.378396
## iter 30 value 963.806433
## iter 40 value 950.006735
## iter 50 value 940.852131
## iter 60 value 937.135953
## iter 70 value 935.540067
## iter 80 value 933.054156
## iter 90 value 931.541687
## iter 100 value 930.975278
## final value 930.975278
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 13
## initial value 1806.077621
## iter 10 value 1238.416058
## iter 20 value 978.836811
## iter 30 value 967.613085
## iter 40 value 953.642408
## iter 50 value 949.003146
## iter 60 value 948.528315
## iter 70 value 947.616515
## iter 80 value 947.390454
## iter 90 value 947.302723
## iter 100 value 947.125067
## final value 947.125067
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 13
## initial value 1983.312249
## iter 10 value 1326.624737
## iter 20 value 1011.216799
## iter 30 value 996.160881
## iter 40 value 991.929798
## iter 50 value 957.804088
## iter 60 value 954.199540
## iter 70 value 953.667655
## iter 80 value 950.608491
## iter 90 value 950.019199
## iter 100 value 949.755181
## final value 949.755181
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 13
## initial value 1734.617032
## iter 10 value 1172.195044
## iter 20 value 976.227646
## iter 30 value 955.991928
## iter 40 value 949.859821
## iter 50 value 947.960889
## iter 60 value 947.828929
## iter 70 value 947.344650
## iter 80 value 947.260590
## final value 947.260561
## converged
## Fitting Repeat 4
##
## # weights: 13
## initial value 1789.769400
## iter 10 value 1423.435688
## iter 20 value 1007.214746
## iter 30 value 981.051229
## iter 40 value 963.000301
## iter 50 value 952.243633
## iter 60 value 950.974503
## iter 70 value 948.405057
## iter 80 value 947.239785
## iter 90 value 947.178854
## iter 100 value 947.091395
## final value 947.091395
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 13
## initial value 1731.475257
## iter 10 value 1046.495069
## iter 20 value 980.232626
## iter 30 value 961.330253
## iter 40 value 954.515923
## iter 50 value 952.041914
## iter 60 value 948.624242
## iter 70 value 948.001634
## iter 80 value 947.557267
## iter 90 value 947.295003
## iter 100 value 947.237211
## final value 947.237211
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 35
## initial value 1670.995873
## iter 10 value 979.148430
## iter 20 value 957.329912
## iter 30 value 935.795364
## iter 40 value 924.803077
## iter 50 value 914.603508
## iter 60 value 911.024533
## iter 70 value 909.308244
## iter 80 value 908.077272
## iter 90 value 906.210644
## iter 100 value 905.650538
## final value 905.650538
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 1950.120612
## iter 10 value 1503.885312
## iter 20 value 1095.348164
## iter 30 value 1012.152492
## iter 40 value 995.972991
## iter 50 value 976.225612
## iter 60 value 970.979789
## iter 70 value 969.910306
## iter 80 value 969.760542
## iter 90 value 969.216108
## iter 100 value 969.156700
## final value 969.156700
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 1804.668878
## iter 10 value 1339.712580
## iter 20 value 1055.226530
## iter 30 value 964.433209
## iter 40 value 940.429079
## iter 50 value 933.941504
## iter 60 value 931.608585
## iter 70 value 929.955143
## iter 80 value 929.628527
## iter 90 value 925.590838
## iter 100 value 922.371286
## final value 922.371286
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 1720.912022
## iter 10 value 1176.881147
## iter 20 value 987.054337
## iter 30 value 950.136649
## iter 40 value 947.996573
## iter 50 value 943.311056
## iter 60 value 937.936636
## iter 70 value 937.748038
## iter 80 value 937.670743
## iter 90 value 937.029229
## iter 100 value 936.684463
## final value 936.684463
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 1714.319868
## iter 10 value 1151.228547
## iter 20 value 988.513967
## iter 30 value 960.131948
## iter 40 value 950.329711
## iter 50 value 943.170550
## iter 60 value 938.373359
## iter 70 value 928.371023
## iter 80 value 924.271059
## iter 90 value 921.798697
## iter 100 value 920.814497
## final value 920.814497
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 57
## initial value 1735.713283
## iter 10 value 1321.453032
## iter 20 value 1000.725111
## iter 30 value 956.206253
## iter 40 value 914.626972
## iter 50 value 910.819835
## iter 60 value 909.814438
## iter 70 value 908.717476
## iter 80 value 903.065411
## iter 90 value 899.331130
## iter 100 value 896.154026
## final value 896.154026
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 57
## initial value 1735.348951
## iter 10 value 1335.782058
## iter 20 value 1009.184369
## iter 30 value 958.009714
## iter 40 value 936.577287
## iter 50 value 920.037728
## iter 60 value 909.509800
## iter 70 value 905.811714
## iter 80 value 902.622162
## iter 90 value 900.799973
## iter 100 value 899.714025
## final value 899.714025
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 57
## initial value 2031.363802
## iter 10 value 1433.259931
## iter 20 value 962.769160
## iter 30 value 937.999297
## iter 40 value 927.351422
## iter 50 value 922.587132
## iter 60 value 912.142129
## iter 70 value 904.553452
## iter 80 value 896.557826
## iter 90 value 888.923973
## iter 100 value 886.295875
## final value 886.295875
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 57
## initial value 1825.243179
## iter 10 value 1454.241656
## iter 20 value 1077.026351
## iter 30 value 973.614499
## iter 40 value 961.144813
## iter 50 value 950.502855
## iter 60 value 943.263268
## iter 70 value 939.850031
## iter 80 value 936.967646
## iter 90 value 934.813709
## iter 100 value 933.596312
## final value 933.596312
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 57
## initial value 1685.114499
## iter 10 value 1011.315936
## iter 20 value 951.224473
## iter 30 value 926.075133
## iter 40 value 916.405326
## iter 50 value 907.255151
## iter 60 value 903.143447
## iter 70 value 898.724484
## iter 80 value 896.406989
## iter 90 value 894.767769
## iter 100 value 894.150307
## final value 894.150307
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 35
## initial value 2105.308771
## iter 10 value 1570.411578
## iter 20 value 1272.459146
## iter 30 value 1178.120475
## iter 40 value 1171.780830
## iter 50 value 1171.235279
## iter 60 value 1170.340637
## iter 70 value 1169.424999
## iter 80 value 1168.908520
## iter 90 value 1168.770002
## iter 100 value 1168.693803
## final value 1168.693803
## stopped after 100 iterations
## Fitting Repeat 2
##
## # weights: 35
## initial value 2212.722540
## iter 10 value 1578.348762
## iter 20 value 1247.761619
## iter 30 value 1213.918327
## iter 40 value 1173.342641
## iter 50 value 1160.262639
## iter 60 value 1150.418290
## iter 70 value 1144.752802
## iter 80 value 1142.467367
## iter 90 value 1141.349703
## iter 100 value 1140.368982
## final value 1140.368982
## stopped after 100 iterations
## Fitting Repeat 3
##
## # weights: 35
## initial value 2119.377290
## iter 10 value 1922.231608
## iter 20 value 1621.639466
## iter 30 value 1317.942898
## iter 40 value 1305.784682
## iter 50 value 1291.864127
## iter 60 value 1291.730357
## iter 70 value 1291.704277
## iter 80 value 1288.348579
## iter 90 value 1286.054606
## iter 100 value 1285.967290
## final value 1285.967290
## stopped after 100 iterations
## Fitting Repeat 4
##
## # weights: 35
## initial value 2313.582439
## iter 10 value 1647.089490
## iter 20 value 1225.431116
## iter 30 value 1179.905558
## iter 40 value 1173.553388
## iter 50 value 1171.980751
## iter 60 value 1171.508513
## iter 70 value 1171.184864
## iter 80 value 1171.149360
## iter 90 value 1171.114320
## iter 100 value 1171.031448
## final value 1171.031448
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 35
## initial value 2133.386286
## iter 10 value 1739.662742
## iter 20 value 1216.901918
## iter 30 value 1176.612593
## iter 40 value 1165.765904
## iter 50 value 1162.909150
## iter 60 value 1161.181240
## iter 70 value 1160.716294
## iter 80 value 1160.608446
## iter 90 value 1160.539443
## iter 100 value 1160.397183
## final value 1160.397183
## stopped after 100 iterationsdata.frame(train = confusionMatrix(predict(mod, dat_train),
dat_train$arr_delay)$overall[1],
test = confusionMatrix(predict(mod, dat_test),
dat_test$arr_delay)$overall[1])## train test
## Accuracy 0.8032196 0.791807ctrl = trainControl(method = "cv", number = 5)
mod2 <- train(arr_delay ~ dep_delay + dewp + temp + humid +
wind_dir + wind_speed + precip +
visib, data = new_dataset,
method = "rf",
preProcess = "scale",
trControl = ctrl)submission <- data.frame(id = seq(nrow(data_test)), arr_status = rep("", nrow(data_test)))
paste0("F",submission$id) -> submission$id
submission$arr_status = predict(mod2, data_test)
write.csv(submission, file = "submission.csv", row.names = F)Conclusion
Write the conclusion of your capstone project Is your goal achieved? Is the problem can be solved by machine learning? What model did you use and how is the performance? What is the potential business implementation of your capstone project?