Exploratory Data Analysis
Your Name
# Import necessary libraries
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readr)
library(ggplot2)
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(DescTools)##
## Attaching package: 'DescTools'## The following objects are masked from 'package:psych':
##
## AUC, ICC, SDlibrary(corrplot)## corrplot 0.92 loadedlibrary(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following objects are masked from 'package:DescTools':
##
## MAE, RMSElibrary(mlr)## Loading required package: ParamHelpers## Warning message: 'mlr' is in 'maintenance-only' mode since July 2019.
## Future development will only happen in 'mlr3'
## (<https://mlr3.mlr-org.com>). Due to the focus on 'mlr3' there might be
## uncaught bugs meanwhile in {mlr} - please consider switching.##
## Attaching package: 'mlr'## The following object is masked from 'package:caret':
##
## trainsuppressWarnings(expr)## function (expr)
## {
## enexpr(expr)
## }
## <bytecode: 0x7f9e9c53d6a0>
## <environment: namespace:rlang># Check current working directory
getwd()## [1] "/Users/crystalhoo/Desktop/house-price-prediction"# Change working directory
#setwd("C:/Users/User/Desktop")Import Dataset
dataset <- read.csv("train.csv")
head(dataset)## Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour
## 1 1 60 RL 65 8450 Pave <NA> Reg Lvl
## 2 2 20 RL 80 9600 Pave <NA> Reg Lvl
## 3 3 60 RL 68 11250 Pave <NA> IR1 Lvl
## 4 4 70 RL 60 9550 Pave <NA> IR1 Lvl
## 5 5 60 RL 84 14260 Pave <NA> IR1 Lvl
## 6 6 50 RL 85 14115 Pave <NA> IR1 Lvl
## Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType
## 1 AllPub Inside Gtl CollgCr Norm Norm 1Fam
## 2 AllPub FR2 Gtl Veenker Feedr Norm 1Fam
## 3 AllPub Inside Gtl CollgCr Norm Norm 1Fam
## 4 AllPub Corner Gtl Crawfor Norm Norm 1Fam
## 5 AllPub FR2 Gtl NoRidge Norm Norm 1Fam
## 6 AllPub Inside Gtl Mitchel Norm Norm 1Fam
## HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle RoofMatl
## 1 2Story 7 5 2003 2003 Gable CompShg
## 2 1Story 6 8 1976 1976 Gable CompShg
## 3 2Story 7 5 2001 2002 Gable CompShg
## 4 2Story 7 5 1915 1970 Gable CompShg
## 5 2Story 8 5 2000 2000 Gable CompShg
## 6 1.5Fin 5 5 1993 1995 Gable CompShg
## Exterior1st Exterior2nd MasVnrType MasVnrArea ExterQual ExterCond Foundation
## 1 VinylSd VinylSd BrkFace 196 Gd TA PConc
## 2 MetalSd MetalSd None 0 TA TA CBlock
## 3 VinylSd VinylSd BrkFace 162 Gd TA PConc
## 4 Wd Sdng Wd Shng None 0 TA TA BrkTil
## 5 VinylSd VinylSd BrkFace 350 Gd TA PConc
## 6 VinylSd VinylSd None 0 TA TA Wood
## BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2
## 1 Gd TA No GLQ 706 Unf
## 2 Gd TA Gd ALQ 978 Unf
## 3 Gd TA Mn GLQ 486 Unf
## 4 TA Gd No ALQ 216 Unf
## 5 Gd TA Av GLQ 655 Unf
## 6 Gd TA No GLQ 732 Unf
## BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical
## 1 0 150 856 GasA Ex Y SBrkr
## 2 0 284 1262 GasA Ex Y SBrkr
## 3 0 434 920 GasA Ex Y SBrkr
## 4 0 540 756 GasA Gd Y SBrkr
## 5 0 490 1145 GasA Ex Y SBrkr
## 6 0 64 796 GasA Ex Y SBrkr
## X1stFlrSF X2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath
## 1 856 854 0 1710 1 0 2
## 2 1262 0 0 1262 0 1 2
## 3 920 866 0 1786 1 0 2
## 4 961 756 0 1717 1 0 1
## 5 1145 1053 0 2198 1 0 2
## 6 796 566 0 1362 1 0 1
## HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Functional
## 1 1 3 1 Gd 8 Typ
## 2 0 3 1 TA 6 Typ
## 3 1 3 1 Gd 6 Typ
## 4 0 3 1 Gd 7 Typ
## 5 1 4 1 Gd 9 Typ
## 6 1 1 1 TA 5 Typ
## Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars
## 1 0 <NA> Attchd 2003 RFn 2
## 2 1 TA Attchd 1976 RFn 2
## 3 1 TA Attchd 2001 RFn 2
## 4 1 Gd Detchd 1998 Unf 3
## 5 1 TA Attchd 2000 RFn 3
## 6 0 <NA> Attchd 1993 Unf 2
## GarageArea GarageQual GarageCond PavedDrive WoodDeckSF OpenPorchSF
## 1 548 TA TA Y 0 61
## 2 460 TA TA Y 298 0
## 3 608 TA TA Y 0 42
## 4 642 TA TA Y 0 35
## 5 836 TA TA Y 192 84
## 6 480 TA TA Y 40 30
## EnclosedPorch X3SsnPorch ScreenPorch PoolArea PoolQC Fence MiscFeature
## 1 0 0 0 0 <NA> <NA> <NA>
## 2 0 0 0 0 <NA> <NA> <NA>
## 3 0 0 0 0 <NA> <NA> <NA>
## 4 272 0 0 0 <NA> <NA> <NA>
## 5 0 0 0 0 <NA> <NA> <NA>
## 6 0 320 0 0 <NA> MnPrv Shed
## MiscVal MoSold YrSold SaleType SaleCondition SalePrice
## 1 0 2 2008 WD Normal 208500
## 2 0 5 2007 WD Normal 181500
## 3 0 9 2008 WD Normal 223500
## 4 0 2 2006 WD Abnorml 140000
## 5 0 12 2008 WD Normal 250000
## 6 700 10 2009 WD Normal 143000Dataset Overview
There are 1460 records (rows) and 81 attributes (columns).
Dataset is mostly made up of numerical values (int64 & float64) and categorical values (object). Statistics are provided for numerical variables only.
cat("Total rows & columns:", dim(dataset), '\n\n')## Total rows & columns: 1460 81str(dataset)## 'data.frame': 1460 obs. of 81 variables:
## $ Id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ MSSubClass : int 60 20 60 70 60 50 20 60 50 190 ...
## $ MSZoning : chr "RL" "RL" "RL" "RL" ...
## $ LotFrontage : int 65 80 68 60 84 85 75 NA 51 50 ...
## $ LotArea : int 8450 9600 11250 9550 14260 14115 10084 10382 6120 7420 ...
## $ Street : chr "Pave" "Pave" "Pave" "Pave" ...
## $ Alley : chr NA NA NA NA ...
## $ LotShape : chr "Reg" "Reg" "IR1" "IR1" ...
## $ LandContour : chr "Lvl" "Lvl" "Lvl" "Lvl" ...
## $ Utilities : chr "AllPub" "AllPub" "AllPub" "AllPub" ...
## $ LotConfig : chr "Inside" "FR2" "Inside" "Corner" ...
## $ LandSlope : chr "Gtl" "Gtl" "Gtl" "Gtl" ...
## $ Neighborhood : chr "CollgCr" "Veenker" "CollgCr" "Crawfor" ...
## $ Condition1 : chr "Norm" "Feedr" "Norm" "Norm" ...
## $ Condition2 : chr "Norm" "Norm" "Norm" "Norm" ...
## $ BldgType : chr "1Fam" "1Fam" "1Fam" "1Fam" ...
## $ HouseStyle : chr "2Story" "1Story" "2Story" "2Story" ...
## $ OverallQual : int 7 6 7 7 8 5 8 7 7 5 ...
## $ OverallCond : int 5 8 5 5 5 5 5 6 5 6 ...
## $ YearBuilt : int 2003 1976 2001 1915 2000 1993 2004 1973 1931 1939 ...
## $ YearRemodAdd : int 2003 1976 2002 1970 2000 1995 2005 1973 1950 1950 ...
## $ RoofStyle : chr "Gable" "Gable" "Gable" "Gable" ...
## $ RoofMatl : chr "CompShg" "CompShg" "CompShg" "CompShg" ...
## $ Exterior1st : chr "VinylSd" "MetalSd" "VinylSd" "Wd Sdng" ...
## $ Exterior2nd : chr "VinylSd" "MetalSd" "VinylSd" "Wd Shng" ...
## $ MasVnrType : chr "BrkFace" "None" "BrkFace" "None" ...
## $ MasVnrArea : int 196 0 162 0 350 0 186 240 0 0 ...
## $ ExterQual : chr "Gd" "TA" "Gd" "TA" ...
## $ ExterCond : chr "TA" "TA" "TA" "TA" ...
## $ Foundation : chr "PConc" "CBlock" "PConc" "BrkTil" ...
## $ BsmtQual : chr "Gd" "Gd" "Gd" "TA" ...
## $ BsmtCond : chr "TA" "TA" "TA" "Gd" ...
## $ BsmtExposure : chr "No" "Gd" "Mn" "No" ...
## $ BsmtFinType1 : chr "GLQ" "ALQ" "GLQ" "ALQ" ...
## $ BsmtFinSF1 : int 706 978 486 216 655 732 1369 859 0 851 ...
## $ BsmtFinType2 : chr "Unf" "Unf" "Unf" "Unf" ...
## $ BsmtFinSF2 : int 0 0 0 0 0 0 0 32 0 0 ...
## $ BsmtUnfSF : int 150 284 434 540 490 64 317 216 952 140 ...
## $ TotalBsmtSF : int 856 1262 920 756 1145 796 1686 1107 952 991 ...
## $ Heating : chr "GasA" "GasA" "GasA" "GasA" ...
## $ HeatingQC : chr "Ex" "Ex" "Ex" "Gd" ...
## $ CentralAir : chr "Y" "Y" "Y" "Y" ...
## $ Electrical : chr "SBrkr" "SBrkr" "SBrkr" "SBrkr" ...
## $ X1stFlrSF : int 856 1262 920 961 1145 796 1694 1107 1022 1077 ...
## $ X2ndFlrSF : int 854 0 866 756 1053 566 0 983 752 0 ...
## $ LowQualFinSF : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GrLivArea : int 1710 1262 1786 1717 2198 1362 1694 2090 1774 1077 ...
## $ BsmtFullBath : int 1 0 1 1 1 1 1 1 0 1 ...
## $ BsmtHalfBath : int 0 1 0 0 0 0 0 0 0 0 ...
## $ FullBath : int 2 2 2 1 2 1 2 2 2 1 ...
## $ HalfBath : int 1 0 1 0 1 1 0 1 0 0 ...
## $ BedroomAbvGr : int 3 3 3 3 4 1 3 3 2 2 ...
## $ KitchenAbvGr : int 1 1 1 1 1 1 1 1 2 2 ...
## $ KitchenQual : chr "Gd" "TA" "Gd" "Gd" ...
## $ TotRmsAbvGrd : int 8 6 6 7 9 5 7 7 8 5 ...
## $ Functional : chr "Typ" "Typ" "Typ" "Typ" ...
## $ Fireplaces : int 0 1 1 1 1 0 1 2 2 2 ...
## $ FireplaceQu : chr NA "TA" "TA" "Gd" ...
## $ GarageType : chr "Attchd" "Attchd" "Attchd" "Detchd" ...
## $ GarageYrBlt : int 2003 1976 2001 1998 2000 1993 2004 1973 1931 1939 ...
## $ GarageFinish : chr "RFn" "RFn" "RFn" "Unf" ...
## $ GarageCars : int 2 2 2 3 3 2 2 2 2 1 ...
## $ GarageArea : int 548 460 608 642 836 480 636 484 468 205 ...
## $ GarageQual : chr "TA" "TA" "TA" "TA" ...
## $ GarageCond : chr "TA" "TA" "TA" "TA" ...
## $ PavedDrive : chr "Y" "Y" "Y" "Y" ...
## $ WoodDeckSF : int 0 298 0 0 192 40 255 235 90 0 ...
## $ OpenPorchSF : int 61 0 42 35 84 30 57 204 0 4 ...
## $ EnclosedPorch: int 0 0 0 272 0 0 0 228 205 0 ...
## $ X3SsnPorch : int 0 0 0 0 0 320 0 0 0 0 ...
## $ ScreenPorch : int 0 0 0 0 0 0 0 0 0 0 ...
## $ PoolArea : int 0 0 0 0 0 0 0 0 0 0 ...
## $ PoolQC : chr NA NA NA NA ...
## $ Fence : chr NA NA NA NA ...
## $ MiscFeature : chr NA NA NA NA ...
## $ MiscVal : int 0 0 0 0 0 700 0 350 0 0 ...
## $ MoSold : int 2 5 9 2 12 10 8 11 4 1 ...
## $ YrSold : int 2008 2007 2008 2006 2008 2009 2007 2009 2008 2008 ...
## $ SaleType : chr "WD" "WD" "WD" "WD" ...
## $ SaleCondition: chr "Normal" "Normal" "Normal" "Abnorml" ...
## $ SalePrice : int 208500 181500 223500 140000 250000 143000 307000 200000 129900 118000 ...summary(dataset)## Id MSSubClass MSZoning LotFrontage
## Min. : 1.0 Min. : 20.0 Length:1460 Min. : 21.00
## 1st Qu.: 365.8 1st Qu.: 20.0 Class :character 1st Qu.: 59.00
## Median : 730.5 Median : 50.0 Mode :character Median : 69.00
## Mean : 730.5 Mean : 56.9 Mean : 70.05
## 3rd Qu.:1095.2 3rd Qu.: 70.0 3rd Qu.: 80.00
## Max. :1460.0 Max. :190.0 Max. :313.00
## NA's :259
## LotArea Street Alley LotShape
## Min. : 1300 Length:1460 Length:1460 Length:1460
## 1st Qu.: 7554 Class :character Class :character Class :character
## Median : 9478 Mode :character Mode :character Mode :character
## Mean : 10517
## 3rd Qu.: 11602
## Max. :215245
##
## LandContour Utilities LotConfig LandSlope
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Neighborhood Condition1 Condition2 BldgType
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## HouseStyle OverallQual OverallCond YearBuilt
## Length:1460 Min. : 1.000 Min. :1.000 Min. :1872
## Class :character 1st Qu.: 5.000 1st Qu.:5.000 1st Qu.:1954
## Mode :character Median : 6.000 Median :5.000 Median :1973
## Mean : 6.099 Mean :5.575 Mean :1971
## 3rd Qu.: 7.000 3rd Qu.:6.000 3rd Qu.:2000
## Max. :10.000 Max. :9.000 Max. :2010
##
## YearRemodAdd RoofStyle RoofMatl Exterior1st
## Min. :1950 Length:1460 Length:1460 Length:1460
## 1st Qu.:1967 Class :character Class :character Class :character
## Median :1994 Mode :character Mode :character Mode :character
## Mean :1985
## 3rd Qu.:2004
## Max. :2010
##
## Exterior2nd MasVnrType MasVnrArea ExterQual
## Length:1460 Length:1460 Min. : 0.0 Length:1460
## Class :character Class :character 1st Qu.: 0.0 Class :character
## Mode :character Mode :character Median : 0.0 Mode :character
## Mean : 103.7
## 3rd Qu.: 166.0
## Max. :1600.0
## NA's :8
## ExterCond Foundation BsmtQual BsmtCond
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2
## Length:1460 Length:1460 Min. : 0.0 Length:1460
## Class :character Class :character 1st Qu.: 0.0 Class :character
## Mode :character Mode :character Median : 383.5 Mode :character
## Mean : 443.6
## 3rd Qu.: 712.2
## Max. :5644.0
##
## BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating
## Min. : 0.00 Min. : 0.0 Min. : 0.0 Length:1460
## 1st Qu.: 0.00 1st Qu.: 223.0 1st Qu.: 795.8 Class :character
## Median : 0.00 Median : 477.5 Median : 991.5 Mode :character
## Mean : 46.55 Mean : 567.2 Mean :1057.4
## 3rd Qu.: 0.00 3rd Qu.: 808.0 3rd Qu.:1298.2
## Max. :1474.00 Max. :2336.0 Max. :6110.0
##
## HeatingQC CentralAir Electrical X1stFlrSF
## Length:1460 Length:1460 Length:1460 Min. : 334
## Class :character Class :character Class :character 1st Qu.: 882
## Mode :character Mode :character Mode :character Median :1087
## Mean :1163
## 3rd Qu.:1391
## Max. :4692
##
## X2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath
## Min. : 0 Min. : 0.000 Min. : 334 Min. :0.0000
## 1st Qu.: 0 1st Qu.: 0.000 1st Qu.:1130 1st Qu.:0.0000
## Median : 0 Median : 0.000 Median :1464 Median :0.0000
## Mean : 347 Mean : 5.845 Mean :1515 Mean :0.4253
## 3rd Qu.: 728 3rd Qu.: 0.000 3rd Qu.:1777 3rd Qu.:1.0000
## Max. :2065 Max. :572.000 Max. :5642 Max. :3.0000
##
## BsmtHalfBath FullBath HalfBath BedroomAbvGr
## Min. :0.00000 Min. :0.000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.00000 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:2.000
## Median :0.00000 Median :2.000 Median :0.0000 Median :3.000
## Mean :0.05753 Mean :1.565 Mean :0.3829 Mean :2.866
## 3rd Qu.:0.00000 3rd Qu.:2.000 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :2.00000 Max. :3.000 Max. :2.0000 Max. :8.000
##
## KitchenAbvGr KitchenQual TotRmsAbvGrd Functional
## Min. :0.000 Length:1460 Min. : 2.000 Length:1460
## 1st Qu.:1.000 Class :character 1st Qu.: 5.000 Class :character
## Median :1.000 Mode :character Median : 6.000 Mode :character
## Mean :1.047 Mean : 6.518
## 3rd Qu.:1.000 3rd Qu.: 7.000
## Max. :3.000 Max. :14.000
##
## Fireplaces FireplaceQu GarageType GarageYrBlt
## Min. :0.000 Length:1460 Length:1460 Min. :1900
## 1st Qu.:0.000 Class :character Class :character 1st Qu.:1961
## Median :1.000 Mode :character Mode :character Median :1980
## Mean :0.613 Mean :1979
## 3rd Qu.:1.000 3rd Qu.:2002
## Max. :3.000 Max. :2010
## NA's :81
## GarageFinish GarageCars GarageArea GarageQual
## Length:1460 Min. :0.000 Min. : 0.0 Length:1460
## Class :character 1st Qu.:1.000 1st Qu.: 334.5 Class :character
## Mode :character Median :2.000 Median : 480.0 Mode :character
## Mean :1.767 Mean : 473.0
## 3rd Qu.:2.000 3rd Qu.: 576.0
## Max. :4.000 Max. :1418.0
##
## GarageCond PavedDrive WoodDeckSF OpenPorchSF
## Length:1460 Length:1460 Min. : 0.00 Min. : 0.00
## Class :character Class :character 1st Qu.: 0.00 1st Qu.: 0.00
## Mode :character Mode :character Median : 0.00 Median : 25.00
## Mean : 94.24 Mean : 46.66
## 3rd Qu.:168.00 3rd Qu.: 68.00
## Max. :857.00 Max. :547.00
##
## EnclosedPorch X3SsnPorch ScreenPorch PoolArea
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.000
## Mean : 21.95 Mean : 3.41 Mean : 15.06 Mean : 2.759
## 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.000
## Max. :552.00 Max. :508.00 Max. :480.00 Max. :738.000
##
## PoolQC Fence MiscFeature MiscVal
## Length:1460 Length:1460 Length:1460 Min. : 0.00
## Class :character Class :character Class :character 1st Qu.: 0.00
## Mode :character Mode :character Mode :character Median : 0.00
## Mean : 43.49
## 3rd Qu.: 0.00
## Max. :15500.00
##
## MoSold YrSold SaleType SaleCondition
## Min. : 1.000 Min. :2006 Length:1460 Length:1460
## 1st Qu.: 5.000 1st Qu.:2007 Class :character Class :character
## Median : 6.000 Median :2008 Mode :character Mode :character
## Mean : 6.322 Mean :2008
## 3rd Qu.: 8.000 3rd Qu.:2009
## Max. :12.000 Max. :2010
##
## SalePrice
## Min. : 34900
## 1st Qu.:129975
## Median :163000
## Mean :180921
## 3rd Qu.:214000
## Max. :755000
## Data Preprocessing
Check for sum of duplicates between columns
No duplication inside the dataset
duplicates <- sum(duplicated(dataset))
cat(paste("Number of duplicates:", duplicates))## Number of duplicates: 0Remove Meaningless Columns
At this stage, we have removed irrelevant columns from the dataset.
‘Id’ column is removed, as it is a unique identifier for each customer.
‘GarageCars’ column is removed, as it provides redundant information already captured by the ‘GarageArea’ column.
We also removed the ‘Exterior2nd’ column, as it represents a limited set of additional choices for exterior covering.
Furthermore, we removed the ‘GarageYrBlt’ column, as the year of construction is typically the same as the ‘YearBuilt’ column.
Finally, we removed the ‘TotRmsAbvGrd’ column, as it provides a similar meaning as the ‘First Floor’ square footage.
Subset the dataset by removing specific columns
dataset <- subset(dataset, select = -c(Id, GarageCars, Exterior2nd, GarageYrBlt, TotRmsAbvGrd))Check for Missing Values
There are a total of 19 columns with missing values
5 of them have missing values higher than 20%
Columns with missing values higher than 20%: PoolQC, MiscFeature, Alley, Fence, FireplaceQu
Column with missing values higher than 20% will be removed in this stage
Calculate missing percentages
missing_percentages <- colSums(is.na(dataset)) / nrow(dataset) * 100Create dataframe with column names and missing percentages
missing_dataset <- data.frame(
column_name = names(missing_percentages),
missing_percentage = missing_percentages
) %>%
# Sort the dataframe by missing percentages in descending order
arrange(desc(missing_percentage)) %>%
# Filter out columns with 0 missing percentages
filter(missing_percentage > 0)Print the missing dataset
print(missing_dataset)## column_name missing_percentage
## PoolQC PoolQC 99.52054795
## MiscFeature MiscFeature 96.30136986
## Alley Alley 93.76712329
## Fence Fence 80.75342466
## FireplaceQu FireplaceQu 47.26027397
## LotFrontage LotFrontage 17.73972603
## GarageType GarageType 5.54794521
## GarageFinish GarageFinish 5.54794521
## GarageQual GarageQual 5.54794521
## GarageCond GarageCond 5.54794521
## BsmtExposure BsmtExposure 2.60273973
## BsmtFinType2 BsmtFinType2 2.60273973
## BsmtQual BsmtQual 2.53424658
## BsmtCond BsmtCond 2.53424658
## BsmtFinType1 BsmtFinType1 2.53424658
## MasVnrType MasVnrType 0.54794521
## MasVnrArea MasVnrArea 0.54794521
## Electrical Electrical 0.06849315Remove columns with missing values more than 20%
dataset <- subset(dataset, select = -c(PoolQC, MiscFeature, Alley, Fence, FireplaceQu))Impute numerical missing data using mean & Impute categorical missing data using mode
Median is chosen to fill in missing values, rather than the mean.
The mean is sensitive to extreme values and can be heavily influenced by outliers in the data, which can lead to biased estimates. In contrast, the median is a robust measure of central tendency that is less sensitive to extreme values and is more appropriate for skewed data.
Identify numerical columns & replace missing numerical values with mean
num_cols <- sapply(dataset, is.numeric)
for (col in names(dataset[, num_cols])) {
col_mean <- mean(dataset[, col], na.rm = TRUE)
dataset[, col][is.na(dataset[, col])] <- rep(col_mean, sum(is.na(dataset[, col])))
}Identify categorical columns & replace missing categorical values with mode
cat_cols <- sapply(dataset, is.factor) | sapply(dataset, is.character)
for (col in names(dataset[, cat_cols])) {
dataset[, col][is.na(dataset[, col])] <- as.character(Mode(dataset[, col], na.rm = TRUE))
}Explore Data - Exploratory Data Analysis (EDA)
Plot Distribution of Numeric Data
Majority of numerical columns’ distribution are right-skewed (31 attributes), however, 3 of them are left-skewed
Select numerical columns using the “is.numeric” function
numerical_cols <- names(dataset)[sapply(dataset, is.numeric)]
categorical_cols <- names(dataset)[sapply(dataset, is.factor) | sapply(dataset, is.character)]Create new data frames with only numerical and categorical columns
numerical_data <- dataset[numerical_cols]
categorical_data <- dataset[categorical_cols]Create a function to plot probability density plot and skewness
plot_density <- function(column, col_name) {
# Compute the skewness of the data
skewness <- skew(column)
# Set the plot color based on the skewness
if (skewness < 0) {
plot_color <- "blue"
} else {
plot_color <- "red"
}
# Create the density plot
plot_title <- paste("Skewness:", round(skewness, 2), "| Column:", col_name)
density_plot <- ggplot(data = data.frame(column), aes(x = column)) +
geom_density(fill = plot_color, alpha = 0.3) +
ggtitle(plot_title)
return(density_plot)
}Apply the plot_density function to each column of numerical data in numerical_data
plots <- lapply(names(numerical_data), function(col_name) {
plot_density(numerical_data[[col_name]], col_name)
})Arrange the resulting plots in a grid with 5 columns
grid.arrange(grobs = plots, ncol = 5)
Check Outliers using boxplot
Create a list of boxplots for the first 20 attributes
Outliers are found in most of the columns - 27 attributes
boxplot_numerical_first20attributes <- lapply(1:20, function(i) {
ggplot(numerical_data, aes(x = 1, y = numerical_data[, i])) +
geom_boxplot() +
ggtitle(paste0("Boxplot of", colnames(numerical_data)[i])) +
xlab("") +
ylab(colnames(numerical_data)[i])
})Create a list of boxplots for the remaining 14 attributes
boxplot_numerical_rest14attributes <- lapply(21:34, function(i) {
ggplot(numerical_data, aes(x = 1, y = numerical_data[, i])) +
geom_boxplot() +
ggtitle(paste0("Boxplot of", colnames(numerical_data)[i])) +
xlab("") +
ylab(colnames(numerical_data)[i])
})
grid.arrange(grobs = boxplot_numerical_first20attributes, ncol = 5)
grid.arrange(grobs = boxplot_numerical_rest14attributes, ncol = 5)
Perform Correlation Matrix
correlation_matrix <- cor(numerical_data, method = "pearson")
print(correlation_matrix)## MSSubClass LotFrontage LotArea OverallQual OverallCond
## MSSubClass 1.000000000 -0.357055878 -0.139781082 0.03262771 -0.059315817
## LotFrontage -0.357055878 1.000000000 0.306794605 0.23419623 -0.052820100
## LotArea -0.139781082 0.306794605 1.000000000 0.10580574 -0.005636270
## OverallQual 0.032627708 0.234196231 0.105805742 1.00000000 -0.091932343
## OverallCond -0.059315817 -0.052820100 -0.005636270 -0.09193234 1.000000000
## YearBuilt 0.027850137 0.117597979 0.014227652 0.57232277 -0.375983196
## YearRemodAdd 0.040581045 0.082745885 0.013788427 0.55068392 0.073741498
## MasVnrArea 0.022894690 0.179283452 0.103960227 0.41023774 -0.127788119
## BsmtFinSF1 -0.069835749 0.215828435 0.214103131 0.23966597 -0.046230856
## BsmtFinSF2 -0.065648579 0.043339566 0.111169745 -0.05911869 0.040229170
## BsmtUnfSF -0.140759481 0.122155600 -0.002618360 0.30815893 -0.136840570
## TotalBsmtSF -0.238518409 0.363357723 0.260833135 0.53780850 -0.171097515
## X1stFlrSF -0.251758352 0.414266394 0.299474579 0.47622383 -0.144202784
## X2ndFlrSF 0.307885721 0.072482666 0.050985948 0.29549288 0.028942116
## LowQualFinSF 0.046473756 0.036848721 0.004778970 -0.03042928 0.025494320
## GrLivArea 0.074853180 0.368391966 0.263116167 0.59300743 -0.079685865
## BsmtFullBath 0.003491026 0.091480992 0.158154531 0.11109779 -0.054941515
## BsmtHalfBath -0.002332535 -0.006419220 0.048045571 -0.04015016 0.117820915
## FullBath 0.131608222 0.180424206 0.126030627 0.55059971 -0.194149489
## HalfBath 0.177354389 0.048258362 0.014259469 0.27345810 -0.060769327
## BedroomAbvGr -0.023438028 0.237023227 0.119689908 0.10167636 0.012980060
## KitchenAbvGr 0.281721040 -0.005804607 -0.017783871 -0.18388223 -0.087000855
## Fireplaces -0.045569340 0.235754565 0.271364010 0.39676504 -0.023819978
## GarageArea -0.098671543 0.323662899 0.180402755 0.56202176 -0.151521371
## WoodDeckSF -0.012579358 0.077106221 0.171697687 0.23892339 -0.003333699
## OpenPorchSF -0.006100121 0.137454496 0.084773809 0.30881882 -0.032588814
## EnclosedPorch -0.012036622 0.009790061 -0.018339734 -0.11393686 0.070356184
## X3SsnPorch -0.043824549 0.062335471 0.020422830 0.03037057 0.025503660
## ScreenPorch -0.026030177 0.037684305 0.043160378 0.06488636 0.054810529
## PoolArea 0.008282708 0.180867647 0.077672392 0.06516584 -0.001984942
## MiscVal -0.007683291 0.001168275 0.038067692 -0.03140621 0.068776806
## MoSold -0.013584643 0.010157807 0.001204988 0.07081517 -0.003510839
## YrSold -0.021407038 0.006768250 -0.014261407 -0.02734671 0.043949746
## SalePrice -0.084284135 0.334900852 0.263843354 0.79098160 -0.077855894
## YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2
## MSSubClass 0.027850137 0.040581045 0.022894690 -0.069835749 -0.065648579
## LotFrontage 0.117597979 0.082745885 0.179283452 0.215828435 0.043339566
## LotArea 0.014227652 0.013788427 0.103960227 0.214103131 0.111169745
## OverallQual 0.572322769 0.550683924 0.410237736 0.239665966 -0.059118693
## OverallCond -0.375983196 0.073741498 -0.127788119 -0.046230856 0.040229170
## YearBuilt 1.000000000 0.592854976 0.314745023 0.249503197 -0.049106831
## YearRemodAdd 0.592854976 1.000000000 0.179186495 0.128450547 -0.067758514
## MasVnrArea 0.314745023 0.179186495 1.000000000 0.263582388 -0.072302247
## BsmtFinSF1 0.249503197 0.128450547 0.263582388 1.000000000 -0.050117400
## BsmtFinSF2 -0.049106831 -0.067758514 -0.072302247 -0.050117400 1.000000000
## BsmtUnfSF 0.149040392 0.181133087 0.114183659 -0.495251469 -0.209294492
## TotalBsmtSF 0.391452002 0.291065583 0.362452094 0.522396052 0.104809538
## X1stFlrSF 0.281985859 0.240379268 0.342160338 0.445862656 0.097117448
## X2ndFlrSF 0.010307660 0.140023779 0.174019344 -0.137078986 -0.099260316
## LowQualFinSF -0.183784344 -0.062419100 -0.069068105 -0.064502597 0.014806998
## GrLivArea 0.199009714 0.287388520 0.389892916 0.208171130 -0.009639892
## BsmtFullBath 0.187598550 0.119469879 0.085054528 0.649211754 0.158678061
## BsmtHalfBath -0.038161806 -0.012337032 0.026668558 0.067418478 0.070948134
## FullBath 0.468270787 0.439046484 0.275729847 0.058543137 -0.076443862
## HalfBath 0.242655910 0.183330612 0.200802357 0.004262424 -0.032147837
## BedroomAbvGr -0.070651217 -0.040580928 0.102417471 -0.107354677 -0.015728114
## KitchenAbvGr -0.174800246 -0.149597521 -0.037364011 -0.081006851 -0.040751236
## Fireplaces 0.147716399 0.112581318 0.247906285 0.260010920 0.046920709
## GarageArea 0.478953820 0.371599809 0.372567013 0.296970385 -0.018226592
## WoodDeckSF 0.224880142 0.205725920 0.159349430 0.204306145 0.067898326
## OpenPorchSF 0.188685840 0.226297633 0.124965275 0.111760613 0.003092562
## EnclosedPorch -0.387267783 -0.193919147 -0.109848652 -0.102303306 0.036543339
## X3SsnPorch 0.031354513 0.045285810 0.018794814 0.026450506 -0.029993398
## ScreenPorch -0.050364435 -0.038740011 0.061453179 0.062020623 0.088871251
## PoolArea 0.004949728 0.005829372 0.011722909 0.140491286 0.041709055
## MiscVal -0.034383139 -0.010286249 -0.029814762 0.003571473 0.004939781
## MoSold 0.012398471 0.021490002 -0.005939579 -0.015726948 -0.015210738
## YrSold -0.013617680 0.035743247 -0.008183639 0.014358922 0.031705637
## SalePrice 0.522897333 0.507100967 0.475241317 0.386419806 -0.011378121
## BsmtUnfSF TotalBsmtSF X1stFlrSF X2ndFlrSF
## MSSubClass -0.140759481 -0.2385184093 -0.251758352 0.307885721
## LotFrontage 0.122155600 0.3633577228 0.414266394 0.072482666
## LotArea -0.002618360 0.2608331345 0.299474579 0.050985948
## OverallQual 0.308158927 0.5378084986 0.476223829 0.295492879
## OverallCond -0.136840570 -0.1710975146 -0.144202784 0.028942116
## YearBuilt 0.149040392 0.3914520021 0.281985859 0.010307660
## YearRemodAdd 0.181133087 0.2910655826 0.240379268 0.140023779
## MasVnrArea 0.114183659 0.3624520941 0.342160338 0.174019344
## BsmtFinSF1 -0.495251469 0.5223960520 0.445862656 -0.137078986
## BsmtFinSF2 -0.209294492 0.1048095376 0.097117448 -0.099260316
## BsmtUnfSF 1.000000000 0.4153596052 0.317987438 0.004469092
## TotalBsmtSF 0.415359605 1.0000000000 0.819529975 -0.174511950
## X1stFlrSF 0.317987438 0.8195299750 1.000000000 -0.202646181
## X2ndFlrSF 0.004469092 -0.1745119501 -0.202646181 1.000000000
## LowQualFinSF 0.028166688 -0.0332453873 -0.014240673 0.063352950
## GrLivArea 0.240257268 0.4548682025 0.566023969 0.687501064
## BsmtFullBath -0.422900477 0.3073505537 0.244671104 -0.169493952
## BsmtHalfBath -0.095804288 -0.0003145818 0.001955654 -0.023854784
## FullBath 0.288886055 0.3237224136 0.380637495 0.421377983
## HalfBath -0.041117530 -0.0488037386 -0.119915909 0.609707300
## BedroomAbvGr 0.166643317 0.0504499555 0.127400749 0.502900613
## KitchenAbvGr 0.030085868 -0.0689006426 0.068100588 0.059305753
## Fireplaces 0.051574882 0.3395193239 0.410531085 0.194560892
## GarageArea 0.183302698 0.4866654638 0.489781654 0.138346959
## WoodDeckSF -0.005316424 0.2320186091 0.235458623 0.092165418
## OpenPorchSF 0.129005415 0.2472637463 0.211671225 0.208026063
## EnclosedPorch -0.002537855 -0.0954777367 -0.065291701 0.061988691
## X3SsnPorch 0.020764006 0.0373837273 0.056104374 -0.024357648
## ScreenPorch -0.012579273 0.0844889859 0.088758073 0.040606448
## PoolArea -0.035092241 0.1260531321 0.131524976 0.081486878
## MiscVal -0.023836645 -0.0184789224 -0.021095719 0.016196875
## MoSold 0.034888443 0.0131961786 0.031371560 0.035164427
## YrSold -0.041258195 -0.0149686480 -0.013603771 -0.028699914
## SalePrice 0.214479106 0.6135805516 0.605852185 0.319333803
## LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath
## MSSubClass 0.0464737559 0.074853180 0.0034910258 -0.0023325346
## LotFrontage 0.0368487208 0.368391966 0.0914809919 -0.0064192197
## LotArea 0.0047789699 0.263116167 0.1581545311 0.0480455709
## OverallQual -0.0304292840 0.593007430 0.1110977861 -0.0401501577
## OverallCond 0.0254943199 -0.079685865 -0.0549415154 0.1178209151
## YearBuilt -0.1837843444 0.199009714 0.1875985500 -0.0381618057
## YearRemodAdd -0.0624191001 0.287388520 0.1194698791 -0.0123370321
## MasVnrArea -0.0690681050 0.389892916 0.0850545278 0.0266685579
## BsmtFinSF1 -0.0645025969 0.208171130 0.6492117536 0.0674184779
## BsmtFinSF2 0.0148069979 -0.009639892 0.1586780608 0.0709481337
## BsmtUnfSF 0.0281666881 0.240257268 -0.4229004774 -0.0958042882
## TotalBsmtSF -0.0332453873 0.454868203 0.3073505537 -0.0003145818
## X1stFlrSF -0.0142406727 0.566023969 0.2446711042 0.0019556536
## X2ndFlrSF 0.0633529501 0.687501064 -0.1694939517 -0.0238547839
## LowQualFinSF 1.0000000000 0.134682813 -0.0471434219 -0.0058415048
## GrLivArea 0.1346828130 1.000000000 0.0348360495 -0.0189184832
## BsmtFullBath -0.0471434219 0.034836050 1.0000000000 -0.1478709605
## BsmtHalfBath -0.0058415048 -0.018918483 -0.1478709605 1.0000000000
## FullBath -0.0007095096 0.630011646 -0.0645120486 -0.0545358120
## HalfBath -0.0270800493 0.415771636 -0.0309049591 -0.0123399001
## BedroomAbvGr 0.1056065685 0.521269511 -0.1506728092 0.0465188484
## KitchenAbvGr 0.0075217443 0.100063165 -0.0415025464 -0.0379443502
## Fireplaces -0.0212721434 0.461679134 0.1379277084 0.0289755866
## GarageArea -0.0676014132 0.468997477 0.1791894804 -0.0245355796
## WoodDeckSF -0.0254436480 0.247432821 0.1753151901 0.0401612233
## OpenPorchSF 0.0182510391 0.330223962 0.0673414614 -0.0253237579
## EnclosedPorch 0.0610812378 0.009113210 -0.0499106491 -0.0085553339
## X3SsnPorch -0.0042956104 0.020643190 -0.0001060915 0.0351136309
## ScreenPorch 0.0267994130 0.101510396 0.0231477258 0.0321214072
## PoolArea 0.0621573723 0.170205336 0.0676155562 0.0200246298
## MiscVal -0.0037928708 -0.002415640 -0.0230470249 -0.0073665245
## MoSold -0.0221739606 0.050239681 -0.0253608943 0.0328727052
## YrSold -0.0289208798 -0.036525820 0.0670491377 -0.0465238818
## SalePrice -0.0256061300 0.708624478 0.2271222331 -0.0168441543
## FullBath HalfBath BedroomAbvGr KitchenAbvGr Fireplaces
## MSSubClass 0.1316082224 0.177354389 -0.023438028 0.281721040 -0.045569340
## LotFrontage 0.1804242055 0.048258362 0.237023227 -0.005804607 0.235754565
## LotArea 0.1260306265 0.014259469 0.119689908 -0.017783871 0.271364010
## OverallQual 0.5505997094 0.273458099 0.101676356 -0.183882235 0.396765038
## OverallCond -0.1941494887 -0.060769327 0.012980060 -0.087000855 -0.023819978
## YearBuilt 0.4682707872 0.242655910 -0.070651217 -0.174800246 0.147716399
## YearRemodAdd 0.4390464839 0.183330612 -0.040580928 -0.149597521 0.112581318
## MasVnrArea 0.2757298473 0.200802357 0.102417471 -0.037364011 0.247906285
## BsmtFinSF1 0.0585431369 0.004262424 -0.107354677 -0.081006851 0.260010920
## BsmtFinSF2 -0.0764438620 -0.032147837 -0.015728114 -0.040751236 0.046920709
## BsmtUnfSF 0.2888860555 -0.041117530 0.166643317 0.030085868 0.051574882
## TotalBsmtSF 0.3237224136 -0.048803739 0.050449956 -0.068900643 0.339519324
## X1stFlrSF 0.3806374950 -0.119915909 0.127400749 0.068100588 0.410531085
## X2ndFlrSF 0.4213779829 0.609707300 0.502900613 0.059305753 0.194560892
## LowQualFinSF -0.0007095096 -0.027080049 0.105606569 0.007521744 -0.021272143
## GrLivArea 0.6300116463 0.415771636 0.521269511 0.100063165 0.461679134
## BsmtFullBath -0.0645120486 -0.030904959 -0.150672809 -0.041502546 0.137927708
## BsmtHalfBath -0.0545358120 -0.012339900 0.046518848 -0.037944350 0.028975587
## FullBath 1.0000000000 0.136380589 0.363251983 0.133115214 0.243670503
## HalfBath 0.1363805887 1.000000000 0.226651484 -0.068262549 0.203648508
## BedroomAbvGr 0.3632519830 0.226651484 1.000000000 0.198596758 0.107569681
## KitchenAbvGr 0.1331152142 -0.068262549 0.198596758 1.000000000 -0.123936235
## Fireplaces 0.2436705031 0.203648508 0.107569681 -0.123936235 1.000000000
## GarageArea 0.4056562085 0.163549364 0.065252530 -0.064433047 0.269141238
## WoodDeckSF 0.1877032138 0.108080303 0.046853773 -0.090130273 0.200018796
## OpenPorchSF 0.2599774255 0.199740148 0.093809572 -0.070090610 0.169405327
## EnclosedPorch -0.1150929635 -0.095316526 0.041570435 0.037312385 -0.024821869
## X3SsnPorch 0.0353530166 -0.004972488 -0.024477796 -0.024600359 0.011257239
## ScreenPorch -0.0081060933 0.072425845 0.044299691 -0.051613366 0.184530270
## PoolArea 0.0496038256 0.022381498 0.070702584 -0.014525116 0.095073522
## MiscVal -0.0142898450 0.001290145 0.007766972 0.062340724 0.001408605
## MoSold 0.0558721290 -0.009049888 0.046543860 0.026588907 0.046357102
## YrSold -0.0196688407 -0.010268669 -0.036013893 0.031687207 -0.024095565
## SalePrice 0.5606637627 0.284107676 0.168213154 -0.135907371 0.466928837
## GarageArea WoodDeckSF OpenPorchSF EnclosedPorch X3SsnPorch
## MSSubClass -0.09867154 -0.012579358 -0.006100121 -0.012036622 -0.0438245492
## LotFrontage 0.32366290 0.077106221 0.137454496 0.009790061 0.0623354713
## LotArea 0.18040276 0.171697687 0.084773809 -0.018339734 0.0204228296
## OverallQual 0.56202176 0.238923392 0.308818823 -0.113936859 0.0303705671
## OverallCond -0.15152137 -0.003333699 -0.032588814 0.070356184 0.0255036600
## YearBuilt 0.47895382 0.224880142 0.188685840 -0.387267783 0.0313545131
## YearRemodAdd 0.37159981 0.205725920 0.226297633 -0.193919147 0.0452858098
## MasVnrArea 0.37256701 0.159349430 0.124965275 -0.109848652 0.0187948145
## BsmtFinSF1 0.29697039 0.204306145 0.111760613 -0.102303306 0.0264505062
## BsmtFinSF2 -0.01822659 0.067898326 0.003092562 0.036543339 -0.0299933980
## BsmtUnfSF 0.18330270 -0.005316424 0.129005415 -0.002537855 0.0207640057
## TotalBsmtSF 0.48666546 0.232018609 0.247263746 -0.095477737 0.0373837273
## X1stFlrSF 0.48978165 0.235458623 0.211671225 -0.065291701 0.0561043745
## X2ndFlrSF 0.13834696 0.092165418 0.208026063 0.061988691 -0.0243576484
## LowQualFinSF -0.06760141 -0.025443648 0.018251039 0.061081238 -0.0042956104
## GrLivArea 0.46899748 0.247432821 0.330223962 0.009113210 0.0206431897
## BsmtFullBath 0.17918948 0.175315190 0.067341461 -0.049910649 -0.0001060915
## BsmtHalfBath -0.02453558 0.040161223 -0.025323758 -0.008555334 0.0351136309
## FullBath 0.40565621 0.187703214 0.259977425 -0.115092963 0.0353530166
## HalfBath 0.16354936 0.108080303 0.199740148 -0.095316526 -0.0049724884
## BedroomAbvGr 0.06525253 0.046853773 0.093809572 0.041570435 -0.0244777964
## KitchenAbvGr -0.06443305 -0.090130273 -0.070090610 0.037312385 -0.0246003587
## Fireplaces 0.26914124 0.200018796 0.169405327 -0.024821869 0.0112572390
## GarageArea 1.00000000 0.224666307 0.241434672 -0.121776720 0.0350867002
## WoodDeckSF 0.22466631 1.000000000 0.058660609 -0.125988888 -0.0327706336
## OpenPorchSF 0.24143467 0.058660609 1.000000000 -0.093079318 -0.0058424993
## EnclosedPorch -0.12177672 -0.125988888 -0.093079318 1.000000000 -0.0373052828
## X3SsnPorch 0.03508670 -0.032770634 -0.005842499 -0.037305283 1.0000000000
## ScreenPorch 0.05141176 -0.074181351 0.074303944 -0.082864245 -0.0314358470
## PoolArea 0.06104727 0.073378207 0.060762111 0.054202562 -0.0079915489
## MiscVal -0.02739991 -0.009551228 -0.018583739 0.018360600 0.0003539653
## MoSold 0.02797380 0.021011044 0.071254885 -0.028887266 0.0294737952
## YrSold -0.02737794 0.022270451 -0.057619360 -0.009915937 0.0186449254
## SalePrice 0.62343144 0.324413445 0.315856227 -0.128577958 0.0445836653
## ScreenPorch PoolArea MiscVal MoSold YrSold
## MSSubClass -0.026030177 0.008282708 -0.0076832913 -0.013584643 -0.021407038
## LotFrontage 0.037684305 0.180867647 0.0011682749 0.010157807 0.006768250
## LotArea 0.043160378 0.077672392 0.0380676920 0.001204988 -0.014261407
## OverallQual 0.064886360 0.065165844 -0.0314062105 0.070815172 -0.027346708
## OverallCond 0.054810529 -0.001984942 0.0687768061 -0.003510839 0.043949746
## YearBuilt -0.050364435 0.004949728 -0.0343831387 0.012398471 -0.013617680
## YearRemodAdd -0.038740011 0.005829372 -0.0102862488 0.021490002 0.035743247
## MasVnrArea 0.061453179 0.011722909 -0.0298147620 -0.005939579 -0.008183639
## BsmtFinSF1 0.062020623 0.140491286 0.0035714735 -0.015726948 0.014358922
## BsmtFinSF2 0.088871251 0.041709055 0.0049397812 -0.015210738 0.031705637
## BsmtUnfSF -0.012579273 -0.035092241 -0.0238366451 0.034888443 -0.041258195
## TotalBsmtSF 0.084488986 0.126053132 -0.0184789224 0.013196179 -0.014968648
## X1stFlrSF 0.088758073 0.131524976 -0.0210957195 0.031371560 -0.013603771
## X2ndFlrSF 0.040606448 0.081486878 0.0161968746 0.035164427 -0.028699914
## LowQualFinSF 0.026799413 0.062157372 -0.0037928708 -0.022173961 -0.028920880
## GrLivArea 0.101510396 0.170205336 -0.0024156396 0.050239681 -0.036525820
## BsmtFullBath 0.023147726 0.067615556 -0.0230470249 -0.025360894 0.067049138
## BsmtHalfBath 0.032121407 0.020024630 -0.0073665245 0.032872705 -0.046523882
## FullBath -0.008106093 0.049603826 -0.0142898450 0.055872129 -0.019668841
## HalfBath 0.072425845 0.022381498 0.0012901448 -0.009049888 -0.010268669
## BedroomAbvGr 0.044299691 0.070702584 0.0077669720 0.046543860 -0.036013893
## KitchenAbvGr -0.051613366 -0.014525116 0.0623407240 0.026588907 0.031687207
## Fireplaces 0.184530270 0.095073522 0.0014086054 0.046357102 -0.024095565
## GarageArea 0.051411762 0.061047272 -0.0273999144 0.027973800 -0.027377940
## WoodDeckSF -0.074181351 0.073378207 -0.0095512282 0.021011044 0.022270451
## OpenPorchSF 0.074303944 0.060762111 -0.0185837390 0.071254885 -0.057619360
## EnclosedPorch -0.082864245 0.054202562 0.0183606001 -0.028887266 -0.009915937
## X3SsnPorch -0.031435847 -0.007991549 0.0003539653 0.029473795 0.018644925
## ScreenPorch 1.000000000 0.051307395 0.0319457608 0.023216992 0.010694106
## PoolArea 0.051307395 1.000000000 0.0296686509 -0.033736640 -0.059688932
## MiscVal 0.031945761 0.029668651 1.0000000000 -0.006494550 0.004906262
## MoSold 0.023216992 -0.033736640 -0.0064945502 1.000000000 -0.145721413
## YrSold 0.010694106 -0.059688932 0.0049062625 -0.145721413 1.000000000
## SalePrice 0.111446571 0.092403549 -0.0211895796 0.046432245 -0.028922585
## SalePrice
## MSSubClass -0.08428414
## LotFrontage 0.33490085
## LotArea 0.26384335
## OverallQual 0.79098160
## OverallCond -0.07785589
## YearBuilt 0.52289733
## YearRemodAdd 0.50710097
## MasVnrArea 0.47524132
## BsmtFinSF1 0.38641981
## BsmtFinSF2 -0.01137812
## BsmtUnfSF 0.21447911
## TotalBsmtSF 0.61358055
## X1stFlrSF 0.60585218
## X2ndFlrSF 0.31933380
## LowQualFinSF -0.02560613
## GrLivArea 0.70862448
## BsmtFullBath 0.22712223
## BsmtHalfBath -0.01684415
## FullBath 0.56066376
## HalfBath 0.28410768
## BedroomAbvGr 0.16821315
## KitchenAbvGr -0.13590737
## Fireplaces 0.46692884
## GarageArea 0.62343144
## WoodDeckSF 0.32441344
## OpenPorchSF 0.31585623
## EnclosedPorch -0.12857796
## X3SsnPorch 0.04458367
## ScreenPorch 0.11144657
## PoolArea 0.09240355
## MiscVal -0.02118958
## MoSold 0.04643225
## YrSold -0.02892259
## SalePrice 1.00000000Create a heatmap of the correlation matrix
corrplot(correlation_matrix, method = "color", type = "upper", order = "hclust", tl.col = "black", tl.cex = 0.3, tl.srt = 90)
print(correlation_matrix)## MSSubClass LotFrontage LotArea OverallQual OverallCond
## MSSubClass 1.000000000 -0.357055878 -0.139781082 0.03262771 -0.059315817
## LotFrontage -0.357055878 1.000000000 0.306794605 0.23419623 -0.052820100
## LotArea -0.139781082 0.306794605 1.000000000 0.10580574 -0.005636270
## OverallQual 0.032627708 0.234196231 0.105805742 1.00000000 -0.091932343
## OverallCond -0.059315817 -0.052820100 -0.005636270 -0.09193234 1.000000000
## YearBuilt 0.027850137 0.117597979 0.014227652 0.57232277 -0.375983196
## YearRemodAdd 0.040581045 0.082745885 0.013788427 0.55068392 0.073741498
## MasVnrArea 0.022894690 0.179283452 0.103960227 0.41023774 -0.127788119
## BsmtFinSF1 -0.069835749 0.215828435 0.214103131 0.23966597 -0.046230856
## BsmtFinSF2 -0.065648579 0.043339566 0.111169745 -0.05911869 0.040229170
## BsmtUnfSF -0.140759481 0.122155600 -0.002618360 0.30815893 -0.136840570
## TotalBsmtSF -0.238518409 0.363357723 0.260833135 0.53780850 -0.171097515
## X1stFlrSF -0.251758352 0.414266394 0.299474579 0.47622383 -0.144202784
## X2ndFlrSF 0.307885721 0.072482666 0.050985948 0.29549288 0.028942116
## LowQualFinSF 0.046473756 0.036848721 0.004778970 -0.03042928 0.025494320
## GrLivArea 0.074853180 0.368391966 0.263116167 0.59300743 -0.079685865
## BsmtFullBath 0.003491026 0.091480992 0.158154531 0.11109779 -0.054941515
## BsmtHalfBath -0.002332535 -0.006419220 0.048045571 -0.04015016 0.117820915
## FullBath 0.131608222 0.180424206 0.126030627 0.55059971 -0.194149489
## HalfBath 0.177354389 0.048258362 0.014259469 0.27345810 -0.060769327
## BedroomAbvGr -0.023438028 0.237023227 0.119689908 0.10167636 0.012980060
## KitchenAbvGr 0.281721040 -0.005804607 -0.017783871 -0.18388223 -0.087000855
## Fireplaces -0.045569340 0.235754565 0.271364010 0.39676504 -0.023819978
## GarageArea -0.098671543 0.323662899 0.180402755 0.56202176 -0.151521371
## WoodDeckSF -0.012579358 0.077106221 0.171697687 0.23892339 -0.003333699
## OpenPorchSF -0.006100121 0.137454496 0.084773809 0.30881882 -0.032588814
## EnclosedPorch -0.012036622 0.009790061 -0.018339734 -0.11393686 0.070356184
## X3SsnPorch -0.043824549 0.062335471 0.020422830 0.03037057 0.025503660
## ScreenPorch -0.026030177 0.037684305 0.043160378 0.06488636 0.054810529
## PoolArea 0.008282708 0.180867647 0.077672392 0.06516584 -0.001984942
## MiscVal -0.007683291 0.001168275 0.038067692 -0.03140621 0.068776806
## MoSold -0.013584643 0.010157807 0.001204988 0.07081517 -0.003510839
## YrSold -0.021407038 0.006768250 -0.014261407 -0.02734671 0.043949746
## SalePrice -0.084284135 0.334900852 0.263843354 0.79098160 -0.077855894
## YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2
## MSSubClass 0.027850137 0.040581045 0.022894690 -0.069835749 -0.065648579
## LotFrontage 0.117597979 0.082745885 0.179283452 0.215828435 0.043339566
## LotArea 0.014227652 0.013788427 0.103960227 0.214103131 0.111169745
## OverallQual 0.572322769 0.550683924 0.410237736 0.239665966 -0.059118693
## OverallCond -0.375983196 0.073741498 -0.127788119 -0.046230856 0.040229170
## YearBuilt 1.000000000 0.592854976 0.314745023 0.249503197 -0.049106831
## YearRemodAdd 0.592854976 1.000000000 0.179186495 0.128450547 -0.067758514
## MasVnrArea 0.314745023 0.179186495 1.000000000 0.263582388 -0.072302247
## BsmtFinSF1 0.249503197 0.128450547 0.263582388 1.000000000 -0.050117400
## BsmtFinSF2 -0.049106831 -0.067758514 -0.072302247 -0.050117400 1.000000000
## BsmtUnfSF 0.149040392 0.181133087 0.114183659 -0.495251469 -0.209294492
## TotalBsmtSF 0.391452002 0.291065583 0.362452094 0.522396052 0.104809538
## X1stFlrSF 0.281985859 0.240379268 0.342160338 0.445862656 0.097117448
## X2ndFlrSF 0.010307660 0.140023779 0.174019344 -0.137078986 -0.099260316
## LowQualFinSF -0.183784344 -0.062419100 -0.069068105 -0.064502597 0.014806998
## GrLivArea 0.199009714 0.287388520 0.389892916 0.208171130 -0.009639892
## BsmtFullBath 0.187598550 0.119469879 0.085054528 0.649211754 0.158678061
## BsmtHalfBath -0.038161806 -0.012337032 0.026668558 0.067418478 0.070948134
## FullBath 0.468270787 0.439046484 0.275729847 0.058543137 -0.076443862
## HalfBath 0.242655910 0.183330612 0.200802357 0.004262424 -0.032147837
## BedroomAbvGr -0.070651217 -0.040580928 0.102417471 -0.107354677 -0.015728114
## KitchenAbvGr -0.174800246 -0.149597521 -0.037364011 -0.081006851 -0.040751236
## Fireplaces 0.147716399 0.112581318 0.247906285 0.260010920 0.046920709
## GarageArea 0.478953820 0.371599809 0.372567013 0.296970385 -0.018226592
## WoodDeckSF 0.224880142 0.205725920 0.159349430 0.204306145 0.067898326
## OpenPorchSF 0.188685840 0.226297633 0.124965275 0.111760613 0.003092562
## EnclosedPorch -0.387267783 -0.193919147 -0.109848652 -0.102303306 0.036543339
## X3SsnPorch 0.031354513 0.045285810 0.018794814 0.026450506 -0.029993398
## ScreenPorch -0.050364435 -0.038740011 0.061453179 0.062020623 0.088871251
## PoolArea 0.004949728 0.005829372 0.011722909 0.140491286 0.041709055
## MiscVal -0.034383139 -0.010286249 -0.029814762 0.003571473 0.004939781
## MoSold 0.012398471 0.021490002 -0.005939579 -0.015726948 -0.015210738
## YrSold -0.013617680 0.035743247 -0.008183639 0.014358922 0.031705637
## SalePrice 0.522897333 0.507100967 0.475241317 0.386419806 -0.011378121
## BsmtUnfSF TotalBsmtSF X1stFlrSF X2ndFlrSF
## MSSubClass -0.140759481 -0.2385184093 -0.251758352 0.307885721
## LotFrontage 0.122155600 0.3633577228 0.414266394 0.072482666
## LotArea -0.002618360 0.2608331345 0.299474579 0.050985948
## OverallQual 0.308158927 0.5378084986 0.476223829 0.295492879
## OverallCond -0.136840570 -0.1710975146 -0.144202784 0.028942116
## YearBuilt 0.149040392 0.3914520021 0.281985859 0.010307660
## YearRemodAdd 0.181133087 0.2910655826 0.240379268 0.140023779
## MasVnrArea 0.114183659 0.3624520941 0.342160338 0.174019344
## BsmtFinSF1 -0.495251469 0.5223960520 0.445862656 -0.137078986
## BsmtFinSF2 -0.209294492 0.1048095376 0.097117448 -0.099260316
## BsmtUnfSF 1.000000000 0.4153596052 0.317987438 0.004469092
## TotalBsmtSF 0.415359605 1.0000000000 0.819529975 -0.174511950
## X1stFlrSF 0.317987438 0.8195299750 1.000000000 -0.202646181
## X2ndFlrSF 0.004469092 -0.1745119501 -0.202646181 1.000000000
## LowQualFinSF 0.028166688 -0.0332453873 -0.014240673 0.063352950
## GrLivArea 0.240257268 0.4548682025 0.566023969 0.687501064
## BsmtFullBath -0.422900477 0.3073505537 0.244671104 -0.169493952
## BsmtHalfBath -0.095804288 -0.0003145818 0.001955654 -0.023854784
## FullBath 0.288886055 0.3237224136 0.380637495 0.421377983
## HalfBath -0.041117530 -0.0488037386 -0.119915909 0.609707300
## BedroomAbvGr 0.166643317 0.0504499555 0.127400749 0.502900613
## KitchenAbvGr 0.030085868 -0.0689006426 0.068100588 0.059305753
## Fireplaces 0.051574882 0.3395193239 0.410531085 0.194560892
## GarageArea 0.183302698 0.4866654638 0.489781654 0.138346959
## WoodDeckSF -0.005316424 0.2320186091 0.235458623 0.092165418
## OpenPorchSF 0.129005415 0.2472637463 0.211671225 0.208026063
## EnclosedPorch -0.002537855 -0.0954777367 -0.065291701 0.061988691
## X3SsnPorch 0.020764006 0.0373837273 0.056104374 -0.024357648
## ScreenPorch -0.012579273 0.0844889859 0.088758073 0.040606448
## PoolArea -0.035092241 0.1260531321 0.131524976 0.081486878
## MiscVal -0.023836645 -0.0184789224 -0.021095719 0.016196875
## MoSold 0.034888443 0.0131961786 0.031371560 0.035164427
## YrSold -0.041258195 -0.0149686480 -0.013603771 -0.028699914
## SalePrice 0.214479106 0.6135805516 0.605852185 0.319333803
## LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath
## MSSubClass 0.0464737559 0.074853180 0.0034910258 -0.0023325346
## LotFrontage 0.0368487208 0.368391966 0.0914809919 -0.0064192197
## LotArea 0.0047789699 0.263116167 0.1581545311 0.0480455709
## OverallQual -0.0304292840 0.593007430 0.1110977861 -0.0401501577
## OverallCond 0.0254943199 -0.079685865 -0.0549415154 0.1178209151
## YearBuilt -0.1837843444 0.199009714 0.1875985500 -0.0381618057
## YearRemodAdd -0.0624191001 0.287388520 0.1194698791 -0.0123370321
## MasVnrArea -0.0690681050 0.389892916 0.0850545278 0.0266685579
## BsmtFinSF1 -0.0645025969 0.208171130 0.6492117536 0.0674184779
## BsmtFinSF2 0.0148069979 -0.009639892 0.1586780608 0.0709481337
## BsmtUnfSF 0.0281666881 0.240257268 -0.4229004774 -0.0958042882
## TotalBsmtSF -0.0332453873 0.454868203 0.3073505537 -0.0003145818
## X1stFlrSF -0.0142406727 0.566023969 0.2446711042 0.0019556536
## X2ndFlrSF 0.0633529501 0.687501064 -0.1694939517 -0.0238547839
## LowQualFinSF 1.0000000000 0.134682813 -0.0471434219 -0.0058415048
## GrLivArea 0.1346828130 1.000000000 0.0348360495 -0.0189184832
## BsmtFullBath -0.0471434219 0.034836050 1.0000000000 -0.1478709605
## BsmtHalfBath -0.0058415048 -0.018918483 -0.1478709605 1.0000000000
## FullBath -0.0007095096 0.630011646 -0.0645120486 -0.0545358120
## HalfBath -0.0270800493 0.415771636 -0.0309049591 -0.0123399001
## BedroomAbvGr 0.1056065685 0.521269511 -0.1506728092 0.0465188484
## KitchenAbvGr 0.0075217443 0.100063165 -0.0415025464 -0.0379443502
## Fireplaces -0.0212721434 0.461679134 0.1379277084 0.0289755866
## GarageArea -0.0676014132 0.468997477 0.1791894804 -0.0245355796
## WoodDeckSF -0.0254436480 0.247432821 0.1753151901 0.0401612233
## OpenPorchSF 0.0182510391 0.330223962 0.0673414614 -0.0253237579
## EnclosedPorch 0.0610812378 0.009113210 -0.0499106491 -0.0085553339
## X3SsnPorch -0.0042956104 0.020643190 -0.0001060915 0.0351136309
## ScreenPorch 0.0267994130 0.101510396 0.0231477258 0.0321214072
## PoolArea 0.0621573723 0.170205336 0.0676155562 0.0200246298
## MiscVal -0.0037928708 -0.002415640 -0.0230470249 -0.0073665245
## MoSold -0.0221739606 0.050239681 -0.0253608943 0.0328727052
## YrSold -0.0289208798 -0.036525820 0.0670491377 -0.0465238818
## SalePrice -0.0256061300 0.708624478 0.2271222331 -0.0168441543
## FullBath HalfBath BedroomAbvGr KitchenAbvGr Fireplaces
## MSSubClass 0.1316082224 0.177354389 -0.023438028 0.281721040 -0.045569340
## LotFrontage 0.1804242055 0.048258362 0.237023227 -0.005804607 0.235754565
## LotArea 0.1260306265 0.014259469 0.119689908 -0.017783871 0.271364010
## OverallQual 0.5505997094 0.273458099 0.101676356 -0.183882235 0.396765038
## OverallCond -0.1941494887 -0.060769327 0.012980060 -0.087000855 -0.023819978
## YearBuilt 0.4682707872 0.242655910 -0.070651217 -0.174800246 0.147716399
## YearRemodAdd 0.4390464839 0.183330612 -0.040580928 -0.149597521 0.112581318
## MasVnrArea 0.2757298473 0.200802357 0.102417471 -0.037364011 0.247906285
## BsmtFinSF1 0.0585431369 0.004262424 -0.107354677 -0.081006851 0.260010920
## BsmtFinSF2 -0.0764438620 -0.032147837 -0.015728114 -0.040751236 0.046920709
## BsmtUnfSF 0.2888860555 -0.041117530 0.166643317 0.030085868 0.051574882
## TotalBsmtSF 0.3237224136 -0.048803739 0.050449956 -0.068900643 0.339519324
## X1stFlrSF 0.3806374950 -0.119915909 0.127400749 0.068100588 0.410531085
## X2ndFlrSF 0.4213779829 0.609707300 0.502900613 0.059305753 0.194560892
## LowQualFinSF -0.0007095096 -0.027080049 0.105606569 0.007521744 -0.021272143
## GrLivArea 0.6300116463 0.415771636 0.521269511 0.100063165 0.461679134
## BsmtFullBath -0.0645120486 -0.030904959 -0.150672809 -0.041502546 0.137927708
## BsmtHalfBath -0.0545358120 -0.012339900 0.046518848 -0.037944350 0.028975587
## FullBath 1.0000000000 0.136380589 0.363251983 0.133115214 0.243670503
## HalfBath 0.1363805887 1.000000000 0.226651484 -0.068262549 0.203648508
## BedroomAbvGr 0.3632519830 0.226651484 1.000000000 0.198596758 0.107569681
## KitchenAbvGr 0.1331152142 -0.068262549 0.198596758 1.000000000 -0.123936235
## Fireplaces 0.2436705031 0.203648508 0.107569681 -0.123936235 1.000000000
## GarageArea 0.4056562085 0.163549364 0.065252530 -0.064433047 0.269141238
## WoodDeckSF 0.1877032138 0.108080303 0.046853773 -0.090130273 0.200018796
## OpenPorchSF 0.2599774255 0.199740148 0.093809572 -0.070090610 0.169405327
## EnclosedPorch -0.1150929635 -0.095316526 0.041570435 0.037312385 -0.024821869
## X3SsnPorch 0.0353530166 -0.004972488 -0.024477796 -0.024600359 0.011257239
## ScreenPorch -0.0081060933 0.072425845 0.044299691 -0.051613366 0.184530270
## PoolArea 0.0496038256 0.022381498 0.070702584 -0.014525116 0.095073522
## MiscVal -0.0142898450 0.001290145 0.007766972 0.062340724 0.001408605
## MoSold 0.0558721290 -0.009049888 0.046543860 0.026588907 0.046357102
## YrSold -0.0196688407 -0.010268669 -0.036013893 0.031687207 -0.024095565
## SalePrice 0.5606637627 0.284107676 0.168213154 -0.135907371 0.466928837
## GarageArea WoodDeckSF OpenPorchSF EnclosedPorch X3SsnPorch
## MSSubClass -0.09867154 -0.012579358 -0.006100121 -0.012036622 -0.0438245492
## LotFrontage 0.32366290 0.077106221 0.137454496 0.009790061 0.0623354713
## LotArea 0.18040276 0.171697687 0.084773809 -0.018339734 0.0204228296
## OverallQual 0.56202176 0.238923392 0.308818823 -0.113936859 0.0303705671
## OverallCond -0.15152137 -0.003333699 -0.032588814 0.070356184 0.0255036600
## YearBuilt 0.47895382 0.224880142 0.188685840 -0.387267783 0.0313545131
## YearRemodAdd 0.37159981 0.205725920 0.226297633 -0.193919147 0.0452858098
## MasVnrArea 0.37256701 0.159349430 0.124965275 -0.109848652 0.0187948145
## BsmtFinSF1 0.29697039 0.204306145 0.111760613 -0.102303306 0.0264505062
## BsmtFinSF2 -0.01822659 0.067898326 0.003092562 0.036543339 -0.0299933980
## BsmtUnfSF 0.18330270 -0.005316424 0.129005415 -0.002537855 0.0207640057
## TotalBsmtSF 0.48666546 0.232018609 0.247263746 -0.095477737 0.0373837273
## X1stFlrSF 0.48978165 0.235458623 0.211671225 -0.065291701 0.0561043745
## X2ndFlrSF 0.13834696 0.092165418 0.208026063 0.061988691 -0.0243576484
## LowQualFinSF -0.06760141 -0.025443648 0.018251039 0.061081238 -0.0042956104
## GrLivArea 0.46899748 0.247432821 0.330223962 0.009113210 0.0206431897
## BsmtFullBath 0.17918948 0.175315190 0.067341461 -0.049910649 -0.0001060915
## BsmtHalfBath -0.02453558 0.040161223 -0.025323758 -0.008555334 0.0351136309
## FullBath 0.40565621 0.187703214 0.259977425 -0.115092963 0.0353530166
## HalfBath 0.16354936 0.108080303 0.199740148 -0.095316526 -0.0049724884
## BedroomAbvGr 0.06525253 0.046853773 0.093809572 0.041570435 -0.0244777964
## KitchenAbvGr -0.06443305 -0.090130273 -0.070090610 0.037312385 -0.0246003587
## Fireplaces 0.26914124 0.200018796 0.169405327 -0.024821869 0.0112572390
## GarageArea 1.00000000 0.224666307 0.241434672 -0.121776720 0.0350867002
## WoodDeckSF 0.22466631 1.000000000 0.058660609 -0.125988888 -0.0327706336
## OpenPorchSF 0.24143467 0.058660609 1.000000000 -0.093079318 -0.0058424993
## EnclosedPorch -0.12177672 -0.125988888 -0.093079318 1.000000000 -0.0373052828
## X3SsnPorch 0.03508670 -0.032770634 -0.005842499 -0.037305283 1.0000000000
## ScreenPorch 0.05141176 -0.074181351 0.074303944 -0.082864245 -0.0314358470
## PoolArea 0.06104727 0.073378207 0.060762111 0.054202562 -0.0079915489
## MiscVal -0.02739991 -0.009551228 -0.018583739 0.018360600 0.0003539653
## MoSold 0.02797380 0.021011044 0.071254885 -0.028887266 0.0294737952
## YrSold -0.02737794 0.022270451 -0.057619360 -0.009915937 0.0186449254
## SalePrice 0.62343144 0.324413445 0.315856227 -0.128577958 0.0445836653
## ScreenPorch PoolArea MiscVal MoSold YrSold
## MSSubClass -0.026030177 0.008282708 -0.0076832913 -0.013584643 -0.021407038
## LotFrontage 0.037684305 0.180867647 0.0011682749 0.010157807 0.006768250
## LotArea 0.043160378 0.077672392 0.0380676920 0.001204988 -0.014261407
## OverallQual 0.064886360 0.065165844 -0.0314062105 0.070815172 -0.027346708
## OverallCond 0.054810529 -0.001984942 0.0687768061 -0.003510839 0.043949746
## YearBuilt -0.050364435 0.004949728 -0.0343831387 0.012398471 -0.013617680
## YearRemodAdd -0.038740011 0.005829372 -0.0102862488 0.021490002 0.035743247
## MasVnrArea 0.061453179 0.011722909 -0.0298147620 -0.005939579 -0.008183639
## BsmtFinSF1 0.062020623 0.140491286 0.0035714735 -0.015726948 0.014358922
## BsmtFinSF2 0.088871251 0.041709055 0.0049397812 -0.015210738 0.031705637
## BsmtUnfSF -0.012579273 -0.035092241 -0.0238366451 0.034888443 -0.041258195
## TotalBsmtSF 0.084488986 0.126053132 -0.0184789224 0.013196179 -0.014968648
## X1stFlrSF 0.088758073 0.131524976 -0.0210957195 0.031371560 -0.013603771
## X2ndFlrSF 0.040606448 0.081486878 0.0161968746 0.035164427 -0.028699914
## LowQualFinSF 0.026799413 0.062157372 -0.0037928708 -0.022173961 -0.028920880
## GrLivArea 0.101510396 0.170205336 -0.0024156396 0.050239681 -0.036525820
## BsmtFullBath 0.023147726 0.067615556 -0.0230470249 -0.025360894 0.067049138
## BsmtHalfBath 0.032121407 0.020024630 -0.0073665245 0.032872705 -0.046523882
## FullBath -0.008106093 0.049603826 -0.0142898450 0.055872129 -0.019668841
## HalfBath 0.072425845 0.022381498 0.0012901448 -0.009049888 -0.010268669
## BedroomAbvGr 0.044299691 0.070702584 0.0077669720 0.046543860 -0.036013893
## KitchenAbvGr -0.051613366 -0.014525116 0.0623407240 0.026588907 0.031687207
## Fireplaces 0.184530270 0.095073522 0.0014086054 0.046357102 -0.024095565
## GarageArea 0.051411762 0.061047272 -0.0273999144 0.027973800 -0.027377940
## WoodDeckSF -0.074181351 0.073378207 -0.0095512282 0.021011044 0.022270451
## OpenPorchSF 0.074303944 0.060762111 -0.0185837390 0.071254885 -0.057619360
## EnclosedPorch -0.082864245 0.054202562 0.0183606001 -0.028887266 -0.009915937
## X3SsnPorch -0.031435847 -0.007991549 0.0003539653 0.029473795 0.018644925
## ScreenPorch 1.000000000 0.051307395 0.0319457608 0.023216992 0.010694106
## PoolArea 0.051307395 1.000000000 0.0296686509 -0.033736640 -0.059688932
## MiscVal 0.031945761 0.029668651 1.0000000000 -0.006494550 0.004906262
## MoSold 0.023216992 -0.033736640 -0.0064945502 1.000000000 -0.145721413
## YrSold 0.010694106 -0.059688932 0.0049062625 -0.145721413 1.000000000
## SalePrice 0.111446571 0.092403549 -0.0211895796 0.046432245 -0.028922585
## SalePrice
## MSSubClass -0.08428414
## LotFrontage 0.33490085
## LotArea 0.26384335
## OverallQual 0.79098160
## OverallCond -0.07785589
## YearBuilt 0.52289733
## YearRemodAdd 0.50710097
## MasVnrArea 0.47524132
## BsmtFinSF1 0.38641981
## BsmtFinSF2 -0.01137812
## BsmtUnfSF 0.21447911
## TotalBsmtSF 0.61358055
## X1stFlrSF 0.60585218
## X2ndFlrSF 0.31933380
## LowQualFinSF -0.02560613
## GrLivArea 0.70862448
## BsmtFullBath 0.22712223
## BsmtHalfBath -0.01684415
## FullBath 0.56066376
## HalfBath 0.28410768
## BedroomAbvGr 0.16821315
## KitchenAbvGr -0.13590737
## Fireplaces 0.46692884
## GarageArea 0.62343144
## WoodDeckSF 0.32441344
## OpenPorchSF 0.31585623
## EnclosedPorch -0.12857796
## X3SsnPorch 0.04458367
## ScreenPorch 0.11144657
## PoolArea 0.09240355
## MiscVal -0.02118958
## MoSold 0.04643225
## YrSold -0.02892259
## SalePrice 1.00000000high_corr <- which(abs((correlation_matrix) > 0.8 | correlation_matrix < -0.8)& correlation_matrix!= 1, arr.ind=TRUE)
high_corr_values <- correlation_matrix[high_corr]
high_corr_df <- data.frame(row = high_corr[, 1], col = high_corr[, 2], corr = high_corr_values)
high_corr_df## row col corr
## X1stFlrSF 13 12 0.81953
## TotalBsmtSF 12 13 0.81953Categorical Data
Display the first six rows of the categorical data frame
head(categorical_data)## MSZoning Street LotShape LandContour Utilities LotConfig LandSlope
## 1 RL Pave Reg Lvl AllPub Inside Gtl
## 2 RL Pave Reg Lvl AllPub FR2 Gtl
## 3 RL Pave IR1 Lvl AllPub Inside Gtl
## 4 RL Pave IR1 Lvl AllPub Corner Gtl
## 5 RL Pave IR1 Lvl AllPub FR2 Gtl
## 6 RL Pave IR1 Lvl AllPub Inside Gtl
## Neighborhood Condition1 Condition2 BldgType HouseStyle RoofStyle RoofMatl
## 1 CollgCr Norm Norm 1Fam 2Story Gable CompShg
## 2 Veenker Feedr Norm 1Fam 1Story Gable CompShg
## 3 CollgCr Norm Norm 1Fam 2Story Gable CompShg
## 4 Crawfor Norm Norm 1Fam 2Story Gable CompShg
## 5 NoRidge Norm Norm 1Fam 2Story Gable CompShg
## 6 Mitchel Norm Norm 1Fam 1.5Fin Gable CompShg
## Exterior1st MasVnrType ExterQual ExterCond Foundation BsmtQual BsmtCond
## 1 VinylSd BrkFace Gd TA PConc Gd TA
## 2 MetalSd None TA TA CBlock Gd TA
## 3 VinylSd BrkFace Gd TA PConc Gd TA
## 4 Wd Sdng None TA TA BrkTil TA Gd
## 5 VinylSd BrkFace Gd TA PConc Gd TA
## 6 VinylSd None TA TA Wood Gd TA
## BsmtExposure BsmtFinType1 BsmtFinType2 Heating HeatingQC CentralAir
## 1 No GLQ Unf GasA Ex Y
## 2 Gd ALQ Unf GasA Ex Y
## 3 Mn GLQ Unf GasA Ex Y
## 4 No ALQ Unf GasA Gd Y
## 5 Av GLQ Unf GasA Ex Y
## 6 No GLQ Unf GasA Ex Y
## Electrical KitchenQual Functional GarageType GarageFinish GarageQual
## 1 SBrkr Gd Typ Attchd RFn TA
## 2 SBrkr TA Typ Attchd RFn TA
## 3 SBrkr Gd Typ Attchd RFn TA
## 4 SBrkr Gd Typ Detchd Unf TA
## 5 SBrkr Gd Typ Attchd RFn TA
## 6 SBrkr TA Typ Attchd Unf TA
## GarageCond PavedDrive SaleType SaleCondition
## 1 TA Y WD Normal
## 2 TA Y WD Normal
## 3 TA Y WD Normal
## 4 TA Y WD Abnorml
## 5 TA Y WD Normal
## 6 TA Y WD NormalPrint bar plots for categorical data for the first 20 attributes
plots_first20attribute <- lapply(colnames(categorical_data)[1:20], function(col) {
ggplot(categorical_data, aes_string(x = col)) +
geom_bar() +
xlab(col) +
ylab("Count") +
ggtitle(paste0("Distribution of", col)) +
scale_x_discrete(guide = guide_axis(check.overlap = TRUE))
})## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.# Print bar plots for categorical data for the remaining 17 attributes
plots_rest23attribute <- lapply(colnames(categorical_data)[21:37], function(col) {
ggplot(categorical_data, aes_string(x = col)) +
geom_bar() +
xlab(col) +
ylab("Count") +
ggtitle(paste0("Distribution of", col)) +
scale_x_discrete(guide = guide_axis(check.overlap = TRUE))
})Print the plots in a grid
grid.arrange(grobs = plots_first20attribute, ncol = 5)
grid.arrange(grobs = plots_rest23attribute, ncol = 5)
Encode Categorical Data
Create a copy of the dataset
dataset_encode <- dataset
for (col in colnames(dataset_encode)) {
if (is.factor(dataset_encode[[col]]) | is.character(dataset_encode[[col]])) {
encoded_col <- predict(dummyVars(formula = as.formula(paste0(“~”, col)), data = dataset_encode), newdata = dataset_encode)
dataset_encode[[col]] <- encoded_col
}
}
library(arules)## Loading required package: Matrix##
## Attaching package: 'arules'## The following object is masked from 'package:dplyr':
##
## recode## The following objects are masked from 'package:base':
##
## abbreviate, writelibrary(caret)
library(catboost)
library(cluster)
library(dplyr)
library(e1071)##
## Attaching package: 'e1071'## The following object is masked from 'package:mlr':
##
## imputelibrary(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(ggplot2)
library(glmnet)## Loaded glmnet 4.1-7library(lattice)
library(lightgbm)## Loading required package: R6##
## Attaching package: 'lightgbm'## The following object is masked from 'package:dplyr':
##
## slicelibrary(mlrMBO)## Loading required package: smoof## Loading required package: checkmatelibrary(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:gridExtra':
##
## combine## The following object is masked from 'package:psych':
##
## outlier## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:dplyr':
##
## combinelibrary(tidyr)##
## Attaching package: 'tidyr'## The following objects are masked from 'package:Matrix':
##
## expand, pack, unpacklibrary(viridis)## Loading required package: viridisLitelibrary(xgboost)##
## Attaching package: 'xgboost'## The following objects are masked from 'package:lightgbm':
##
## getinfo, setinfo, slice## The following object is masked from 'package:dplyr':
##
## slicelibrary(Metrics)##
## Attaching package: 'Metrics'## The following objects are masked from 'package:caret':
##
## precision, recalllibrary(Rtsne)
library(DiceKriging)##
## Attaching package: 'DiceKriging'## The following object is masked from 'package:checkmate':
##
## checkNamessuppressWarnings(expr)## function (expr)
## {
## enexpr(expr)
## }
## <bytecode: 0x7f9e9c53d6a0>
## <environment: namespace:rlang>#source("data_cleaning.r")Data Transformation/Scaling
Data preprocessing steps such as log transformation, scaling, and one-hot encoding.
Data splitted into train, validation, and test sets and combines the training and validation sets for model training.
Calculates and displays the skewness of the numerical features and target variable before and after the log transformations.
Split the numerical variables into features and the target variable.
X_num <- subset(numerical_data, select = -c(SalePrice))
y <- subset(numerical_data, select = c(SalePrice))$SalePriceLog Transformation for numerical features.
skewness_before <- sapply(X_num, function(x) {
e1071::skewness(x)
})
X_num_skewed <- skewness_before[abs(skewness_before) > 0.75]
for (x in names(X_num_skewed)) {
# bc <- BoxCoxTrans(X_num[[x]], lambda = 0.15)
# X_num[[x]] <- predict(bc, X_num[[x]])
X_num[[x]] <- log1p(X_num[[x]])
}
skewness_after <- sapply(X_num, function(x) {
e1071::skewness(x)
})
data.frame(skewness_before, skewness_after)## skewness_before skewness_after
## MSSubClass 1.40476562 0.248485705
## LotFrontage 2.38005182 -0.890144743
## LotArea 12.18261502 -0.137122272
## OverallQual 0.21649836 0.216498356
## OverallCond 0.69164401 0.691644012
## YearBuilt -0.61220121 -0.612201211
## YearRemodAdd -0.50252776 -0.502527759
## MasVnrArea 2.67091482 0.480131975
## BsmtFinSF1 1.68204129 -0.617139693
## BsmtFinSF2 4.24652141 2.518510460
## BsmtUnfSF 0.91837835 -2.182012816
## TotalBsmtSF 1.52112395 -5.144083032
## X1stFlrSF 1.37392896 0.079949547
## X2ndFlrSF 0.81135997 0.289048572
## LowQualFinSF 8.99283329 7.444994097
## GrLivArea 1.36375364 -0.006127642
## BsmtFullBath 0.59484237 0.594842375
## BsmtHalfBath 4.09497490 3.924985577
## FullBath 0.03648647 0.036486466
## HalfBath 0.67450925 0.674509252
## BedroomAbvGr 0.21135511 0.211355110
## KitchenAbvGr 4.47917826 3.861466484
## Fireplaces 0.64823107 0.648231070
## GarageArea 0.17961125 0.179611252
## WoodDeckSF 1.53820999 0.153221248
## OpenPorchSF 2.35948572 -0.023349240
## EnclosedPorch 3.08352575 2.107936639
## X3SsnPorch 10.28317840 7.719088344
## ScreenPorch 4.11374731 3.143938375
## PoolArea 14.79791829 14.333602712
## MiscVal 24.42652237 5.160083979
## MoSold 0.21161746 0.211617459
## YrSold 0.09607079 0.096070792Log Transformation for the target variable.
skewness_before <- e1071::skewness(y)
y_t <- log1p(y)
skewness_after <- e1071::skewness(y_t)
sprintf("Before: %f, After: %f", skewness_before, skewness_after)## [1] "Before: 1.879009, After: 0.121097"Scaling
X_num <- scale(X_num)One-Hot Encoding for categorical variables.
encoder <- dummyVars(~., data = categorical_data)
X_cat <- predict(encoder, newdata = categorical_data)
X_cat <- data.frame(X_cat)Split into train and validation and test sets.
X <- cbind(X_cat, X_num)
train_idx <- createDataPartition(y_t, p = 0.7, list = F)
X_train <- X[train_idx, ]
y_train <- y_t[train_idx]
X_test <- X[-train_idx, ]
y_test <- y_t[-train_idx]
train_val_idx <- createDataPartition(y_train, p = 0.8, list = FALSE)
X_train <- X_train[train_val_idx, ]
y_train <- y_train[train_val_idx]
X_val <- X_train[-train_val_idx, ]
y_val <- y_train[-train_val_idx]
X_train_val <- rbind(X_train, X_val)
y_train_val <- c(y_train, y_val)dim(X)## [1] 1460 251length(y)## [1] 1460dim(X_train)## [1] 821 251length(y_train)## [1] 821dim(X_val)## [1] 154 251length(y_val)## [1] 154#names(X)Clustering
The code executes PCA (Principal Component Analysis) to reduce the data’s dimensionality and extract the principal components.
Elbow method used to determine the best number of clusters. Result: 3.
K-means clustering is performed on the principal components, assigning each observation to a specific cluster.
The cluster assignments are appended to the principal components data, and a scatter plot is generated to visualize the resulting clusters.
# Load the necessary libraries
library(stats)
library(factoextra)
# Perform PCA on the data matrix
pca_result <- prcomp(X)
# Extract the principal components
principal_components <- as.data.frame(pca_result$x)
# Determine the optimal number of clusters using the elbow method
fviz_nbclust(principal_components, kmeans, method = "wss")
k <- 3
# Perform K-means clustering on the principal components
kmeans_result <- kmeans(principal_components, centers = k)
cluster_assignments <- kmeans_result$cluster
print(cluster_assignments)## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 3 2 3 2 3 2 3 3 2 2 2 3 2 3 2 2
## 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
## 2 2 2 2 3 2 3 1 2 3 2 3 2 2 2 2
## 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
## 3 2 3 3 3 2 2 2 2 2 2 2 2 3 3 3
## 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
## 2 2 3 2 2 3 2 2 1 3 3 2 3 2 1 2
## 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 3 3 3 3 2 2 3 2 3 2 2 1 2 2 2 2
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
## 3 1 3 2 3 3 3 1 2 2 2 2 2 2 3 3
## 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
## 3 2 2 2 3 3 2 3 2 3 2 2 2 3 2 3
## 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
## 3 3 2 1 2 2 3 3 2 2 2 1 2 2 1 2
## 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 2 2 3 3 2 3 2 3 2 3 3 3 2 3 2 3
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
## 2 1 2 3 2 2 2 3 3 2 2 2 2 3 3 3
## 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
## 2 3 3 2 2 2 2 3 3 3 2 3 1 2 3 2
## 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
## 3 2 3 2 1 2 2 3 2 3 3 2 2 1 3 2
## 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
## 3 1 2 1 3 3 2 3 2 2 2 1 2 3 2 2
## 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
## 3 2 2 3 3 2 2 2 3 2 3 1 3 3 3 2
## 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
## 3 1 3 1 2 1 2 3 1 2 3 1 3 3 3 2
## 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
## 3 2 2 2 3 3 2 2 3 3 2 1 3 2 2 3
## 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
## 3 3 3 2 2 3 2 2 2 3 3 2 2 2 3 2
## 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
## 3 2 2 2 3 2 3 3 3 3 1 3 1 1 2 2
## 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
## 2 2 3 2 2 3 2 2 2 3 2 2 2 3 3 2
## 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
## 3 3 3 2 2 3 3 2 2 3 2 3 3 3 3 3
## 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
## 3 3 3 2 3 2 1 2 2 2 2 2 3 1 3 3
## 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
## 3 3 3 2 3 2 2 3 1 2 2 2 1 3 3 3
## 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
## 2 2 2 3 3 1 2 3 2 2 3 1 3 2 3 2
## 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
## 2 2 3 2 1 2 3 2 2 3 3 3 2 3 3 2
## 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
## 3 1 2 2 3 3 2 3 2 2 2 2 2 2 2 3
## 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
## 1 3 2 3 3 2 2 2 3 3 2 2 3 2 3 3
## 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
## 2 2 2 2 1 3 2 3 2 2 3 2 3 2 1 2
## 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
## 1 3 1 3 2 2 2 2 3 2 2 1 3 2 3 3
## 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
## 2 2 2 3 3 3 2 2 2 3 2 2 3 2 2 2
## 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
## 2 1 2 2 3 3 1 3 1 3 1 2 3 3 3 2
## 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
## 3 3 2 1 2 2 2 2 2 1 1 2 3 2 2 2
## 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
## 3 2 2 2 1 3 2 3 1 2 3 3 2 2 2 1
## 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
## 2 2 2 3 3 3 3 2 2 2 2 3 3 3 2 3
## 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
## 2 3 3 2 2 2 3 2 3 2 2 3 3 3 3 1
## 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
## 3 3 2 2 2 3 1 2 3 2 3 2 2 2 3 1
## 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
## 2 2 2 2 3 2 3 3 3 2 2 2 3 3 2 2
## 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
## 2 2 1 2 2 3 2 3 2 3 2 2 2 2 3 3
## 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
## 2 1 2 3 2 1 2 1 3 2 3 1 3 3 2 2
## 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
## 3 2 3 2 3 2 1 2 3 2 3 3 2 3 2 1
## 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
## 3 2 2 2 3 2 2 1 3 2 2 2 2 2 2 1
## 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
## 3 3 3 2 3 2 2 2 2 1 3 2 3 2 3 1
## 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
## 2 2 2 2 3 3 2 2 3 3 3 3 2 2 3 2
## 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
## 2 3 2 1 2 2 3 2 1 2 1 3 3 3 3 1
## 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
## 3 3 1 3 3 2 2 2 2 2 2 1 3 2 3 2
## 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
## 3 2 3 1 3 2 2 2 1 2 3 2 2 2 3 2
## 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
## 3 1 2 2 3 2 3 3 2 2 1 3 3 2 2 2
## 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
## 2 3 2 3 2 2 3 2 1 3 3 2 3 2 2 3
## 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
## 3 3 2 1 3 3 1 3 2 2 3 3 1 3 3 2
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
## 3 3 2 2 2 2 3 1 3 2 2 2 3 3 3 1
## 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
## 2 2 2 3 2 3 1 2 3 3 3 3 2 2 3 2
## 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
## 3 2 3 3 2 3 2 3 2 2 2 1 2 2 2 3
## 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
## 2 3 2 1 3 2 3 2 3 3 2 3 2 1 2 1
## 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
## 3 2 2 2 2 1 2 2 2 2 2 2 2 2 3 2
## 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
## 3 3 1 1 2 2 2 2 2 3 2 3 2 2 2 2
## 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
## 3 2 3 2 2 3 2 3 2 2 2 3 2 3 2 2
## 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
## 2 3 3 2 2 1 3 2 3 2 2 3 2 2 2 2
## 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
## 2 2 3 2 2 2 3 3 2 2 3 2 2 3 2 2
## 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
## 2 2 1 1 2 2 3 2 3 2 3 1 3 2 3 3
## 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
## 3 3 3 2 3 3 3 2 3 3 3 2 2 3 2 2
## 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
## 2 2 2 3 3 2 2 2 2 3 1 2 1 2 3 1
## 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
## 2 3 1 3 3 3 2 2 2 2 2 1 2 3 2 1
## 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
## 2 1 2 2 2 3 1 3 2 2 2 3 3 3 3 2
## 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
## 3 3 3 2 2 2 2 3 2 2 3 2 1 2 2 1
## 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
## 3 2 2 2 2 2 2 3 3 1 3 1 3 3 2 1
## 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
## 3 2 2 3 2 1 2 3 3 3 2 2 3 3 1 1
## 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
## 2 2 1 3 3 2 3 2 2 2 3 3 2 2 3 2
## 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
## 1 3 3 2 1 2 2 2 2 3 3 2 1 2 2 2
## 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
## 2 2 3 2 2 2 1 2 3 2 3 2 3 2 1 3
## 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
## 1 1 2 1 2 2 2 3 2 1 2 3 2 2 2 2
## 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
## 1 3 3 3 3 3 3 3 2 2 2 3 3 2 2 2
## 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
## 2 3 2 2 3 2 1 3 3 1 2 2 2 3 3 2
## 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
## 2 2 3 2 2 3 3 2 2 2 3 2 2 2 2 3
## 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
## 2 2 3 3 2 1 3 3 1 3 2 2 3 3 3 3
## 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
## 2 3 2 2 1 2 2 3 2 2 2 2 3 3 3 2
## 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
## 3 2 2 3 3 3 3 1 2 1 2 3 3 2 3 2
## 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
## 2 3 2 3 2 3 2 3 2 3 3 3 2 2 2 2
## 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
## 2 3 2 1 2 2 2 2 3 2 3 2 3 2 3 2
## 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
## 2 2 2 2 1 3 2 3 3 3 3 3 2 3 3 2
## 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
## 2 2 3 1 2 3 3 2 3 2 3 2 3 2 2 2
## 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
## 1 1 2 3 3 2 3 2 2 2 2 2 2 3 3 2
## 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
## 3 3 2 2 2 2 2 2 1 3 2 1 2 3 2 2
## 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
## 2 1 3 2 3 2 3 3 1 3 1 2 2 3 3 3
## 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
## 3 3 2 3 3 1 3 2 2 2 3 2 3 2 2 2
## 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
## 2 3 3 2 2 2 1 2 2 2 3 2 2 2 3 2
## 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
## 3 2 3 3 3 2 3 2 2 3 3 3 2 2 1 3
## 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
## 2 3 2 3 1 3 3 1 1 3 2 2 3 3 3 3
## 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
## 2 2 1 3 2 3 2 2 2 2 3 2 3 2 3 2
## 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
## 2 2 1 3 2 2 2 2 2 3 3 3 2 1 2 2
## 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
## 2 3 3 2 2 3 2 1 2 3 2 3 3 1 1 3
## 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
## 2 2 3 2 2 3 3 1 2 3 2 3 2 3 2 3
## 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
## 2 1 3 2 3 2 2 3 2 1 2 3 1 2 3 3
## 1457 1458 1459 1460
## 3 3 2 2# Add the cluster assignments to the principal components data
principal_components$cluster <- as.factor(cluster_assignments)
ggplot(principal_components, aes(PC1, PC2, color = cluster)) +
geom_point() +
labs(x = "Principal Component 1", y = "Principal Component 2") +
scale_color_discrete(name = "Cluster") +
theme_minimal()
t-SNE
t-SNE (t-Distributed Stochastic Neighbor Embedding) is perfomed for dimensionality reduction and visualization of the data.
Function:
Calculates the t-SNE coordinates
Creates a dendrogram through hierarchical clustering
Determines the number of clusters visually, assigns data points to clusters
Computes centroids for each cluster
Generates scatter plots to visualize the t-SNE coordinates colored by the original variable and cluster assignments
Includes a bar plot showing the cluster frequencies and evaluates clustering quality using silhouette analysis.
tsne <- Rtsne(X)
tsne_df <- data.frame(tsne)dist_mat <- dist(tsne$Y, method = "euclidean")
hclust_avg <- hclust(dist_mat, method = "average")
dend <- as.dendrogram(hclust_avg)
plot(dend)
k = 15
cut_avg <- cutree(hclust_avg, k)
tsne_df$cluster <- cut_avg
getCentroid <- function(points) {
xy <- numeric(2)
xy[1] = mean(points[, 1])
xy[2] = mean(points[, 2])
return(xy)
}
centroids = matrix(0, k, 2)
for (i in unique(cut_avg)) centroids[i, ] <- getCentroid(tsne$Y[cut_avg == i,])Silhouette Graph: Graph width represents the number of data point; longer length indicates a stronger separation and better-defined clusters.
The lighter the color, the higher the house price.
tsne_df## N Y.1 Y.2 costs itercosts origD perplexity
## 1 1460 1.986541e+01 0.61696248 6.312128e-04 73.771148 50 30
## 2 1460 3.704116e+00 17.12829597 8.232540e-04 71.150987 50 30
## 3 1460 2.014823e+01 -0.70238402 8.511462e-04 71.034367 50 30
## 4 1460 -2.776097e+01 7.42146480 3.518235e-04 71.035404 50 30
## 5 1460 2.049439e+01 -2.68081918 5.377578e-04 71.037731 50 30
## 6 1460 8.551878e+00 -9.86640159 1.835220e-04 1.555216 50 30
## 7 1460 -1.365457e-01 -19.74222937 1.439053e-03 1.312321 50 30
## 8 1460 7.018594e+00 24.49087648 8.079059e-04 1.228241 50 30
## 9 1460 -2.974571e+01 -9.98391754 5.951550e-04 1.196858 50 30
## 10 1460 -3.572762e+00 -2.29372078 8.088329e-04 1.176920 50 30
## 11 1460 -8.876179e+00 5.88658564 1.742382e-03 1.164909 50 30
## 12 1460 2.327253e+01 -6.33412442 3.948003e-04 1.155632 50 30
## 13 1460 3.767144e+00 -0.01074932 8.627069e-04 1.148755 50 30
## 14 1460 -2.724445e+00 -27.21039124 5.896907e-04 1.143827 50 30
## 15 1460 -6.929143e+00 -2.70460989 9.837242e-04 1.139748 50 30
## 16 1460 -1.866745e+01 8.81821619 7.690628e-04 1.135437 50 30
## 17 1460 -5.033253e+00 19.69411388 1.129143e-03 1.131049 50 30
## 18 1460 -2.249028e+01 29.56426190 2.261055e-04 1.127886 50 30
## 19 1460 -1.260445e+01 -11.32737087 1.099444e-03 1.125117 50 30
## 20 1460 -1.189045e+01 4.14226659 1.052478e-03 1.123256 50 30
## 21 1460 2.828610e+01 -3.24507358 4.173264e-04 73.771148 50 30
## 22 1460 -2.255078e+01 11.97767458 7.400350e-04 71.150987 50 30
## 23 1460 -4.039918e+00 -27.97222117 7.656355e-04 71.034367 50 30
## 24 1460 7.770521e+00 -5.53522565 1.558227e-03 71.035404 50 30
## 25 1460 7.334040e-01 10.27577087 7.199786e-04 71.037731 50 30
## 26 1460 -2.375303e+00 -25.78658325 4.094778e-04 1.555216 50 30
## 27 1460 2.885874e+00 19.20802716 4.868814e-04 1.312321 50 30
## 28 1460 -3.038099e-01 -21.78084086 1.050677e-03 1.228241 50 30
## 29 1460 -4.256321e+00 -5.49863836 5.379093e-04 1.196858 50 30
## 30 1460 -1.994594e+01 13.78177044 2.389868e-04 1.176920 50 30
## 31 1460 -2.439541e+01 13.23623861 9.066452e-04 1.164909 50 30
## 32 1460 -1.414430e+01 -5.07683992 1.486820e-03 1.155632 50 30
## 33 1460 -8.564948e+00 -25.51218076 2.359553e-04 1.148755 50 30
## 34 1460 4.226560e+00 14.97345959 5.513542e-04 1.143827 50 30
## 35 1460 1.025058e+01 -22.37488610 6.141026e-04 1.139748 50 30
## 36 1460 2.780281e+01 -2.87848877 4.092666e-04 1.135437 50 30
## 37 1460 -1.511928e+01 -12.21702278 1.154713e-03 1.131049 50 30
## 38 1460 4.070995e+00 16.12912804 8.755048e-04 1.127886 50 30
## 39 1460 -8.898812e+00 9.37688585 8.266751e-04 1.125117 50 30
## 40 1460 -2.208428e+01 30.39641844 1.668854e-04 1.123256 50 30
## 41 1460 -8.200685e+00 -3.66998674 1.044667e-03 73.771148 50 30
## 42 1460 4.179280e+00 15.00092016 6.410947e-04 71.150987 50 30
## 43 1460 -2.915433e+00 5.21787108 7.706347e-04 71.034367 50 30
## 44 1460 -7.690188e-01 7.63334436 4.243941e-04 71.035404 50 30
## 45 1460 -1.810113e+00 8.53811196 1.030059e-03 71.037731 50 30
## 46 1460 9.933275e+00 -22.66429833 4.929111e-04 1.555216 50 30
## 47 1460 1.337784e+01 -14.37573786 7.182959e-04 1.312321 50 30
## 48 1460 -5.131762e+00 -24.05535921 4.866584e-04 1.228241 50 30
## 49 1460 -2.911821e+01 -11.21216077 2.437323e-04 1.196858 50 30
## 50 1460 -8.860755e+00 6.10262860 1.134323e-03 1.176920 50 30
## 51 1460 8.619451e+00 17.62880279 7.982909e-04 1.164909 50 30
## 52 1460 -3.615796e+01 6.94109880 6.071872e-04 1.155632 50 30
## 53 1460 4.220717e-01 5.80851697 3.188263e-04 1.148755 50 30
## 54 1460 1.335188e+01 -10.28527035 7.715632e-04 1.143827 50 30
## 55 1460 -1.307171e+01 1.08902214 9.764671e-04 1.139748 50 30
## 56 1460 7.070279e+00 -10.40681970 6.254991e-04 1.135437 50 30
## 57 1460 3.922133e+01 -1.67168525 3.399996e-04 1.131049 50 30
## 58 1460 2.877218e+01 3.83968560 5.696405e-04 1.127886 50 30
## 59 1460 2.759604e+01 -4.56450990 2.092763e-04 1.125117 50 30
## 60 1460 -9.355720e+00 5.14343379 8.723019e-04 1.123256 50 30
## 61 1460 -1.278215e+01 -11.46681586 6.454594e-04 73.771148 50 30
## 62 1460 -2.743416e+01 12.32781556 1.314707e-03 71.150987 50 30
## 63 1460 1.238512e+01 -23.13568151 6.718501e-04 71.034367 50 30
## 64 1460 -2.816901e+01 9.28321673 6.910749e-04 71.035404 50 30
## 65 1460 1.884554e+01 -0.62760277 5.203892e-04 71.037731 50 30
## 66 1460 2.767114e+01 -2.30007907 7.324863e-04 1.555216 50 30
## 67 1460 -1.358972e+00 -13.04916481 8.538453e-04 1.312321 50 30
## 68 1460 -1.854762e+00 -19.00474759 1.186887e-03 1.228241 50 30
## 69 1460 -1.600896e+01 6.76850500 6.052313e-04 1.196858 50 30
## 70 1460 -2.502587e+01 7.93620533 7.736814e-04 1.176920 50 30
## 71 1460 -1.760768e+00 -12.65743519 7.980831e-04 1.164909 50 30
## 72 1460 -9.611671e+00 7.21427195 1.164541e-03 1.155632 50 30
## 73 1460 1.958520e+01 13.47004709 6.768649e-04 1.148755 50 30
## 74 1460 -1.042616e+00 9.97945517 1.129621e-03 1.143827 50 30
## 75 1460 -2.902196e+01 -10.39267675 5.901708e-04 1.139748 50 30
## 76 1460 4.437559e+01 4.15370884 4.325591e-04 1.135437 50 30
## 77 1460 -1.192425e+01 5.83571674 9.663715e-04 1.131049 50 30
## 78 1460 -1.829393e+01 1.41226079 1.142798e-03 1.127886 50 30
## 79 1460 -2.469038e+01 -12.14785163 6.003784e-04 1.125117 50 30
## 80 1460 -2.658901e+01 11.18415777 9.079538e-04 1.123256 50 30
## 81 1460 1.698581e+01 13.79843493 1.214593e-03 73.771148 50 30
## 82 1460 2.024836e+01 -21.64242229 4.668457e-04 71.150987 50 30
## 83 1460 -3.664736e+00 -27.17467033 8.833755e-04 71.034367 50 30
## 84 1460 -1.109128e+01 -1.95825988 9.807902e-04 71.035404 50 30
## 85 1460 7.127931e+00 25.90282402 9.266649e-04 71.037731 50 30
## 86 1460 2.633685e+01 -2.03124625 3.126195e-04 1.555216 50 30
## 87 1460 2.485586e+01 5.75887278 4.349472e-04 1.312321 50 30
## 88 1460 3.718150e+01 -2.80876040 2.901393e-04 1.228241 50 30
## 89 1460 -3.632818e+01 5.76311003 1.928239e-04 1.196858 50 30
## 90 1460 -1.392597e+01 -9.45359175 1.032273e-03 1.176920 50 30
## 91 1460 -2.005805e+01 28.31187351 3.961537e-04 1.164909 50 30
## 92 1460 -1.005284e+01 -1.11494547 6.325959e-04 1.155632 50 30
## 93 1460 -1.595622e+01 13.01731213 9.136438e-04 1.148755 50 30
## 94 1460 -2.623611e+01 -10.26380918 4.114362e-04 1.143827 50 30
## 95 1460 1.895318e+01 3.19340075 6.777869e-04 1.139748 50 30
## 96 1460 6.654960e+00 26.11493597 7.113825e-04 1.135437 50 30
## 97 1460 -2.072006e+00 -20.42717900 7.832183e-04 1.131049 50 30
## 98 1460 -7.201559e+00 1.72107892 8.273412e-04 1.127886 50 30
## 99 1460 -7.281512e+00 19.28261324 5.517415e-04 1.125117 50 30
## 100 1460 -5.089245e+00 18.67278260 6.161473e-04 1.123256 50 30
## 101 1460 -2.206904e+00 -14.61161370 8.013740e-04 73.771148 50 30
## 102 1460 2.417159e+01 7.29329871 9.488142e-04 71.150987 50 30
## 103 1460 -2.167335e+01 31.11099711 2.304384e-04 71.034367 50 30
## 104 1460 -8.496256e+00 -25.64624591 6.008806e-04 71.035404 50 30
## 105 1460 -2.825285e+01 -3.21897823 1.290395e-03 71.037731 50 30
## 106 1460 2.726106e+01 -1.31048036 1.000007e-03 1.555216 50 30
## 107 1460 -7.863999e+00 20.67658224 6.287204e-04 1.312321 50 30
## 108 1460 -3.422086e+00 9.90921782 1.087887e-03 1.228241 50 30
## 109 1460 -2.716128e+01 13.13410608 2.685448e-04 1.196858 50 30
## 110 1460 -9.582727e+00 -6.82197574 8.370237e-04 1.176920 50 30
## 111 1460 -2.091008e+01 -0.58198317 1.098286e-03 1.164909 50 30
## 112 1460 2.131084e+01 5.70368050 5.420783e-04 1.155632 50 30
## 113 1460 1.608023e+01 -5.91052847 8.251326e-04 1.148755 50 30
## 114 1460 -5.012581e+00 -10.30130502 8.238789e-04 1.143827 50 30
## 115 1460 1.056112e+01 6.06373713 1.625292e-03 1.139748 50 30
## 116 1460 3.874242e+01 -0.42494905 3.841055e-04 1.135437 50 30
## 117 1460 3.738359e+00 19.86458682 6.418124e-04 1.131049 50 30
## 118 1460 -1.502649e+01 -12.00802221 7.869551e-04 1.127886 50 30
## 119 1460 1.830076e+01 -6.42559329 5.213700e-04 1.125117 50 30
## 120 1460 3.001868e+01 5.35248815 5.505252e-04 1.123256 50 30
## 121 1460 7.334809e+00 -9.44760832 2.572020e-04 73.771148 50 30
## 122 1460 -2.419105e+01 12.15461534 1.507383e-03 71.150987 50 30
## 123 1460 4.120086e+00 1.29424362 7.420127e-04 71.034367 50 30
## 124 1460 1.435726e+01 -20.62767390 1.845382e-03 71.035404 50 30
## 125 1460 -1.291203e+01 -5.19306719 1.013023e-03 71.037731 50 30
## 126 1460 -3.730329e+01 6.95943862 3.176853e-04 1.555216 50 30
## 127 1460 3.910732e+00 -5.78763390 1.441835e-03 1.312321 50 30
## 128 1460 -1.724765e+01 6.95094712 4.395826e-04 1.228241 50 30
## 129 1460 1.253096e+01 11.33471675 9.596910e-04 1.196858 50 30
## 130 1460 5.696393e+00 -10.81802535 3.825430e-04 1.176920 50 30
## 131 1460 1.417729e+01 10.37146970 6.913022e-04 1.164909 50 30
## 132 1460 1.967303e+01 -0.39855385 1.110492e-03 1.155632 50 30
## 133 1460 -8.342952e+00 7.89504759 1.121345e-03 1.148755 50 30
## 134 1460 -1.486188e+00 -18.24404505 7.998188e-04 1.143827 50 30
## 135 1460 -7.413830e+00 -9.15386728 1.674109e-03 1.139748 50 30
## 136 1460 -1.047453e+01 -5.64493515 1.159753e-03 1.135437 50 30
## 137 1460 -8.081743e+00 -3.61005849 1.182396e-03 1.131049 50 30
## 138 1460 -2.456443e+01 -13.37693959 2.252292e-04 1.127886 50 30
## 139 1460 2.135602e+01 -1.92788478 2.354585e-04 1.125117 50 30
## 140 1460 1.832442e+01 3.21088350 7.594926e-04 1.123256 50 30
## 141 1460 -1.116294e+01 2.83094711 8.652286e-04 73.771148 50 30
## 142 1460 -3.419359e+00 -17.64065061 2.881910e-04 71.150987 50 30
## 143 1460 -2.006844e+01 -0.36047702 6.720297e-04 71.034367 50 30
## 144 1460 -1.776241e+00 -18.61217454 1.288332e-03 71.035404 50 30
## 145 1460 -2.421249e+01 -14.06098377 5.177088e-04 71.037731 50 30
## 146 1460 3.894871e+01 -3.89623811 5.117821e-04 1.555216 50 30
## 147 1460 -1.476117e+01 8.19571503 5.807937e-04 1.312321 50 30
## 148 1460 2.482850e+01 7.02249565 1.524578e-03 1.228241 50 30
## 149 1460 -1.371421e+01 -9.16384488 8.562325e-04 1.196858 50 30
## 150 1460 -2.453902e+01 2.89759883 9.695830e-04 1.176920 50 30
## 151 1460 -9.652709e+00 4.12984066 1.941347e-04 1.164909 50 30
## 152 1460 1.791418e+00 -22.14558894 3.530796e-04 1.155632 50 30
## 153 1460 1.407980e+01 11.08180811 9.803545e-04 1.148755 50 30
## 154 1460 1.253750e+00 8.35831800 7.190795e-04 1.143827 50 30
## 155 1460 -2.301209e+01 12.74142416 2.433453e-04 1.139748 50 30
## 156 1460 -2.387960e+01 13.76582189 6.880175e-04 1.135437 50 30
## 157 1460 -2.002843e+01 28.06697168 2.501324e-04 1.131049 50 30
## 158 1460 2.683683e+01 0.17794690 4.286774e-04 1.127886 50 30
## 159 1460 2.155502e+01 2.16527546 2.605565e-04 1.125117 50 30
## 160 1460 8.540352e+00 -11.17741556 2.636143e-04 1.123256 50 30
## 161 1460 -1.233782e+01 -3.62170771 1.968736e-03 73.771148 50 30
## 162 1460 2.003961e+01 -5.98370886 2.111396e-04 71.150987 50 30
## 163 1460 -1.521346e+00 -23.37325815 6.587628e-04 71.034367 50 30
## 164 1460 -1.657966e+01 7.15239109 9.592530e-04 71.035404 50 30
## 165 1460 -2.864850e+01 13.63275457 4.503540e-04 71.037731 50 30
## 166 1460 -2.450512e+01 -10.01725614 1.395207e-03 1.555216 50 30
## 167 1460 5.046641e+00 4.98541434 4.769108e-04 1.312321 50 30
## 168 1460 2.160706e+01 -7.44705891 6.720856e-04 1.228241 50 30
## 169 1460 2.730815e+01 6.05147544 1.299810e-03 1.196858 50 30
## 170 1460 -3.488854e+00 -30.69427888 1.133689e-03 1.176920 50 30
## 171 1460 -3.746138e+01 6.41047834 4.294120e-04 1.164909 50 30
## 172 1460 -6.780054e+00 -5.52746480 6.532527e-04 1.155632 50 30
## 173 1460 1.249225e+01 3.03806671 1.219290e-03 1.148755 50 30
## 174 1460 1.338367e+00 3.37206759 1.775087e-03 1.143827 50 30
## 175 1460 -1.873476e+00 -10.12780175 5.285481e-04 1.139748 50 30
## 176 1460 -7.762656e+00 -10.55833510 1.127410e-03 1.135437 50 30
## 177 1460 1.115455e+01 14.98303092 9.340449e-04 1.131049 50 30
## 178 1460 -1.809355e+01 -0.58381055 1.105026e-03 1.127886 50 30
## 179 1460 3.874120e+00 -22.15806267 1.984772e-04 1.125117 50 30
## 180 1460 -2.040661e+01 11.79329995 1.437896e-03 1.123256 50 30
## 181 1460 3.763842e+01 -0.22600518 4.316873e-04 73.771148 50 30
## 182 1460 -2.645816e+01 7.07253522 1.343445e-03 71.150987 50 30
## 183 1460 -1.761788e+01 27.27155820 1.217026e-03 71.034367 50 30
## 184 1460 2.636693e+01 3.88424061 2.392966e-04 71.035404 50 30
## 185 1460 -2.129276e+01 8.06282418 5.930913e-04 71.037731 50 30
## 186 1460 -3.874935e+01 4.53579146 3.979569e-04 1.555216 50 30
## 187 1460 3.161236e-01 -0.98829493 1.867086e-03 1.312321 50 30
## 188 1460 -3.984815e+01 5.81149256 1.286645e-03 1.228241 50 30
## 189 1460 -9.896775e+00 24.14308233 1.161216e-03 1.196858 50 30
## 190 1460 1.307859e+01 -14.84867983 5.896989e-04 1.176920 50 30
## 191 1460 -6.591539e+00 25.64242072 1.471104e-03 1.164909 50 30
## 192 1460 1.341416e+01 8.34337781 1.024645e-03 1.155632 50 30
## 193 1460 -5.468078e+00 -19.30586063 8.806553e-04 1.148755 50 30
## 194 1460 3.895017e+01 -3.87377135 5.313662e-04 1.143827 50 30
## 195 1460 -1.051333e+01 5.50511152 1.027154e-03 1.139748 50 30
## 196 1460 4.075436e+01 3.02257519 6.662581e-04 1.135437 50 30
## 197 1460 2.967977e+00 -25.71216564 1.183563e-03 1.131049 50 30
## 198 1460 1.110633e+01 -3.71882649 3.790902e-04 1.127886 50 30
## 199 1460 -3.704875e+01 5.24339447 3.255766e-04 1.125117 50 30
## 200 1460 2.743008e+00 -24.79858432 9.546337e-04 1.123256 50 30
## 201 1460 -9.640526e+00 -24.97474731 8.821825e-04 73.771148 50 30
## 202 1460 4.248365e+00 18.41016734 4.925151e-04 71.150987 50 30
## 203 1460 -3.101506e+01 7.93297395 1.163894e-03 71.034367 50 30
## 204 1460 1.780886e+01 -18.95497608 2.328933e-04 71.035404 50 30
## 205 1460 -1.949140e+01 4.46974740 1.286041e-03 71.037731 50 30
## 206 1460 7.362171e+00 -10.63976968 3.871395e-04 1.555216 50 30
## 207 1460 -1.373118e+01 -4.95241686 6.854000e-04 1.312321 50 30
## 208 1460 -2.201276e-01 7.95152493 6.647683e-04 1.228241 50 30
## 209 1460 1.578498e+01 -5.01790732 1.408267e-03 1.196858 50 30
## 210 1460 -7.023498e+00 0.70510842 9.208801e-04 1.176920 50 30
## 211 1460 -1.779104e+01 14.57801538 1.713277e-03 1.164909 50 30
## 212 1460 -9.188847e+00 -25.06690982 1.150896e-03 1.155632 50 30
## 213 1460 1.846270e+01 2.64617451 2.761416e-04 1.148755 50 30
## 214 1460 2.237939e+00 17.82268464 7.811057e-04 1.143827 50 30
## 215 1460 6.521190e+00 26.08385185 1.111685e-03 1.139748 50 30
## 216 1460 3.525486e+00 16.26546783 9.143506e-04 1.135437 50 30
## 217 1460 -2.223517e+00 -18.41879649 8.424945e-04 1.131049 50 30
## 218 1460 -2.428900e+01 13.07340304 1.246090e-03 1.127886 50 30
## 219 1460 1.111586e+01 16.22585352 9.079080e-04 1.125117 50 30
## 220 1460 1.412602e+01 -24.86122246 2.808823e-04 1.123256 50 30
## 221 1460 -6.063724e+00 -24.94063000 3.288021e-04 73.771148 50 30
## 222 1460 2.752910e+01 4.99579331 3.875417e-04 71.150987 50 30
## 223 1460 1.828097e+01 7.20240975 4.307268e-04 71.034367 50 30
## 224 1460 -9.501102e-01 8.58703769 8.112615e-04 71.035404 50 30
## 225 1460 3.496019e+00 -20.96529877 3.828500e-04 71.037731 50 30
## 226 1460 4.196276e+01 1.05729372 2.046074e-04 1.555216 50 30
## 227 1460 2.214660e+01 -2.00043891 1.177225e-03 1.312321 50 30
## 228 1460 4.307925e+01 1.75334271 2.974175e-04 1.228241 50 30
## 229 1460 -1.103925e+01 2.88897156 1.060936e-03 1.196858 50 30
## 230 1460 1.416413e+01 -23.39942158 5.074691e-04 1.176920 50 30
## 231 1460 -3.476474e+00 2.80865150 1.186662e-03 1.164909 50 30
## 232 1460 1.776762e+01 -3.39585742 2.535677e-04 1.155632 50 30
## 233 1460 4.264275e+01 0.82268140 9.023346e-05 1.148755 50 30
## 234 1460 5.968135e-02 6.27103750 5.685036e-04 1.143827 50 30
## 235 1460 1.841465e+01 0.18477361 7.402057e-04 1.139748 50 30
## 236 1460 4.321908e+01 1.59967163 1.083273e-04 1.135437 50 30
## 237 1460 -6.058450e+00 -26.92288008 1.294523e-03 1.131049 50 30
## 238 1460 8.216382e+00 -10.76234596 3.688609e-04 1.127886 50 30
## 239 1460 -1.841806e+00 -28.15887486 2.470247e-04 1.125117 50 30
## 240 1460 -1.989808e+01 3.85218578 6.982344e-04 1.123256 50 30
## 241 1460 -3.216189e+00 -19.10622698 6.389683e-04 73.771148 50 30
## 242 1460 -1.336239e+01 9.65060629 7.113466e-04 71.150987 50 30
## 243 1460 -2.405684e+01 12.35857862 6.312351e-04 71.034367 50 30
## 244 1460 2.756287e+01 10.09057830 1.166346e-03 71.035404 50 30
## 245 1460 1.953288e+01 1.85363919 3.834696e-04 71.037731 50 30
## 246 1460 5.247188e+00 16.65974244 9.484306e-04 1.555216 50 30
## 247 1460 -2.884705e+01 -9.66111559 6.177633e-04 1.312321 50 30
## 248 1460 -1.345978e+01 -0.85197958 1.291728e-03 1.228241 50 30
## 249 1460 2.627084e+01 2.17484886 1.263170e-03 1.196858 50 30
## 250 1460 4.270069e+00 22.73175640 5.710435e-04 1.176920 50 30
## 251 1460 -4.911740e+00 18.12520514 5.284618e-04 1.164909 50 30
## 252 1460 9.140468e+00 -17.23282402 6.228260e-04 1.155632 50 30
## 253 1460 2.715623e+01 4.74012911 5.467972e-04 1.148755 50 30
## 254 1460 4.182146e+00 19.25998391 5.897637e-04 1.143827 50 30
## 255 1460 -6.740981e+00 3.38807140 7.577568e-04 1.139748 50 30
## 256 1460 2.557146e+01 -0.36446343 5.428444e-04 1.135437 50 30
## 257 1460 2.362477e+01 3.29990592 9.226400e-04 1.131049 50 30
## 258 1460 -2.048770e+00 -17.82706534 8.149137e-04 1.127886 50 30
## 259 1460 8.122014e+00 -10.93125464 6.619612e-04 1.125117 50 30
## 260 1460 -2.005763e+01 28.21633420 1.468647e-04 1.123256 50 30
## 261 1460 -1.313876e+00 3.56611385 6.292153e-04 73.771148 50 30
## 262 1460 2.745715e+01 -3.82669350 4.917907e-04 71.150987 50 30
## 263 1460 5.472324e-01 -4.56412587 1.021054e-03 71.034367 50 30
## 264 1460 -3.790023e+01 7.02648986 3.503606e-04 71.035404 50 30
## 265 1460 -1.627125e+01 8.62861933 3.791751e-04 71.037731 50 30
## 266 1460 -2.848470e+00 -11.10911671 1.404586e-03 1.555216 50 30
## 267 1460 1.919681e+01 6.83166081 7.299037e-04 1.312321 50 30
## 268 1460 -3.810994e+01 5.20488852 6.355066e-04 1.228241 50 30
## 269 1460 -3.496739e+00 10.16467661 4.275139e-04 1.196858 50 30
## 270 1460 -5.984834e+00 -1.87968160 8.177119e-04 1.176920 50 30
## 271 1460 2.973392e+01 1.42200364 4.359797e-04 1.164909 50 30
## 272 1460 2.583710e+00 8.81930059 9.669885e-04 1.155632 50 30
## 273 1460 2.306402e+01 -1.50208930 1.144263e-03 1.148755 50 30
## 274 1460 -1.487347e+00 -9.75029629 1.399459e-03 1.143827 50 30
## 275 1460 -9.300969e+00 5.03652475 1.201290e-03 1.139748 50 30
## 276 1460 -2.837171e+01 -0.24845547 3.874686e-04 1.135437 50 30
## 277 1460 -8.685287e+00 -26.73329413 4.787269e-04 1.131049 50 30
## 278 1460 -1.296788e+01 5.52161123 1.390304e-04 1.127886 50 30
## 279 1460 2.403414e+00 -26.21796856 8.652725e-04 1.125117 50 30
## 280 1460 1.432256e+01 10.17404509 1.607117e-03 1.123256 50 30
## 281 1460 8.188254e+00 -10.86957672 4.154401e-04 73.771148 50 30
## 282 1460 -4.587985e+00 -21.56720100 3.286752e-04 71.150987 50 30
## 283 1460 1.253157e+01 -21.54850267 5.569947e-04 71.034367 50 30
## 284 1460 -2.098597e+00 -27.55795260 1.039053e-04 71.035404 50 30
## 285 1460 1.471200e+01 -19.92488856 7.525857e-04 71.037731 50 30
## 286 1460 3.642173e+01 -2.77899508 4.641454e-04 1.555216 50 30
## 287 1460 -2.646024e+01 -2.76258629 2.714243e-03 1.312321 50 30
## 288 1460 -1.275998e+01 2.72986506 7.945098e-04 1.228241 50 30
## 289 1460 -1.029607e+01 1.91400075 4.416515e-04 1.196858 50 30
## 290 1460 -2.876275e+01 -0.60713315 5.990596e-04 1.176920 50 30
## 291 1460 3.060012e+01 2.87264376 4.422003e-04 1.164909 50 30
## 292 1460 -2.292717e+01 0.94791105 4.710499e-04 1.155632 50 30
## 293 1460 -2.571193e+01 -3.00334600 1.019857e-03 1.148755 50 30
## 294 1460 1.511237e+01 7.47568393 7.027138e-04 1.143827 50 30
## 295 1460 -7.055334e+00 -5.06460718 1.264736e-03 1.139748 50 30
## 296 1460 -1.535909e+00 -0.93090856 6.323626e-04 1.135437 50 30
## 297 1460 -2.104833e+01 1.47238834 9.214308e-04 1.131049 50 30
## 298 1460 2.004719e+01 12.77774067 1.557506e-03 1.127886 50 30
## 299 1460 9.760887e+00 15.48593811 8.238875e-04 1.125117 50 30
## 300 1460 2.125734e+00 15.08254859 1.023462e-03 1.123256 50 30
## 301 1460 -2.922355e+00 -2.41907872 4.605612e-04 73.771148 50 30
## 302 1460 2.257684e+01 4.20282735 1.171985e-03 71.150987 50 30
## 303 1460 -3.600213e+00 -29.80323252 6.420475e-04 71.034367 50 30
## 304 1460 -5.876582e+00 5.84056104 6.778935e-04 71.035404 50 30
## 305 1460 -2.978177e+00 26.62741671 1.699262e-03 71.037731 50 30
## 306 1460 -6.222852e-01 -19.95543102 1.711796e-04 1.555216 50 30
## 307 1460 1.680356e+01 -2.39932323 2.125490e-04 1.312321 50 30
## 308 1460 -1.971617e+01 6.37056919 3.297583e-04 1.228241 50 30
## 309 1460 -1.259783e+01 5.87395635 4.953887e-04 1.196858 50 30
## 310 1460 3.149605e+00 -18.18836562 2.495890e-04 1.176920 50 30
## 311 1460 2.054950e+01 7.90485976 1.104604e-03 1.164909 50 30
## 312 1460 -9.967867e+00 8.42863650 1.399706e-03 1.155632 50 30
## 313 1460 -2.653111e+01 -1.39387828 7.734785e-04 1.148755 50 30
## 314 1460 3.448202e+00 9.15444680 6.993630e-04 1.143827 50 30
## 315 1460 -2.929951e+01 5.18190253 1.084633e-03 1.139748 50 30
## 316 1460 1.941284e+01 4.68785527 1.208263e-03 1.135437 50 30
## 317 1460 1.579191e+01 8.76627857 1.291003e-03 1.131049 50 30
## 318 1460 2.949175e+01 1.30092238 4.382751e-04 1.127886 50 30
## 319 1460 1.732116e+01 -2.48859157 3.855104e-04 1.125117 50 30
## 320 1460 -1.136534e+00 -13.37133366 1.153150e-03 1.123256 50 30
## 321 1460 2.848775e+01 -3.26366892 2.611410e-04 73.771148 50 30
## 322 1460 2.043410e+01 -4.09564169 7.264160e-04 71.150987 50 30
## 323 1460 1.122104e+01 5.94491320 8.015451e-04 71.034367 50 30
## 324 1460 -1.325526e+01 9.65102317 6.207932e-04 71.035404 50 30
## 325 1460 2.450007e+01 -2.29547878 4.087909e-04 71.037731 50 30
## 326 1460 -2.179294e+01 13.86570524 4.575753e-04 1.555216 50 30
## 327 1460 1.329138e+01 -17.51472844 7.256770e-04 1.312321 50 30
## 328 1460 -8.811685e+00 -2.31261644 8.841935e-04 1.228241 50 30
## 329 1460 -2.885013e+01 9.20067248 3.858514e-04 1.196858 50 30
## 330 1460 -2.462407e+01 13.05402976 8.174061e-04 1.176920 50 30
## 331 1460 -2.471152e+01 -14.63016663 3.419479e-04 1.164909 50 30
## 332 1460 -8.754787e+00 7.76149419 2.717259e-03 1.155632 50 30
## 333 1460 -7.517659e-01 -21.57209953 4.169789e-04 1.148755 50 30
## 334 1460 1.197872e+01 -21.84640970 9.497920e-04 1.143827 50 30
## 335 1460 1.933951e+01 6.60110252 5.429321e-04 1.139748 50 30
## 336 1460 3.929590e+00 9.68093345 8.691114e-04 1.135437 50 30
## 337 1460 1.892432e+00 -19.65833915 7.962809e-04 1.131049 50 30
## 338 1460 -1.901586e+00 -19.73733526 6.455989e-04 1.127886 50 30
## 339 1460 1.738025e+00 -14.12390255 2.223526e-03 1.125117 50 30
## 340 1460 4.260088e+00 1.50764734 6.192101e-04 1.123256 50 30
## 341 1460 2.485961e+01 4.82879279 2.938889e-04 73.771148 50 30
## 342 1460 -1.938650e+01 13.43813376 1.155860e-03 71.150987 50 30
## 343 1460 -2.169899e+01 29.97600902 7.080161e-05 71.034367 50 30
## 344 1460 9.994735e+00 -23.17207503 6.413588e-04 71.035404 50 30
## 345 1460 4.170483e+01 4.46409917 5.828406e-04 71.037731 50 30
## 346 1460 -2.477500e+01 4.50717389 1.329284e-03 1.555216 50 30
## 347 1460 -6.500312e+00 20.97382354 4.071512e-04 1.312321 50 30
## 348 1460 -7.267837e+00 -4.78389435 8.662980e-04 1.228241 50 30
## 349 1460 3.833498e+01 -2.24192866 6.983390e-04 1.196858 50 30
## 350 1460 2.239261e+01 -5.78746524 2.307012e-04 1.176920 50 30
## 351 1460 1.007769e+01 -23.86128594 6.012112e-04 1.164909 50 30
## 352 1460 1.271039e+01 -14.07509994 1.141331e-03 1.155632 50 30
## 353 1460 2.063524e+00 19.80203016 6.871371e-04 1.148755 50 30
## 354 1460 -1.895327e+01 9.56595697 6.625996e-04 1.143827 50 30
## 355 1460 -2.423617e+01 7.46329895 9.202206e-04 1.139748 50 30
## 356 1460 -4.968230e+00 -14.70919719 1.049767e-03 1.135437 50 30
## 357 1460 -2.029239e+00 -15.98870824 9.039312e-04 1.131049 50 30
## 358 1460 7.806202e+00 -5.52987587 1.946275e-03 1.127886 50 30
## 359 1460 4.149076e+00 19.89213289 4.128937e-04 1.125117 50 30
## 360 1460 1.573502e+01 -5.11565317 1.499780e-03 1.123256 50 30
## 361 1460 4.518216e+00 -1.45639326 1.074916e-03 73.771148 50 30
## 362 1460 -1.957209e+01 2.09264805 2.235062e-03 71.150987 50 30
## 363 1460 -1.902641e+01 30.64735908 5.210367e-04 71.034367 50 30
## 364 1460 4.243552e+01 2.14999191 1.330102e-05 71.035404 50 30
## 365 1460 1.896722e+01 7.64606775 5.186977e-04 71.037731 50 30
## 366 1460 -2.790682e+01 14.16114989 7.267119e-04 1.555216 50 30
## 367 1460 6.136283e+00 1.43265130 1.017658e-03 1.312321 50 30
## 368 1460 6.113001e+00 16.90974169 5.168090e-04 1.228241 50 30
## 369 1460 -7.437514e+00 -1.82440203 6.182052e-04 1.196858 50 30
## 370 1460 3.670358e+00 6.06147996 7.433506e-04 1.176920 50 30
## 371 1460 2.687620e+01 7.55641549 5.196637e-04 1.164909 50 30
## 372 1460 -1.910057e+01 29.67269548 4.530280e-04 1.155632 50 30
## 373 1460 1.201783e+00 5.71129851 8.609385e-04 1.148755 50 30
## 374 1460 -2.622044e+00 6.32561800 8.096084e-04 1.143827 50 30
## 375 1460 2.760770e+01 5.78942818 9.768819e-04 1.139748 50 30
## 376 1460 -1.961303e+01 16.55521867 3.857489e-04 1.135437 50 30
## 377 1460 -1.213199e+00 -1.60970014 7.402418e-04 1.131049 50 30
## 378 1460 2.916579e+01 -0.70712196 5.080819e-04 1.127886 50 30
## 379 1460 4.974499e+00 -22.29986169 1.456674e-04 1.125117 50 30
## 380 1460 2.539767e+01 7.61283643 1.169477e-03 1.123256 50 30
## 381 1460 -2.580266e+01 8.54798003 6.050805e-04 73.771148 50 30
## 382 1460 -5.983846e+00 -24.46466296 2.919690e-04 71.150987 50 30
## 383 1460 2.570070e+01 3.83352612 6.433520e-04 71.034367 50 30
## 384 1460 -2.190135e+01 15.31821202 6.060571e-04 71.035404 50 30
## 385 1460 1.688990e+01 5.46079312 7.672329e-04 71.037731 50 30
## 386 1460 1.463924e+01 -22.80663156 2.514379e-04 1.555216 50 30
## 387 1460 -2.401270e+01 1.28103009 7.159739e-04 1.312321 50 30
## 388 1460 -4.927485e+00 0.67953417 6.250284e-04 1.228241 50 30
## 389 1460 -5.039282e+00 -27.32601478 7.973343e-04 1.196858 50 30
## 390 1460 2.156044e+01 -7.17328460 1.265690e-03 1.176920 50 30
## 391 1460 -2.886223e+01 7.18906399 9.038790e-04 1.164909 50 30
## 392 1460 1.893357e+01 5.55134684 5.967223e-04 1.155632 50 30
## 393 1460 -2.144163e+01 27.96605517 3.305699e-04 1.148755 50 30
## 394 1460 -1.213056e+01 2.68930231 4.357740e-04 1.143827 50 30
## 395 1460 -2.001596e+01 5.89800839 2.049431e-03 1.139748 50 30
## 396 1460 -7.010887e+00 3.49378328 1.564476e-03 1.135437 50 30
## 397 1460 -1.045004e+01 4.61110701 3.344379e-04 1.131049 50 30
## 398 1460 1.320776e+01 11.25555663 1.043409e-03 1.127886 50 30
## 399 1460 -2.193669e+01 15.75067345 7.713660e-04 1.125117 50 30
## 400 1460 1.640917e+01 -0.31769033 1.640506e-03 1.123256 50 30
## 401 1460 1.316963e+01 -14.65901161 6.173112e-04 73.771148 50 30
## 402 1460 -6.537601e+00 -22.61235551 3.327634e-04 71.150987 50 30
## 403 1460 -1.857340e+01 9.57072458 1.118804e-03 71.034367 50 30
## 404 1460 2.646756e+01 -2.04265894 2.803920e-04 71.035404 50 30
## 405 1460 2.731176e+01 8.67790114 7.401665e-04 71.037731 50 30
## 406 1460 -5.267248e+00 -5.15927064 7.462555e-04 1.555216 50 30
## 407 1460 -3.728562e+01 5.93974269 6.365995e-04 1.312321 50 30
## 408 1460 -2.821251e+01 9.44384633 1.032843e-03 1.228241 50 30
## 409 1460 2.833858e+01 -2.71060374 3.636766e-04 1.196858 50 30
## 410 1460 2.144751e+01 -5.88635020 5.907466e-04 1.176920 50 30
## 411 1460 -1.437770e+01 2.46853869 9.412653e-04 1.164909 50 30
## 412 1460 -1.721943e+00 -4.52837242 4.964704e-04 1.155632 50 30
## 413 1460 5.816546e+00 -21.28578842 5.631263e-04 1.148755 50 30
## 414 1460 -2.145332e+01 11.89353873 6.236558e-04 1.143827 50 30
## 415 1460 9.199219e+00 17.62249745 7.679060e-04 1.139748 50 30
## 416 1460 -6.447015e+00 -22.94165115 6.977926e-04 1.135437 50 30
## 417 1460 1.145853e+01 11.17183105 1.181069e-03 1.131049 50 30
## 418 1460 -1.323094e+01 18.77413898 8.981573e-04 1.127886 50 30
## 419 1460 -2.010187e+01 1.97527496 1.106200e-03 1.125117 50 30
## 420 1460 -1.105466e+01 12.71814327 1.304623e-03 1.123256 50 30
## 421 1460 -2.258114e+01 -14.63909089 1.079826e-03 73.771148 50 30
## 422 1460 4.651101e+00 16.70457352 6.967053e-04 71.150987 50 30
## 423 1460 -1.351018e+01 4.76603328 4.999285e-04 71.034367 50 30
## 424 1460 2.135592e+01 -3.96362861 7.021847e-04 71.035404 50 30
## 425 1460 -7.350752e+00 -1.88986487 1.039222e-03 71.037731 50 30
## 426 1460 -2.943644e+01 12.84635391 5.982384e-04 1.555216 50 30
## 427 1460 9.074455e+00 -17.12600880 1.175171e-03 1.312321 50 30
## 428 1460 -1.176031e+01 5.65474777 1.235792e-03 1.228241 50 30
## 429 1460 -3.476039e+00 -20.45425304 7.800801e-04 1.196858 50 30
## 430 1460 -8.115656e+00 -10.60421352 9.945789e-04 1.176920 50 30
## 431 1460 4.304057e+01 1.68944874 2.168098e-04 1.164909 50 30
## 432 1460 -1.961196e+01 6.66861592 7.691562e-04 1.155632 50 30
## 433 1460 4.367875e+01 1.49189299 2.074010e-04 1.148755 50 30
## 434 1460 2.674088e+01 8.69619174 8.542614e-04 1.143827 50 30
## 435 1460 4.543205e+01 4.44252217 7.299529e-04 1.139748 50 30
## 436 1460 1.601204e+01 4.18056500 8.669716e-04 1.135437 50 30
## 437 1460 -2.009834e+01 8.13192881 8.134473e-04 1.131049 50 30
## 438 1460 -2.019691e+01 11.43094047 1.461930e-03 1.127886 50 30
## 439 1460 -1.695824e+01 13.87807869 8.126458e-04 1.125117 50 30
## 440 1460 -7.802407e+00 21.13762531 1.090235e-03 1.123256 50 30
## 441 1460 2.907380e+00 -24.42348860 3.006040e-04 73.771148 50 30
## 442 1460 -2.490307e+01 -12.28983581 3.416162e-04 71.150987 50 30
## 443 1460 -2.419467e+01 4.80743564 1.469842e-03 71.034367 50 30
## 444 1460 1.348610e+01 -24.79459299 2.706636e-04 71.035404 50 30
## 445 1460 1.904945e+01 3.93674267 5.260827e-04 71.037731 50 30
## 446 1460 -1.349414e+01 -0.94783014 1.243189e-03 1.555216 50 30
## 447 1460 -4.311174e+00 -12.05643377 9.683563e-04 1.312321 50 30
## 448 1460 2.761980e+01 6.63751128 4.586871e-04 1.228241 50 30
## 449 1460 -2.450321e+01 4.72224575 1.019009e-03 1.196858 50 30
## 450 1460 -1.928717e+01 4.76749305 7.913772e-04 1.176920 50 30
## 451 1460 -1.855068e+01 8.71993872 4.649789e-04 1.164909 50 30
## 452 1460 -3.742812e+00 -7.33275920 9.259003e-04 1.155632 50 30
## 453 1460 1.767260e+01 6.15214055 6.118245e-04 1.148755 50 30
## 454 1460 2.791871e+01 3.09220352 9.149265e-04 1.143827 50 30
## 455 1460 -2.308473e+01 -14.16012547 1.134111e-03 1.139748 50 30
## 456 1460 -9.326810e+00 -2.16574366 1.246767e-03 1.135437 50 30
## 457 1460 -2.462054e+01 12.36187518 3.212847e-04 1.131049 50 30
## 458 1460 -3.831132e+00 -7.03761240 1.122110e-03 1.127886 50 30
## 459 1460 -2.191547e+01 6.80792066 8.697882e-04 1.125117 50 30
## 460 1460 -2.470106e+01 8.74636532 7.596417e-04 1.123256 50 30
## 461 1460 1.671388e+01 -0.26237104 5.415069e-04 73.771148 50 30
## 462 1460 -1.542525e+01 -2.88066964 1.544733e-03 71.150987 50 30
## 463 1460 -2.148271e+00 5.52097175 3.372242e-04 71.034367 50 30
## 464 1460 -2.146890e+01 3.36890863 1.348575e-03 71.035404 50 30
## 465 1460 -1.027281e+01 0.60605271 3.408177e-04 71.037731 50 30
## 466 1460 1.363370e+01 -24.47392807 1.842695e-04 1.555216 50 30
## 467 1460 4.293391e+00 -0.38583380 8.175293e-04 1.312321 50 30
## 468 1460 1.156660e+01 10.24978747 1.906096e-03 1.228241 50 30
## 469 1460 -2.302219e+00 -25.86487171 8.603418e-04 1.196858 50 30
## 470 1460 2.194531e+01 0.23102935 1.723808e-03 1.176920 50 30
## 471 1460 1.506480e+01 -13.77830207 8.796821e-04 1.164909 50 30
## 472 1460 1.847926e+01 14.25283553 1.590644e-03 1.155632 50 30
## 473 1460 2.013369e+01 -20.45669870 9.087069e-04 1.148755 50 30
## 474 1460 1.459364e+00 -21.22087951 5.222866e-04 1.143827 50 30
## 475 1460 1.291419e+01 -20.05066128 6.375597e-04 1.139748 50 30
## 476 1460 4.015660e+00 0.07360296 1.003230e-03 1.135437 50 30
## 477 1460 -1.244418e+00 -17.29459811 1.091664e-03 1.131049 50 30
## 478 1460 2.598299e+01 -2.95300345 4.980846e-04 1.127886 50 30
## 479 1460 3.447437e-01 -19.83276838 9.520887e-04 1.125117 50 30
## 480 1460 -1.119670e+01 9.97998999 5.048328e-04 1.123256 50 30
## 481 1460 1.896126e+00 -20.16963485 3.415721e-04 73.771148 50 30
## 482 1460 2.417502e+00 -19.78096148 3.197607e-04 71.150987 50 30
## 483 1460 -2.136529e+01 5.23618141 6.207092e-04 71.034367 50 30
## 484 1460 2.019896e+01 -21.66092062 7.344977e-04 71.035404 50 30
## 485 1460 -8.803049e+00 9.44038126 7.494664e-04 71.037731 50 30
## 486 1460 -1.096153e+01 12.61522572 1.264356e-03 1.555216 50 30
## 487 1460 -7.133004e+00 0.35267569 1.326341e-03 1.312321 50 30
## 488 1460 3.633432e+00 -0.88627729 8.232670e-04 1.228241 50 30
## 489 1460 -2.646229e+01 -10.98978341 2.706980e-04 1.196858 50 30
## 490 1460 4.552290e+01 4.40778514 6.478605e-04 1.176920 50 30
## 491 1460 3.882000e+01 5.06716488 1.183632e-03 1.164909 50 30
## 492 1460 -1.838693e+01 0.33064865 8.679506e-04 1.155632 50 30
## 493 1460 3.095306e+01 6.12697912 2.743322e-04 1.148755 50 30
## 494 1460 -2.568468e+00 8.53821251 1.054643e-03 1.143827 50 30
## 495 1460 -1.857515e+01 11.32396789 4.679563e-04 1.139748 50 30
## 496 1460 -1.844214e+01 15.05050835 5.617185e-04 1.135437 50 30
## 497 1460 -7.630339e+00 -15.08657749 7.683931e-04 1.131049 50 30
## 498 1460 -2.224083e+01 1.05641901 1.120288e-03 1.127886 50 30
## 499 1460 -8.992679e+00 1.50820129 7.061060e-04 1.125117 50 30
## 500 1460 3.024859e+00 21.45630895 5.889422e-04 1.123256 50 30
## 501 1460 4.308882e+01 1.32982574 3.306015e-04 73.771148 50 30
## 502 1460 2.170022e+01 2.55889574 4.979317e-04 71.150987 50 30
## 503 1460 -6.746106e+00 19.59342107 3.386458e-04 71.034367 50 30
## 504 1460 6.512048e+00 -1.96434525 6.147546e-04 71.035404 50 30
## 505 1460 4.080217e+01 4.11846397 3.464924e-04 71.037731 50 30
## 506 1460 -2.691407e+01 -12.37583082 7.203407e-04 1.555216 50 30
## 507 1460 2.191958e+01 -1.65022891 1.252034e-03 1.312321 50 30
## 508 1460 -5.265324e+00 -22.63227148 3.740599e-04 1.228241 50 30
## 509 1460 -2.871961e+01 13.43795919 6.608176e-04 1.196858 50 30
## 510 1460 -6.603257e+00 -0.10713844 6.016523e-04 1.176920 50 30
## 511 1460 -4.643249e+00 20.66060293 4.483416e-04 1.164909 50 30
## 512 1460 1.223877e+01 -24.81189813 4.518948e-04 1.155632 50 30
## 513 1460 -1.587996e+00 8.21035915 4.612736e-04 1.148755 50 30
## 514 1460 -1.302546e+01 13.15891134 7.107550e-04 1.143827 50 30
## 515 1460 -2.120793e+01 13.77368864 1.446634e-03 1.139748 50 30
## 516 1460 4.659053e+00 -21.16678365 7.338002e-04 1.135437 50 30
## 517 1460 1.460625e+01 12.03536518 6.566890e-04 1.131049 50 30
## 518 1460 2.471131e+01 -1.35480316 5.979843e-04 1.127886 50 30
## 519 1460 1.842891e+01 3.67797913 7.037558e-04 1.125117 50 30
## 520 1460 -2.809991e+01 0.21542238 5.585239e-04 1.123256 50 30
## 521 1460 -2.282676e+01 30.89195603 1.719798e-04 73.771148 50 30
## 522 1460 -7.417574e+00 -7.17555775 1.479063e-03 71.150987 50 30
## 523 1460 -2.530839e+01 6.57862714 5.041411e-04 71.034367 50 30
## 524 1460 2.353630e+01 -7.63966844 4.182036e-04 71.035404 50 30
## 525 1460 2.041113e+01 -3.36366025 8.998011e-04 71.037731 50 30
## 526 1460 -6.623857e+00 -24.23908598 6.172425e-04 1.555216 50 30
## 527 1460 -1.378393e+01 12.98090629 7.880469e-04 1.312321 50 30
## 528 1460 2.176231e+01 -7.25194591 6.738106e-04 1.228241 50 30
## 529 1460 -1.617071e+01 13.57074940 5.172334e-04 1.196858 50 30
## 530 1460 -8.229159e+00 -14.70616202 6.490563e-04 1.176920 50 30
## 531 1460 -8.238348e-01 -14.08066303 1.435055e-03 1.164909 50 30
## 532 1460 -2.827979e+01 11.21793176 3.801689e-04 1.155632 50 30
## 533 1460 -1.909364e+01 27.84688629 3.401093e-04 1.148755 50 30
## 534 1460 -2.043685e+01 26.68142565 4.647230e-04 1.143827 50 30
## 535 1460 2.866939e+01 6.17986209 1.102916e-04 1.139748 50 30
## 536 1460 -3.066923e+01 12.03898922 7.718533e-04 1.135437 50 30
## 537 1460 2.630810e+01 4.86849802 7.332753e-04 1.131049 50 30
## 538 1460 -1.074053e+01 4.88044014 6.214949e-04 1.127886 50 30
## 539 1460 -8.958793e+00 -1.67238152 1.046164e-03 1.125117 50 30
## 540 1460 1.735045e+00 -14.23503021 9.589189e-04 1.123256 50 30
## 541 1460 3.122758e+00 -21.02994578 6.693679e-04 73.771148 50 30
## 542 1460 2.679529e+01 -0.16482673 1.677949e-03 71.150987 50 30
## 543 1460 -4.669241e+00 -13.71326188 1.623202e-03 71.034367 50 30
## 544 1460 2.016274e+01 -19.54833997 7.508928e-04 71.035404 50 30
## 545 1460 1.970335e+01 4.99333025 1.260922e-04 71.037731 50 30
## 546 1460 1.446981e+01 5.36671295 6.269915e-04 1.555216 50 30
## 547 1460 7.892530e+00 -10.07025169 1.021435e-03 1.312321 50 30
## 548 1460 -1.005147e+00 -0.22055258 1.038275e-03 1.228241 50 30
## 549 1460 -4.404943e-02 10.59839836 4.144349e-04 1.196858 50 30
## 550 1460 2.757872e+01 4.78248408 6.476184e-04 1.176920 50 30
## 551 1460 4.748784e+00 -4.92864804 8.758953e-04 1.164909 50 30
## 552 1460 -1.254526e+01 4.67825329 4.786072e-04 1.155632 50 30
## 553 1460 -2.868802e+00 -26.99517952 8.341831e-04 1.148755 50 30
## 554 1460 -1.916128e+01 28.32743547 2.349107e-04 1.143827 50 30
## 555 1460 1.992181e+01 -2.72733607 6.531584e-04 1.139748 50 30
## 556 1460 -2.183218e+01 12.86406817 1.613389e-03 1.135437 50 30
## 557 1460 3.720723e+00 -0.49552735 8.252895e-04 1.131049 50 30
## 558 1460 -3.108366e+01 7.90298197 1.019565e-03 1.127886 50 30
## 559 1460 8.680555e+00 17.51585255 1.084507e-03 1.125117 50 30
## 560 1460 1.346220e+01 -24.36410973 2.396546e-04 1.123256 50 30
## 561 1460 5.194513e+00 1.54515629 4.660125e-04 73.771148 50 30
## 562 1460 8.573181e-01 8.09050290 1.222951e-03 71.150987 50 30
## 563 1460 -6.709611e+00 4.03352849 1.539496e-03 71.034367 50 30
## 564 1460 -3.032913e+01 -0.53397261 7.895552e-04 71.035404 50 30
## 565 1460 1.720095e+01 3.43525842 7.473622e-04 71.037731 50 30
## 566 1460 -2.492797e+01 14.01334842 5.012356e-04 1.555216 50 30
## 567 1460 2.587256e+01 -2.09109052 7.109570e-04 1.312321 50 30
## 568 1460 -7.002781e+00 -27.28026020 7.862648e-04 1.228241 50 30
## 569 1460 1.368239e+01 -9.81957151 1.165684e-03 1.196858 50 30
## 570 1460 5.672740e-01 -4.17794287 5.581279e-04 1.176920 50 30
## 571 1460 -2.464837e+01 -13.85328971 5.404778e-04 1.164909 50 30
## 572 1460 -7.896938e+00 0.47781935 3.158434e-04 1.155632 50 30
## 573 1460 2.580802e+01 4.45643350 3.620577e-05 1.148755 50 30
## 574 1460 2.908977e+01 8.09799946 2.402080e-04 1.143827 50 30
## 575 1460 6.517697e+00 18.11810709 1.084960e-03 1.139748 50 30
## 576 1460 -2.393342e+01 9.28937551 8.541304e-04 1.135437 50 30
## 577 1460 -2.804349e+01 4.04103869 9.329850e-04 1.131049 50 30
## 578 1460 -1.055540e+00 2.71149763 8.434651e-04 1.127886 50 30
## 579 1460 3.704467e+01 -3.40085842 4.881953e-04 1.125117 50 30
## 580 1460 -2.512675e+01 3.68337257 6.634011e-04 1.123256 50 30
## 581 1460 4.429733e+00 18.36830611 8.021975e-04 73.771148 50 30
## 582 1460 -7.293159e-01 -26.91691441 3.115105e-04 71.150987 50 30
## 583 1460 -4.165734e-01 -4.05339430 6.675898e-04 71.034367 50 30
## 584 1460 -2.979228e+00 26.62892174 1.533987e-03 71.035404 50 30
## 585 1460 -2.917385e+01 -0.34466409 8.936555e-04 71.037731 50 30
## 586 1460 1.884367e+00 -21.59620677 3.697297e-04 1.555216 50 30
## 587 1460 -1.141782e+00 5.56248949 7.047223e-04 1.312321 50 30
## 588 1460 -1.652065e+00 -0.02234930 1.207404e-03 1.228241 50 30
## 589 1460 6.689857e+00 -2.09605885 7.113890e-04 1.196858 50 30
## 590 1460 -3.610469e+01 6.99380401 6.994104e-04 1.176920 50 30
## 591 1460 2.256856e+01 2.46938116 1.367596e-03 1.164909 50 30
## 592 1460 2.104595e+01 -7.05872084 5.531657e-04 1.155632 50 30
## 593 1460 -6.154044e+00 7.07694577 5.826703e-04 1.148755 50 30
## 594 1460 1.822506e+01 -18.74104285 5.022994e-04 1.143827 50 30
## 595 1460 -1.394320e+01 4.13584440 8.282915e-04 1.139748 50 30
## 596 1460 3.035896e+00 -25.69079084 1.055981e-03 1.135437 50 30
## 597 1460 -1.983928e+01 7.47616562 5.733514e-04 1.131049 50 30
## 598 1460 1.639966e+01 -24.05582832 9.860895e-04 1.127886 50 30
## 599 1460 -1.309698e+00 -9.81436657 8.847079e-04 1.125117 50 30
## 600 1460 4.098706e+01 4.47935257 4.509971e-04 1.123256 50 30
## 601 1460 2.040181e+01 -3.81175686 5.947409e-04 73.771148 50 30
## 602 1460 -2.758649e+01 -1.44144723 4.215169e-04 71.150987 50 30
## 603 1460 1.814465e+01 4.98545306 2.707119e-04 71.034367 50 30
## 604 1460 3.675836e+01 -2.75256410 3.595628e-04 71.035404 50 30
## 605 1460 -1.149006e+00 -18.84239095 1.597506e-03 71.037731 50 30
## 606 1460 1.679185e+01 14.34439157 1.015922e-03 1.555216 50 30
## 607 1460 -1.174873e+01 -10.54041229 8.136728e-04 1.312321 50 30
## 608 1460 -2.065851e+01 -2.37524868 1.180344e-03 1.228241 50 30
## 609 1460 -6.614184e+00 25.62244425 1.894883e-03 1.196858 50 30
## 610 1460 -6.788084e+00 1.99150137 6.481296e-04 1.176920 50 30
## 611 1460 2.003338e+01 -6.00746199 6.939891e-04 1.164909 50 30
## 612 1460 4.503398e+00 22.41769958 7.584179e-04 1.155632 50 30
## 613 1460 2.005023e+01 -2.31063934 3.195006e-04 1.148755 50 30
## 614 1460 -1.460244e+01 -9.92546007 5.238478e-04 1.143827 50 30
## 615 1460 4.471158e+01 4.90489374 7.102305e-04 1.139748 50 30
## 616 1460 -4.353115e+00 0.16299291 4.966626e-04 1.135437 50 30
## 617 1460 1.902916e+01 5.11513591 7.089324e-04 1.131049 50 30
## 618 1460 -1.525481e+01 5.39267907 5.257170e-04 1.127886 50 30
## 619 1460 2.597719e+00 -25.89468683 6.833954e-04 1.125117 50 30
## 620 1460 1.986371e+01 -5.48250206 6.697584e-04 1.123256 50 30
## 621 1460 -1.883758e+01 15.67022384 8.330778e-04 73.771148 50 30
## 622 1460 1.083854e+01 7.36685524 8.387830e-04 71.150987 50 30
## 623 1460 -9.029162e+00 1.10813437 1.490848e-03 71.034367 50 30
## 624 1460 3.762623e+01 -0.29350282 5.342249e-04 71.035404 50 30
## 625 1460 1.657781e+01 15.22062583 9.708006e-04 71.037731 50 30
## 626 1460 4.152890e+00 2.02475514 6.638789e-04 1.555216 50 30
## 627 1460 -6.082987e+00 20.17447772 5.731256e-04 1.312321 50 30
## 628 1460 -1.401862e+00 2.92004379 7.047441e-04 1.228241 50 30
## 629 1460 9.551813e+00 15.42677395 4.723549e-04 1.196858 50 30
## 630 1460 4.326480e-01 2.88113949 6.205918e-04 1.176920 50 30
## 631 1460 -2.691437e+01 12.88271072 4.201072e-04 1.164909 50 30
## 632 1460 1.269987e+01 -23.06476207 4.242296e-04 1.155632 50 30
## 633 1460 -9.803134e+00 -6.06098553 1.407079e-03 1.148755 50 30
## 634 1460 3.008219e+00 19.17862195 7.034329e-04 1.143827 50 30
## 635 1460 -9.554808e+00 23.76253940 7.707715e-04 1.139748 50 30
## 636 1460 -3.694822e+01 4.24246813 3.117546e-04 1.135437 50 30
## 637 1460 -1.996376e+01 16.10412903 7.517336e-04 1.131049 50 30
## 638 1460 -2.736310e+01 -12.05974151 7.031615e-04 1.127886 50 30
## 639 1460 -1.944996e+01 13.34477513 6.182995e-04 1.125117 50 30
## 640 1460 1.076225e+01 -20.77780411 4.129261e-04 1.123256 50 30
## 641 1460 9.682999e+00 -23.09457241 2.332046e-04 73.771148 50 30
## 642 1460 1.950230e+01 2.51821194 6.128312e-04 71.150987 50 30
## 643 1460 1.854751e+01 -7.22841013 1.102481e-03 71.034367 50 30
## 644 1460 9.550971e+00 8.08914760 8.612479e-04 71.035404 50 30
## 645 1460 5.388461e+00 -21.19202343 6.282775e-04 71.037731 50 30
## 646 1460 -4.146962e+00 8.18549394 9.042371e-04 1.555216 50 30
## 647 1460 -1.985340e+01 28.40705040 3.622607e-04 1.312321 50 30
## 648 1460 5.703873e+00 1.26429469 7.134621e-04 1.228241 50 30
## 649 1460 1.383751e+01 13.21810576 8.317222e-04 1.196858 50 30
## 650 1460 4.479546e+01 4.93267149 6.251568e-04 1.176920 50 30
## 651 1460 2.797109e+01 2.80459570 5.005022e-04 1.164909 50 30
## 652 1460 -2.307845e+01 -0.32869522 1.143575e-03 1.155632 50 30
## 653 1460 2.784385e+01 3.49937231 8.379598e-04 1.148755 50 30
## 654 1460 -2.745081e+01 11.77271292 1.539421e-03 1.143827 50 30
## 655 1460 1.868230e+00 -18.29225096 2.544229e-04 1.139748 50 30
## 656 1460 4.210119e+01 1.11849635 1.220828e-04 1.135437 50 30
## 657 1460 -7.058886e+00 10.04247846 1.138310e-03 1.131049 50 30
## 658 1460 -2.927176e+01 12.12331162 7.716387e-04 1.127886 50 30
## 659 1460 -2.764637e+01 3.75757946 8.890601e-04 1.125117 50 30
## 660 1460 -7.443119e+00 7.91989886 3.793702e-04 1.123256 50 30
## 661 1460 1.376422e+01 12.99409105 6.322327e-04 73.771148 50 30
## 662 1460 1.833784e+01 -4.56804713 3.991281e-04 71.150987 50 30
## 663 1460 -1.163421e+01 -6.44094405 7.209528e-04 71.034367 50 30
## 664 1460 -1.630048e+00 4.48877208 4.984948e-04 71.035404 50 30
## 665 1460 5.004462e+00 -19.70663023 4.633547e-04 71.037731 50 30
## 666 1460 2.830756e+01 -1.06725706 9.493797e-04 1.555216 50 30
## 667 1460 1.126268e+01 7.38077555 9.794121e-04 1.312321 50 30
## 668 1460 -1.443193e+00 -16.41738995 1.009461e-03 1.228241 50 30
## 669 1460 -5.292962e+00 4.37049011 9.359319e-04 1.196858 50 30
## 670 1460 -2.164469e+01 13.06714237 1.034380e-03 1.176920 50 30
## 671 1460 2.055957e+01 4.02057161 6.510343e-04 1.164909 50 30
## 672 1460 -2.181802e+01 1.00277877 9.036070e-04 1.155632 50 30
## 673 1460 -5.390704e+00 -3.15295003 1.581709e-03 1.148755 50 30
## 674 1460 6.339164e+00 -1.68610064 1.294872e-03 1.143827 50 30
## 675 1460 -6.496328e+00 3.39650778 2.874553e-03 1.139748 50 30
## 676 1460 4.083475e+01 2.95283515 6.039307e-04 1.135437 50 30
## 677 1460 -2.800203e+01 -10.48698490 3.474407e-04 1.131049 50 30
## 678 1460 -1.936298e+01 11.04552278 7.507546e-04 1.127886 50 30
## 679 1460 -1.224678e+00 -27.44134888 6.708822e-04 1.125117 50 30
## 680 1460 -7.815404e+00 1.42540968 1.082327e-03 1.123256 50 30
## 681 1460 3.086168e+00 -6.39686524 1.505139e-03 73.771148 50 30
## 682 1460 -2.477632e+01 9.88905068 1.552325e-03 71.150987 50 30
## 683 1460 1.221244e+01 -18.39077750 1.062580e-03 71.034367 50 30
## 684 1460 1.438048e+00 -19.53731738 1.067356e-03 71.035404 50 30
## 685 1460 2.507689e+01 8.26023863 6.135199e-04 71.037731 50 30
## 686 1460 1.248700e+01 3.05848135 9.661869e-04 1.555216 50 30
## 687 1460 3.136005e+01 4.39389998 3.425657e-04 1.312321 50 30
## 688 1460 3.612073e+01 -1.43689480 5.990875e-04 1.228241 50 30
## 689 1460 -3.424322e+00 -20.94255138 5.457347e-04 1.196858 50 30
## 690 1460 1.227739e+01 -23.69558473 4.006391e-04 1.176920 50 30
## 691 1460 1.789605e+01 -18.98383078 4.465372e-04 1.164909 50 30
## 692 1460 2.442497e+01 -7.59468167 4.441197e-04 1.155632 50 30
## 693 1460 1.889436e+01 -3.63476008 4.367080e-04 1.148755 50 30
## 694 1460 -2.161034e+01 13.13855543 4.149035e-04 1.143827 50 30
## 695 1460 -2.333679e+01 5.39643656 7.660458e-04 1.139748 50 30
## 696 1460 -2.565663e+00 -10.87409251 1.338448e-03 1.135437 50 30
## 697 1460 -1.618647e+01 15.08105419 1.213146e-03 1.131049 50 30
## 698 1460 2.570237e+00 19.81816772 5.507834e-04 1.127886 50 30
## 699 1460 6.845822e-01 10.37403592 7.043316e-04 1.125117 50 30
## 700 1460 1.412107e+01 -20.85942892 7.239074e-04 1.123256 50 30
## 701 1460 1.679249e+00 -20.10353543 6.600851e-04 73.771148 50 30
## 702 1460 -1.019165e+01 -2.22300216 6.716856e-04 71.150987 50 30
## 703 1460 2.682982e+01 -3.65781458 7.133076e-04 71.034367 50 30
## 704 1460 -2.911949e+01 -9.19978179 5.195393e-04 71.035404 50 30
## 705 1460 7.536280e+00 -11.22096329 3.784772e-04 71.037731 50 30
## 706 1460 -2.303427e+01 29.49932211 1.553983e-04 1.555216 50 30
## 707 1460 -3.720740e+00 -7.37497496 9.638722e-04 1.312321 50 30
## 708 1460 1.073712e+01 -21.87166150 6.119797e-04 1.228241 50 30
## 709 1460 2.911520e+01 6.21328371 2.280439e-04 1.196858 50 30
## 710 1460 -1.123911e+01 0.10398111 7.728730e-04 1.176920 50 30
## 711 1460 -1.619126e+01 7.77349480 4.569368e-04 1.164909 50 30
## 712 1460 -2.301958e+01 10.63556917 4.117245e-04 1.155632 50 30
## 713 1460 1.237367e+01 -19.53388967 7.168108e-04 1.148755 50 30
## 714 1460 -2.224065e+00 -3.14488297 4.784842e-04 1.143827 50 30
## 715 1460 1.367575e+01 7.61208951 1.043678e-03 1.139748 50 30
## 716 1460 -1.072716e+01 -5.62492484 1.129083e-03 1.135437 50 30
## 717 1460 -2.943573e+01 10.70714665 5.043769e-04 1.131049 50 30
## 718 1460 4.002863e+00 16.16871573 7.566931e-04 1.127886 50 30
## 719 1460 1.729761e+01 -2.60185939 4.382159e-04 1.125117 50 30
## 720 1460 -9.790131e+00 3.72048303 1.297105e-03 1.123256 50 30
## 721 1460 1.110097e+01 -18.91373846 1.036662e-03 73.771148 50 30
## 722 1460 1.810284e+01 -18.60022066 4.040835e-04 71.150987 50 30
## 723 1460 -1.169076e+01 6.18201167 1.006554e-03 71.034367 50 30
## 724 1460 -2.914300e+01 -0.83435030 6.915578e-04 71.035404 50 30
## 725 1460 2.943450e+00 -20.88921708 6.338018e-04 71.037731 50 30
## 726 1460 -6.404090e+00 18.72673603 4.972312e-04 1.555216 50 30
## 727 1460 6.645976e+00 -11.25389812 3.261266e-04 1.312321 50 30
## 728 1460 -3.634046e+00 -18.68278863 5.658092e-04 1.228241 50 30
## 729 1460 -2.514202e+01 -13.32378810 2.644032e-04 1.196858 50 30
## 730 1460 -3.729079e+01 6.32771473 3.703490e-04 1.176920 50 30
## 731 1460 1.248711e+01 -20.48491930 4.199789e-04 1.164909 50 30
## 732 1460 8.652283e+00 -20.02131146 1.317242e-03 1.155632 50 30
## 733 1460 2.195046e+01 -1.46740564 1.264267e-03 1.148755 50 30
## 734 1460 -5.326517e+00 19.30508825 1.809109e-03 1.143827 50 30
## 735 1460 -1.427792e+01 3.32516289 1.206862e-03 1.139748 50 30
## 736 1460 -2.178052e+01 4.55138314 5.489717e-04 1.135437 50 30
## 737 1460 -2.177057e+01 30.02826284 1.479734e-04 1.131049 50 30
## 738 1460 2.890263e+01 4.14505555 3.861936e-04 1.127886 50 30
## 739 1460 -2.099719e+01 -14.25802046 1.125981e-03 1.125117 50 30
## 740 1460 2.556715e+01 3.91697117 1.054354e-03 1.123256 50 30
## 741 1460 -2.798393e+01 11.25673997 4.142167e-04 73.771148 50 30
## 742 1460 2.169522e+00 16.54556442 4.438907e-04 71.150987 50 30
## 743 1460 -4.814150e+00 -28.37912926 3.671899e-04 71.034367 50 30
## 744 1460 6.187233e+00 15.57039746 7.631868e-04 71.035404 50 30
## 745 1460 7.559228e+00 -12.02292008 3.577492e-04 71.037731 50 30
## 746 1460 1.178771e+01 16.24549816 5.674587e-04 1.555216 50 30
## 747 1460 2.246596e+01 4.28952609 1.126202e-03 1.312321 50 30
## 748 1460 -2.968880e+01 10.45589608 2.622847e-04 1.228241 50 30
## 749 1460 -5.072651e-01 -17.23283443 5.042530e-04 1.196858 50 30
## 750 1460 -1.963845e+01 29.79043578 1.594933e-04 1.176920 50 30
## 751 1460 -1.936383e+01 6.61422816 6.113545e-04 1.164909 50 30
## 752 1460 2.930763e+01 4.87696978 6.673490e-04 1.155632 50 30
## 753 1460 -3.852747e+00 -16.95891005 9.496635e-04 1.148755 50 30
## 754 1460 2.704339e+01 -1.14268328 3.524269e-04 1.143827 50 30
## 755 1460 -3.933417e-02 10.12089174 1.199103e-03 1.139748 50 30
## 756 1460 3.879902e+01 -0.51003229 4.884304e-04 1.135437 50 30
## 757 1460 2.475184e+01 3.66005593 8.469082e-04 1.131049 50 30
## 758 1460 1.037870e+01 11.01589798 7.829668e-04 1.127886 50 30
## 759 1460 3.926948e+01 -0.60449109 6.601305e-04 1.125117 50 30
## 760 1460 2.224363e+01 -2.33128151 1.054734e-03 1.123256 50 30
## 761 1460 -6.684798e+00 19.36281889 1.139636e-03 73.771148 50 30
## 762 1460 -1.676625e+01 3.70564975 1.167355e-03 71.150987 50 30
## 763 1460 2.439121e+01 3.63068339 5.235475e-04 71.034367 50 30
## 764 1460 1.577685e+01 -5.20349584 1.612920e-03 71.035404 50 30
## 765 1460 1.507139e+01 -13.79277418 7.125012e-04 71.037731 50 30
## 766 1460 -2.509491e+00 -24.98061502 2.059733e-04 1.555216 50 30
## 767 1460 6.884204e+00 25.05184719 8.398767e-04 1.312321 50 30
## 768 1460 6.102698e+00 24.47278114 2.653130e-03 1.228241 50 30
## 769 1460 -7.484325e+00 -22.87800944 9.951604e-04 1.196858 50 30
## 770 1460 1.681532e+01 -5.42980493 2.360376e-04 1.176920 50 30
## 771 1460 -1.331178e+00 -0.18089438 1.097406e-03 1.164909 50 30
## 772 1460 -1.275985e+01 1.46994717 1.242895e-03 1.155632 50 30
## 773 1460 -1.398837e-01 4.62520677 8.643886e-04 1.148755 50 30
## 774 1460 -1.238427e+01 3.85167183 1.142203e-03 1.143827 50 30
## 775 1460 -1.689352e+00 -28.27458782 2.400482e-04 1.139748 50 30
## 776 1460 2.011053e+01 -21.72872424 7.335255e-04 1.135437 50 30
## 777 1460 -4.185983e+00 -26.01796732 5.830547e-04 1.131049 50 30
## 778 1460 -5.941990e+00 3.04858623 1.239864e-03 1.127886 50 30
## 779 1460 -2.103539e+01 31.40418001 1.426553e-04 1.125117 50 30
## 780 1460 -2.584114e-01 -4.11333114 1.402951e-03 1.123256 50 30
## 781 1460 -5.117227e+00 -29.04365058 1.377946e-03 73.771148 50 30
## 782 1460 2.287722e+01 2.45546364 9.045856e-04 71.150987 50 30
## 783 1460 -8.053641e+00 -25.49248111 8.946531e-04 71.034367 50 30
## 784 1460 8.764121e-01 -4.86282066 1.072917e-03 71.035404 50 30
## 785 1460 -2.641605e+01 12.32514241 2.837450e-04 71.037731 50 30
## 786 1460 3.905587e+00 4.46224804 8.899246e-04 1.555216 50 30
## 787 1460 -1.873486e+01 -4.55845615 2.024694e-03 1.312321 50 30
## 788 1460 2.051408e+01 2.53578256 9.031469e-05 1.228241 50 30
## 789 1460 -1.575695e+01 8.06201271 1.304618e-03 1.196858 50 30
## 790 1460 1.362909e+01 8.78828823 6.220816e-04 1.176920 50 30
## 791 1460 1.409328e+01 -23.08478368 5.972777e-04 1.164909 50 30
## 792 1460 -3.086786e+00 -1.23957666 1.042633e-03 1.155632 50 30
## 793 1460 1.780693e+01 4.74816212 1.060077e-03 1.148755 50 30
## 794 1460 -4.441382e+00 -25.51706943 4.063564e-04 1.143827 50 30
## 795 1460 7.543824e+00 25.87526410 1.127606e-03 1.139748 50 30
## 796 1460 1.785943e+01 13.49473342 1.121127e-03 1.135437 50 30
## 797 1460 -1.070138e+01 -2.59360976 9.667791e-04 1.131049 50 30
## 798 1460 -1.237423e+01 4.04655235 1.941546e-03 1.127886 50 30
## 799 1460 2.611046e+01 -4.92802406 5.346232e-04 1.125117 50 30
## 800 1460 -2.632408e+01 6.81924534 4.903104e-04 1.123256 50 30
## 801 1460 6.405846e+00 24.62813179 6.377030e-04 73.771148 50 30
## 802 1460 -1.447666e+01 8.38592161 1.099303e-03 71.150987 50 30
## 803 1460 1.963638e+01 4.46122387 4.196261e-04 71.034367 50 30
## 804 1460 2.605040e+01 -5.37520070 7.967552e-04 71.035404 50 30
## 805 1460 -1.230028e+01 3.34197801 2.055436e-03 71.037731 50 30
## 806 1460 -3.186808e+00 -24.86406256 5.659359e-04 1.555216 50 30
## 807 1460 -2.017632e+00 7.33048962 4.919036e-04 1.312321 50 30
## 808 1460 1.724130e+01 1.93318048 3.805844e-04 1.228241 50 30
## 809 1460 -1.734877e-01 2.49383947 9.828810e-04 1.196858 50 30
## 810 1460 -2.786801e+01 -11.57664139 1.129358e-04 1.176920 50 30
## 811 1460 1.109776e+01 -3.18795762 4.840593e-04 1.164909 50 30
## 812 1460 1.797678e+01 -18.95566053 5.103896e-04 1.155632 50 30
## 813 1460 -8.096568e+00 18.32090961 1.238616e-03 1.148755 50 30
## 814 1460 -6.008360e+00 20.38045459 5.219803e-04 1.143827 50 30
## 815 1460 -2.931262e+01 5.19634737 6.474079e-04 1.139748 50 30
## 816 1460 -6.870308e+00 -27.36059946 5.367721e-04 1.135437 50 30
## 817 1460 -1.372463e+01 12.61403325 1.778605e-03 1.131049 50 30
## 818 1460 1.146903e+00 -18.84434272 5.244501e-04 1.127886 50 30
## 819 1460 -4.147130e+00 0.57531692 9.612342e-04 1.125117 50 30
## 820 1460 1.235455e+01 -21.47314085 2.402009e-04 1.123256 50 30
## 821 1460 2.808087e+01 3.65805961 1.121237e-03 73.771148 50 30
## 822 1460 -2.151373e+01 11.97559806 3.023670e-04 71.150987 50 30
## 823 1460 2.830178e+01 6.14182740 5.438239e-04 71.034367 50 30
## 824 1460 -2.369053e+01 4.50663235 1.205880e-03 71.035404 50 30
## 825 1460 -5.117647e+00 -24.11812589 3.973401e-04 71.037731 50 30
## 826 1460 3.910414e+00 -21.21990149 2.520965e-04 1.555216 50 30
## 827 1460 -1.617699e+01 15.07975685 1.053945e-03 1.312321 50 30
## 828 1460 -7.716327e+00 -24.61190831 1.127631e-03 1.228241 50 30
## 829 1460 7.664280e+00 14.63099310 7.289996e-04 1.196858 50 30
## 830 1460 3.460204e+01 -3.97106910 5.593832e-04 1.176920 50 30
## 831 1460 5.928051e+00 1.42888417 1.072174e-03 1.164909 50 30
## 832 1460 3.459170e+01 -3.97107326 6.793280e-04 1.155632 50 30
## 833 1460 1.882464e+01 -0.49220703 3.584957e-04 1.148755 50 30
## 834 1460 3.879620e+00 2.79670058 5.961071e-04 1.143827 50 30
## 835 1460 -7.396141e+00 0.84497981 1.709262e-03 1.139748 50 30
## 836 1460 -8.321456e+00 4.37620874 7.426639e-04 1.135437 50 30
## 837 1460 -9.777520e+00 13.75793237 8.828906e-04 1.131049 50 30
## 838 1460 4.343245e+01 1.82054679 1.954341e-04 1.127886 50 30
## 839 1460 -1.496040e+01 -12.05774143 1.607872e-03 1.125117 50 30
## 840 1460 -1.958996e+01 2.40639111 1.880676e-03 1.123256 50 30
## 841 1460 -2.818267e+01 9.10943816 5.145517e-04 73.771148 50 30
## 842 1460 -2.874630e+01 10.82583611 1.143370e-03 71.150987 50 30
## 843 1460 6.472080e-01 2.98572861 9.148531e-04 71.034367 50 30
## 844 1460 -2.455601e+01 -12.20342016 2.721760e-04 71.035404 50 30
## 845 1460 -2.720029e+01 10.12167635 1.430057e-04 71.037731 50 30
## 846 1460 -7.897835e-01 -7.22811114 1.141861e-03 1.555216 50 30
## 847 1460 1.733338e+01 -2.16842825 4.810059e-04 1.312321 50 30
## 848 1460 -6.956219e+00 5.38203684 4.940974e-04 1.228241 50 30
## 849 1460 -3.052905e+01 -0.46806334 7.118081e-04 1.196858 50 30
## 850 1460 1.603989e+01 10.90769594 1.521286e-03 1.176920 50 30
## 851 1460 1.865359e+01 -19.25549844 3.318715e-04 1.164909 50 30
## 852 1460 1.355331e+01 -24.40585094 3.378667e-04 1.155632 50 30
## 853 1460 -2.774107e+01 -2.57780187 7.653433e-04 1.148755 50 30
## 854 1460 -4.599633e+00 -0.97290983 8.194796e-04 1.143827 50 30
## 855 1460 4.918089e+00 4.42666956 8.270815e-04 1.139748 50 30
## 856 1460 -8.670341e+00 9.71740608 4.426899e-04 1.135437 50 30
## 857 1460 -1.838698e+00 5.00713322 6.132729e-04 1.131049 50 30
## 858 1460 2.734701e+01 9.27614069 8.051543e-04 1.127886 50 30
## 859 1460 -1.061908e+01 -5.75299418 1.289396e-03 1.125117 50 30
## 860 1460 1.635145e+01 13.68853378 7.827747e-04 1.123256 50 30
## 861 1460 -2.181758e+01 6.45902431 8.850175e-04 73.771148 50 30
## 862 1460 -2.407966e+00 0.27798965 7.756711e-04 71.150987 50 30
## 863 1460 -1.237136e+01 -3.58474399 1.191341e-03 71.034367 50 30
## 864 1460 -7.908847e+00 3.41937365 1.108086e-03 71.035404 50 30
## 865 1460 -5.288801e+00 -23.41238988 6.897561e-04 71.037731 50 30
## 866 1460 -7.848009e+00 1.69095824 8.222223e-04 1.555216 50 30
## 867 1460 -2.521124e+00 -27.55759962 3.792179e-04 1.312321 50 30
## 868 1460 -5.302855e+00 18.93204904 7.287320e-04 1.228241 50 30
## 869 1460 -1.823561e+01 29.83942695 3.466033e-04 1.196858 50 30
## 870 1460 1.879473e+01 -1.40241438 1.185860e-03 1.176920 50 30
## 871 1460 -1.495464e+01 5.12110164 1.947584e-03 1.164909 50 30
## 872 1460 2.072226e+01 0.80593961 7.369730e-04 1.155632 50 30
## 873 1460 -1.851630e+01 11.49243712 4.521833e-04 1.148755 50 30
## 874 1460 -3.742910e+01 6.13454960 4.658005e-04 1.143827 50 30
## 875 1460 -2.304248e+01 5.04488117 1.526219e-03 1.139748 50 30
## 876 1460 2.448369e+01 2.05752209 7.602905e-04 1.135437 50 30
## 877 1460 -8.382555e+00 5.34261569 3.729694e-04 1.131049 50 30
## 878 1460 2.012176e+01 -5.66732859 3.846487e-04 1.127886 50 30
## 879 1460 -5.770101e+00 18.72527054 1.127221e-03 1.125117 50 30
## 880 1460 -7.413071e+00 10.02193220 9.502276e-04 1.123256 50 30
## 881 1460 -1.262589e+01 -12.01881110 1.093420e-03 73.771148 50 30
## 882 1460 2.225349e+01 -0.48587598 7.017228e-04 71.150987 50 30
## 883 1460 2.901456e+01 8.23230254 5.468146e-04 71.034367 50 30
## 884 1460 -3.723967e+01 5.19944078 2.715811e-04 71.035404 50 30
## 885 1460 -9.780668e+00 0.99038400 1.511591e-03 71.037731 50 30
## 886 1460 1.161320e+01 -21.12774451 2.040082e-04 1.555216 50 30
## 887 1460 -2.427067e+01 -13.40257762 1.775299e-04 1.312321 50 30
## 888 1460 -2.905965e+01 -1.32287300 5.154863e-04 1.228241 50 30
## 889 1460 -5.947290e+00 -12.97735564 1.418002e-03 1.196858 50 30
## 890 1460 7.074888e+00 -10.11819565 4.267903e-04 1.176920 50 30
## 891 1460 -7.665835e+00 21.17480941 7.582184e-04 1.164909 50 30
## 892 1460 1.018901e+01 8.02446504 1.185549e-03 1.155632 50 30
## 893 1460 2.762826e+00 16.85885197 5.948341e-04 1.148755 50 30
## 894 1460 -5.106265e+00 -4.79572723 1.562450e-03 1.143827 50 30
## 895 1460 -2.167032e+01 31.10299047 2.518628e-04 1.139748 50 30
## 896 1460 1.328718e+01 11.80285313 7.238856e-04 1.135437 50 30
## 897 1460 -1.498444e+01 9.85801870 1.423634e-03 1.131049 50 30
## 898 1460 -2.161810e+01 31.71941595 2.133146e-04 1.127886 50 30
## 899 1460 4.324324e+00 -22.16813291 2.984918e-04 1.125117 50 30
## 900 1460 -1.008016e+01 3.77654627 1.259998e-03 1.123256 50 30
## 901 1460 -1.066760e+01 5.85222374 1.338883e-03 73.771148 50 30
## 902 1460 -7.251384e+00 7.77469717 8.171737e-04 71.150987 50 30
## 903 1460 2.927122e+01 5.13136770 8.754676e-04 71.034367 50 30
## 904 1460 -2.938389e+00 -28.64184087 3.251980e-04 71.035404 50 30
## 905 1460 -9.661420e+00 1.00883189 1.493846e-03 71.037731 50 30
## 906 1460 -2.588583e+00 3.20957689 1.344890e-03 1.555216 50 30
## 907 1460 -3.527648e-01 -22.55932353 1.201499e-03 1.312321 50 30
## 908 1460 -2.642811e+01 -2.79570475 1.265630e-03 1.228241 50 30
## 909 1460 -5.715670e-01 7.64189375 3.913766e-04 1.196858 50 30
## 910 1460 2.488500e+01 5.78853402 4.949353e-04 1.176920 50 30
## 911 1460 -2.629791e+01 -13.01236266 4.473070e-04 1.164909 50 30
## 912 1460 -8.832117e+00 3.58957797 7.231230e-04 1.155632 50 30
## 913 1460 -7.382108e+00 19.94059649 6.967006e-04 1.148755 50 30
## 914 1460 -2.639376e+01 -11.99151991 6.168830e-04 1.143827 50 30
## 915 1460 3.741110e+01 -2.55474759 2.896978e-04 1.139748 50 30
## 916 1460 4.216568e+01 0.81457089 1.649476e-04 1.135437 50 30
## 917 1460 -1.832633e+01 16.59674457 7.137848e-04 1.131049 50 30
## 918 1460 -1.189144e+01 4.23733884 4.232675e-04 1.127886 50 30
## 919 1460 1.459040e+01 2.28958290 8.878879e-04 1.125117 50 30
## 920 1460 3.726775e+00 5.92879045 1.393006e-03 1.123256 50 30
## 921 1460 9.638114e+00 17.32767660 1.495686e-03 73.771148 50 30
## 922 1460 -1.641162e+01 21.19277620 1.532595e-03 71.150987 50 30
## 923 1460 -7.034183e+00 -22.04224508 9.114816e-04 71.034367 50 30
## 924 1460 1.404905e+01 -18.92013700 1.788638e-03 71.035404 50 30
## 925 1460 -1.538059e+00 -10.00375421 8.535428e-04 71.037731 50 30
## 926 1460 3.359925e+00 18.35333014 5.426393e-04 1.555216 50 30
## 927 1460 2.618259e+01 -2.08479908 1.104932e-03 1.312321 50 30
## 928 1460 1.373536e+01 11.05747207 1.098890e-03 1.228241 50 30
## 929 1460 -7.343864e+00 -26.10971586 6.050349e-04 1.196858 50 30
## 930 1460 2.405582e+01 9.56053316 8.451054e-04 1.176920 50 30
## 931 1460 -7.558511e+00 -24.60629073 1.051999e-03 1.164909 50 30
## 932 1460 2.678020e+00 19.68099588 9.797034e-04 1.155632 50 30
## 933 1460 -1.410178e+00 -25.66540734 2.417150e-04 1.148755 50 30
## 934 1460 -5.727191e+00 -26.97408589 1.138565e-03 1.143827 50 30
## 935 1460 -7.255126e+00 -17.84143556 6.009890e-04 1.139748 50 30
## 936 1460 -2.056951e+01 13.79547416 2.025919e-04 1.135437 50 30
## 937 1460 -4.813568e+00 -19.23097887 1.477363e-03 1.131049 50 30
## 938 1460 2.145533e+01 2.24465034 5.470939e-04 1.127886 50 30
## 939 1460 3.123538e+01 2.61884139 7.768049e-04 1.125117 50 30
## 940 1460 -2.706266e+01 8.55841306 2.185743e-04 1.123256 50 30
## 941 1460 -2.374342e+01 -13.11667454 5.582116e-04 73.771148 50 30
## 942 1460 8.124232e+00 -11.02768175 4.871321e-04 71.150987 50 30
## 943 1460 -2.136237e+01 -14.23266214 8.466606e-04 71.034367 50 30
## 944 1460 -2.450247e+01 -12.61376889 4.212107e-04 71.035404 50 30
## 945 1460 5.718875e+00 13.92957504 6.286667e-04 71.037731 50 30
## 946 1460 -3.793411e+01 6.94319465 3.391381e-04 1.555216 50 30
## 947 1460 -1.730079e-01 2.51112376 8.394412e-04 1.312321 50 30
## 948 1460 4.990729e-01 -19.45185003 3.134796e-04 1.228241 50 30
## 949 1460 2.573341e+01 0.23175510 1.056345e-03 1.196858 50 30
## 950 1460 -6.683567e+00 8.84251118 9.470681e-04 1.176920 50 30
## 951 1460 -1.836154e-01 10.05961913 6.305012e-04 1.164909 50 30
## 952 1460 -6.353323e+00 5.07722391 1.482111e-03 1.155632 50 30
## 953 1460 1.993069e+00 16.55959172 7.695409e-04 1.148755 50 30
## 954 1460 5.445397e+00 22.52539411 5.444666e-04 1.143827 50 30
## 955 1460 6.391592e+00 18.55317332 2.655221e-04 1.139748 50 30
## 956 1460 -2.628905e+01 -12.36518772 4.838217e-04 1.135437 50 30
## 957 1460 4.126466e+01 2.99622020 3.887690e-04 1.131049 50 30
## 958 1460 -1.120897e+01 4.90180385 1.252633e-03 1.127886 50 30
## 959 1460 -5.269110e+00 -19.20754897 1.102381e-03 1.125117 50 30
## 960 1460 3.791865e+01 -1.48672125 1.798283e-04 1.123256 50 30
## 961 1460 -9.589550e+00 8.77370572 5.121740e-04 73.771148 50 30
## 962 1460 1.579365e+01 8.85317676 8.090588e-04 71.150987 50 30
## 963 1460 4.049732e+01 2.93123821 4.819172e-04 71.034367 50 30
## 964 1460 -8.708506e+00 -26.98347209 3.462337e-04 71.035404 50 30
## 965 1460 1.907061e+01 0.44338010 3.483558e-04 71.037731 50 30
## 966 1460 2.998110e+01 5.40395623 4.792935e-04 1.555216 50 30
## 967 1460 -2.172626e+01 2.87436222 4.485017e-04 1.312321 50 30
## 968 1460 -7.341139e+00 0.97400422 1.060335e-03 1.228241 50 30
## 969 1460 -1.826862e+01 6.67485588 1.059746e-03 1.196858 50 30
## 970 1460 -2.230239e+00 -2.86042153 5.952667e-04 1.176920 50 30
## 971 1460 -2.352155e+01 1.98654884 6.260106e-04 1.164909 50 30
## 972 1460 3.818989e+01 -2.44527587 6.441393e-04 1.155632 50 30
## 973 1460 3.104352e+00 -6.40610498 1.299169e-03 1.148755 50 30
## 974 1460 -3.755354e+00 -24.04735352 2.896583e-04 1.143827 50 30
## 975 1460 -2.195958e+01 7.15546895 6.071963e-04 1.139748 50 30
## 976 1460 3.612938e+01 -1.09290900 6.792112e-04 1.135437 50 30
## 977 1460 -1.690144e+01 7.42284011 4.395690e-04 1.131049 50 30
## 978 1460 1.443646e+01 -17.52825775 6.999923e-04 1.127886 50 30
## 979 1460 -1.178708e+01 5.29204716 1.087859e-03 1.125117 50 30
## 980 1460 -7.724412e+00 6.80768194 1.888220e-03 1.123256 50 30
## 981 1460 8.020856e+00 4.96183228 1.535517e-03 73.771148 50 30
## 982 1460 1.968250e+01 -5.12770431 5.464849e-04 71.150987 50 30
## 983 1460 1.471621e+01 -24.47806271 6.803336e-04 71.034367 50 30
## 984 1460 2.783601e+01 1.72962700 4.325823e-04 71.035404 50 30
## 985 1460 -2.158671e+01 31.26907605 2.864610e-04 71.037731 50 30
## 986 1460 -2.607944e+00 -3.44212844 1.391212e-03 1.555216 50 30
## 987 1460 -2.134642e+01 8.13712425 4.106864e-04 1.312321 50 30
## 988 1460 4.557601e+00 -21.80832785 6.219017e-04 1.228241 50 30
## 989 1460 1.417626e+01 11.04893260 1.713319e-03 1.196858 50 30
## 990 1460 3.061973e+01 4.26477921 6.776980e-04 1.176920 50 30
## 991 1460 2.117599e+01 -3.91065150 6.564205e-04 1.164909 50 30
## 992 1460 -2.176293e+01 6.10124669 4.295297e-04 1.155632 50 30
## 993 1460 1.062470e+01 8.16595345 7.895153e-04 1.148755 50 30
## 994 1460 3.045763e+01 4.49522915 5.624527e-04 1.143827 50 30
## 995 1460 3.264151e+00 -20.54928398 7.032715e-04 1.139748 50 30
## 996 1460 -2.601531e+01 -1.18130728 8.891386e-04 1.135437 50 30
## 997 1460 -8.133781e+00 6.13425107 8.209659e-04 1.131049 50 30
## 998 1460 -1.040749e+01 -5.73512771 9.879143e-04 1.127886 50 30
## 999 1460 -2.262029e+01 13.59590148 4.590973e-04 1.125117 50 30
## 1000 1460 -3.564150e+00 -18.75894573 6.930639e-04 1.123256 50 30
## 1001 1460 -1.980448e+01 28.63818226 1.690025e-04 73.771148 50 30
## 1002 1460 -2.068857e+01 14.22379325 6.025474e-04 71.150987 50 30
## 1003 1460 -3.589992e+00 -26.93875057 1.065344e-03 71.034367 50 30
## 1004 1460 -2.487918e+01 -13.22496524 4.383308e-04 71.035404 50 30
## 1005 1460 1.437607e+01 -23.34258497 2.707118e-04 71.037731 50 30
## 1006 1460 -5.167054e+00 1.41679120 6.452862e-04 1.555216 50 30
## 1007 1460 5.871516e+00 14.82983774 7.049077e-04 1.312321 50 30
## 1008 1460 4.186937e+01 4.49670153 4.818595e-04 1.228241 50 30
## 1009 1460 1.047803e+00 -27.42429027 7.510193e-04 1.196858 50 30
## 1010 1460 -3.697289e+01 5.59647443 3.697329e-04 1.176920 50 30
## 1011 1460 -2.673887e+01 8.77739651 4.728464e-04 1.164909 50 30
## 1012 1460 -2.189431e+01 31.05650196 1.440689e-04 1.155632 50 30
## 1013 1460 -2.817575e+01 0.72344540 5.862147e-04 1.148755 50 30
## 1014 1460 -4.323166e+00 11.77740404 1.202479e-03 1.143827 50 30
## 1015 1460 -8.205287e+00 -3.37864833 5.465144e-04 1.139748 50 30
## 1016 1460 2.005548e+01 3.29042530 6.903821e-04 1.135437 50 30
## 1017 1460 -8.193068e-01 -17.65906847 1.365826e-03 1.131049 50 30
## 1018 1460 1.228137e+01 -18.73250335 6.134088e-04 1.127886 50 30
## 1019 1460 2.328508e+01 8.42413411 9.284490e-04 1.125117 50 30
## 1020 1460 1.377834e+01 -23.99715623 4.154421e-04 1.123256 50 30
## 1021 1460 -1.258762e+01 -12.12771134 8.560971e-04 73.771148 50 30
## 1022 1460 -3.462766e+00 -20.50986217 7.139224e-04 71.150987 50 30
## 1023 1460 -2.383539e+01 11.77749299 1.302100e-03 71.034367 50 30
## 1024 1460 1.407295e+01 -23.48333219 7.359243e-04 71.035404 50 30
## 1025 1460 -5.147080e+00 -11.27880136 6.344892e-04 71.037731 50 30
## 1026 1460 -9.972752e-01 8.04493407 1.091412e-03 1.555216 50 30
## 1027 1460 5.657853e+00 1.36390165 5.813470e-04 1.312321 50 30
## 1028 1460 7.001663e-01 -20.84256916 6.321172e-04 1.228241 50 30
## 1029 1460 -1.844255e+01 0.48910147 2.076280e-03 1.196858 50 30
## 1030 1460 4.364912e+01 0.46750954 1.657429e-04 1.176920 50 30
## 1031 1460 -2.853037e+01 -9.38409230 7.204691e-04 1.164909 50 30
## 1032 1460 -3.817476e+01 4.74453533 4.190340e-04 1.155632 50 30
## 1033 1460 1.652645e+01 3.21243803 4.222562e-04 1.148755 50 30
## 1034 1460 -2.818841e+00 -18.02887861 1.521139e-04 1.143827 50 30
## 1035 1460 -1.704991e+01 7.23627672 7.662017e-04 1.139748 50 30
## 1036 1460 -1.971160e+01 28.91558882 1.023423e-04 1.135437 50 30
## 1037 1460 3.022442e+00 -19.63453419 3.085591e-04 1.131049 50 30
## 1038 1460 1.994407e+01 13.09933373 1.441116e-03 1.127886 50 30
## 1039 1460 4.127823e+01 1.63140635 2.201967e-04 1.125117 50 30
## 1040 1460 4.537101e+01 4.35158322 5.252710e-04 1.123256 50 30
## 1041 1460 -1.031867e+00 -8.93196966 1.162782e-03 73.771148 50 30
## 1042 1460 1.007323e+01 15.60718383 8.063991e-04 71.150987 50 30
## 1043 1460 1.254308e+01 -21.64209343 2.546243e-04 71.034367 50 30
## 1044 1460 1.864228e+01 -2.41512881 1.539050e-03 71.035404 50 30
## 1045 1460 -6.999058e+00 -15.11873767 1.378971e-03 71.037731 50 30
## 1046 1460 -1.907912e+01 29.19548138 2.234189e-04 1.555216 50 30
## 1047 1460 2.418016e+01 -6.63498701 1.031961e-03 1.312321 50 30
## 1048 1460 2.140431e+00 17.78827739 9.270836e-04 1.228241 50 30
## 1049 1460 -1.867491e+01 28.87391053 1.816445e-04 1.196858 50 30
## 1050 1460 -1.909584e+01 27.84326050 3.209966e-04 1.176920 50 30
## 1051 1460 -6.142740e+00 -22.91401802 6.922881e-04 1.164909 50 30
## 1052 1460 -6.155017e+00 -22.77491376 3.536190e-04 1.155632 50 30
## 1053 1460 1.026026e+01 15.48380483 1.216494e-03 1.148755 50 30
## 1054 1460 -9.060549e+00 -0.29086434 4.367892e-04 1.143827 50 30
## 1055 1460 2.051690e+01 -2.79043242 3.109383e-04 1.139748 50 30
## 1056 1460 5.847102e+00 14.89364697 9.630960e-04 1.135437 50 30
## 1057 1460 1.208611e+01 -22.07207184 7.110301e-04 1.131049 50 30
## 1058 1460 2.214374e+01 4.54869411 4.139658e-04 1.127886 50 30
## 1059 1460 2.112798e+01 -6.16285948 4.727771e-04 1.125117 50 30
## 1060 1460 8.174872e+00 7.54460569 1.068494e-03 1.123256 50 30
## 1061 1460 1.478651e+01 -19.88307347 3.548927e-04 73.771148 50 30
## 1062 1460 -8.154381e+00 18.23042425 3.734978e-04 71.150987 50 30
## 1063 1460 -2.731527e+01 -11.18626815 7.146663e-04 71.034367 50 30
## 1064 1460 -1.419262e+01 9.80009944 5.980559e-04 71.035404 50 30
## 1065 1460 1.372570e+00 3.45849508 6.827556e-04 71.037731 50 30
## 1066 1460 1.768549e+01 1.96237138 6.175204e-04 1.555216 50 30
## 1067 1460 2.751481e+01 9.26864289 6.591100e-04 1.312321 50 30
## 1068 1460 8.572639e+00 8.98571465 9.291747e-04 1.228241 50 30
## 1069 1460 1.944673e+01 9.11775877 1.178114e-03 1.196858 50 30
## 1070 1460 2.597496e+00 16.43130068 1.371747e-03 1.176920 50 30
## 1071 1460 4.243406e+00 0.65664540 8.397438e-04 1.164909 50 30
## 1072 1460 -2.089183e+01 2.31920578 1.619287e-03 1.155632 50 30
## 1073 1460 -2.678710e+01 3.58507481 5.303087e-04 1.148755 50 30
## 1074 1460 1.042911e+01 11.06109095 1.259943e-03 1.143827 50 30
## 1075 1460 -6.361345e+00 -25.11208264 4.757638e-04 1.139748 50 30
## 1076 1460 -2.158568e+01 3.54721098 2.881532e-03 1.135437 50 30
## 1077 1460 4.329044e+00 22.44539961 8.164504e-04 1.131049 50 30
## 1078 1460 -1.441909e-01 7.98345234 2.253319e-04 1.127886 50 30
## 1079 1460 1.819425e+01 -19.44504838 2.876382e-04 1.125117 50 30
## 1080 1460 -1.383772e+01 -10.02153423 1.413055e-03 1.123256 50 30
## 1081 1460 5.831098e+00 -11.00920940 5.112421e-04 73.771148 50 30
## 1082 1460 -1.144198e+01 12.96223503 9.371284e-04 71.150987 50 30
## 1083 1460 -4.435156e+00 -27.77659229 1.146039e-03 71.034367 50 30
## 1084 1460 -4.813117e+00 19.94900560 5.939228e-04 71.035404 50 30
## 1085 1460 1.994919e+01 7.54940443 5.303659e-04 71.037731 50 30
## 1086 1460 -2.210414e-01 -0.58001709 2.042410e-03 1.555216 50 30
## 1087 1460 3.900609e+01 4.99569252 1.181484e-03 1.312321 50 30
## 1088 1460 2.904169e+01 1.47959201 5.704814e-04 1.228241 50 30
## 1089 1460 3.894373e+01 -3.88794126 5.276992e-04 1.196858 50 30
## 1090 1460 1.431649e+01 -17.52117446 6.281480e-04 1.176920 50 30
## 1091 1460 -2.174912e+01 30.13354231 1.417364e-04 1.164909 50 30
## 1092 1460 3.824233e+01 -1.29794466 4.435356e-04 1.155632 50 30
## 1093 1460 -2.591579e+01 8.35332532 7.270456e-04 1.148755 50 30
## 1094 1460 -7.560737e+00 11.64821996 5.407934e-04 1.143827 50 30
## 1095 1460 3.255168e+00 6.13436882 4.724381e-04 1.139748 50 30
## 1096 1460 -7.731910e+00 -24.01352774 7.567033e-04 1.135437 50 30
## 1097 1460 -2.839410e+01 12.13425109 6.324476e-04 1.131049 50 30
## 1098 1460 1.556701e+01 -20.82423143 9.944073e-04 1.127886 50 30
## 1099 1460 -1.864419e+01 3.46990186 1.003315e-03 1.125117 50 30
## 1100 1460 -9.750588e+00 -6.55959103 1.575634e-03 1.123256 50 30
## 1101 1460 -1.657438e+01 16.63467097 9.757795e-04 73.771148 50 30
## 1102 1460 -2.286390e+00 8.11857246 1.262305e-03 71.150987 50 30
## 1103 1460 -1.028093e+01 9.51520354 1.370087e-03 71.034367 50 30
## 1104 1460 7.224637e-01 17.99542854 7.833013e-04 71.035404 50 30
## 1105 1460 4.207185e+01 0.89357968 2.464858e-04 71.037731 50 30
## 1106 1460 1.927078e+01 -5.69171672 5.116360e-04 1.555216 50 30
## 1107 1460 -6.319127e+00 -17.44892980 7.646354e-04 1.312321 50 30
## 1108 1460 3.059302e+01 3.00658068 2.990408e-04 1.228241 50 30
## 1109 1460 2.860724e+01 7.93169654 7.724965e-04 1.196858 50 30
## 1110 1460 2.342625e+00 -20.92381104 6.745184e-04 1.176920 50 30
## 1111 1460 1.942486e+01 6.74230739 6.666168e-04 1.164909 50 30
## 1112 1460 1.375941e+01 11.57648627 1.214632e-03 1.155632 50 30
## 1113 1460 -1.017098e+01 6.91305971 6.271881e-04 1.148755 50 30
## 1114 1460 -9.030985e+00 8.57849474 1.628434e-03 1.143827 50 30
## 1115 1460 -1.186297e+01 7.06521630 7.451035e-04 1.139748 50 30
## 1116 1460 7.020349e-01 -22.09189625 2.419479e-04 1.135437 50 30
## 1117 1460 2.087913e+01 5.26408781 3.467293e-04 1.131049 50 30
## 1118 1460 -7.699209e+00 8.92649953 8.856490e-04 1.127886 50 30
## 1119 1460 7.131393e+00 17.64318281 1.234086e-03 1.125117 50 30
## 1120 1460 -1.365253e+01 12.87736692 1.675510e-03 1.123256 50 30
## 1121 1460 -2.177901e+01 12.46767044 1.145040e-03 73.771148 50 30
## 1122 1460 -4.323909e+00 -25.93162286 7.241208e-04 71.150987 50 30
## 1123 1460 -1.488771e+01 2.33531476 4.678111e-04 71.034367 50 30
## 1124 1460 1.902285e+00 14.94254567 6.426207e-04 71.035404 50 30
## 1125 1460 2.323238e+01 8.48104911 1.391401e-03 71.037731 50 30
## 1126 1460 -1.525815e+01 4.41172360 9.412034e-04 1.555216 50 30
## 1127 1460 1.420263e+01 -23.79490437 1.792364e-04 1.312321 50 30
## 1128 1460 -6.784427e+00 -17.79898311 1.000777e-03 1.228241 50 30
## 1129 1460 2.790675e+01 6.10443510 6.654976e-04 1.196858 50 30
## 1130 1460 5.578832e-04 -4.11400103 1.264280e-03 1.176920 50 30
## 1131 1460 -2.311567e+01 -1.04893578 7.008254e-04 1.164909 50 30
## 1132 1460 -1.446506e+01 -9.81021520 1.316617e-03 1.155632 50 30
## 1133 1460 -2.472974e+01 2.15184306 5.626217e-04 1.148755 50 30
## 1134 1460 2.110838e+01 2.01568581 6.882406e-04 1.143827 50 30
## 1135 1460 2.752842e+01 7.72394426 6.590629e-04 1.139748 50 30
## 1136 1460 -1.625696e+01 10.31502777 1.470908e-03 1.135437 50 30
## 1137 1460 -2.431237e+01 9.01241964 2.325498e-03 1.131049 50 30
## 1138 1460 -2.671382e+01 14.03859705 7.014493e-04 1.127886 50 30
## 1139 1460 -2.908112e+00 -14.50276719 9.470780e-04 1.125117 50 30
## 1140 1460 -2.362709e+01 -3.71723207 1.711662e-03 1.123256 50 30
## 1141 1460 -1.006735e+01 6.59472056 3.169763e-04 73.771148 50 30
## 1142 1460 1.577277e+01 8.95402236 5.938933e-04 71.150987 50 30
## 1143 1460 2.326193e+01 -6.30793676 4.229547e-04 71.034367 50 30
## 1144 1460 -1.309979e+01 2.57857678 1.286458e-03 71.035404 50 30
## 1145 1460 -1.838513e+01 2.67581196 8.221708e-04 71.037731 50 30
## 1146 1460 -2.455448e+01 4.69107882 1.460148e-03 1.555216 50 30
## 1147 1460 -1.732819e+00 -15.43779647 1.165786e-03 1.312321 50 30
## 1148 1460 -2.136864e+01 3.06793655 1.482529e-03 1.228241 50 30
## 1149 1460 -2.541797e+01 11.08488888 8.971552e-04 1.196858 50 30
## 1150 1460 -2.861912e+01 3.94421667 1.545307e-03 1.176920 50 30
## 1151 1460 -2.111498e+01 12.94200347 6.416376e-04 1.164909 50 30
## 1152 1460 6.882393e+00 2.04552578 1.012599e-03 1.155632 50 30
## 1153 1460 -2.335847e+00 12.05244588 1.497803e-03 1.148755 50 30
## 1154 1460 -1.646850e+01 12.65731182 9.387315e-04 1.143827 50 30
## 1155 1460 1.668047e+01 14.58396039 8.523476e-04 1.139748 50 30
## 1156 1460 5.777439e+00 -1.61421878 8.586945e-04 1.135437 50 30
## 1157 1460 5.705424e+00 -10.96830026 4.983862e-04 1.131049 50 30
## 1158 1460 1.248023e+01 -21.54590171 6.122136e-04 1.127886 50 30
## 1159 1460 -2.927891e+00 -25.17525752 4.102469e-04 1.125117 50 30
## 1160 1460 1.377661e+01 11.45552110 8.218717e-04 1.123256 50 30
## 1161 1460 4.075486e+01 2.99703147 5.425477e-04 73.771148 50 30
## 1162 1460 6.696109e+00 -11.10798818 4.174358e-04 71.150987 50 30
## 1163 1460 -1.106164e+01 3.63380807 6.734053e-04 71.034367 50 30
## 1164 1460 -9.854036e+00 24.09378540 1.185915e-03 71.035404 50 30
## 1165 1460 1.886538e+00 8.17287176 7.848757e-04 71.037731 50 30
## 1166 1460 -3.207786e+00 -24.89152940 4.362029e-04 1.555216 50 30
## 1167 1460 -6.555557e+00 -27.88720360 6.219395e-04 1.312321 50 30
## 1168 1460 1.857838e+01 6.07486250 4.617541e-04 1.228241 50 30
## 1169 1460 -2.731390e+01 -3.16804614 1.461541e-03 1.196858 50 30
## 1170 1460 2.394386e+01 -7.47059207 5.124862e-04 1.176920 50 30
## 1171 1460 1.111988e+01 -3.22518254 6.495679e-04 1.164909 50 30
## 1172 1460 -5.232046e+00 20.51781611 7.428318e-04 1.155632 50 30
## 1173 1460 3.643738e+01 -2.71119230 5.408639e-04 1.148755 50 30
## 1174 1460 -3.847530e+01 5.30755199 2.723561e-04 1.143827 50 30
## 1175 1460 -1.324855e+01 18.74955209 2.468791e-03 1.139748 50 30
## 1176 1460 2.215166e+01 -3.98269169 5.667313e-04 1.135437 50 30
## 1177 1460 -6.623236e+00 5.12806702 5.869408e-04 1.131049 50 30
## 1178 1460 -2.853650e+01 13.76604497 6.920433e-04 1.127886 50 30
## 1179 1460 -2.645943e+01 10.46297446 6.947023e-04 1.125117 50 30
## 1180 1460 -2.011332e+01 28.90342472 2.898501e-05 1.123256 50 30
## 1181 1460 1.481600e+01 5.36193596 7.395257e-04 73.771148 50 30
## 1182 1460 6.736955e+00 -12.21303489 2.332707e-04 71.150987 50 30
## 1183 1460 1.154234e+01 -3.55128410 5.944638e-04 71.034367 50 30
## 1184 1460 -1.147857e+01 8.58264895 8.274665e-04 71.035404 50 30
## 1185 1460 6.746459e+00 1.43837173 2.765337e-04 71.037731 50 30
## 1186 1460 -2.837433e+01 7.51539665 1.158424e-03 1.555216 50 30
## 1187 1460 -2.659295e+01 -11.58906054 3.174480e-04 1.312321 50 30
## 1188 1460 -5.874203e+00 -16.65658335 1.412758e-03 1.228241 50 30
## 1189 1460 2.603268e+01 2.66571558 1.444675e-03 1.196858 50 30
## 1190 1460 2.593993e+01 7.03373175 5.563269e-04 1.176920 50 30
## 1191 1460 -1.889696e+00 -5.23128809 5.828768e-04 1.164909 50 30
## 1192 1460 3.951889e+01 -1.88633824 2.472624e-04 1.155632 50 30
## 1193 1460 -2.760298e+01 12.30026092 1.041782e-03 1.148755 50 30
## 1194 1460 2.005789e+01 -21.82105495 6.881331e-04 1.143827 50 30
## 1195 1460 1.040208e+01 11.09688786 8.550760e-04 1.139748 50 30
## 1196 1460 2.657702e+01 5.76044375 9.569298e-04 1.135437 50 30
## 1197 1460 2.867321e+01 6.20854607 2.368792e-04 1.131049 50 30
## 1198 1460 -2.587669e+01 10.24392548 8.321973e-04 1.127886 50 30
## 1199 1460 -7.889795e+00 -25.17995675 6.210642e-04 1.125117 50 30
## 1200 1460 -8.807661e+00 -2.72110931 1.391958e-03 1.123256 50 30
## 1201 1460 -1.442622e+01 3.91066794 1.123287e-03 73.771148 50 30
## 1202 1460 2.538944e+01 4.07563732 4.246981e-04 71.150987 50 30
## 1203 1460 -2.711044e+01 11.83053509 8.216213e-04 71.034367 50 30
## 1204 1460 -4.395245e+00 -27.95915051 3.286065e-04 71.035404 50 30
## 1205 1460 -5.540404e+00 -3.10662396 1.637921e-03 71.037731 50 30
## 1206 1460 -3.437525e+00 -11.57352414 7.628132e-04 1.555216 50 30
## 1207 1460 -1.224924e+01 1.23510876 1.093222e-03 1.312321 50 30
## 1208 1460 -2.228525e+00 -19.07317049 6.595967e-04 1.228241 50 30
## 1209 1460 -1.866669e+00 9.30115801 8.656144e-04 1.196858 50 30
## 1210 1460 9.769608e-01 -20.79071314 1.616656e-04 1.176920 50 30
## 1211 1460 7.496468e+00 25.83443119 1.217891e-03 1.164909 50 30
## 1212 1460 1.623134e+01 1.75041933 8.171104e-04 1.155632 50 30
## 1213 1460 -1.231975e+01 8.16510489 1.190880e-03 1.148755 50 30
## 1214 1460 1.188064e+00 16.44244860 4.235370e-04 1.143827 50 30
## 1215 1460 -3.003149e+00 -3.77376462 1.367820e-03 1.139748 50 30
## 1216 1460 -2.173778e+00 8.07927966 6.805363e-04 1.135437 50 30
## 1217 1460 -2.161580e+01 31.41839393 2.122230e-04 1.131049 50 30
## 1218 1460 -3.391459e+00 -20.17890218 3.500453e-04 1.127886 50 30
## 1219 1460 -2.045769e+01 28.01313571 1.910477e-04 1.125117 50 30
## 1220 1460 4.250691e+01 0.64155535 9.184533e-05 1.123256 50 30
## 1221 1460 -2.813641e+00 6.42023444 5.312029e-04 73.771148 50 30
## 1222 1460 -2.888345e+00 8.67955170 1.085611e-03 71.150987 50 30
## 1223 1460 1.253417e+01 10.17693910 1.535863e-03 71.034367 50 30
## 1224 1460 -9.590047e+00 -3.55697167 5.013378e-04 71.035404 50 30
## 1225 1460 1.891630e+01 5.08433348 5.587539e-04 71.037731 50 30
## 1226 1460 3.226715e+00 17.19553699 1.097474e-03 1.555216 50 30
## 1227 1460 2.545328e+01 -0.44929614 4.711555e-04 1.312321 50 30
## 1228 1460 -7.559439e+00 11.49657433 6.978101e-04 1.228241 50 30
## 1229 1460 1.275424e+01 -15.23083077 6.815360e-04 1.196858 50 30
## 1230 1460 -2.027348e+01 -0.64457492 1.494166e-03 1.176920 50 30
## 1231 1460 7.304213e+00 24.33729174 3.076218e-04 1.164909 50 30
## 1232 1460 -2.298007e+00 -2.93148379 1.209761e-03 1.155632 50 30
## 1233 1460 -2.171329e+01 30.35183687 1.667421e-04 1.148755 50 30
## 1234 1460 -6.631719e+00 0.99936389 5.054517e-04 1.143827 50 30
## 1235 1460 -2.377807e+01 2.17853681 5.650417e-04 1.139748 50 30
## 1236 1460 -2.452523e+01 2.51870359 5.885619e-04 1.135437 50 30
## 1237 1460 3.777672e+01 -2.70981236 3.715396e-04 1.131049 50 30
## 1238 1460 2.798300e+01 6.11889026 9.251118e-05 1.127886 50 30
## 1239 1460 -1.465409e+01 -12.01168423 6.902102e-04 1.125117 50 30
## 1240 1460 -2.195586e+00 -21.73201766 8.814921e-04 1.123256 50 30
## 1241 1460 2.004363e+01 0.37400684 1.079062e-03 73.771148 50 30
## 1242 1460 -4.748583e+00 -24.33522660 2.407952e-04 71.150987 50 30
## 1243 1460 -6.830488e-01 -10.12432917 5.708259e-04 71.034367 50 30
## 1244 1460 4.842665e+00 -20.46118357 8.297578e-04 71.035404 50 30
## 1245 1460 -2.540166e+01 5.14054193 4.846155e-04 71.037731 50 30
## 1246 1460 1.822073e+01 12.62852049 7.134899e-04 1.555216 50 30
## 1247 1460 3.059456e+01 4.26546151 4.838790e-04 1.312321 50 30
## 1248 1460 -3.884728e+00 0.52393439 6.720847e-04 1.228241 50 30
## 1249 1460 -2.652016e+01 7.27578547 7.951229e-04 1.196858 50 30
## 1250 1460 -2.734123e+00 8.74333801 7.943333e-04 1.176920 50 30
## 1251 1460 -7.047101e+00 -7.86275722 1.128152e-03 1.164909 50 30
## 1252 1460 1.268796e+01 -24.54415123 2.434529e-04 1.155632 50 30
## 1253 1460 -6.075204e+00 19.19902284 1.166881e-03 1.148755 50 30
## 1254 1460 1.095615e+01 6.11424815 1.171564e-03 1.143827 50 30
## 1255 1460 2.560167e+01 6.48476602 1.122767e-03 1.139748 50 30
## 1256 1460 -2.748764e+01 -1.86136962 1.131068e-03 1.135437 50 30
## 1257 1460 1.549194e+00 -18.77620954 6.145089e-04 1.131049 50 30
## 1258 1460 -1.714101e+01 7.83338498 2.365001e-04 1.127886 50 30
## 1259 1460 8.807345e+00 -20.01122653 1.837459e-03 1.125117 50 30
## 1260 1460 1.686083e+00 6.78015255 5.891155e-04 1.123256 50 30
## 1261 1460 2.650805e+01 8.79269085 3.557316e-04 73.771148 50 30
## 1262 1460 -1.224665e+01 4.58749803 1.519423e-03 71.150987 50 30
## 1263 1460 -2.301016e+01 -0.58319763 6.173417e-04 71.034367 50 30
## 1264 1460 -2.647977e+01 9.51812568 1.524213e-03 71.035404 50 30
## 1265 1460 1.335539e+01 -20.16209216 5.209157e-04 71.037731 50 30
## 1266 1460 3.804521e+01 -1.11009780 4.450407e-04 1.555216 50 30
## 1267 1460 -2.977194e+01 -9.99048355 4.511929e-04 1.312321 50 30
## 1268 1460 -8.546546e+00 -27.29141790 6.403968e-04 1.228241 50 30
## 1269 1460 1.315322e+01 16.03351545 1.596197e-03 1.196858 50 30
## 1270 1460 -2.079354e+01 1.53972731 2.011339e-03 1.176920 50 30
## 1271 1460 1.350818e+01 -10.08043664 9.514990e-04 1.164909 50 30
## 1272 1460 -1.140663e+01 -4.92229329 7.413112e-04 1.155632 50 30
## 1273 1460 -3.026996e+00 6.12620086 4.651014e-04 1.148755 50 30
## 1274 1460 4.900909e+00 -1.70005341 5.629987e-04 1.143827 50 30
## 1275 1460 -2.792041e+01 10.91173388 2.846806e-04 1.139748 50 30
## 1276 1460 -2.628841e+01 -12.58959628 6.336748e-04 1.135437 50 30
## 1277 1460 8.084532e+00 17.38720636 9.253381e-04 1.131049 50 30
## 1278 1460 -4.437983e+00 -1.00495019 7.486887e-04 1.127886 50 30
## 1279 1460 1.974963e+01 1.18861282 9.401900e-04 1.125117 50 30
## 1280 1460 -2.313806e+01 10.68184663 3.956677e-04 1.123256 50 30
## 1281 1460 -2.553256e+00 -18.28157578 8.377548e-04 73.771148 50 30
## 1282 1460 5.396652e+00 -0.33463111 6.964195e-04 71.150987 50 30
## 1283 1460 3.714374e+00 4.54991388 6.175788e-04 71.034367 50 30
## 1284 1460 -1.621096e+01 21.68983027 1.486470e-03 71.035404 50 30
## 1285 1460 -2.972198e+01 0.19433069 4.773389e-04 71.037731 50 30
## 1286 1460 -2.356292e+01 4.86935222 1.119647e-03 1.555216 50 30
## 1287 1460 1.529338e+00 3.31870083 8.585373e-04 1.312321 50 30
## 1288 1460 5.446334e+00 16.97106812 3.804077e-04 1.228241 50 30
## 1289 1460 1.250634e+01 -19.86023111 7.389092e-04 1.196858 50 30
## 1290 1460 2.749205e+01 -3.63065731 7.034816e-04 1.176920 50 30
## 1291 1460 -4.303314e+00 -0.20610935 5.811313e-04 1.164909 50 30
## 1292 1460 4.207010e+01 1.92191243 1.433816e-04 1.155632 50 30
## 1293 1460 -2.744753e+01 -11.35344998 4.411814e-04 1.148755 50 30
## 1294 1460 1.800078e+01 14.03130550 1.107602e-03 1.143827 50 30
## 1295 1460 -9.902561e+00 7.33933224 1.118413e-03 1.139748 50 30
## 1296 1460 -7.702431e+00 -0.47944687 3.341449e-04 1.135437 50 30
## 1297 1460 -6.533595e+00 -0.69021909 7.261761e-04 1.131049 50 30
## 1298 1460 2.054247e+01 -20.24396203 6.394960e-04 1.127886 50 30
## 1299 1460 1.176144e+01 -3.74954835 4.905038e-04 1.125117 50 30
## 1300 1460 -2.076496e-01 6.33577148 1.114348e-03 1.123256 50 30
## 1301 1460 2.123520e+01 0.38334018 7.094497e-04 73.771148 50 30
## 1302 1460 -2.731292e+01 -2.44252735 5.585333e-04 71.150987 50 30
## 1303 1460 2.052682e+01 -2.84287412 1.983558e-04 71.034367 50 30
## 1304 1460 -4.679950e+00 -26.44682402 5.531966e-04 71.035404 50 30
## 1305 1460 3.891173e+01 -3.89162125 5.901078e-04 71.037731 50 30
## 1306 1460 2.993982e+00 -19.08183295 1.795725e-04 1.555216 50 30
## 1307 1460 1.210126e+01 -24.62262605 6.037417e-04 1.312321 50 30
## 1308 1460 -1.261369e+01 -10.63398444 1.533238e-03 1.228241 50 30
## 1309 1460 -9.074730e-01 10.23020899 6.808699e-04 1.196858 50 30
## 1310 1460 -1.912910e+00 -15.86942742 1.029424e-03 1.176920 50 30
## 1311 1460 -7.686308e+00 -10.85756114 6.716461e-04 1.164909 50 30
## 1312 1460 -2.851621e+00 -18.77196002 2.266495e-04 1.155632 50 30
## 1313 1460 2.270620e+01 -2.53526167 6.592077e-04 1.148755 50 30
## 1314 1460 2.482795e+01 -2.04747028 5.244640e-04 1.143827 50 30
## 1315 1460 -1.191013e+01 8.10524631 1.300564e-03 1.139748 50 30
## 1316 1460 1.042493e+01 7.58144323 1.153833e-03 1.135437 50 30
## 1317 1460 -6.509026e+00 -28.67571521 9.103305e-04 1.131049 50 30
## 1318 1460 1.230147e+01 -25.57257527 7.496107e-04 1.127886 50 30
## 1319 1460 -3.301944e+00 -28.51996835 5.512957e-04 1.125117 50 30
## 1320 1460 -9.316102e+00 2.63327796 6.367968e-04 1.123256 50 30
## 1321 1460 4.951557e+00 4.72043121 5.022200e-04 73.771148 50 30
## 1322 1460 -2.007404e+01 27.99770980 1.867389e-04 71.150987 50 30
## 1323 1460 1.794273e+01 2.25681985 1.035978e-03 71.034367 50 30
## 1324 1460 -1.364476e+01 7.29529943 4.635868e-04 71.035404 50 30
## 1325 1460 -1.937071e+00 -25.39459310 1.798315e-04 71.037731 50 30
## 1326 1460 -2.028989e+01 15.28298993 6.660403e-04 1.555216 50 30
## 1327 1460 -1.602465e+01 14.85328847 2.948999e-04 1.312321 50 30
## 1328 1460 2.140701e+00 15.30602364 7.064330e-04 1.228241 50 30
## 1329 1460 -3.079910e+01 -2.87859875 1.576978e-03 1.196858 50 30
## 1330 1460 2.746870e+01 8.38715131 4.123686e-04 1.176920 50 30
## 1331 1460 -3.193789e+00 -27.99531387 4.237986e-04 1.164909 50 30
## 1332 1460 -1.379448e+00 -1.53323193 1.477692e-03 1.155632 50 30
## 1333 1460 -1.104080e+01 7.85451212 1.067980e-03 1.148755 50 30
## 1334 1460 -2.639026e+01 12.03696150 7.480435e-04 1.143827 50 30
## 1335 1460 4.313784e+01 2.67665727 3.102439e-04 1.139748 50 30
## 1336 1460 5.018972e+00 16.20276766 1.138778e-03 1.135437 50 30
## 1337 1460 -2.502603e+01 -13.33991386 3.055606e-04 1.131049 50 30
## 1338 1460 -1.675406e+01 10.10527106 4.260767e-04 1.127886 50 30
## 1339 1460 1.767987e+01 4.48867884 3.778574e-04 1.125117 50 30
## 1340 1460 -1.197186e+01 6.55698869 1.325789e-04 1.123256 50 30
## 1341 1460 -1.446913e+01 4.22400006 1.849201e-03 73.771148 50 30
## 1342 1460 -1.249353e+01 -11.01174687 1.456894e-03 71.150987 50 30
## 1343 1460 2.729598e+01 -1.16597113 1.898964e-03 71.034367 50 30
## 1344 1460 -2.356637e+01 5.37631394 7.713671e-04 71.035404 50 30
## 1345 1460 3.047090e+01 5.97558296 4.666967e-04 71.037731 50 30
## 1346 1460 -2.190837e+01 13.97418251 8.189207e-04 1.555216 50 30
## 1347 1460 6.762327e+00 -10.50273444 1.322443e-04 1.312321 50 30
## 1348 1460 4.318283e-01 -22.51522626 2.532287e-04 1.228241 50 30
## 1349 1460 -3.656722e+00 -16.62937037 1.278241e-03 1.196858 50 30
## 1350 1460 -3.702042e+01 5.17766375 2.694002e-04 1.176920 50 30
## 1351 1460 -2.584654e+01 -14.72906381 5.283051e-04 1.164909 50 30
## 1352 1460 1.701152e+01 13.87875352 1.497029e-03 1.155632 50 30
## 1353 1460 -1.961894e+01 9.07431859 9.310188e-04 1.148755 50 30
## 1354 1460 1.880640e+01 -5.27069659 4.004588e-04 1.143827 50 30
## 1355 1460 1.942626e+01 2.44647447 8.521893e-04 1.139748 50 30
## 1356 1460 1.515471e+01 11.29349565 5.487230e-04 1.135437 50 30
## 1357 1460 -9.867016e+00 0.77479541 9.631889e-04 1.131049 50 30
## 1358 1460 -6.964562e+00 8.84894628 9.500521e-04 1.127886 50 30
## 1359 1460 3.764908e+01 -0.34270177 4.035667e-04 1.125117 50 30
## 1360 1460 1.989695e+00 -19.84011817 5.314296e-04 1.123256 50 30
## 1361 1460 -2.919856e+01 9.65331295 3.336737e-04 73.771148 50 30
## 1362 1460 3.187539e-01 -17.76952879 6.637997e-04 71.150987 50 30
## 1363 1460 -2.174909e+01 -1.02780931 8.458363e-04 71.034367 50 30
## 1364 1460 3.013531e+01 6.10894286 4.053416e-04 71.035404 50 30
## 1365 1460 3.459583e+01 -3.96961758 6.101300e-04 71.037731 50 30
## 1366 1460 2.070526e+01 2.67537288 3.730291e-04 1.555216 50 30
## 1367 1460 2.000883e+01 0.32907817 8.719287e-04 1.312321 50 30
## 1368 1460 4.067834e+01 5.00298494 1.340106e-03 1.228241 50 30
## 1369 1460 1.824514e+01 -18.74733841 5.184798e-04 1.196858 50 30
## 1370 1460 -4.590964e+00 -13.73293333 8.141778e-04 1.176920 50 30
## 1371 1460 -2.399639e+01 9.66807337 4.438865e-04 1.164909 50 30
## 1372 1460 4.998440e+00 -1.82405653 1.209042e-03 1.155632 50 30
## 1373 1460 1.751266e+01 4.57877052 3.443515e-04 1.148755 50 30
## 1374 1460 2.916348e+00 -21.91188529 2.690864e-04 1.143827 50 30
## 1375 1460 2.906671e+01 1.57611099 7.561695e-04 1.139748 50 30
## 1376 1460 -2.366398e+00 -27.64143362 4.422657e-04 1.135437 50 30
## 1377 1460 -1.542553e+01 10.21345837 7.706226e-04 1.131049 50 30
## 1378 1460 -1.866683e+01 -1.05064069 1.516564e-03 1.127886 50 30
## 1379 1460 4.310220e+01 1.35819238 1.725718e-04 1.125117 50 30
## 1380 1460 2.622680e+01 6.10666584 3.817043e-04 1.123256 50 30
## 1381 1460 -1.895693e+01 15.72106196 7.224166e-04 73.771148 50 30
## 1382 1460 -7.306908e+00 -7.54582628 8.661589e-04 71.150987 50 30
## 1383 1460 -2.696610e+01 9.97051628 1.283917e-04 71.034367 50 30
## 1384 1460 -2.598000e+01 15.73877384 1.166908e-03 71.035404 50 30
## 1385 1460 -1.878012e+01 3.19652685 1.779332e-03 71.037731 50 30
## 1386 1460 -1.861003e+01 5.42054716 8.674972e-04 1.555216 50 30
## 1387 1460 1.135432e+01 -3.12270130 2.958409e-04 1.312321 50 30
## 1388 1460 -1.789877e+01 -1.58739302 9.518334e-04 1.228241 50 30
## 1389 1460 3.223137e+00 -18.76080570 1.670713e-04 1.196858 50 30
## 1390 1460 -2.826381e+01 3.99850850 1.621857e-03 1.176920 50 30
## 1391 1460 -1.942626e+00 -19.58142467 6.960169e-04 1.164909 50 30
## 1392 1460 -2.508929e+01 -12.95259306 3.634438e-04 1.155632 50 30
## 1393 1460 -3.217513e+00 -3.99328407 8.343260e-04 1.148755 50 30
## 1394 1460 -2.910464e+01 -9.66275390 7.397813e-04 1.143827 50 30
## 1395 1460 1.082041e+01 -20.99354492 6.115731e-04 1.139748 50 30
## 1396 1460 2.584270e+01 -1.61483855 6.766643e-04 1.135437 50 30
## 1397 1460 3.090891e+00 8.95257883 7.130339e-04 1.131049 50 30
## 1398 1460 -2.819301e+01 11.88027754 4.380803e-04 1.127886 50 30
## 1399 1460 -1.750433e+01 0.92893274 1.026517e-03 1.125117 50 30
## 1400 1460 -2.295668e+01 4.02285008 1.137997e-03 1.123256 50 30
## 1401 1460 -2.550102e+01 10.97773251 1.289437e-03 73.771148 50 30
## 1402 1460 1.906586e+01 5.92726893 6.251599e-04 71.150987 50 30
## 1403 1460 -6.030812e+00 -24.70304990 5.400615e-04 71.034367 50 30
## 1404 1460 -1.038455e+00 -19.29325654 9.850615e-05 71.035404 50 30
## 1405 1460 -2.386656e+01 11.36117545 8.398040e-05 71.037731 50 30
## 1406 1460 9.230135e+00 -17.38942026 5.442093e-04 1.555216 50 30
## 1407 1460 -9.327454e-01 -0.14965968 1.155527e-03 1.312321 50 30
## 1408 1460 -8.854525e+00 6.70130812 8.467396e-04 1.228241 50 30
## 1409 1460 -2.899469e+01 -0.85822727 4.657984e-04 1.196858 50 30
## 1410 1460 2.268907e+01 9.64053484 9.739213e-04 1.176920 50 30
## 1411 1460 2.006537e+01 2.54681089 9.351594e-04 1.164909 50 30
## 1412 1460 -2.078524e+01 3.16538571 6.392506e-04 1.155632 50 30
## 1413 1460 -2.179679e+01 30.22033195 1.942632e-04 1.148755 50 30
## 1414 1460 1.277761e+00 -18.70173162 3.241941e-04 1.143827 50 30
## 1415 1460 -2.830772e+01 -0.16233698 1.168225e-03 1.139748 50 30
## 1416 1460 1.580743e+01 -23.78245187 6.510689e-04 1.135437 50 30
## 1417 1460 -2.889879e+01 -10.13346604 7.792210e-04 1.131049 50 30
## 1418 1460 2.096871e+01 -4.48546571 3.990922e-04 1.127886 50 30
## 1419 1460 -2.770916e+00 8.40843064 1.196091e-03 1.125117 50 30
## 1420 1460 -6.531665e+00 -9.51499605 1.223070e-03 1.123256 50 30
## 1421 1460 1.716970e+01 13.71371564 5.184436e-04 73.771148 50 30
## 1422 1460 4.742453e+00 -4.95434696 9.666950e-04 71.150987 50 30
## 1423 1460 1.847358e+01 -19.11311497 3.802887e-04 71.034367 50 30
## 1424 1460 1.124661e+01 -3.27266249 5.747808e-04 71.035404 50 30
## 1425 1460 4.712412e-01 8.16717441 9.501021e-04 71.037731 50 30
## 1426 1460 -1.183659e+01 -1.05178630 4.994076e-04 1.555216 50 30
## 1427 1460 1.975865e+01 -2.33915744 1.054720e-03 1.312321 50 30
## 1428 1460 -2.033919e+01 1.81794261 1.478647e-03 1.228241 50 30
## 1429 1460 -1.087534e+01 11.77413812 6.370088e-04 1.196858 50 30
## 1430 1460 -8.826696e+00 -8.80315052 9.085545e-04 1.176920 50 30
## 1431 1460 2.810761e+01 6.71918546 1.235895e-05 1.164909 50 30
## 1432 1460 3.868118e+00 -5.37952424 6.288693e-04 1.155632 50 30
## 1433 1460 -1.661957e+01 5.17810626 1.096805e-03 1.148755 50 30
## 1434 1460 2.535641e+01 9.06612780 4.559541e-04 1.143827 50 30
## 1435 1460 -4.935467e+00 -4.11613447 1.539246e-03 1.139748 50 30
## 1436 1460 -6.494504e+00 10.44968801 6.199137e-04 1.135437 50 30
## 1437 1460 -1.050849e+01 5.68464705 1.963096e-03 1.131049 50 30
## 1438 1460 7.103009e+00 -11.60549147 2.151471e-04 1.127886 50 30
## 1439 1460 -9.786456e+00 13.71638794 6.997396e-04 1.125117 50 30
## 1440 1460 1.337203e+01 12.78608475 9.893252e-04 1.123256 50 30
## 1441 1460 -3.824934e+01 5.20511904 6.129777e-04 73.771148 50 30
## 1442 1460 1.791757e+01 -18.96874872 4.387507e-04 71.150987 50 30
## 1443 1460 2.088602e+01 -6.26097887 4.572060e-04 71.034367 50 30
## 1444 1460 -1.621145e+01 10.50017360 4.934299e-04 71.035404 50 30
## 1445 1460 -4.787609e+00 -27.51355008 4.254095e-04 71.037731 50 30
## 1446 1460 -1.659919e+00 5.01261706 6.946730e-04 1.555216 50 30
## 1447 1460 -8.850115e+00 0.23989059 3.445671e-04 1.312321 50 30
## 1448 1460 2.029528e+01 -1.74577380 4.550161e-04 1.228241 50 30
## 1449 1460 -2.112497e+01 7.39873074 8.625991e-04 1.196858 50 30
## 1450 1460 4.543679e+01 4.46023390 6.728958e-04 1.176920 50 30
## 1451 1460 -1.623457e+01 21.62679191 1.708126e-03 1.164909 50 30
## 1452 1460 -2.317150e+00 -25.15562445 3.244397e-04 1.155632 50 30
## 1453 1460 2.044313e+01 -20.20933600 9.929341e-04 1.148755 50 30
## 1454 1460 -1.486533e+01 -10.55844168 6.205043e-04 1.143827 50 30
## 1455 1460 -5.134882e+00 -19.33441084 9.290986e-04 1.139748 50 30
## 1456 1460 2.816420e+01 8.06133960 1.117811e-03 1.135437 50 30
## 1457 1460 -1.921733e+00 -12.02716207 8.692068e-04 1.131049 50 30
## 1458 1460 5.645777e+00 26.29267797 1.536823e-03 1.127886 50 30
## 1459 1460 -5.443215e-01 6.01642272 4.247624e-04 1.125117 50 30
## 1460 1460 5.800857e-01 7.68324594 8.081546e-04 1.123256 50 30
## theta max_iter stop_lying_iter mom_switch_iter momentum final_momentum eta
## 1 0.5 1000 250 250 0.5 0.8 200
## 2 0.5 1000 250 250 0.5 0.8 200
## 3 0.5 1000 250 250 0.5 0.8 200
## 4 0.5 1000 250 250 0.5 0.8 200
## 5 0.5 1000 250 250 0.5 0.8 200
## 6 0.5 1000 250 250 0.5 0.8 200
## 7 0.5 1000 250 250 0.5 0.8 200
## 8 0.5 1000 250 250 0.5 0.8 200
## 9 0.5 1000 250 250 0.5 0.8 200
## 10 0.5 1000 250 250 0.5 0.8 200
## 11 0.5 1000 250 250 0.5 0.8 200
## 12 0.5 1000 250 250 0.5 0.8 200
## 13 0.5 1000 250 250 0.5 0.8 200
## 14 0.5 1000 250 250 0.5 0.8 200
## 15 0.5 1000 250 250 0.5 0.8 200
## 16 0.5 1000 250 250 0.5 0.8 200
## 17 0.5 1000 250 250 0.5 0.8 200
## 18 0.5 1000 250 250 0.5 0.8 200
## 19 0.5 1000 250 250 0.5 0.8 200
## 20 0.5 1000 250 250 0.5 0.8 200
## 21 0.5 1000 250 250 0.5 0.8 200
## 22 0.5 1000 250 250 0.5 0.8 200
## 23 0.5 1000 250 250 0.5 0.8 200
## 24 0.5 1000 250 250 0.5 0.8 200
## 25 0.5 1000 250 250 0.5 0.8 200
## 26 0.5 1000 250 250 0.5 0.8 200
## 27 0.5 1000 250 250 0.5 0.8 200
## 28 0.5 1000 250 250 0.5 0.8 200
## 29 0.5 1000 250 250 0.5 0.8 200
## 30 0.5 1000 250 250 0.5 0.8 200
## 31 0.5 1000 250 250 0.5 0.8 200
## 32 0.5 1000 250 250 0.5 0.8 200
## 33 0.5 1000 250 250 0.5 0.8 200
## 34 0.5 1000 250 250 0.5 0.8 200
## 35 0.5 1000 250 250 0.5 0.8 200
## 36 0.5 1000 250 250 0.5 0.8 200
## 37 0.5 1000 250 250 0.5 0.8 200
## 38 0.5 1000 250 250 0.5 0.8 200
## 39 0.5 1000 250 250 0.5 0.8 200
## 40 0.5 1000 250 250 0.5 0.8 200
## 41 0.5 1000 250 250 0.5 0.8 200
## 42 0.5 1000 250 250 0.5 0.8 200
## 43 0.5 1000 250 250 0.5 0.8 200
## 44 0.5 1000 250 250 0.5 0.8 200
## 45 0.5 1000 250 250 0.5 0.8 200
## 46 0.5 1000 250 250 0.5 0.8 200
## 47 0.5 1000 250 250 0.5 0.8 200
## 48 0.5 1000 250 250 0.5 0.8 200
## 49 0.5 1000 250 250 0.5 0.8 200
## 50 0.5 1000 250 250 0.5 0.8 200
## 51 0.5 1000 250 250 0.5 0.8 200
## 52 0.5 1000 250 250 0.5 0.8 200
## 53 0.5 1000 250 250 0.5 0.8 200
## 54 0.5 1000 250 250 0.5 0.8 200
## 55 0.5 1000 250 250 0.5 0.8 200
## 56 0.5 1000 250 250 0.5 0.8 200
## 57 0.5 1000 250 250 0.5 0.8 200
## 58 0.5 1000 250 250 0.5 0.8 200
## 59 0.5 1000 250 250 0.5 0.8 200
## 60 0.5 1000 250 250 0.5 0.8 200
## 61 0.5 1000 250 250 0.5 0.8 200
## 62 0.5 1000 250 250 0.5 0.8 200
## 63 0.5 1000 250 250 0.5 0.8 200
## 64 0.5 1000 250 250 0.5 0.8 200
## 65 0.5 1000 250 250 0.5 0.8 200
## 66 0.5 1000 250 250 0.5 0.8 200
## 67 0.5 1000 250 250 0.5 0.8 200
## 68 0.5 1000 250 250 0.5 0.8 200
## 69 0.5 1000 250 250 0.5 0.8 200
## 70 0.5 1000 250 250 0.5 0.8 200
## 71 0.5 1000 250 250 0.5 0.8 200
## 72 0.5 1000 250 250 0.5 0.8 200
## 73 0.5 1000 250 250 0.5 0.8 200
## 74 0.5 1000 250 250 0.5 0.8 200
## 75 0.5 1000 250 250 0.5 0.8 200
## 76 0.5 1000 250 250 0.5 0.8 200
## 77 0.5 1000 250 250 0.5 0.8 200
## 78 0.5 1000 250 250 0.5 0.8 200
## 79 0.5 1000 250 250 0.5 0.8 200
## 80 0.5 1000 250 250 0.5 0.8 200
## 81 0.5 1000 250 250 0.5 0.8 200
## 82 0.5 1000 250 250 0.5 0.8 200
## 83 0.5 1000 250 250 0.5 0.8 200
## 84 0.5 1000 250 250 0.5 0.8 200
## 85 0.5 1000 250 250 0.5 0.8 200
## 86 0.5 1000 250 250 0.5 0.8 200
## 87 0.5 1000 250 250 0.5 0.8 200
## 88 0.5 1000 250 250 0.5 0.8 200
## 89 0.5 1000 250 250 0.5 0.8 200
## 90 0.5 1000 250 250 0.5 0.8 200
## 91 0.5 1000 250 250 0.5 0.8 200
## 92 0.5 1000 250 250 0.5 0.8 200
## 93 0.5 1000 250 250 0.5 0.8 200
## 94 0.5 1000 250 250 0.5 0.8 200
## 95 0.5 1000 250 250 0.5 0.8 200
## 96 0.5 1000 250 250 0.5 0.8 200
## 97 0.5 1000 250 250 0.5 0.8 200
## 98 0.5 1000 250 250 0.5 0.8 200
## 99 0.5 1000 250 250 0.5 0.8 200
## 100 0.5 1000 250 250 0.5 0.8 200
## 101 0.5 1000 250 250 0.5 0.8 200
## 102 0.5 1000 250 250 0.5 0.8 200
## 103 0.5 1000 250 250 0.5 0.8 200
## 104 0.5 1000 250 250 0.5 0.8 200
## 105 0.5 1000 250 250 0.5 0.8 200
## 106 0.5 1000 250 250 0.5 0.8 200
## 107 0.5 1000 250 250 0.5 0.8 200
## 108 0.5 1000 250 250 0.5 0.8 200
## 109 0.5 1000 250 250 0.5 0.8 200
## 110 0.5 1000 250 250 0.5 0.8 200
## 111 0.5 1000 250 250 0.5 0.8 200
## 112 0.5 1000 250 250 0.5 0.8 200
## 113 0.5 1000 250 250 0.5 0.8 200
## 114 0.5 1000 250 250 0.5 0.8 200
## 115 0.5 1000 250 250 0.5 0.8 200
## 116 0.5 1000 250 250 0.5 0.8 200
## 117 0.5 1000 250 250 0.5 0.8 200
## 118 0.5 1000 250 250 0.5 0.8 200
## 119 0.5 1000 250 250 0.5 0.8 200
## 120 0.5 1000 250 250 0.5 0.8 200
## 121 0.5 1000 250 250 0.5 0.8 200
## 122 0.5 1000 250 250 0.5 0.8 200
## 123 0.5 1000 250 250 0.5 0.8 200
## 124 0.5 1000 250 250 0.5 0.8 200
## 125 0.5 1000 250 250 0.5 0.8 200
## 126 0.5 1000 250 250 0.5 0.8 200
## 127 0.5 1000 250 250 0.5 0.8 200
## 128 0.5 1000 250 250 0.5 0.8 200
## 129 0.5 1000 250 250 0.5 0.8 200
## 130 0.5 1000 250 250 0.5 0.8 200
## 131 0.5 1000 250 250 0.5 0.8 200
## 132 0.5 1000 250 250 0.5 0.8 200
## 133 0.5 1000 250 250 0.5 0.8 200
## 134 0.5 1000 250 250 0.5 0.8 200
## 135 0.5 1000 250 250 0.5 0.8 200
## 136 0.5 1000 250 250 0.5 0.8 200
## 137 0.5 1000 250 250 0.5 0.8 200
## 138 0.5 1000 250 250 0.5 0.8 200
## 139 0.5 1000 250 250 0.5 0.8 200
## 140 0.5 1000 250 250 0.5 0.8 200
## 141 0.5 1000 250 250 0.5 0.8 200
## 142 0.5 1000 250 250 0.5 0.8 200
## 143 0.5 1000 250 250 0.5 0.8 200
## 144 0.5 1000 250 250 0.5 0.8 200
## 145 0.5 1000 250 250 0.5 0.8 200
## 146 0.5 1000 250 250 0.5 0.8 200
## 147 0.5 1000 250 250 0.5 0.8 200
## 148 0.5 1000 250 250 0.5 0.8 200
## 149 0.5 1000 250 250 0.5 0.8 200
## 150 0.5 1000 250 250 0.5 0.8 200
## 151 0.5 1000 250 250 0.5 0.8 200
## 152 0.5 1000 250 250 0.5 0.8 200
## 153 0.5 1000 250 250 0.5 0.8 200
## 154 0.5 1000 250 250 0.5 0.8 200
## 155 0.5 1000 250 250 0.5 0.8 200
## 156 0.5 1000 250 250 0.5 0.8 200
## 157 0.5 1000 250 250 0.5 0.8 200
## 158 0.5 1000 250 250 0.5 0.8 200
## 159 0.5 1000 250 250 0.5 0.8 200
## 160 0.5 1000 250 250 0.5 0.8 200
## 161 0.5 1000 250 250 0.5 0.8 200
## 162 0.5 1000 250 250 0.5 0.8 200
## 163 0.5 1000 250 250 0.5 0.8 200
## 164 0.5 1000 250 250 0.5 0.8 200
## 165 0.5 1000 250 250 0.5 0.8 200
## 166 0.5 1000 250 250 0.5 0.8 200
## 167 0.5 1000 250 250 0.5 0.8 200
## 168 0.5 1000 250 250 0.5 0.8 200
## 169 0.5 1000 250 250 0.5 0.8 200
## 170 0.5 1000 250 250 0.5 0.8 200
## 171 0.5 1000 250 250 0.5 0.8 200
## 172 0.5 1000 250 250 0.5 0.8 200
## 173 0.5 1000 250 250 0.5 0.8 200
## 174 0.5 1000 250 250 0.5 0.8 200
## 175 0.5 1000 250 250 0.5 0.8 200
## 176 0.5 1000 250 250 0.5 0.8 200
## 177 0.5 1000 250 250 0.5 0.8 200
## 178 0.5 1000 250 250 0.5 0.8 200
## 179 0.5 1000 250 250 0.5 0.8 200
## 180 0.5 1000 250 250 0.5 0.8 200
## 181 0.5 1000 250 250 0.5 0.8 200
## 182 0.5 1000 250 250 0.5 0.8 200
## 183 0.5 1000 250 250 0.5 0.8 200
## 184 0.5 1000 250 250 0.5 0.8 200
## 185 0.5 1000 250 250 0.5 0.8 200
## 186 0.5 1000 250 250 0.5 0.8 200
## 187 0.5 1000 250 250 0.5 0.8 200
## 188 0.5 1000 250 250 0.5 0.8 200
## 189 0.5 1000 250 250 0.5 0.8 200
## 190 0.5 1000 250 250 0.5 0.8 200
## 191 0.5 1000 250 250 0.5 0.8 200
## 192 0.5 1000 250 250 0.5 0.8 200
## 193 0.5 1000 250 250 0.5 0.8 200
## 194 0.5 1000 250 250 0.5 0.8 200
## 195 0.5 1000 250 250 0.5 0.8 200
## 196 0.5 1000 250 250 0.5 0.8 200
## 197 0.5 1000 250 250 0.5 0.8 200
## 198 0.5 1000 250 250 0.5 0.8 200
## 199 0.5 1000 250 250 0.5 0.8 200
## 200 0.5 1000 250 250 0.5 0.8 200
## 201 0.5 1000 250 250 0.5 0.8 200
## 202 0.5 1000 250 250 0.5 0.8 200
## 203 0.5 1000 250 250 0.5 0.8 200
## 204 0.5 1000 250 250 0.5 0.8 200
## 205 0.5 1000 250 250 0.5 0.8 200
## 206 0.5 1000 250 250 0.5 0.8 200
## 207 0.5 1000 250 250 0.5 0.8 200
## 208 0.5 1000 250 250 0.5 0.8 200
## 209 0.5 1000 250 250 0.5 0.8 200
## 210 0.5 1000 250 250 0.5 0.8 200
## 211 0.5 1000 250 250 0.5 0.8 200
## 212 0.5 1000 250 250 0.5 0.8 200
## 213 0.5 1000 250 250 0.5 0.8 200
## 214 0.5 1000 250 250 0.5 0.8 200
## 215 0.5 1000 250 250 0.5 0.8 200
## 216 0.5 1000 250 250 0.5 0.8 200
## 217 0.5 1000 250 250 0.5 0.8 200
## 218 0.5 1000 250 250 0.5 0.8 200
## 219 0.5 1000 250 250 0.5 0.8 200
## 220 0.5 1000 250 250 0.5 0.8 200
## 221 0.5 1000 250 250 0.5 0.8 200
## 222 0.5 1000 250 250 0.5 0.8 200
## 223 0.5 1000 250 250 0.5 0.8 200
## 224 0.5 1000 250 250 0.5 0.8 200
## 225 0.5 1000 250 250 0.5 0.8 200
## 226 0.5 1000 250 250 0.5 0.8 200
## 227 0.5 1000 250 250 0.5 0.8 200
## 228 0.5 1000 250 250 0.5 0.8 200
## 229 0.5 1000 250 250 0.5 0.8 200
## 230 0.5 1000 250 250 0.5 0.8 200
## 231 0.5 1000 250 250 0.5 0.8 200
## 232 0.5 1000 250 250 0.5 0.8 200
## 233 0.5 1000 250 250 0.5 0.8 200
## 234 0.5 1000 250 250 0.5 0.8 200
## 235 0.5 1000 250 250 0.5 0.8 200
## 236 0.5 1000 250 250 0.5 0.8 200
## 237 0.5 1000 250 250 0.5 0.8 200
## 238 0.5 1000 250 250 0.5 0.8 200
## 239 0.5 1000 250 250 0.5 0.8 200
## 240 0.5 1000 250 250 0.5 0.8 200
## 241 0.5 1000 250 250 0.5 0.8 200
## 242 0.5 1000 250 250 0.5 0.8 200
## 243 0.5 1000 250 250 0.5 0.8 200
## 244 0.5 1000 250 250 0.5 0.8 200
## 245 0.5 1000 250 250 0.5 0.8 200
## 246 0.5 1000 250 250 0.5 0.8 200
## 247 0.5 1000 250 250 0.5 0.8 200
## 248 0.5 1000 250 250 0.5 0.8 200
## 249 0.5 1000 250 250 0.5 0.8 200
## 250 0.5 1000 250 250 0.5 0.8 200
## 251 0.5 1000 250 250 0.5 0.8 200
## 252 0.5 1000 250 250 0.5 0.8 200
## 253 0.5 1000 250 250 0.5 0.8 200
## 254 0.5 1000 250 250 0.5 0.8 200
## 255 0.5 1000 250 250 0.5 0.8 200
## 256 0.5 1000 250 250 0.5 0.8 200
## 257 0.5 1000 250 250 0.5 0.8 200
## 258 0.5 1000 250 250 0.5 0.8 200
## 259 0.5 1000 250 250 0.5 0.8 200
## 260 0.5 1000 250 250 0.5 0.8 200
## 261 0.5 1000 250 250 0.5 0.8 200
## 262 0.5 1000 250 250 0.5 0.8 200
## 263 0.5 1000 250 250 0.5 0.8 200
## 264 0.5 1000 250 250 0.5 0.8 200
## 265 0.5 1000 250 250 0.5 0.8 200
## 266 0.5 1000 250 250 0.5 0.8 200
## 267 0.5 1000 250 250 0.5 0.8 200
## 268 0.5 1000 250 250 0.5 0.8 200
## 269 0.5 1000 250 250 0.5 0.8 200
## 270 0.5 1000 250 250 0.5 0.8 200
## 271 0.5 1000 250 250 0.5 0.8 200
## 272 0.5 1000 250 250 0.5 0.8 200
## 273 0.5 1000 250 250 0.5 0.8 200
## 274 0.5 1000 250 250 0.5 0.8 200
## 275 0.5 1000 250 250 0.5 0.8 200
## 276 0.5 1000 250 250 0.5 0.8 200
## 277 0.5 1000 250 250 0.5 0.8 200
## 278 0.5 1000 250 250 0.5 0.8 200
## 279 0.5 1000 250 250 0.5 0.8 200
## 280 0.5 1000 250 250 0.5 0.8 200
## 281 0.5 1000 250 250 0.5 0.8 200
## 282 0.5 1000 250 250 0.5 0.8 200
## 283 0.5 1000 250 250 0.5 0.8 200
## 284 0.5 1000 250 250 0.5 0.8 200
## 285 0.5 1000 250 250 0.5 0.8 200
## 286 0.5 1000 250 250 0.5 0.8 200
## 287 0.5 1000 250 250 0.5 0.8 200
## 288 0.5 1000 250 250 0.5 0.8 200
## 289 0.5 1000 250 250 0.5 0.8 200
## 290 0.5 1000 250 250 0.5 0.8 200
## 291 0.5 1000 250 250 0.5 0.8 200
## 292 0.5 1000 250 250 0.5 0.8 200
## 293 0.5 1000 250 250 0.5 0.8 200
## 294 0.5 1000 250 250 0.5 0.8 200
## 295 0.5 1000 250 250 0.5 0.8 200
## 296 0.5 1000 250 250 0.5 0.8 200
## 297 0.5 1000 250 250 0.5 0.8 200
## 298 0.5 1000 250 250 0.5 0.8 200
## 299 0.5 1000 250 250 0.5 0.8 200
## 300 0.5 1000 250 250 0.5 0.8 200
## 301 0.5 1000 250 250 0.5 0.8 200
## 302 0.5 1000 250 250 0.5 0.8 200
## 303 0.5 1000 250 250 0.5 0.8 200
## 304 0.5 1000 250 250 0.5 0.8 200
## 305 0.5 1000 250 250 0.5 0.8 200
## 306 0.5 1000 250 250 0.5 0.8 200
## 307 0.5 1000 250 250 0.5 0.8 200
## 308 0.5 1000 250 250 0.5 0.8 200
## 309 0.5 1000 250 250 0.5 0.8 200
## 310 0.5 1000 250 250 0.5 0.8 200
## 311 0.5 1000 250 250 0.5 0.8 200
## 312 0.5 1000 250 250 0.5 0.8 200
## 313 0.5 1000 250 250 0.5 0.8 200
## 314 0.5 1000 250 250 0.5 0.8 200
## 315 0.5 1000 250 250 0.5 0.8 200
## 316 0.5 1000 250 250 0.5 0.8 200
## 317 0.5 1000 250 250 0.5 0.8 200
## 318 0.5 1000 250 250 0.5 0.8 200
## 319 0.5 1000 250 250 0.5 0.8 200
## 320 0.5 1000 250 250 0.5 0.8 200
## 321 0.5 1000 250 250 0.5 0.8 200
## 322 0.5 1000 250 250 0.5 0.8 200
## 323 0.5 1000 250 250 0.5 0.8 200
## 324 0.5 1000 250 250 0.5 0.8 200
## 325 0.5 1000 250 250 0.5 0.8 200
## 326 0.5 1000 250 250 0.5 0.8 200
## 327 0.5 1000 250 250 0.5 0.8 200
## 328 0.5 1000 250 250 0.5 0.8 200
## 329 0.5 1000 250 250 0.5 0.8 200
## 330 0.5 1000 250 250 0.5 0.8 200
## 331 0.5 1000 250 250 0.5 0.8 200
## 332 0.5 1000 250 250 0.5 0.8 200
## 333 0.5 1000 250 250 0.5 0.8 200
## 334 0.5 1000 250 250 0.5 0.8 200
## 335 0.5 1000 250 250 0.5 0.8 200
## 336 0.5 1000 250 250 0.5 0.8 200
## 337 0.5 1000 250 250 0.5 0.8 200
## 338 0.5 1000 250 250 0.5 0.8 200
## 339 0.5 1000 250 250 0.5 0.8 200
## 340 0.5 1000 250 250 0.5 0.8 200
## 341 0.5 1000 250 250 0.5 0.8 200
## 342 0.5 1000 250 250 0.5 0.8 200
## 343 0.5 1000 250 250 0.5 0.8 200
## 344 0.5 1000 250 250 0.5 0.8 200
## 345 0.5 1000 250 250 0.5 0.8 200
## 346 0.5 1000 250 250 0.5 0.8 200
## 347 0.5 1000 250 250 0.5 0.8 200
## 348 0.5 1000 250 250 0.5 0.8 200
## 349 0.5 1000 250 250 0.5 0.8 200
## 350 0.5 1000 250 250 0.5 0.8 200
## 351 0.5 1000 250 250 0.5 0.8 200
## 352 0.5 1000 250 250 0.5 0.8 200
## 353 0.5 1000 250 250 0.5 0.8 200
## 354 0.5 1000 250 250 0.5 0.8 200
## 355 0.5 1000 250 250 0.5 0.8 200
## 356 0.5 1000 250 250 0.5 0.8 200
## 357 0.5 1000 250 250 0.5 0.8 200
## 358 0.5 1000 250 250 0.5 0.8 200
## 359 0.5 1000 250 250 0.5 0.8 200
## 360 0.5 1000 250 250 0.5 0.8 200
## 361 0.5 1000 250 250 0.5 0.8 200
## 362 0.5 1000 250 250 0.5 0.8 200
## 363 0.5 1000 250 250 0.5 0.8 200
## 364 0.5 1000 250 250 0.5 0.8 200
## 365 0.5 1000 250 250 0.5 0.8 200
## 366 0.5 1000 250 250 0.5 0.8 200
## 367 0.5 1000 250 250 0.5 0.8 200
## 368 0.5 1000 250 250 0.5 0.8 200
## 369 0.5 1000 250 250 0.5 0.8 200
## 370 0.5 1000 250 250 0.5 0.8 200
## 371 0.5 1000 250 250 0.5 0.8 200
## 372 0.5 1000 250 250 0.5 0.8 200
## 373 0.5 1000 250 250 0.5 0.8 200
## 374 0.5 1000 250 250 0.5 0.8 200
## 375 0.5 1000 250 250 0.5 0.8 200
## 376 0.5 1000 250 250 0.5 0.8 200
## 377 0.5 1000 250 250 0.5 0.8 200
## 378 0.5 1000 250 250 0.5 0.8 200
## 379 0.5 1000 250 250 0.5 0.8 200
## 380 0.5 1000 250 250 0.5 0.8 200
## 381 0.5 1000 250 250 0.5 0.8 200
## 382 0.5 1000 250 250 0.5 0.8 200
## 383 0.5 1000 250 250 0.5 0.8 200
## 384 0.5 1000 250 250 0.5 0.8 200
## 385 0.5 1000 250 250 0.5 0.8 200
## 386 0.5 1000 250 250 0.5 0.8 200
## 387 0.5 1000 250 250 0.5 0.8 200
## 388 0.5 1000 250 250 0.5 0.8 200
## 389 0.5 1000 250 250 0.5 0.8 200
## 390 0.5 1000 250 250 0.5 0.8 200
## 391 0.5 1000 250 250 0.5 0.8 200
## 392 0.5 1000 250 250 0.5 0.8 200
## 393 0.5 1000 250 250 0.5 0.8 200
## 394 0.5 1000 250 250 0.5 0.8 200
## 395 0.5 1000 250 250 0.5 0.8 200
## 396 0.5 1000 250 250 0.5 0.8 200
## 397 0.5 1000 250 250 0.5 0.8 200
## 398 0.5 1000 250 250 0.5 0.8 200
## 399 0.5 1000 250 250 0.5 0.8 200
## 400 0.5 1000 250 250 0.5 0.8 200
## 401 0.5 1000 250 250 0.5 0.8 200
## 402 0.5 1000 250 250 0.5 0.8 200
## 403 0.5 1000 250 250 0.5 0.8 200
## 404 0.5 1000 250 250 0.5 0.8 200
## 405 0.5 1000 250 250 0.5 0.8 200
## 406 0.5 1000 250 250 0.5 0.8 200
## 407 0.5 1000 250 250 0.5 0.8 200
## 408 0.5 1000 250 250 0.5 0.8 200
## 409 0.5 1000 250 250 0.5 0.8 200
## 410 0.5 1000 250 250 0.5 0.8 200
## 411 0.5 1000 250 250 0.5 0.8 200
## 412 0.5 1000 250 250 0.5 0.8 200
## 413 0.5 1000 250 250 0.5 0.8 200
## 414 0.5 1000 250 250 0.5 0.8 200
## 415 0.5 1000 250 250 0.5 0.8 200
## 416 0.5 1000 250 250 0.5 0.8 200
## 417 0.5 1000 250 250 0.5 0.8 200
## 418 0.5 1000 250 250 0.5 0.8 200
## 419 0.5 1000 250 250 0.5 0.8 200
## 420 0.5 1000 250 250 0.5 0.8 200
## 421 0.5 1000 250 250 0.5 0.8 200
## 422 0.5 1000 250 250 0.5 0.8 200
## 423 0.5 1000 250 250 0.5 0.8 200
## 424 0.5 1000 250 250 0.5 0.8 200
## 425 0.5 1000 250 250 0.5 0.8 200
## 426 0.5 1000 250 250 0.5 0.8 200
## 427 0.5 1000 250 250 0.5 0.8 200
## 428 0.5 1000 250 250 0.5 0.8 200
## 429 0.5 1000 250 250 0.5 0.8 200
## 430 0.5 1000 250 250 0.5 0.8 200
## 431 0.5 1000 250 250 0.5 0.8 200
## 432 0.5 1000 250 250 0.5 0.8 200
## 433 0.5 1000 250 250 0.5 0.8 200
## 434 0.5 1000 250 250 0.5 0.8 200
## 435 0.5 1000 250 250 0.5 0.8 200
## 436 0.5 1000 250 250 0.5 0.8 200
## 437 0.5 1000 250 250 0.5 0.8 200
## 438 0.5 1000 250 250 0.5 0.8 200
## 439 0.5 1000 250 250 0.5 0.8 200
## 440 0.5 1000 250 250 0.5 0.8 200
## 441 0.5 1000 250 250 0.5 0.8 200
## 442 0.5 1000 250 250 0.5 0.8 200
## 443 0.5 1000 250 250 0.5 0.8 200
## 444 0.5 1000 250 250 0.5 0.8 200
## 445 0.5 1000 250 250 0.5 0.8 200
## 446 0.5 1000 250 250 0.5 0.8 200
## 447 0.5 1000 250 250 0.5 0.8 200
## 448 0.5 1000 250 250 0.5 0.8 200
## 449 0.5 1000 250 250 0.5 0.8 200
## 450 0.5 1000 250 250 0.5 0.8 200
## 451 0.5 1000 250 250 0.5 0.8 200
## 452 0.5 1000 250 250 0.5 0.8 200
## 453 0.5 1000 250 250 0.5 0.8 200
## 454 0.5 1000 250 250 0.5 0.8 200
## 455 0.5 1000 250 250 0.5 0.8 200
## 456 0.5 1000 250 250 0.5 0.8 200
## 457 0.5 1000 250 250 0.5 0.8 200
## 458 0.5 1000 250 250 0.5 0.8 200
## 459 0.5 1000 250 250 0.5 0.8 200
## 460 0.5 1000 250 250 0.5 0.8 200
## 461 0.5 1000 250 250 0.5 0.8 200
## 462 0.5 1000 250 250 0.5 0.8 200
## 463 0.5 1000 250 250 0.5 0.8 200
## 464 0.5 1000 250 250 0.5 0.8 200
## 465 0.5 1000 250 250 0.5 0.8 200
## 466 0.5 1000 250 250 0.5 0.8 200
## 467 0.5 1000 250 250 0.5 0.8 200
## 468 0.5 1000 250 250 0.5 0.8 200
## 469 0.5 1000 250 250 0.5 0.8 200
## 470 0.5 1000 250 250 0.5 0.8 200
## 471 0.5 1000 250 250 0.5 0.8 200
## 472 0.5 1000 250 250 0.5 0.8 200
## 473 0.5 1000 250 250 0.5 0.8 200
## 474 0.5 1000 250 250 0.5 0.8 200
## 475 0.5 1000 250 250 0.5 0.8 200
## 476 0.5 1000 250 250 0.5 0.8 200
## 477 0.5 1000 250 250 0.5 0.8 200
## 478 0.5 1000 250 250 0.5 0.8 200
## 479 0.5 1000 250 250 0.5 0.8 200
## 480 0.5 1000 250 250 0.5 0.8 200
## 481 0.5 1000 250 250 0.5 0.8 200
## 482 0.5 1000 250 250 0.5 0.8 200
## 483 0.5 1000 250 250 0.5 0.8 200
## 484 0.5 1000 250 250 0.5 0.8 200
## 485 0.5 1000 250 250 0.5 0.8 200
## 486 0.5 1000 250 250 0.5 0.8 200
## 487 0.5 1000 250 250 0.5 0.8 200
## 488 0.5 1000 250 250 0.5 0.8 200
## 489 0.5 1000 250 250 0.5 0.8 200
## 490 0.5 1000 250 250 0.5 0.8 200
## 491 0.5 1000 250 250 0.5 0.8 200
## 492 0.5 1000 250 250 0.5 0.8 200
## 493 0.5 1000 250 250 0.5 0.8 200
## 494 0.5 1000 250 250 0.5 0.8 200
## 495 0.5 1000 250 250 0.5 0.8 200
## 496 0.5 1000 250 250 0.5 0.8 200
## 497 0.5 1000 250 250 0.5 0.8 200
## 498 0.5 1000 250 250 0.5 0.8 200
## 499 0.5 1000 250 250 0.5 0.8 200
## 500 0.5 1000 250 250 0.5 0.8 200
## 501 0.5 1000 250 250 0.5 0.8 200
## 502 0.5 1000 250 250 0.5 0.8 200
## 503 0.5 1000 250 250 0.5 0.8 200
## 504 0.5 1000 250 250 0.5 0.8 200
## 505 0.5 1000 250 250 0.5 0.8 200
## 506 0.5 1000 250 250 0.5 0.8 200
## 507 0.5 1000 250 250 0.5 0.8 200
## 508 0.5 1000 250 250 0.5 0.8 200
## 509 0.5 1000 250 250 0.5 0.8 200
## 510 0.5 1000 250 250 0.5 0.8 200
## 511 0.5 1000 250 250 0.5 0.8 200
## 512 0.5 1000 250 250 0.5 0.8 200
## 513 0.5 1000 250 250 0.5 0.8 200
## 514 0.5 1000 250 250 0.5 0.8 200
## 515 0.5 1000 250 250 0.5 0.8 200
## 516 0.5 1000 250 250 0.5 0.8 200
## 517 0.5 1000 250 250 0.5 0.8 200
## 518 0.5 1000 250 250 0.5 0.8 200
## 519 0.5 1000 250 250 0.5 0.8 200
## 520 0.5 1000 250 250 0.5 0.8 200
## 521 0.5 1000 250 250 0.5 0.8 200
## 522 0.5 1000 250 250 0.5 0.8 200
## 523 0.5 1000 250 250 0.5 0.8 200
## 524 0.5 1000 250 250 0.5 0.8 200
## 525 0.5 1000 250 250 0.5 0.8 200
## 526 0.5 1000 250 250 0.5 0.8 200
## 527 0.5 1000 250 250 0.5 0.8 200
## 528 0.5 1000 250 250 0.5 0.8 200
## 529 0.5 1000 250 250 0.5 0.8 200
## 530 0.5 1000 250 250 0.5 0.8 200
## 531 0.5 1000 250 250 0.5 0.8 200
## 532 0.5 1000 250 250 0.5 0.8 200
## 533 0.5 1000 250 250 0.5 0.8 200
## 534 0.5 1000 250 250 0.5 0.8 200
## 535 0.5 1000 250 250 0.5 0.8 200
## 536 0.5 1000 250 250 0.5 0.8 200
## 537 0.5 1000 250 250 0.5 0.8 200
## 538 0.5 1000 250 250 0.5 0.8 200
## 539 0.5 1000 250 250 0.5 0.8 200
## 540 0.5 1000 250 250 0.5 0.8 200
## 541 0.5 1000 250 250 0.5 0.8 200
## 542 0.5 1000 250 250 0.5 0.8 200
## 543 0.5 1000 250 250 0.5 0.8 200
## 544 0.5 1000 250 250 0.5 0.8 200
## 545 0.5 1000 250 250 0.5 0.8 200
## 546 0.5 1000 250 250 0.5 0.8 200
## 547 0.5 1000 250 250 0.5 0.8 200
## 548 0.5 1000 250 250 0.5 0.8 200
## 549 0.5 1000 250 250 0.5 0.8 200
## 550 0.5 1000 250 250 0.5 0.8 200
## 551 0.5 1000 250 250 0.5 0.8 200
## 552 0.5 1000 250 250 0.5 0.8 200
## 553 0.5 1000 250 250 0.5 0.8 200
## 554 0.5 1000 250 250 0.5 0.8 200
## 555 0.5 1000 250 250 0.5 0.8 200
## 556 0.5 1000 250 250 0.5 0.8 200
## 557 0.5 1000 250 250 0.5 0.8 200
## 558 0.5 1000 250 250 0.5 0.8 200
## 559 0.5 1000 250 250 0.5 0.8 200
## 560 0.5 1000 250 250 0.5 0.8 200
## 561 0.5 1000 250 250 0.5 0.8 200
## 562 0.5 1000 250 250 0.5 0.8 200
## 563 0.5 1000 250 250 0.5 0.8 200
## 564 0.5 1000 250 250 0.5 0.8 200
## 565 0.5 1000 250 250 0.5 0.8 200
## 566 0.5 1000 250 250 0.5 0.8 200
## 567 0.5 1000 250 250 0.5 0.8 200
## 568 0.5 1000 250 250 0.5 0.8 200
## 569 0.5 1000 250 250 0.5 0.8 200
## 570 0.5 1000 250 250 0.5 0.8 200
## 571 0.5 1000 250 250 0.5 0.8 200
## 572 0.5 1000 250 250 0.5 0.8 200
## 573 0.5 1000 250 250 0.5 0.8 200
## 574 0.5 1000 250 250 0.5 0.8 200
## 575 0.5 1000 250 250 0.5 0.8 200
## 576 0.5 1000 250 250 0.5 0.8 200
## 577 0.5 1000 250 250 0.5 0.8 200
## 578 0.5 1000 250 250 0.5 0.8 200
## 579 0.5 1000 250 250 0.5 0.8 200
## 580 0.5 1000 250 250 0.5 0.8 200
## 581 0.5 1000 250 250 0.5 0.8 200
## 582 0.5 1000 250 250 0.5 0.8 200
## 583 0.5 1000 250 250 0.5 0.8 200
## 584 0.5 1000 250 250 0.5 0.8 200
## 585 0.5 1000 250 250 0.5 0.8 200
## 586 0.5 1000 250 250 0.5 0.8 200
## 587 0.5 1000 250 250 0.5 0.8 200
## 588 0.5 1000 250 250 0.5 0.8 200
## 589 0.5 1000 250 250 0.5 0.8 200
## 590 0.5 1000 250 250 0.5 0.8 200
## 591 0.5 1000 250 250 0.5 0.8 200
## 592 0.5 1000 250 250 0.5 0.8 200
## 593 0.5 1000 250 250 0.5 0.8 200
## 594 0.5 1000 250 250 0.5 0.8 200
## 595 0.5 1000 250 250 0.5 0.8 200
## 596 0.5 1000 250 250 0.5 0.8 200
## 597 0.5 1000 250 250 0.5 0.8 200
## 598 0.5 1000 250 250 0.5 0.8 200
## 599 0.5 1000 250 250 0.5 0.8 200
## 600 0.5 1000 250 250 0.5 0.8 200
## 601 0.5 1000 250 250 0.5 0.8 200
## 602 0.5 1000 250 250 0.5 0.8 200
## 603 0.5 1000 250 250 0.5 0.8 200
## 604 0.5 1000 250 250 0.5 0.8 200
## 605 0.5 1000 250 250 0.5 0.8 200
## 606 0.5 1000 250 250 0.5 0.8 200
## 607 0.5 1000 250 250 0.5 0.8 200
## 608 0.5 1000 250 250 0.5 0.8 200
## 609 0.5 1000 250 250 0.5 0.8 200
## 610 0.5 1000 250 250 0.5 0.8 200
## 611 0.5 1000 250 250 0.5 0.8 200
## 612 0.5 1000 250 250 0.5 0.8 200
## 613 0.5 1000 250 250 0.5 0.8 200
## 614 0.5 1000 250 250 0.5 0.8 200
## 615 0.5 1000 250 250 0.5 0.8 200
## 616 0.5 1000 250 250 0.5 0.8 200
## 617 0.5 1000 250 250 0.5 0.8 200
## 618 0.5 1000 250 250 0.5 0.8 200
## 619 0.5 1000 250 250 0.5 0.8 200
## 620 0.5 1000 250 250 0.5 0.8 200
## 621 0.5 1000 250 250 0.5 0.8 200
## 622 0.5 1000 250 250 0.5 0.8 200
## 623 0.5 1000 250 250 0.5 0.8 200
## 624 0.5 1000 250 250 0.5 0.8 200
## 625 0.5 1000 250 250 0.5 0.8 200
## 626 0.5 1000 250 250 0.5 0.8 200
## 627 0.5 1000 250 250 0.5 0.8 200
## 628 0.5 1000 250 250 0.5 0.8 200
## 629 0.5 1000 250 250 0.5 0.8 200
## 630 0.5 1000 250 250 0.5 0.8 200
## 631 0.5 1000 250 250 0.5 0.8 200
## 632 0.5 1000 250 250 0.5 0.8 200
## 633 0.5 1000 250 250 0.5 0.8 200
## 634 0.5 1000 250 250 0.5 0.8 200
## 635 0.5 1000 250 250 0.5 0.8 200
## 636 0.5 1000 250 250 0.5 0.8 200
## 637 0.5 1000 250 250 0.5 0.8 200
## 638 0.5 1000 250 250 0.5 0.8 200
## 639 0.5 1000 250 250 0.5 0.8 200
## 640 0.5 1000 250 250 0.5 0.8 200
## 641 0.5 1000 250 250 0.5 0.8 200
## 642 0.5 1000 250 250 0.5 0.8 200
## 643 0.5 1000 250 250 0.5 0.8 200
## 644 0.5 1000 250 250 0.5 0.8 200
## 645 0.5 1000 250 250 0.5 0.8 200
## 646 0.5 1000 250 250 0.5 0.8 200
## 647 0.5 1000 250 250 0.5 0.8 200
## 648 0.5 1000 250 250 0.5 0.8 200
## 649 0.5 1000 250 250 0.5 0.8 200
## 650 0.5 1000 250 250 0.5 0.8 200
## 651 0.5 1000 250 250 0.5 0.8 200
## 652 0.5 1000 250 250 0.5 0.8 200
## 653 0.5 1000 250 250 0.5 0.8 200
## 654 0.5 1000 250 250 0.5 0.8 200
## 655 0.5 1000 250 250 0.5 0.8 200
## 656 0.5 1000 250 250 0.5 0.8 200
## 657 0.5 1000 250 250 0.5 0.8 200
## 658 0.5 1000 250 250 0.5 0.8 200
## 659 0.5 1000 250 250 0.5 0.8 200
## 660 0.5 1000 250 250 0.5 0.8 200
## 661 0.5 1000 250 250 0.5 0.8 200
## 662 0.5 1000 250 250 0.5 0.8 200
## 663 0.5 1000 250 250 0.5 0.8 200
## 664 0.5 1000 250 250 0.5 0.8 200
## 665 0.5 1000 250 250 0.5 0.8 200
## 666 0.5 1000 250 250 0.5 0.8 200
## 667 0.5 1000 250 250 0.5 0.8 200
## 668 0.5 1000 250 250 0.5 0.8 200
## 669 0.5 1000 250 250 0.5 0.8 200
## 670 0.5 1000 250 250 0.5 0.8 200
## 671 0.5 1000 250 250 0.5 0.8 200
## 672 0.5 1000 250 250 0.5 0.8 200
## 673 0.5 1000 250 250 0.5 0.8 200
## 674 0.5 1000 250 250 0.5 0.8 200
## 675 0.5 1000 250 250 0.5 0.8 200
## 676 0.5 1000 250 250 0.5 0.8 200
## 677 0.5 1000 250 250 0.5 0.8 200
## 678 0.5 1000 250 250 0.5 0.8 200
## 679 0.5 1000 250 250 0.5 0.8 200
## 680 0.5 1000 250 250 0.5 0.8 200
## 681 0.5 1000 250 250 0.5 0.8 200
## 682 0.5 1000 250 250 0.5 0.8 200
## 683 0.5 1000 250 250 0.5 0.8 200
## 684 0.5 1000 250 250 0.5 0.8 200
## 685 0.5 1000 250 250 0.5 0.8 200
## 686 0.5 1000 250 250 0.5 0.8 200
## 687 0.5 1000 250 250 0.5 0.8 200
## 688 0.5 1000 250 250 0.5 0.8 200
## 689 0.5 1000 250 250 0.5 0.8 200
## 690 0.5 1000 250 250 0.5 0.8 200
## 691 0.5 1000 250 250 0.5 0.8 200
## 692 0.5 1000 250 250 0.5 0.8 200
## 693 0.5 1000 250 250 0.5 0.8 200
## 694 0.5 1000 250 250 0.5 0.8 200
## 695 0.5 1000 250 250 0.5 0.8 200
## 696 0.5 1000 250 250 0.5 0.8 200
## 697 0.5 1000 250 250 0.5 0.8 200
## 698 0.5 1000 250 250 0.5 0.8 200
## 699 0.5 1000 250 250 0.5 0.8 200
## 700 0.5 1000 250 250 0.5 0.8 200
## 701 0.5 1000 250 250 0.5 0.8 200
## 702 0.5 1000 250 250 0.5 0.8 200
## 703 0.5 1000 250 250 0.5 0.8 200
## 704 0.5 1000 250 250 0.5 0.8 200
## 705 0.5 1000 250 250 0.5 0.8 200
## 706 0.5 1000 250 250 0.5 0.8 200
## 707 0.5 1000 250 250 0.5 0.8 200
## 708 0.5 1000 250 250 0.5 0.8 200
## 709 0.5 1000 250 250 0.5 0.8 200
## 710 0.5 1000 250 250 0.5 0.8 200
## 711 0.5 1000 250 250 0.5 0.8 200
## 712 0.5 1000 250 250 0.5 0.8 200
## 713 0.5 1000 250 250 0.5 0.8 200
## 714 0.5 1000 250 250 0.5 0.8 200
## 715 0.5 1000 250 250 0.5 0.8 200
## 716 0.5 1000 250 250 0.5 0.8 200
## 717 0.5 1000 250 250 0.5 0.8 200
## 718 0.5 1000 250 250 0.5 0.8 200
## 719 0.5 1000 250 250 0.5 0.8 200
## 720 0.5 1000 250 250 0.5 0.8 200
## 721 0.5 1000 250 250 0.5 0.8 200
## 722 0.5 1000 250 250 0.5 0.8 200
## 723 0.5 1000 250 250 0.5 0.8 200
## 724 0.5 1000 250 250 0.5 0.8 200
## 725 0.5 1000 250 250 0.5 0.8 200
## 726 0.5 1000 250 250 0.5 0.8 200
## 727 0.5 1000 250 250 0.5 0.8 200
## 728 0.5 1000 250 250 0.5 0.8 200
## 729 0.5 1000 250 250 0.5 0.8 200
## 730 0.5 1000 250 250 0.5 0.8 200
## 731 0.5 1000 250 250 0.5 0.8 200
## 732 0.5 1000 250 250 0.5 0.8 200
## 733 0.5 1000 250 250 0.5 0.8 200
## 734 0.5 1000 250 250 0.5 0.8 200
## 735 0.5 1000 250 250 0.5 0.8 200
## 736 0.5 1000 250 250 0.5 0.8 200
## 737 0.5 1000 250 250 0.5 0.8 200
## 738 0.5 1000 250 250 0.5 0.8 200
## 739 0.5 1000 250 250 0.5 0.8 200
## 740 0.5 1000 250 250 0.5 0.8 200
## 741 0.5 1000 250 250 0.5 0.8 200
## 742 0.5 1000 250 250 0.5 0.8 200
## 743 0.5 1000 250 250 0.5 0.8 200
## 744 0.5 1000 250 250 0.5 0.8 200
## 745 0.5 1000 250 250 0.5 0.8 200
## 746 0.5 1000 250 250 0.5 0.8 200
## 747 0.5 1000 250 250 0.5 0.8 200
## 748 0.5 1000 250 250 0.5 0.8 200
## 749 0.5 1000 250 250 0.5 0.8 200
## 750 0.5 1000 250 250 0.5 0.8 200
## 751 0.5 1000 250 250 0.5 0.8 200
## 752 0.5 1000 250 250 0.5 0.8 200
## 753 0.5 1000 250 250 0.5 0.8 200
## 754 0.5 1000 250 250 0.5 0.8 200
## 755 0.5 1000 250 250 0.5 0.8 200
## 756 0.5 1000 250 250 0.5 0.8 200
## 757 0.5 1000 250 250 0.5 0.8 200
## 758 0.5 1000 250 250 0.5 0.8 200
## 759 0.5 1000 250 250 0.5 0.8 200
## 760 0.5 1000 250 250 0.5 0.8 200
## 761 0.5 1000 250 250 0.5 0.8 200
## 762 0.5 1000 250 250 0.5 0.8 200
## 763 0.5 1000 250 250 0.5 0.8 200
## 764 0.5 1000 250 250 0.5 0.8 200
## 765 0.5 1000 250 250 0.5 0.8 200
## 766 0.5 1000 250 250 0.5 0.8 200
## 767 0.5 1000 250 250 0.5 0.8 200
## 768 0.5 1000 250 250 0.5 0.8 200
## 769 0.5 1000 250 250 0.5 0.8 200
## 770 0.5 1000 250 250 0.5 0.8 200
## 771 0.5 1000 250 250 0.5 0.8 200
## 772 0.5 1000 250 250 0.5 0.8 200
## 773 0.5 1000 250 250 0.5 0.8 200
## 774 0.5 1000 250 250 0.5 0.8 200
## 775 0.5 1000 250 250 0.5 0.8 200
## 776 0.5 1000 250 250 0.5 0.8 200
## 777 0.5 1000 250 250 0.5 0.8 200
## 778 0.5 1000 250 250 0.5 0.8 200
## 779 0.5 1000 250 250 0.5 0.8 200
## 780 0.5 1000 250 250 0.5 0.8 200
## 781 0.5 1000 250 250 0.5 0.8 200
## 782 0.5 1000 250 250 0.5 0.8 200
## 783 0.5 1000 250 250 0.5 0.8 200
## 784 0.5 1000 250 250 0.5 0.8 200
## 785 0.5 1000 250 250 0.5 0.8 200
## 786 0.5 1000 250 250 0.5 0.8 200
## 787 0.5 1000 250 250 0.5 0.8 200
## 788 0.5 1000 250 250 0.5 0.8 200
## 789 0.5 1000 250 250 0.5 0.8 200
## 790 0.5 1000 250 250 0.5 0.8 200
## 791 0.5 1000 250 250 0.5 0.8 200
## 792 0.5 1000 250 250 0.5 0.8 200
## 793 0.5 1000 250 250 0.5 0.8 200
## 794 0.5 1000 250 250 0.5 0.8 200
## 795 0.5 1000 250 250 0.5 0.8 200
## 796 0.5 1000 250 250 0.5 0.8 200
## 797 0.5 1000 250 250 0.5 0.8 200
## 798 0.5 1000 250 250 0.5 0.8 200
## 799 0.5 1000 250 250 0.5 0.8 200
## 800 0.5 1000 250 250 0.5 0.8 200
## 801 0.5 1000 250 250 0.5 0.8 200
## 802 0.5 1000 250 250 0.5 0.8 200
## 803 0.5 1000 250 250 0.5 0.8 200
## 804 0.5 1000 250 250 0.5 0.8 200
## 805 0.5 1000 250 250 0.5 0.8 200
## 806 0.5 1000 250 250 0.5 0.8 200
## 807 0.5 1000 250 250 0.5 0.8 200
## 808 0.5 1000 250 250 0.5 0.8 200
## 809 0.5 1000 250 250 0.5 0.8 200
## 810 0.5 1000 250 250 0.5 0.8 200
## 811 0.5 1000 250 250 0.5 0.8 200
## 812 0.5 1000 250 250 0.5 0.8 200
## 813 0.5 1000 250 250 0.5 0.8 200
## 814 0.5 1000 250 250 0.5 0.8 200
## 815 0.5 1000 250 250 0.5 0.8 200
## 816 0.5 1000 250 250 0.5 0.8 200
## 817 0.5 1000 250 250 0.5 0.8 200
## 818 0.5 1000 250 250 0.5 0.8 200
## 819 0.5 1000 250 250 0.5 0.8 200
## 820 0.5 1000 250 250 0.5 0.8 200
## 821 0.5 1000 250 250 0.5 0.8 200
## 822 0.5 1000 250 250 0.5 0.8 200
## 823 0.5 1000 250 250 0.5 0.8 200
## 824 0.5 1000 250 250 0.5 0.8 200
## 825 0.5 1000 250 250 0.5 0.8 200
## 826 0.5 1000 250 250 0.5 0.8 200
## 827 0.5 1000 250 250 0.5 0.8 200
## 828 0.5 1000 250 250 0.5 0.8 200
## 829 0.5 1000 250 250 0.5 0.8 200
## 830 0.5 1000 250 250 0.5 0.8 200
## 831 0.5 1000 250 250 0.5 0.8 200
## 832 0.5 1000 250 250 0.5 0.8 200
## 833 0.5 1000 250 250 0.5 0.8 200
## 834 0.5 1000 250 250 0.5 0.8 200
## 835 0.5 1000 250 250 0.5 0.8 200
## 836 0.5 1000 250 250 0.5 0.8 200
## 837 0.5 1000 250 250 0.5 0.8 200
## 838 0.5 1000 250 250 0.5 0.8 200
## 839 0.5 1000 250 250 0.5 0.8 200
## 840 0.5 1000 250 250 0.5 0.8 200
## 841 0.5 1000 250 250 0.5 0.8 200
## 842 0.5 1000 250 250 0.5 0.8 200
## 843 0.5 1000 250 250 0.5 0.8 200
## 844 0.5 1000 250 250 0.5 0.8 200
## 845 0.5 1000 250 250 0.5 0.8 200
## 846 0.5 1000 250 250 0.5 0.8 200
## 847 0.5 1000 250 250 0.5 0.8 200
## 848 0.5 1000 250 250 0.5 0.8 200
## 849 0.5 1000 250 250 0.5 0.8 200
## 850 0.5 1000 250 250 0.5 0.8 200
## 851 0.5 1000 250 250 0.5 0.8 200
## 852 0.5 1000 250 250 0.5 0.8 200
## 853 0.5 1000 250 250 0.5 0.8 200
## 854 0.5 1000 250 250 0.5 0.8 200
## 855 0.5 1000 250 250 0.5 0.8 200
## 856 0.5 1000 250 250 0.5 0.8 200
## 857 0.5 1000 250 250 0.5 0.8 200
## 858 0.5 1000 250 250 0.5 0.8 200
## 859 0.5 1000 250 250 0.5 0.8 200
## 860 0.5 1000 250 250 0.5 0.8 200
## 861 0.5 1000 250 250 0.5 0.8 200
## 862 0.5 1000 250 250 0.5 0.8 200
## 863 0.5 1000 250 250 0.5 0.8 200
## 864 0.5 1000 250 250 0.5 0.8 200
## 865 0.5 1000 250 250 0.5 0.8 200
## 866 0.5 1000 250 250 0.5 0.8 200
## 867 0.5 1000 250 250 0.5 0.8 200
## 868 0.5 1000 250 250 0.5 0.8 200
## 869 0.5 1000 250 250 0.5 0.8 200
## 870 0.5 1000 250 250 0.5 0.8 200
## 871 0.5 1000 250 250 0.5 0.8 200
## 872 0.5 1000 250 250 0.5 0.8 200
## 873 0.5 1000 250 250 0.5 0.8 200
## 874 0.5 1000 250 250 0.5 0.8 200
## 875 0.5 1000 250 250 0.5 0.8 200
## 876 0.5 1000 250 250 0.5 0.8 200
## 877 0.5 1000 250 250 0.5 0.8 200
## 878 0.5 1000 250 250 0.5 0.8 200
## 879 0.5 1000 250 250 0.5 0.8 200
## 880 0.5 1000 250 250 0.5 0.8 200
## 881 0.5 1000 250 250 0.5 0.8 200
## 882 0.5 1000 250 250 0.5 0.8 200
## 883 0.5 1000 250 250 0.5 0.8 200
## 884 0.5 1000 250 250 0.5 0.8 200
## 885 0.5 1000 250 250 0.5 0.8 200
## 886 0.5 1000 250 250 0.5 0.8 200
## 887 0.5 1000 250 250 0.5 0.8 200
## 888 0.5 1000 250 250 0.5 0.8 200
## 889 0.5 1000 250 250 0.5 0.8 200
## 890 0.5 1000 250 250 0.5 0.8 200
## 891 0.5 1000 250 250 0.5 0.8 200
## 892 0.5 1000 250 250 0.5 0.8 200
## 893 0.5 1000 250 250 0.5 0.8 200
## 894 0.5 1000 250 250 0.5 0.8 200
## 895 0.5 1000 250 250 0.5 0.8 200
## 896 0.5 1000 250 250 0.5 0.8 200
## 897 0.5 1000 250 250 0.5 0.8 200
## 898 0.5 1000 250 250 0.5 0.8 200
## 899 0.5 1000 250 250 0.5 0.8 200
## 900 0.5 1000 250 250 0.5 0.8 200
## 901 0.5 1000 250 250 0.5 0.8 200
## 902 0.5 1000 250 250 0.5 0.8 200
## 903 0.5 1000 250 250 0.5 0.8 200
## 904 0.5 1000 250 250 0.5 0.8 200
## 905 0.5 1000 250 250 0.5 0.8 200
## 906 0.5 1000 250 250 0.5 0.8 200
## 907 0.5 1000 250 250 0.5 0.8 200
## 908 0.5 1000 250 250 0.5 0.8 200
## 909 0.5 1000 250 250 0.5 0.8 200
## 910 0.5 1000 250 250 0.5 0.8 200
## 911 0.5 1000 250 250 0.5 0.8 200
## 912 0.5 1000 250 250 0.5 0.8 200
## 913 0.5 1000 250 250 0.5 0.8 200
## 914 0.5 1000 250 250 0.5 0.8 200
## 915 0.5 1000 250 250 0.5 0.8 200
## 916 0.5 1000 250 250 0.5 0.8 200
## 917 0.5 1000 250 250 0.5 0.8 200
## 918 0.5 1000 250 250 0.5 0.8 200
## 919 0.5 1000 250 250 0.5 0.8 200
## 920 0.5 1000 250 250 0.5 0.8 200
## 921 0.5 1000 250 250 0.5 0.8 200
## 922 0.5 1000 250 250 0.5 0.8 200
## 923 0.5 1000 250 250 0.5 0.8 200
## 924 0.5 1000 250 250 0.5 0.8 200
## 925 0.5 1000 250 250 0.5 0.8 200
## 926 0.5 1000 250 250 0.5 0.8 200
## 927 0.5 1000 250 250 0.5 0.8 200
## 928 0.5 1000 250 250 0.5 0.8 200
## 929 0.5 1000 250 250 0.5 0.8 200
## 930 0.5 1000 250 250 0.5 0.8 200
## 931 0.5 1000 250 250 0.5 0.8 200
## 932 0.5 1000 250 250 0.5 0.8 200
## 933 0.5 1000 250 250 0.5 0.8 200
## 934 0.5 1000 250 250 0.5 0.8 200
## 935 0.5 1000 250 250 0.5 0.8 200
## 936 0.5 1000 250 250 0.5 0.8 200
## 937 0.5 1000 250 250 0.5 0.8 200
## 938 0.5 1000 250 250 0.5 0.8 200
## 939 0.5 1000 250 250 0.5 0.8 200
## 940 0.5 1000 250 250 0.5 0.8 200
## 941 0.5 1000 250 250 0.5 0.8 200
## 942 0.5 1000 250 250 0.5 0.8 200
## 943 0.5 1000 250 250 0.5 0.8 200
## 944 0.5 1000 250 250 0.5 0.8 200
## 945 0.5 1000 250 250 0.5 0.8 200
## 946 0.5 1000 250 250 0.5 0.8 200
## 947 0.5 1000 250 250 0.5 0.8 200
## 948 0.5 1000 250 250 0.5 0.8 200
## 949 0.5 1000 250 250 0.5 0.8 200
## 950 0.5 1000 250 250 0.5 0.8 200
## 951 0.5 1000 250 250 0.5 0.8 200
## 952 0.5 1000 250 250 0.5 0.8 200
## 953 0.5 1000 250 250 0.5 0.8 200
## 954 0.5 1000 250 250 0.5 0.8 200
## 955 0.5 1000 250 250 0.5 0.8 200
## 956 0.5 1000 250 250 0.5 0.8 200
## 957 0.5 1000 250 250 0.5 0.8 200
## 958 0.5 1000 250 250 0.5 0.8 200
## 959 0.5 1000 250 250 0.5 0.8 200
## 960 0.5 1000 250 250 0.5 0.8 200
## 961 0.5 1000 250 250 0.5 0.8 200
## 962 0.5 1000 250 250 0.5 0.8 200
## 963 0.5 1000 250 250 0.5 0.8 200
## 964 0.5 1000 250 250 0.5 0.8 200
## 965 0.5 1000 250 250 0.5 0.8 200
## 966 0.5 1000 250 250 0.5 0.8 200
## 967 0.5 1000 250 250 0.5 0.8 200
## 968 0.5 1000 250 250 0.5 0.8 200
## 969 0.5 1000 250 250 0.5 0.8 200
## 970 0.5 1000 250 250 0.5 0.8 200
## 971 0.5 1000 250 250 0.5 0.8 200
## 972 0.5 1000 250 250 0.5 0.8 200
## 973 0.5 1000 250 250 0.5 0.8 200
## 974 0.5 1000 250 250 0.5 0.8 200
## 975 0.5 1000 250 250 0.5 0.8 200
## 976 0.5 1000 250 250 0.5 0.8 200
## 977 0.5 1000 250 250 0.5 0.8 200
## 978 0.5 1000 250 250 0.5 0.8 200
## 979 0.5 1000 250 250 0.5 0.8 200
## 980 0.5 1000 250 250 0.5 0.8 200
## 981 0.5 1000 250 250 0.5 0.8 200
## 982 0.5 1000 250 250 0.5 0.8 200
## 983 0.5 1000 250 250 0.5 0.8 200
## 984 0.5 1000 250 250 0.5 0.8 200
## 985 0.5 1000 250 250 0.5 0.8 200
## 986 0.5 1000 250 250 0.5 0.8 200
## 987 0.5 1000 250 250 0.5 0.8 200
## 988 0.5 1000 250 250 0.5 0.8 200
## 989 0.5 1000 250 250 0.5 0.8 200
## 990 0.5 1000 250 250 0.5 0.8 200
## 991 0.5 1000 250 250 0.5 0.8 200
## 992 0.5 1000 250 250 0.5 0.8 200
## 993 0.5 1000 250 250 0.5 0.8 200
## 994 0.5 1000 250 250 0.5 0.8 200
## 995 0.5 1000 250 250 0.5 0.8 200
## 996 0.5 1000 250 250 0.5 0.8 200
## 997 0.5 1000 250 250 0.5 0.8 200
## 998 0.5 1000 250 250 0.5 0.8 200
## 999 0.5 1000 250 250 0.5 0.8 200
## 1000 0.5 1000 250 250 0.5 0.8 200
## 1001 0.5 1000 250 250 0.5 0.8 200
## 1002 0.5 1000 250 250 0.5 0.8 200
## 1003 0.5 1000 250 250 0.5 0.8 200
## 1004 0.5 1000 250 250 0.5 0.8 200
## 1005 0.5 1000 250 250 0.5 0.8 200
## 1006 0.5 1000 250 250 0.5 0.8 200
## 1007 0.5 1000 250 250 0.5 0.8 200
## 1008 0.5 1000 250 250 0.5 0.8 200
## 1009 0.5 1000 250 250 0.5 0.8 200
## 1010 0.5 1000 250 250 0.5 0.8 200
## 1011 0.5 1000 250 250 0.5 0.8 200
## 1012 0.5 1000 250 250 0.5 0.8 200
## 1013 0.5 1000 250 250 0.5 0.8 200
## 1014 0.5 1000 250 250 0.5 0.8 200
## 1015 0.5 1000 250 250 0.5 0.8 200
## 1016 0.5 1000 250 250 0.5 0.8 200
## 1017 0.5 1000 250 250 0.5 0.8 200
## 1018 0.5 1000 250 250 0.5 0.8 200
## 1019 0.5 1000 250 250 0.5 0.8 200
## 1020 0.5 1000 250 250 0.5 0.8 200
## 1021 0.5 1000 250 250 0.5 0.8 200
## 1022 0.5 1000 250 250 0.5 0.8 200
## 1023 0.5 1000 250 250 0.5 0.8 200
## 1024 0.5 1000 250 250 0.5 0.8 200
## 1025 0.5 1000 250 250 0.5 0.8 200
## 1026 0.5 1000 250 250 0.5 0.8 200
## 1027 0.5 1000 250 250 0.5 0.8 200
## 1028 0.5 1000 250 250 0.5 0.8 200
## 1029 0.5 1000 250 250 0.5 0.8 200
## 1030 0.5 1000 250 250 0.5 0.8 200
## 1031 0.5 1000 250 250 0.5 0.8 200
## 1032 0.5 1000 250 250 0.5 0.8 200
## 1033 0.5 1000 250 250 0.5 0.8 200
## 1034 0.5 1000 250 250 0.5 0.8 200
## 1035 0.5 1000 250 250 0.5 0.8 200
## 1036 0.5 1000 250 250 0.5 0.8 200
## 1037 0.5 1000 250 250 0.5 0.8 200
## 1038 0.5 1000 250 250 0.5 0.8 200
## 1039 0.5 1000 250 250 0.5 0.8 200
## 1040 0.5 1000 250 250 0.5 0.8 200
## 1041 0.5 1000 250 250 0.5 0.8 200
## 1042 0.5 1000 250 250 0.5 0.8 200
## 1043 0.5 1000 250 250 0.5 0.8 200
## 1044 0.5 1000 250 250 0.5 0.8 200
## 1045 0.5 1000 250 250 0.5 0.8 200
## 1046 0.5 1000 250 250 0.5 0.8 200
## 1047 0.5 1000 250 250 0.5 0.8 200
## 1048 0.5 1000 250 250 0.5 0.8 200
## 1049 0.5 1000 250 250 0.5 0.8 200
## 1050 0.5 1000 250 250 0.5 0.8 200
## 1051 0.5 1000 250 250 0.5 0.8 200
## 1052 0.5 1000 250 250 0.5 0.8 200
## 1053 0.5 1000 250 250 0.5 0.8 200
## 1054 0.5 1000 250 250 0.5 0.8 200
## 1055 0.5 1000 250 250 0.5 0.8 200
## 1056 0.5 1000 250 250 0.5 0.8 200
## 1057 0.5 1000 250 250 0.5 0.8 200
## 1058 0.5 1000 250 250 0.5 0.8 200
## 1059 0.5 1000 250 250 0.5 0.8 200
## 1060 0.5 1000 250 250 0.5 0.8 200
## 1061 0.5 1000 250 250 0.5 0.8 200
## 1062 0.5 1000 250 250 0.5 0.8 200
## 1063 0.5 1000 250 250 0.5 0.8 200
## 1064 0.5 1000 250 250 0.5 0.8 200
## 1065 0.5 1000 250 250 0.5 0.8 200
## 1066 0.5 1000 250 250 0.5 0.8 200
## 1067 0.5 1000 250 250 0.5 0.8 200
## 1068 0.5 1000 250 250 0.5 0.8 200
## 1069 0.5 1000 250 250 0.5 0.8 200
## 1070 0.5 1000 250 250 0.5 0.8 200
## 1071 0.5 1000 250 250 0.5 0.8 200
## 1072 0.5 1000 250 250 0.5 0.8 200
## 1073 0.5 1000 250 250 0.5 0.8 200
## 1074 0.5 1000 250 250 0.5 0.8 200
## 1075 0.5 1000 250 250 0.5 0.8 200
## 1076 0.5 1000 250 250 0.5 0.8 200
## 1077 0.5 1000 250 250 0.5 0.8 200
## 1078 0.5 1000 250 250 0.5 0.8 200
## 1079 0.5 1000 250 250 0.5 0.8 200
## 1080 0.5 1000 250 250 0.5 0.8 200
## 1081 0.5 1000 250 250 0.5 0.8 200
## 1082 0.5 1000 250 250 0.5 0.8 200
## 1083 0.5 1000 250 250 0.5 0.8 200
## 1084 0.5 1000 250 250 0.5 0.8 200
## 1085 0.5 1000 250 250 0.5 0.8 200
## 1086 0.5 1000 250 250 0.5 0.8 200
## 1087 0.5 1000 250 250 0.5 0.8 200
## 1088 0.5 1000 250 250 0.5 0.8 200
## 1089 0.5 1000 250 250 0.5 0.8 200
## 1090 0.5 1000 250 250 0.5 0.8 200
## 1091 0.5 1000 250 250 0.5 0.8 200
## 1092 0.5 1000 250 250 0.5 0.8 200
## 1093 0.5 1000 250 250 0.5 0.8 200
## 1094 0.5 1000 250 250 0.5 0.8 200
## 1095 0.5 1000 250 250 0.5 0.8 200
## 1096 0.5 1000 250 250 0.5 0.8 200
## 1097 0.5 1000 250 250 0.5 0.8 200
## 1098 0.5 1000 250 250 0.5 0.8 200
## 1099 0.5 1000 250 250 0.5 0.8 200
## 1100 0.5 1000 250 250 0.5 0.8 200
## 1101 0.5 1000 250 250 0.5 0.8 200
## 1102 0.5 1000 250 250 0.5 0.8 200
## 1103 0.5 1000 250 250 0.5 0.8 200
## 1104 0.5 1000 250 250 0.5 0.8 200
## 1105 0.5 1000 250 250 0.5 0.8 200
## 1106 0.5 1000 250 250 0.5 0.8 200
## 1107 0.5 1000 250 250 0.5 0.8 200
## 1108 0.5 1000 250 250 0.5 0.8 200
## 1109 0.5 1000 250 250 0.5 0.8 200
## 1110 0.5 1000 250 250 0.5 0.8 200
## 1111 0.5 1000 250 250 0.5 0.8 200
## 1112 0.5 1000 250 250 0.5 0.8 200
## 1113 0.5 1000 250 250 0.5 0.8 200
## 1114 0.5 1000 250 250 0.5 0.8 200
## 1115 0.5 1000 250 250 0.5 0.8 200
## 1116 0.5 1000 250 250 0.5 0.8 200
## 1117 0.5 1000 250 250 0.5 0.8 200
## 1118 0.5 1000 250 250 0.5 0.8 200
## 1119 0.5 1000 250 250 0.5 0.8 200
## 1120 0.5 1000 250 250 0.5 0.8 200
## 1121 0.5 1000 250 250 0.5 0.8 200
## 1122 0.5 1000 250 250 0.5 0.8 200
## 1123 0.5 1000 250 250 0.5 0.8 200
## 1124 0.5 1000 250 250 0.5 0.8 200
## 1125 0.5 1000 250 250 0.5 0.8 200
## 1126 0.5 1000 250 250 0.5 0.8 200
## 1127 0.5 1000 250 250 0.5 0.8 200
## 1128 0.5 1000 250 250 0.5 0.8 200
## 1129 0.5 1000 250 250 0.5 0.8 200
## 1130 0.5 1000 250 250 0.5 0.8 200
## 1131 0.5 1000 250 250 0.5 0.8 200
## 1132 0.5 1000 250 250 0.5 0.8 200
## 1133 0.5 1000 250 250 0.5 0.8 200
## 1134 0.5 1000 250 250 0.5 0.8 200
## 1135 0.5 1000 250 250 0.5 0.8 200
## 1136 0.5 1000 250 250 0.5 0.8 200
## 1137 0.5 1000 250 250 0.5 0.8 200
## 1138 0.5 1000 250 250 0.5 0.8 200
## 1139 0.5 1000 250 250 0.5 0.8 200
## 1140 0.5 1000 250 250 0.5 0.8 200
## 1141 0.5 1000 250 250 0.5 0.8 200
## 1142 0.5 1000 250 250 0.5 0.8 200
## 1143 0.5 1000 250 250 0.5 0.8 200
## 1144 0.5 1000 250 250 0.5 0.8 200
## 1145 0.5 1000 250 250 0.5 0.8 200
## 1146 0.5 1000 250 250 0.5 0.8 200
## 1147 0.5 1000 250 250 0.5 0.8 200
## 1148 0.5 1000 250 250 0.5 0.8 200
## 1149 0.5 1000 250 250 0.5 0.8 200
## 1150 0.5 1000 250 250 0.5 0.8 200
## 1151 0.5 1000 250 250 0.5 0.8 200
## 1152 0.5 1000 250 250 0.5 0.8 200
## 1153 0.5 1000 250 250 0.5 0.8 200
## 1154 0.5 1000 250 250 0.5 0.8 200
## 1155 0.5 1000 250 250 0.5 0.8 200
## 1156 0.5 1000 250 250 0.5 0.8 200
## 1157 0.5 1000 250 250 0.5 0.8 200
## 1158 0.5 1000 250 250 0.5 0.8 200
## 1159 0.5 1000 250 250 0.5 0.8 200
## 1160 0.5 1000 250 250 0.5 0.8 200
## 1161 0.5 1000 250 250 0.5 0.8 200
## 1162 0.5 1000 250 250 0.5 0.8 200
## 1163 0.5 1000 250 250 0.5 0.8 200
## 1164 0.5 1000 250 250 0.5 0.8 200
## 1165 0.5 1000 250 250 0.5 0.8 200
## 1166 0.5 1000 250 250 0.5 0.8 200
## 1167 0.5 1000 250 250 0.5 0.8 200
## 1168 0.5 1000 250 250 0.5 0.8 200
## 1169 0.5 1000 250 250 0.5 0.8 200
## 1170 0.5 1000 250 250 0.5 0.8 200
## 1171 0.5 1000 250 250 0.5 0.8 200
## 1172 0.5 1000 250 250 0.5 0.8 200
## 1173 0.5 1000 250 250 0.5 0.8 200
## 1174 0.5 1000 250 250 0.5 0.8 200
## 1175 0.5 1000 250 250 0.5 0.8 200
## 1176 0.5 1000 250 250 0.5 0.8 200
## 1177 0.5 1000 250 250 0.5 0.8 200
## 1178 0.5 1000 250 250 0.5 0.8 200
## 1179 0.5 1000 250 250 0.5 0.8 200
## 1180 0.5 1000 250 250 0.5 0.8 200
## 1181 0.5 1000 250 250 0.5 0.8 200
## 1182 0.5 1000 250 250 0.5 0.8 200
## 1183 0.5 1000 250 250 0.5 0.8 200
## 1184 0.5 1000 250 250 0.5 0.8 200
## 1185 0.5 1000 250 250 0.5 0.8 200
## 1186 0.5 1000 250 250 0.5 0.8 200
## 1187 0.5 1000 250 250 0.5 0.8 200
## 1188 0.5 1000 250 250 0.5 0.8 200
## 1189 0.5 1000 250 250 0.5 0.8 200
## 1190 0.5 1000 250 250 0.5 0.8 200
## 1191 0.5 1000 250 250 0.5 0.8 200
## 1192 0.5 1000 250 250 0.5 0.8 200
## 1193 0.5 1000 250 250 0.5 0.8 200
## 1194 0.5 1000 250 250 0.5 0.8 200
## 1195 0.5 1000 250 250 0.5 0.8 200
## 1196 0.5 1000 250 250 0.5 0.8 200
## 1197 0.5 1000 250 250 0.5 0.8 200
## 1198 0.5 1000 250 250 0.5 0.8 200
## 1199 0.5 1000 250 250 0.5 0.8 200
## 1200 0.5 1000 250 250 0.5 0.8 200
## 1201 0.5 1000 250 250 0.5 0.8 200
## 1202 0.5 1000 250 250 0.5 0.8 200
## 1203 0.5 1000 250 250 0.5 0.8 200
## 1204 0.5 1000 250 250 0.5 0.8 200
## 1205 0.5 1000 250 250 0.5 0.8 200
## 1206 0.5 1000 250 250 0.5 0.8 200
## 1207 0.5 1000 250 250 0.5 0.8 200
## 1208 0.5 1000 250 250 0.5 0.8 200
## 1209 0.5 1000 250 250 0.5 0.8 200
## 1210 0.5 1000 250 250 0.5 0.8 200
## 1211 0.5 1000 250 250 0.5 0.8 200
## 1212 0.5 1000 250 250 0.5 0.8 200
## 1213 0.5 1000 250 250 0.5 0.8 200
## 1214 0.5 1000 250 250 0.5 0.8 200
## 1215 0.5 1000 250 250 0.5 0.8 200
## 1216 0.5 1000 250 250 0.5 0.8 200
## 1217 0.5 1000 250 250 0.5 0.8 200
## 1218 0.5 1000 250 250 0.5 0.8 200
## 1219 0.5 1000 250 250 0.5 0.8 200
## 1220 0.5 1000 250 250 0.5 0.8 200
## 1221 0.5 1000 250 250 0.5 0.8 200
## 1222 0.5 1000 250 250 0.5 0.8 200
## 1223 0.5 1000 250 250 0.5 0.8 200
## 1224 0.5 1000 250 250 0.5 0.8 200
## 1225 0.5 1000 250 250 0.5 0.8 200
## 1226 0.5 1000 250 250 0.5 0.8 200
## 1227 0.5 1000 250 250 0.5 0.8 200
## 1228 0.5 1000 250 250 0.5 0.8 200
## 1229 0.5 1000 250 250 0.5 0.8 200
## 1230 0.5 1000 250 250 0.5 0.8 200
## 1231 0.5 1000 250 250 0.5 0.8 200
## 1232 0.5 1000 250 250 0.5 0.8 200
## 1233 0.5 1000 250 250 0.5 0.8 200
## 1234 0.5 1000 250 250 0.5 0.8 200
## 1235 0.5 1000 250 250 0.5 0.8 200
## 1236 0.5 1000 250 250 0.5 0.8 200
## 1237 0.5 1000 250 250 0.5 0.8 200
## 1238 0.5 1000 250 250 0.5 0.8 200
## 1239 0.5 1000 250 250 0.5 0.8 200
## 1240 0.5 1000 250 250 0.5 0.8 200
## 1241 0.5 1000 250 250 0.5 0.8 200
## 1242 0.5 1000 250 250 0.5 0.8 200
## 1243 0.5 1000 250 250 0.5 0.8 200
## 1244 0.5 1000 250 250 0.5 0.8 200
## 1245 0.5 1000 250 250 0.5 0.8 200
## 1246 0.5 1000 250 250 0.5 0.8 200
## 1247 0.5 1000 250 250 0.5 0.8 200
## 1248 0.5 1000 250 250 0.5 0.8 200
## 1249 0.5 1000 250 250 0.5 0.8 200
## 1250 0.5 1000 250 250 0.5 0.8 200
## 1251 0.5 1000 250 250 0.5 0.8 200
## 1252 0.5 1000 250 250 0.5 0.8 200
## 1253 0.5 1000 250 250 0.5 0.8 200
## 1254 0.5 1000 250 250 0.5 0.8 200
## 1255 0.5 1000 250 250 0.5 0.8 200
## 1256 0.5 1000 250 250 0.5 0.8 200
## 1257 0.5 1000 250 250 0.5 0.8 200
## 1258 0.5 1000 250 250 0.5 0.8 200
## 1259 0.5 1000 250 250 0.5 0.8 200
## 1260 0.5 1000 250 250 0.5 0.8 200
## 1261 0.5 1000 250 250 0.5 0.8 200
## 1262 0.5 1000 250 250 0.5 0.8 200
## 1263 0.5 1000 250 250 0.5 0.8 200
## 1264 0.5 1000 250 250 0.5 0.8 200
## 1265 0.5 1000 250 250 0.5 0.8 200
## 1266 0.5 1000 250 250 0.5 0.8 200
## 1267 0.5 1000 250 250 0.5 0.8 200
## 1268 0.5 1000 250 250 0.5 0.8 200
## 1269 0.5 1000 250 250 0.5 0.8 200
## 1270 0.5 1000 250 250 0.5 0.8 200
## 1271 0.5 1000 250 250 0.5 0.8 200
## 1272 0.5 1000 250 250 0.5 0.8 200
## 1273 0.5 1000 250 250 0.5 0.8 200
## 1274 0.5 1000 250 250 0.5 0.8 200
## 1275 0.5 1000 250 250 0.5 0.8 200
## 1276 0.5 1000 250 250 0.5 0.8 200
## 1277 0.5 1000 250 250 0.5 0.8 200
## 1278 0.5 1000 250 250 0.5 0.8 200
## 1279 0.5 1000 250 250 0.5 0.8 200
## 1280 0.5 1000 250 250 0.5 0.8 200
## 1281 0.5 1000 250 250 0.5 0.8 200
## 1282 0.5 1000 250 250 0.5 0.8 200
## 1283 0.5 1000 250 250 0.5 0.8 200
## 1284 0.5 1000 250 250 0.5 0.8 200
## 1285 0.5 1000 250 250 0.5 0.8 200
## 1286 0.5 1000 250 250 0.5 0.8 200
## 1287 0.5 1000 250 250 0.5 0.8 200
## 1288 0.5 1000 250 250 0.5 0.8 200
## 1289 0.5 1000 250 250 0.5 0.8 200
## 1290 0.5 1000 250 250 0.5 0.8 200
## 1291 0.5 1000 250 250 0.5 0.8 200
## 1292 0.5 1000 250 250 0.5 0.8 200
## 1293 0.5 1000 250 250 0.5 0.8 200
## 1294 0.5 1000 250 250 0.5 0.8 200
## 1295 0.5 1000 250 250 0.5 0.8 200
## 1296 0.5 1000 250 250 0.5 0.8 200
## 1297 0.5 1000 250 250 0.5 0.8 200
## 1298 0.5 1000 250 250 0.5 0.8 200
## 1299 0.5 1000 250 250 0.5 0.8 200
## 1300 0.5 1000 250 250 0.5 0.8 200
## 1301 0.5 1000 250 250 0.5 0.8 200
## 1302 0.5 1000 250 250 0.5 0.8 200
## 1303 0.5 1000 250 250 0.5 0.8 200
## 1304 0.5 1000 250 250 0.5 0.8 200
## 1305 0.5 1000 250 250 0.5 0.8 200
## 1306 0.5 1000 250 250 0.5 0.8 200
## 1307 0.5 1000 250 250 0.5 0.8 200
## 1308 0.5 1000 250 250 0.5 0.8 200
## 1309 0.5 1000 250 250 0.5 0.8 200
## 1310 0.5 1000 250 250 0.5 0.8 200
## 1311 0.5 1000 250 250 0.5 0.8 200
## 1312 0.5 1000 250 250 0.5 0.8 200
## 1313 0.5 1000 250 250 0.5 0.8 200
## 1314 0.5 1000 250 250 0.5 0.8 200
## 1315 0.5 1000 250 250 0.5 0.8 200
## 1316 0.5 1000 250 250 0.5 0.8 200
## 1317 0.5 1000 250 250 0.5 0.8 200
## 1318 0.5 1000 250 250 0.5 0.8 200
## 1319 0.5 1000 250 250 0.5 0.8 200
## 1320 0.5 1000 250 250 0.5 0.8 200
## 1321 0.5 1000 250 250 0.5 0.8 200
## 1322 0.5 1000 250 250 0.5 0.8 200
## 1323 0.5 1000 250 250 0.5 0.8 200
## 1324 0.5 1000 250 250 0.5 0.8 200
## 1325 0.5 1000 250 250 0.5 0.8 200
## 1326 0.5 1000 250 250 0.5 0.8 200
## 1327 0.5 1000 250 250 0.5 0.8 200
## 1328 0.5 1000 250 250 0.5 0.8 200
## 1329 0.5 1000 250 250 0.5 0.8 200
## 1330 0.5 1000 250 250 0.5 0.8 200
## 1331 0.5 1000 250 250 0.5 0.8 200
## 1332 0.5 1000 250 250 0.5 0.8 200
## 1333 0.5 1000 250 250 0.5 0.8 200
## 1334 0.5 1000 250 250 0.5 0.8 200
## 1335 0.5 1000 250 250 0.5 0.8 200
## 1336 0.5 1000 250 250 0.5 0.8 200
## 1337 0.5 1000 250 250 0.5 0.8 200
## 1338 0.5 1000 250 250 0.5 0.8 200
## 1339 0.5 1000 250 250 0.5 0.8 200
## 1340 0.5 1000 250 250 0.5 0.8 200
## 1341 0.5 1000 250 250 0.5 0.8 200
## 1342 0.5 1000 250 250 0.5 0.8 200
## 1343 0.5 1000 250 250 0.5 0.8 200
## 1344 0.5 1000 250 250 0.5 0.8 200
## 1345 0.5 1000 250 250 0.5 0.8 200
## 1346 0.5 1000 250 250 0.5 0.8 200
## 1347 0.5 1000 250 250 0.5 0.8 200
## 1348 0.5 1000 250 250 0.5 0.8 200
## 1349 0.5 1000 250 250 0.5 0.8 200
## 1350 0.5 1000 250 250 0.5 0.8 200
## 1351 0.5 1000 250 250 0.5 0.8 200
## 1352 0.5 1000 250 250 0.5 0.8 200
## 1353 0.5 1000 250 250 0.5 0.8 200
## 1354 0.5 1000 250 250 0.5 0.8 200
## 1355 0.5 1000 250 250 0.5 0.8 200
## 1356 0.5 1000 250 250 0.5 0.8 200
## 1357 0.5 1000 250 250 0.5 0.8 200
## 1358 0.5 1000 250 250 0.5 0.8 200
## 1359 0.5 1000 250 250 0.5 0.8 200
## 1360 0.5 1000 250 250 0.5 0.8 200
## 1361 0.5 1000 250 250 0.5 0.8 200
## 1362 0.5 1000 250 250 0.5 0.8 200
## 1363 0.5 1000 250 250 0.5 0.8 200
## 1364 0.5 1000 250 250 0.5 0.8 200
## 1365 0.5 1000 250 250 0.5 0.8 200
## 1366 0.5 1000 250 250 0.5 0.8 200
## 1367 0.5 1000 250 250 0.5 0.8 200
## 1368 0.5 1000 250 250 0.5 0.8 200
## 1369 0.5 1000 250 250 0.5 0.8 200
## 1370 0.5 1000 250 250 0.5 0.8 200
## 1371 0.5 1000 250 250 0.5 0.8 200
## 1372 0.5 1000 250 250 0.5 0.8 200
## 1373 0.5 1000 250 250 0.5 0.8 200
## 1374 0.5 1000 250 250 0.5 0.8 200
## 1375 0.5 1000 250 250 0.5 0.8 200
## 1376 0.5 1000 250 250 0.5 0.8 200
## 1377 0.5 1000 250 250 0.5 0.8 200
## 1378 0.5 1000 250 250 0.5 0.8 200
## 1379 0.5 1000 250 250 0.5 0.8 200
## 1380 0.5 1000 250 250 0.5 0.8 200
## 1381 0.5 1000 250 250 0.5 0.8 200
## 1382 0.5 1000 250 250 0.5 0.8 200
## 1383 0.5 1000 250 250 0.5 0.8 200
## 1384 0.5 1000 250 250 0.5 0.8 200
## 1385 0.5 1000 250 250 0.5 0.8 200
## 1386 0.5 1000 250 250 0.5 0.8 200
## 1387 0.5 1000 250 250 0.5 0.8 200
## 1388 0.5 1000 250 250 0.5 0.8 200
## 1389 0.5 1000 250 250 0.5 0.8 200
## 1390 0.5 1000 250 250 0.5 0.8 200
## 1391 0.5 1000 250 250 0.5 0.8 200
## 1392 0.5 1000 250 250 0.5 0.8 200
## 1393 0.5 1000 250 250 0.5 0.8 200
## 1394 0.5 1000 250 250 0.5 0.8 200
## 1395 0.5 1000 250 250 0.5 0.8 200
## 1396 0.5 1000 250 250 0.5 0.8 200
## 1397 0.5 1000 250 250 0.5 0.8 200
## 1398 0.5 1000 250 250 0.5 0.8 200
## 1399 0.5 1000 250 250 0.5 0.8 200
## 1400 0.5 1000 250 250 0.5 0.8 200
## 1401 0.5 1000 250 250 0.5 0.8 200
## 1402 0.5 1000 250 250 0.5 0.8 200
## 1403 0.5 1000 250 250 0.5 0.8 200
## 1404 0.5 1000 250 250 0.5 0.8 200
## 1405 0.5 1000 250 250 0.5 0.8 200
## 1406 0.5 1000 250 250 0.5 0.8 200
## 1407 0.5 1000 250 250 0.5 0.8 200
## 1408 0.5 1000 250 250 0.5 0.8 200
## 1409 0.5 1000 250 250 0.5 0.8 200
## 1410 0.5 1000 250 250 0.5 0.8 200
## 1411 0.5 1000 250 250 0.5 0.8 200
## 1412 0.5 1000 250 250 0.5 0.8 200
## 1413 0.5 1000 250 250 0.5 0.8 200
## 1414 0.5 1000 250 250 0.5 0.8 200
## 1415 0.5 1000 250 250 0.5 0.8 200
## 1416 0.5 1000 250 250 0.5 0.8 200
## 1417 0.5 1000 250 250 0.5 0.8 200
## 1418 0.5 1000 250 250 0.5 0.8 200
## 1419 0.5 1000 250 250 0.5 0.8 200
## 1420 0.5 1000 250 250 0.5 0.8 200
## 1421 0.5 1000 250 250 0.5 0.8 200
## 1422 0.5 1000 250 250 0.5 0.8 200
## 1423 0.5 1000 250 250 0.5 0.8 200
## 1424 0.5 1000 250 250 0.5 0.8 200
## 1425 0.5 1000 250 250 0.5 0.8 200
## 1426 0.5 1000 250 250 0.5 0.8 200
## 1427 0.5 1000 250 250 0.5 0.8 200
## 1428 0.5 1000 250 250 0.5 0.8 200
## 1429 0.5 1000 250 250 0.5 0.8 200
## 1430 0.5 1000 250 250 0.5 0.8 200
## 1431 0.5 1000 250 250 0.5 0.8 200
## 1432 0.5 1000 250 250 0.5 0.8 200
## 1433 0.5 1000 250 250 0.5 0.8 200
## 1434 0.5 1000 250 250 0.5 0.8 200
## 1435 0.5 1000 250 250 0.5 0.8 200
## 1436 0.5 1000 250 250 0.5 0.8 200
## 1437 0.5 1000 250 250 0.5 0.8 200
## 1438 0.5 1000 250 250 0.5 0.8 200
## 1439 0.5 1000 250 250 0.5 0.8 200
## 1440 0.5 1000 250 250 0.5 0.8 200
## 1441 0.5 1000 250 250 0.5 0.8 200
## 1442 0.5 1000 250 250 0.5 0.8 200
## 1443 0.5 1000 250 250 0.5 0.8 200
## 1444 0.5 1000 250 250 0.5 0.8 200
## 1445 0.5 1000 250 250 0.5 0.8 200
## 1446 0.5 1000 250 250 0.5 0.8 200
## 1447 0.5 1000 250 250 0.5 0.8 200
## 1448 0.5 1000 250 250 0.5 0.8 200
## 1449 0.5 1000 250 250 0.5 0.8 200
## 1450 0.5 1000 250 250 0.5 0.8 200
## 1451 0.5 1000 250 250 0.5 0.8 200
## 1452 0.5 1000 250 250 0.5 0.8 200
## 1453 0.5 1000 250 250 0.5 0.8 200
## 1454 0.5 1000 250 250 0.5 0.8 200
## 1455 0.5 1000 250 250 0.5 0.8 200
## 1456 0.5 1000 250 250 0.5 0.8 200
## 1457 0.5 1000 250 250 0.5 0.8 200
## 1458 0.5 1000 250 250 0.5 0.8 200
## 1459 0.5 1000 250 250 0.5 0.8 200
## 1460 0.5 1000 250 250 0.5 0.8 200
## exaggeration_factor cluster
## 1 12 1
## 2 12 2
## 3 12 1
## 4 12 3
## 5 12 1
## 6 12 4
## 7 12 5
## 8 12 2
## 9 12 6
## 10 12 7
## 11 12 8
## 12 12 1
## 13 12 9
## 14 12 5
## 15 12 7
## 16 12 3
## 17 12 10
## 18 12 11
## 19 12 7
## 20 12 8
## 21 12 1
## 22 12 3
## 23 12 5
## 24 12 4
## 25 12 9
## 26 12 5
## 27 12 2
## 28 12 5
## 29 12 7
## 30 12 3
## 31 12 3
## 32 12 7
## 33 12 5
## 34 12 2
## 35 12 12
## 36 12 1
## 37 12 7
## 38 12 2
## 39 12 8
## 40 12 11
## 41 12 7
## 42 12 2
## 43 12 9
## 44 12 9
## 45 12 9
## 46 12 12
## 47 12 4
## 48 12 5
## 49 12 6
## 50 12 8
## 51 12 2
## 52 12 13
## 53 12 9
## 54 12 4
## 55 12 7
## 56 12 4
## 57 12 14
## 58 12 1
## 59 12 1
## 60 12 8
## 61 12 7
## 62 12 3
## 63 12 12
## 64 12 3
## 65 12 1
## 66 12 1
## 67 12 7
## 68 12 5
## 69 12 3
## 70 12 3
## 71 12 7
## 72 12 8
## 73 12 15
## 74 12 9
## 75 12 6
## 76 12 14
## 77 12 8
## 78 12 3
## 79 12 6
## 80 12 3
## 81 12 15
## 82 12 12
## 83 12 5
## 84 12 7
## 85 12 2
## 86 12 1
## 87 12 1
## 88 12 14
## 89 12 13
## 90 12 7
## 91 12 11
## 92 12 7
## 93 12 3
## 94 12 6
## 95 12 1
## 96 12 2
## 97 12 5
## 98 12 7
## 99 12 10
## 100 12 10
## 101 12 5
## 102 12 1
## 103 12 11
## 104 12 5
## 105 12 3
## 106 12 1
## 107 12 10
## 108 12 9
## 109 12 3
## 110 12 7
## 111 12 3
## 112 12 1
## 113 12 1
## 114 12 7
## 115 12 15
## 116 12 14
## 117 12 2
## 118 12 7
## 119 12 1
## 120 12 1
## 121 12 4
## 122 12 3
## 123 12 9
## 124 12 12
## 125 12 7
## 126 12 13
## 127 12 4
## 128 12 3
## 129 12 15
## 130 12 4
## 131 12 15
## 132 12 1
## 133 12 8
## 134 12 5
## 135 12 7
## 136 12 7
## 137 12 7
## 138 12 6
## 139 12 1
## 140 12 1
## 141 12 8
## 142 12 5
## 143 12 3
## 144 12 5
## 145 12 6
## 146 12 14
## 147 12 3
## 148 12 1
## 149 12 7
## 150 12 3
## 151 12 8
## 152 12 5
## 153 12 15
## 154 12 9
## 155 12 3
## 156 12 3
## 157 12 11
## 158 12 1
## 159 12 1
## 160 12 4
## 161 12 7
## 162 12 1
## 163 12 5
## 164 12 3
## 165 12 3
## 166 12 6
## 167 12 9
## 168 12 1
## 169 12 1
## 170 12 5
## 171 12 13
## 172 12 7
## 173 12 1
## 174 12 9
## 175 12 7
## 176 12 7
## 177 12 2
## 178 12 3
## 179 12 5
## 180 12 3
## 181 12 14
## 182 12 3
## 183 12 11
## 184 12 1
## 185 12 3
## 186 12 13
## 187 12 7
## 188 12 13
## 189 12 10
## 190 12 4
## 191 12 10
## 192 12 15
## 193 12 5
## 194 12 14
## 195 12 8
## 196 12 14
## 197 12 5
## 198 12 9
## 199 12 13
## 200 12 5
## 201 12 5
## 202 12 2
## 203 12 3
## 204 12 12
## 205 12 3
## 206 12 4
## 207 12 7
## 208 12 9
## 209 12 1
## 210 12 7
## 211 12 3
## 212 12 5
## 213 12 1
## 214 12 2
## 215 12 2
## 216 12 2
## 217 12 5
## 218 12 3
## 219 12 2
## 220 12 12
## 221 12 5
## 222 12 1
## 223 12 1
## 224 12 9
## 225 12 5
## 226 12 14
## 227 12 1
## 228 12 14
## 229 12 8
## 230 12 12
## 231 12 9
## 232 12 1
## 233 12 14
## 234 12 9
## 235 12 1
## 236 12 14
## 237 12 5
## 238 12 4
## 239 12 5
## 240 12 3
## 241 12 5
## 242 12 3
## 243 12 3
## 244 12 1
## 245 12 1
## 246 12 2
## 247 12 6
## 248 12 7
## 249 12 1
## 250 12 2
## 251 12 10
## 252 12 12
## 253 12 1
## 254 12 2
## 255 12 8
## 256 12 1
## 257 12 1
## 258 12 5
## 259 12 4
## 260 12 11
## 261 12 9
## 262 12 1
## 263 12 7
## 264 12 13
## 265 12 3
## 266 12 7
## 267 12 1
## 268 12 13
## 269 12 9
## 270 12 7
## 271 12 1
## 272 12 9
## 273 12 1
## 274 12 7
## 275 12 8
## 276 12 3
## 277 12 5
## 278 12 8
## 279 12 5
## 280 12 15
## 281 12 4
## 282 12 5
## 283 12 12
## 284 12 5
## 285 12 12
## 286 12 14
## 287 12 3
## 288 12 8
## 289 12 8
## 290 12 3
## 291 12 1
## 292 12 3
## 293 12 3
## 294 12 15
## 295 12 7
## 296 12 7
## 297 12 3
## 298 12 15
## 299 12 2
## 300 12 2
## 301 12 7
## 302 12 1
## 303 12 5
## 304 12 8
## 305 12 10
## 306 12 5
## 307 12 1
## 308 12 3
## 309 12 8
## 310 12 5
## 311 12 1
## 312 12 8
## 313 12 3
## 314 12 9
## 315 12 3
## 316 12 1
## 317 12 15
## 318 12 1
## 319 12 1
## 320 12 7
## 321 12 1
## 322 12 1
## 323 12 15
## 324 12 3
## 325 12 1
## 326 12 3
## 327 12 12
## 328 12 7
## 329 12 3
## 330 12 3
## 331 12 6
## 332 12 8
## 333 12 5
## 334 12 12
## 335 12 1
## 336 12 9
## 337 12 5
## 338 12 5
## 339 12 7
## 340 12 9
## 341 12 1
## 342 12 3
## 343 12 11
## 344 12 12
## 345 12 14
## 346 12 3
## 347 12 10
## 348 12 7
## 349 12 14
## 350 12 1
## 351 12 12
## 352 12 4
## 353 12 2
## 354 12 3
## 355 12 3
## 356 12 5
## 357 12 5
## 358 12 4
## 359 12 2
## 360 12 1
## 361 12 9
## 362 12 3
## 363 12 11
## 364 12 14
## 365 12 1
## 366 12 3
## 367 12 9
## 368 12 2
## 369 12 7
## 370 12 9
## 371 12 1
## 372 12 11
## 373 12 9
## 374 12 9
## 375 12 1
## 376 12 3
## 377 12 7
## 378 12 1
## 379 12 5
## 380 12 1
## 381 12 3
## 382 12 5
## 383 12 1
## 384 12 3
## 385 12 1
## 386 12 12
## 387 12 3
## 388 12 7
## 389 12 5
## 390 12 1
## 391 12 3
## 392 12 1
## 393 12 11
## 394 12 8
## 395 12 3
## 396 12 8
## 397 12 8
## 398 12 15
## 399 12 3
## 400 12 1
## 401 12 4
## 402 12 5
## 403 12 3
## 404 12 1
## 405 12 1
## 406 12 7
## 407 12 13
## 408 12 3
## 409 12 1
## 410 12 1
## 411 12 8
## 412 12 7
## 413 12 5
## 414 12 3
## 415 12 2
## 416 12 5
## 417 12 15
## 418 12 10
## 419 12 3
## 420 12 8
## 421 12 6
## 422 12 2
## 423 12 8
## 424 12 1
## 425 12 7
## 426 12 3
## 427 12 12
## 428 12 8
## 429 12 5
## 430 12 7
## 431 12 14
## 432 12 3
## 433 12 14
## 434 12 1
## 435 12 14
## 436 12 1
## 437 12 3
## 438 12 3
## 439 12 3
## 440 12 10
## 441 12 5
## 442 12 6
## 443 12 3
## 444 12 12
## 445 12 1
## 446 12 7
## 447 12 7
## 448 12 1
## 449 12 3
## 450 12 3
## 451 12 3
## 452 12 7
## 453 12 1
## 454 12 1
## 455 12 6
## 456 12 7
## 457 12 3
## 458 12 7
## 459 12 3
## 460 12 3
## 461 12 1
## 462 12 7
## 463 12 9
## 464 12 3
## 465 12 7
## 466 12 12
## 467 12 9
## 468 12 15
## 469 12 5
## 470 12 1
## 471 12 4
## 472 12 15
## 473 12 12
## 474 12 5
## 475 12 12
## 476 12 9
## 477 12 5
## 478 12 1
## 479 12 5
## 480 12 8
## 481 12 5
## 482 12 5
## 483 12 3
## 484 12 12
## 485 12 8
## 486 12 8
## 487 12 7
## 488 12 9
## 489 12 6
## 490 12 14
## 491 12 14
## 492 12 3
## 493 12 1
## 494 12 9
## 495 12 3
## 496 12 3
## 497 12 5
## 498 12 3
## 499 12 7
## 500 12 2
## 501 12 14
## 502 12 1
## 503 12 10
## 504 12 9
## 505 12 14
## 506 12 6
## 507 12 1
## 508 12 5
## 509 12 3
## 510 12 7
## 511 12 10
## 512 12 12
## 513 12 9
## 514 12 8
## 515 12 3
## 516 12 5
## 517 12 15
## 518 12 1
## 519 12 1
## 520 12 3
## 521 12 11
## 522 12 7
## 523 12 3
## 524 12 1
## 525 12 1
## 526 12 5
## 527 12 8
## 528 12 1
## 529 12 3
## 530 12 5
## 531 12 7
## 532 12 3
## 533 12 11
## 534 12 11
## 535 12 1
## 536 12 3
## 537 12 1
## 538 12 8
## 539 12 7
## 540 12 7
## 541 12 5
## 542 12 1
## 543 12 5
## 544 12 12
## 545 12 1
## 546 12 1
## 547 12 4
## 548 12 7
## 549 12 9
## 550 12 1
## 551 12 4
## 552 12 8
## 553 12 5
## 554 12 11
## 555 12 1
## 556 12 3
## 557 12 9
## 558 12 3
## 559 12 2
## 560 12 12
## 561 12 9
## 562 12 9
## 563 12 8
## 564 12 3
## 565 12 1
## 566 12 3
## 567 12 1
## 568 12 5
## 569 12 4
## 570 12 7
## 571 12 6
## 572 12 7
## 573 12 1
## 574 12 1
## 575 12 2
## 576 12 3
## 577 12 3
## 578 12 9
## 579 12 14
## 580 12 3
## 581 12 2
## 582 12 5
## 583 12 7
## 584 12 10
## 585 12 3
## 586 12 5
## 587 12 9
## 588 12 7
## 589 12 9
## 590 12 13
## 591 12 1
## 592 12 1
## 593 12 8
## 594 12 12
## 595 12 8
## 596 12 5
## 597 12 3
## 598 12 12
## 599 12 7
## 600 12 14
## 601 12 1
## 602 12 3
## 603 12 1
## 604 12 14
## 605 12 5
## 606 12 15
## 607 12 7
## 608 12 3
## 609 12 10
## 610 12 7
## 611 12 1
## 612 12 2
## 613 12 1
## 614 12 7
## 615 12 14
## 616 12 7
## 617 12 1
## 618 12 8
## 619 12 5
## 620 12 1
## 621 12 3
## 622 12 15
## 623 12 7
## 624 12 14
## 625 12 15
## 626 12 9
## 627 12 10
## 628 12 9
## 629 12 2
## 630 12 9
## 631 12 3
## 632 12 12
## 633 12 7
## 634 12 2
## 635 12 10
## 636 12 13
## 637 12 3
## 638 12 6
## 639 12 3
## 640 12 12
## 641 12 12
## 642 12 1
## 643 12 1
## 644 12 15
## 645 12 5
## 646 12 9
## 647 12 11
## 648 12 9
## 649 12 15
## 650 12 14
## 651 12 1
## 652 12 3
## 653 12 1
## 654 12 3
## 655 12 5
## 656 12 14
## 657 12 8
## 658 12 3
## 659 12 3
## 660 12 8
## 661 12 15
## 662 12 1
## 663 12 7
## 664 12 9
## 665 12 5
## 666 12 1
## 667 12 15
## 668 12 5
## 669 12 8
## 670 12 3
## 671 12 1
## 672 12 3
## 673 12 7
## 674 12 9
## 675 12 8
## 676 12 14
## 677 12 6
## 678 12 3
## 679 12 5
## 680 12 7
## 681 12 4
## 682 12 3
## 683 12 12
## 684 12 5
## 685 12 1
## 686 12 1
## 687 12 1
## 688 12 14
## 689 12 5
## 690 12 12
## 691 12 12
## 692 12 1
## 693 12 1
## 694 12 3
## 695 12 3
## 696 12 7
## 697 12 3
## 698 12 2
## 699 12 9
## 700 12 12
## 701 12 5
## 702 12 7
## 703 12 1
## 704 12 6
## 705 12 4
## 706 12 11
## 707 12 7
## 708 12 12
## 709 12 1
## 710 12 7
## 711 12 3
## 712 12 3
## 713 12 12
## 714 12 7
## 715 12 15
## 716 12 7
## 717 12 3
## 718 12 2
## 719 12 1
## 720 12 8
## 721 12 12
## 722 12 12
## 723 12 8
## 724 12 3
## 725 12 5
## 726 12 10
## 727 12 4
## 728 12 5
## 729 12 6
## 730 12 13
## 731 12 12
## 732 12 12
## 733 12 1
## 734 12 10
## 735 12 8
## 736 12 3
## 737 12 11
## 738 12 1
## 739 12 6
## 740 12 1
## 741 12 3
## 742 12 2
## 743 12 5
## 744 12 2
## 745 12 4
## 746 12 2
## 747 12 1
## 748 12 3
## 749 12 5
## 750 12 11
## 751 12 3
## 752 12 1
## 753 12 5
## 754 12 1
## 755 12 9
## 756 12 14
## 757 12 1
## 758 12 15
## 759 12 14
## 760 12 1
## 761 12 10
## 762 12 8
## 763 12 1
## 764 12 1
## 765 12 4
## 766 12 5
## 767 12 2
## 768 12 2
## 769 12 5
## 770 12 1
## 771 12 7
## 772 12 7
## 773 12 9
## 774 12 8
## 775 12 5
## 776 12 12
## 777 12 5
## 778 12 8
## 779 12 11
## 780 12 7
## 781 12 5
## 782 12 1
## 783 12 5
## 784 12 7
## 785 12 3
## 786 12 9
## 787 12 3
## 788 12 1
## 789 12 3
## 790 12 15
## 791 12 12
## 792 12 7
## 793 12 1
## 794 12 5
## 795 12 2
## 796 12 15
## 797 12 7
## 798 12 8
## 799 12 1
## 800 12 3
## 801 12 2
## 802 12 3
## 803 12 1
## 804 12 1
## 805 12 8
## 806 12 5
## 807 12 9
## 808 12 1
## 809 12 9
## 810 12 6
## 811 12 9
## 812 12 12
## 813 12 10
## 814 12 10
## 815 12 3
## 816 12 5
## 817 12 8
## 818 12 5
## 819 12 7
## 820 12 12
## 821 12 1
## 822 12 3
## 823 12 1
## 824 12 3
## 825 12 5
## 826 12 5
## 827 12 3
## 828 12 5
## 829 12 2
## 830 12 14
## 831 12 9
## 832 12 14
## 833 12 1
## 834 12 9
## 835 12 7
## 836 12 8
## 837 12 8
## 838 12 14
## 839 12 7
## 840 12 3
## 841 12 3
## 842 12 3
## 843 12 9
## 844 12 6
## 845 12 3
## 846 12 7
## 847 12 1
## 848 12 8
## 849 12 3
## 850 12 15
## 851 12 12
## 852 12 12
## 853 12 3
## 854 12 7
## 855 12 9
## 856 12 8
## 857 12 9
## 858 12 1
## 859 12 7
## 860 12 15
## 861 12 3
## 862 12 7
## 863 12 7
## 864 12 8
## 865 12 5
## 866 12 7
## 867 12 5
## 868 12 10
## 869 12 11
## 870 12 1
## 871 12 8
## 872 12 1
## 873 12 3
## 874 12 13
## 875 12 3
## 876 12 1
## 877 12 8
## 878 12 1
## 879 12 10
## 880 12 8
## 881 12 7
## 882 12 1
## 883 12 1
## 884 12 13
## 885 12 7
## 886 12 12
## 887 12 6
## 888 12 3
## 889 12 5
## 890 12 4
## 891 12 10
## 892 12 15
## 893 12 2
## 894 12 7
## 895 12 11
## 896 12 15
## 897 12 3
## 898 12 11
## 899 12 5
## 900 12 8
## 901 12 8
## 902 12 8
## 903 12 1
## 904 12 5
## 905 12 7
## 906 12 9
## 907 12 5
## 908 12 3
## 909 12 9
## 910 12 1
## 911 12 6
## 912 12 8
## 913 12 10
## 914 12 6
## 915 12 14
## 916 12 14
## 917 12 3
## 918 12 8
## 919 12 1
## 920 12 9
## 921 12 2
## 922 12 10
## 923 12 5
## 924 12 12
## 925 12 7
## 926 12 2
## 927 12 1
## 928 12 15
## 929 12 5
## 930 12 1
## 931 12 5
## 932 12 2
## 933 12 5
## 934 12 5
## 935 12 5
## 936 12 3
## 937 12 5
## 938 12 1
## 939 12 1
## 940 12 3
## 941 12 6
## 942 12 4
## 943 12 6
## 944 12 6
## 945 12 2
## 946 12 13
## 947 12 9
## 948 12 5
## 949 12 1
## 950 12 8
## 951 12 9
## 952 12 8
## 953 12 2
## 954 12 2
## 955 12 2
## 956 12 6
## 957 12 14
## 958 12 8
## 959 12 5
## 960 12 14
## 961 12 8
## 962 12 15
## 963 12 14
## 964 12 5
## 965 12 1
## 966 12 1
## 967 12 3
## 968 12 7
## 969 12 3
## 970 12 7
## 971 12 3
## 972 12 14
## 973 12 4
## 974 12 5
## 975 12 3
## 976 12 14
## 977 12 3
## 978 12 12
## 979 12 8
## 980 12 8
## 981 12 15
## 982 12 1
## 983 12 12
## 984 12 1
## 985 12 11
## 986 12 7
## 987 12 3
## 988 12 5
## 989 12 15
## 990 12 1
## 991 12 1
## 992 12 3
## 993 12 15
## 994 12 1
## 995 12 5
## 996 12 3
## 997 12 8
## 998 12 7
## 999 12 3
## 1000 12 5
## 1001 12 11
## 1002 12 3
## 1003 12 5
## 1004 12 6
## 1005 12 12
## 1006 12 7
## 1007 12 2
## 1008 12 14
## 1009 12 5
## 1010 12 13
## 1011 12 3
## 1012 12 11
## 1013 12 3
## 1014 12 9
## 1015 12 7
## 1016 12 1
## 1017 12 5
## 1018 12 12
## 1019 12 1
## 1020 12 12
## 1021 12 7
## 1022 12 5
## 1023 12 3
## 1024 12 12
## 1025 12 7
## 1026 12 9
## 1027 12 9
## 1028 12 5
## 1029 12 3
## 1030 12 14
## 1031 12 6
## 1032 12 13
## 1033 12 1
## 1034 12 5
## 1035 12 3
## 1036 12 11
## 1037 12 5
## 1038 12 15
## 1039 12 14
## 1040 12 14
## 1041 12 7
## 1042 12 2
## 1043 12 12
## 1044 12 1
## 1045 12 5
## 1046 12 11
## 1047 12 1
## 1048 12 2
## 1049 12 11
## 1050 12 11
## 1051 12 5
## 1052 12 5
## 1053 12 2
## 1054 12 7
## 1055 12 1
## 1056 12 2
## 1057 12 12
## 1058 12 1
## 1059 12 1
## 1060 12 15
## 1061 12 12
## 1062 12 10
## 1063 12 6
## 1064 12 3
## 1065 12 9
## 1066 12 1
## 1067 12 1
## 1068 12 15
## 1069 12 1
## 1070 12 2
## 1071 12 9
## 1072 12 3
## 1073 12 3
## 1074 12 15
## 1075 12 5
## 1076 12 3
## 1077 12 2
## 1078 12 9
## 1079 12 12
## 1080 12 7
## 1081 12 4
## 1082 12 8
## 1083 12 5
## 1084 12 10
## 1085 12 1
## 1086 12 7
## 1087 12 14
## 1088 12 1
## 1089 12 14
## 1090 12 12
## 1091 12 11
## 1092 12 14
## 1093 12 3
## 1094 12 8
## 1095 12 9
## 1096 12 5
## 1097 12 3
## 1098 12 12
## 1099 12 3
## 1100 12 7
## 1101 12 3
## 1102 12 9
## 1103 12 8
## 1104 12 2
## 1105 12 14
## 1106 12 1
## 1107 12 5
## 1108 12 1
## 1109 12 1
## 1110 12 5
## 1111 12 1
## 1112 12 15
## 1113 12 8
## 1114 12 8
## 1115 12 8
## 1116 12 5
## 1117 12 1
## 1118 12 8
## 1119 12 2
## 1120 12 8
## 1121 12 3
## 1122 12 5
## 1123 12 8
## 1124 12 2
## 1125 12 1
## 1126 12 8
## 1127 12 12
## 1128 12 5
## 1129 12 1
## 1130 12 7
## 1131 12 3
## 1132 12 7
## 1133 12 3
## 1134 12 1
## 1135 12 1
## 1136 12 3
## 1137 12 3
## 1138 12 3
## 1139 12 5
## 1140 12 3
## 1141 12 8
## 1142 12 15
## 1143 12 1
## 1144 12 8
## 1145 12 3
## 1146 12 3
## 1147 12 5
## 1148 12 3
## 1149 12 3
## 1150 12 3
## 1151 12 3
## 1152 12 9
## 1153 12 9
## 1154 12 3
## 1155 12 15
## 1156 12 9
## 1157 12 4
## 1158 12 12
## 1159 12 5
## 1160 12 15
## 1161 12 14
## 1162 12 4
## 1163 12 8
## 1164 12 10
## 1165 12 9
## 1166 12 5
## 1167 12 5
## 1168 12 1
## 1169 12 3
## 1170 12 1
## 1171 12 9
## 1172 12 10
## 1173 12 14
## 1174 12 13
## 1175 12 10
## 1176 12 1
## 1177 12 8
## 1178 12 3
## 1179 12 3
## 1180 12 11
## 1181 12 1
## 1182 12 4
## 1183 12 9
## 1184 12 8
## 1185 12 9
## 1186 12 3
## 1187 12 6
## 1188 12 5
## 1189 12 1
## 1190 12 1
## 1191 12 7
## 1192 12 14
## 1193 12 3
## 1194 12 12
## 1195 12 15
## 1196 12 1
## 1197 12 1
## 1198 12 3
## 1199 12 5
## 1200 12 7
## 1201 12 8
## 1202 12 1
## 1203 12 3
## 1204 12 5
## 1205 12 7
## 1206 12 7
## 1207 12 7
## 1208 12 5
## 1209 12 9
## 1210 12 5
## 1211 12 2
## 1212 12 1
## 1213 12 8
## 1214 12 2
## 1215 12 7
## 1216 12 9
## 1217 12 11
## 1218 12 5
## 1219 12 11
## 1220 12 14
## 1221 12 9
## 1222 12 9
## 1223 12 15
## 1224 12 7
## 1225 12 1
## 1226 12 2
## 1227 12 1
## 1228 12 8
## 1229 12 4
## 1230 12 3
## 1231 12 2
## 1232 12 7
## 1233 12 11
## 1234 12 7
## 1235 12 3
## 1236 12 3
## 1237 12 14
## 1238 12 1
## 1239 12 7
## 1240 12 5
## 1241 12 1
## 1242 12 5
## 1243 12 7
## 1244 12 5
## 1245 12 3
## 1246 12 15
## 1247 12 1
## 1248 12 7
## 1249 12 3
## 1250 12 9
## 1251 12 7
## 1252 12 12
## 1253 12 10
## 1254 12 15
## 1255 12 1
## 1256 12 3
## 1257 12 5
## 1258 12 3
## 1259 12 12
## 1260 12 9
## 1261 12 1
## 1262 12 8
## 1263 12 3
## 1264 12 3
## 1265 12 12
## 1266 12 14
## 1267 12 6
## 1268 12 5
## 1269 12 2
## 1270 12 3
## 1271 12 4
## 1272 12 7
## 1273 12 9
## 1274 12 9
## 1275 12 3
## 1276 12 6
## 1277 12 2
## 1278 12 7
## 1279 12 1
## 1280 12 3
## 1281 12 5
## 1282 12 9
## 1283 12 9
## 1284 12 10
## 1285 12 3
## 1286 12 3
## 1287 12 9
## 1288 12 2
## 1289 12 12
## 1290 12 1
## 1291 12 7
## 1292 12 14
## 1293 12 6
## 1294 12 15
## 1295 12 8
## 1296 12 7
## 1297 12 7
## 1298 12 12
## 1299 12 9
## 1300 12 9
## 1301 12 1
## 1302 12 3
## 1303 12 1
## 1304 12 5
## 1305 12 14
## 1306 12 5
## 1307 12 12
## 1308 12 7
## 1309 12 9
## 1310 12 5
## 1311 12 7
## 1312 12 5
## 1313 12 1
## 1314 12 1
## 1315 12 8
## 1316 12 15
## 1317 12 5
## 1318 12 12
## 1319 12 5
## 1320 12 8
## 1321 12 9
## 1322 12 11
## 1323 12 1
## 1324 12 3
## 1325 12 5
## 1326 12 3
## 1327 12 3
## 1328 12 2
## 1329 12 3
## 1330 12 1
## 1331 12 5
## 1332 12 7
## 1333 12 8
## 1334 12 3
## 1335 12 14
## 1336 12 2
## 1337 12 6
## 1338 12 3
## 1339 12 1
## 1340 12 8
## 1341 12 8
## 1342 12 7
## 1343 12 1
## 1344 12 3
## 1345 12 1
## 1346 12 3
## 1347 12 4
## 1348 12 5
## 1349 12 5
## 1350 12 13
## 1351 12 6
## 1352 12 15
## 1353 12 3
## 1354 12 1
## 1355 12 1
## 1356 12 15
## 1357 12 7
## 1358 12 8
## 1359 12 14
## 1360 12 5
## 1361 12 3
## 1362 12 5
## 1363 12 3
## 1364 12 1
## 1365 12 14
## 1366 12 1
## 1367 12 1
## 1368 12 14
## 1369 12 12
## 1370 12 5
## 1371 12 3
## 1372 12 9
## 1373 12 1
## 1374 12 5
## 1375 12 1
## 1376 12 5
## 1377 12 3
## 1378 12 3
## 1379 12 14
## 1380 12 1
## 1381 12 3
## 1382 12 7
## 1383 12 3
## 1384 12 3
## 1385 12 3
## 1386 12 3
## 1387 12 9
## 1388 12 3
## 1389 12 5
## 1390 12 3
## 1391 12 5
## 1392 12 6
## 1393 12 7
## 1394 12 6
## 1395 12 12
## 1396 12 1
## 1397 12 9
## 1398 12 3
## 1399 12 3
## 1400 12 3
## 1401 12 3
## 1402 12 1
## 1403 12 5
## 1404 12 5
## 1405 12 3
## 1406 12 12
## 1407 12 7
## 1408 12 8
## 1409 12 3
## 1410 12 1
## 1411 12 1
## 1412 12 3
## 1413 12 11
## 1414 12 5
## 1415 12 3
## 1416 12 12
## 1417 12 6
## 1418 12 1
## 1419 12 9
## 1420 12 7
## 1421 12 15
## 1422 12 4
## 1423 12 12
## 1424 12 9
## 1425 12 9
## 1426 12 7
## 1427 12 1
## 1428 12 3
## 1429 12 8
## 1430 12 7
## 1431 12 1
## 1432 12 4
## 1433 12 8
## 1434 12 1
## 1435 12 7
## 1436 12 8
## 1437 12 8
## 1438 12 4
## 1439 12 8
## 1440 12 15
## 1441 12 13
## 1442 12 12
## 1443 12 1
## 1444 12 3
## 1445 12 5
## 1446 12 9
## 1447 12 7
## 1448 12 1
## 1449 12 3
## 1450 12 14
## 1451 12 10
## 1452 12 5
## 1453 12 12
## 1454 12 7
## 1455 12 5
## 1456 12 1
## 1457 12 7
## 1458 12 2
## 1459 12 9
## 1460 12 9ggplot(tsne_df, aes(x=Y.1, y=Y.2, color=y)) +
geom_point() +
scale_color_gradientn(colours = heat.colors(10))
ggplot(data.frame(table(tsne_df$cluster)), aes(x=Var1, y=Freq)) +
geom_bar(stat = "identity") +
coord_flip()
ggplot(tsne_df, aes(x=Y.1, y=Y.2, color=cluster)) +
geom_point() +
scale_color_viridis() +
scale_fill_viridis(discrete = T) +
geom_point(data = data.frame(centroids), aes(x=X1, y=X2), color="black", fill="white", shape=21, size=8) +
geom_text(data = data.frame(centroids), aes(x=X1, y=X2, label=1:k), color="black")
fviz_silhouette(silhouette(cutree(hclust_avg, k = k), dist_mat))## cluster size ave.sil.width
## 1 1 241 0.32
## 2 2 71 0.48
## 3 3 240 0.19
## 4 4 39 0.50
## 5 5 175 0.52
## 6 6 42 0.83
## 7 7 152 0.17
## 8 8 104 0.52
## 9 9 105 0.32
## 10 10 35 0.57
## 11 11 37 0.85
## 12 12 77 0.60
## 13 13 21 0.90
## 14 14 66 0.72
## 15 15 55 0.56
Baselines
Sets up empty lists to store RMSE values for baseline models on the training and testing datasets.
Calculate performance metrics (MAE, MAPE, RMSE, MSE, R2) by converting predicted and actual values from transformed to original scale.
Focus on data structures and the metrics calculation function.
baselines_rmse <- list()
baselines_rmse_test <- list()
actual_metrics_test <- list()
metrics_fusion <- function(y_pred, y) {
y_pred_inv <- expm1(y_pred)
y_inv <- expm1(y)
a <- mae(y_pred_inv, y_inv)
b <- mape(y_pred_inv, y_inv)
c <- rmse(y_pred_inv, y_inv)
d <- mse(y_pred_inv, y_inv)
e <- R2(y_pred_inv, y_inv)
return(c("mae" = a, "mape" = b, "rmse" = c, "mse" = d, "r2" = e))
}Linear Regression
Evaluates the performance of a linear regression model for a regression problem.
Cross-validation used to train the model on the training and validation data.
Resulting model is applied to make predictions on the validation dataset.
Root mean squared error (RMSE) is calculated as a measure of prediction accuracy for the model on the validation data.
Trained linear regression model used to make predictions on the test dataset.
###3 RMSE is calculated by comparing the predicted values with the actual test values. #### Stores additional performance metrics (MAE, MAPE, RMSE, MSE, R2) for the linear regression model on the test data.
linreg_tc <- trainControl(method = "cv", number = 5)
linreg_cv <- caret::train(
SalePrice ~ .,
data = cbind(X_train_val, SalePrice = y_train_val),
trControl = linreg_tc,
method = "lm"
)## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient fit
## may be misleading
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient fit
## may be misleading
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient fit
## may be misleading
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient fit
## may be misleading
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient fit
## may be misleading# Validation predictions and metrics
score_val <- linreg_cv$results$RMSE
baselines_rmse$linear_regression <- score_val
# Test predictions and metrics
linreg <- lm(SalePrice ~ ., data = cbind(X_train_val, SalePrice = y_train_val))
y_pred_test <- predict(linreg, newdata = X_test)## Warning in predict.lm(linreg, newdata = X_test): prediction from a
## rank-deficient fit may be misleadingscore_test <- rmse(y_pred_test, y_test)
baselines_rmse_test$linear_regression <- score_test
actual_metrics_test$linear_regression <- metrics_fusion(y_pred_test, y_test)
# Display scores
score_val## [1] 0.1549031score_test## [1] 0.1218889Lasso Regression
Lasso Regression applied to a regression problem.
Model trained by using cross-validation and selects the optimal regularization parameter.
Root mean squared error (RMSE) calculated for the validation set.
For the test set, target variable predicted by using the trained model, calculates the RMSE
Actual performance metrics (MAE, MAPE, RMSE, MSE, R2) computed for the Lasso Regression model on the test set
Displays the validation RMSE and test RMSE.
lasso <- cv.glmnet(x = as.matrix(X_train_val), y = y_train_val, alpha = 1)
# Validation predictions and metrics
score_val <- mean(sqrt(lasso$cvm))
baselines_rmse$lasso <- score_val
# Test predictions and metrics
y_pred_test <- predict(lasso, newx = as.matrix(X_test))
score_test <- rmse(y_pred_test, y_test)
baselines_rmse_test$lasso <- score_test
actual_metrics_test$lasso <- metrics_fusion(y_pred_test, y_test)
# Display scores
score_val## [1] 0.1712791score_test## [1] 0.1148247Ridge Regression
Ridge Regression applied to a regression problem.
Model trained by using cross-validation and selects the optimal regularization parameter.
Root mean squared error (RMSE) calculated for the validation set.
For the test set, it predicts the target variable using the trained model, calculates the RMSE.
Actual performance metrics (MAE, MAPE, RMSE, MSE, R2) computed for the Ridge Regression model on the test set.
Validation RMSE and test RMSE dsiplayed.
ridge <- cv.glmnet(x = as.matrix(X_train_val), y = y_train_val, alpha = 0)
# Validation predictions and metrics
score_val <- mean(sqrt(ridge$cvm))
baselines_rmse$ridge <- score_val
# Test predictions and metrics
y_pred_test <- predict(ridge, newx = as.matrix(X_test))
score_test <- rmse(y_pred_test, y_test)
baselines_rmse_test$ridge <- score_test
actual_metrics_test$ridge <- metrics_fusion(y_pred_test, y_test)
# Display scores
score_val## [1] 0.2462979score_test## [1] 0.1265604Elastic Net
Elastic Net Regression applied to a regression problem.
Cross-validation peformed to select the optimal alpha parameter, which controls the balance between L1 (Lasso) and L2 (Ridge) regularization.
The root mean squared error (RMSE) calculated for the validation set.
For the test set, it predicts the target variable using the trained Elastic Net model with the selected alpha value, calculates the RMSE.
Actual performance metrics (MAE, MAPE, RMSE, MSE, R2) computed for the Elastic Net model on the test set.
Selected alpha, validation RMSE, and test RMSE.
results <- data.frame()
for (i in 0:20) {
elasticnet <- cv.glmnet(x = as.matrix(X_train_val), y = y_train_val, alpha = i/20)
row <- data.frame(alpha = i/20, rmse_val = mean(sqrt(elasticnet$cvm)))
results <- rbind(results, row)
}
best_alpha <- results$alpha[which.min(results$rmse_val)]
# Validation predictions and metrics
score_val <- min(results$rmse_val)
baselines_rmse$elasticnet <- score_val
# Test predictions and metrics
elasticnet <- cv.glmnet(x = as.matrix(X_train_val), y = y_train_val, alpha = best_alpha)
y_pred_test <- predict(elasticnet, newx = as.matrix(X_test))
score_test <- rmse(y_pred_test, y_test)
baselines_rmse_test$elasticnet <- score_test
actual_metrics_test$elasticnet <- metrics_fusion(y_pred_test, y_test)
# Display scores
best_alpha## [1] 1score_val## [1] 0.1711617score_test## [1] 0.1173051K-Nearest Neighbors Regression
K-Nearest Neighbors Regression applied to the data
Calculates the RMSE for validation and test sets, and stores the results.
Additional performance metrics computed and displays the RMSE values.
knn_tc <- trainControl(method = "cv", number = 5)
knn_cv <- caret::train(
SalePrice ~ .,
data = cbind(X_train_val, SalePrice = y_train_val),
trControl = knn_tc,
method = "knn"
)
# Validation predictions and metrics
score_val <- mean(knn_cv$results$RMSE)
baselines_rmse$knn <- score_val
# Test predictions and metrics
knn <- knnreg(x = X_train_val, y = y_train_val)
y_pred_test <- predict(knn, newdata = X_test)
score_test <- rmse(y_pred_test, y_test)
baselines_rmse_test$knn <- score_test
actual_metrics_test$knn <- metrics_fusion(y_pred_test, y_test)
# Display scores
score_val## [1] 0.1832919score_test## [1] 0.1792911Support Vector Regression
Support Vector Regression (SVR) applied to the data
Calculates the RMSE for validation and test sets, and stores the results.
Additional performance metrics computed and displays the RMSE values.
svr_tc <- trainControl(method = "cv", number = 5)
svr_cv <- caret::train(
SalePrice ~ .,
data = cbind(X_train_val, SalePrice = y_train_val),
trControl = svr_tc,
method = "svmLinear2"
)## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition2RRAe' and 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran'
## and 'RoofMatlMetal' and 'Exterior1stAsphShn' and 'Exterior1stCBlock' and
## 'Exterior1stImStucc' and 'Exterior1stStone' and 'ExterCondPo' and 'BsmtCondPo'
## and 'HeatingFloor' and 'HeatingQCPo' and 'ElectricalMix' and 'GarageQualPo'
## constant. Cannot scale data.
## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition2RRAe' and 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran'
## and 'RoofMatlMetal' and 'Exterior1stAsphShn' and 'Exterior1stCBlock' and
## 'Exterior1stImStucc' and 'Exterior1stStone' and 'ExterCondPo' and 'BsmtCondPo'
## and 'HeatingFloor' and 'HeatingQCPo' and 'ElectricalMix' and 'GarageQualPo'
## constant. Cannot scale data.
## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition2RRAe' and 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran'
## and 'RoofMatlMetal' and 'Exterior1stAsphShn' and 'Exterior1stCBlock' and
## 'Exterior1stImStucc' and 'Exterior1stStone' and 'ExterCondPo' and 'BsmtCondPo'
## and 'HeatingFloor' and 'HeatingQCPo' and 'ElectricalMix' and 'GarageQualPo'
## constant. Cannot scale data.## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition1RRNe' and 'Condition2PosA' and 'Condition2RRAe' and 'Condition2RRAn'
## and 'RoofStyleShed' and 'RoofMatlClyTile' and 'RoofMatlMembran' and
## 'RoofMatlMetal' and 'Exterior1stBrkComm' and 'Exterior1stCBlock' and
## 'Exterior1stStone' and 'ExterCondPo' and 'HeatingFloor' and 'HeatingQCPo' and
## 'ElectricalMix' constant. Cannot scale data.
## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition1RRNe' and 'Condition2PosA' and 'Condition2RRAe' and 'Condition2RRAn'
## and 'RoofStyleShed' and 'RoofMatlClyTile' and 'RoofMatlMembran' and
## 'RoofMatlMetal' and 'Exterior1stBrkComm' and 'Exterior1stCBlock' and
## 'Exterior1stStone' and 'ExterCondPo' and 'HeatingFloor' and 'HeatingQCPo' and
## 'ElectricalMix' constant. Cannot scale data.
## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition1RRNe' and 'Condition2PosA' and 'Condition2RRAe' and 'Condition2RRAn'
## and 'RoofStyleShed' and 'RoofMatlClyTile' and 'RoofMatlMembran' and
## 'RoofMatlMetal' and 'Exterior1stBrkComm' and 'Exterior1stCBlock' and
## 'Exterior1stStone' and 'ExterCondPo' and 'HeatingFloor' and 'HeatingQCPo' and
## 'ElectricalMix' constant. Cannot scale data.## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'NeighborhoodBlueste' and 'Condition2RRAe' and 'Condition2RRAn' and
## 'RoofStyleShed' and 'RoofMatlMembran' and 'RoofMatlMetal' and
## 'Exterior1stCBlock' and 'Exterior1stStone' and 'ExterCondPo' and 'HeatingFloor'
## and 'HeatingQCPo' and 'ElectricalMix' constant. Cannot scale data.
## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'NeighborhoodBlueste' and 'Condition2RRAe' and 'Condition2RRAn' and
## 'RoofStyleShed' and 'RoofMatlMembran' and 'RoofMatlMetal' and
## 'Exterior1stCBlock' and 'Exterior1stStone' and 'ExterCondPo' and 'HeatingFloor'
## and 'HeatingQCPo' and 'ElectricalMix' constant. Cannot scale data.
## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'NeighborhoodBlueste' and 'Condition2RRAe' and 'Condition2RRAn' and
## 'RoofStyleShed' and 'RoofMatlMembran' and 'RoofMatlMetal' and
## 'Exterior1stCBlock' and 'Exterior1stStone' and 'ExterCondPo' and 'HeatingFloor'
## and 'HeatingQCPo' and 'ElectricalMix' constant. Cannot scale data.## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition2RRAe' and 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran'
## and 'RoofMatlMetal' and 'RoofMatlWdShake' and 'Exterior1stCBlock' and
## 'Exterior1stStone' and 'ExterCondPo' and 'FoundationWood' and 'HeatingFloor'
## and 'HeatingQCPo' and 'ElectricalMix' and 'FunctionalSev' and
## 'SaleConditionAdjLand' constant. Cannot scale data.
## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition2RRAe' and 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran'
## and 'RoofMatlMetal' and 'RoofMatlWdShake' and 'Exterior1stCBlock' and
## 'Exterior1stStone' and 'ExterCondPo' and 'FoundationWood' and 'HeatingFloor'
## and 'HeatingQCPo' and 'ElectricalMix' and 'FunctionalSev' and
## 'SaleConditionAdjLand' constant. Cannot scale data.
## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition2RRAe' and 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran'
## and 'RoofMatlMetal' and 'RoofMatlWdShake' and 'Exterior1stCBlock' and
## 'Exterior1stStone' and 'ExterCondPo' and 'FoundationWood' and 'HeatingFloor'
## and 'HeatingQCPo' and 'ElectricalMix' and 'FunctionalSev' and
## 'SaleConditionAdjLand' constant. Cannot scale data.## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition2RRAe' and 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran'
## and 'RoofMatlMetal' and 'Exterior1stCBlock' and 'Exterior1stStone' and
## 'ExterCondPo' and 'HeatingFloor' and 'HeatingQCPo' and 'ElectricalMix' and
## 'GarageCondEx' constant. Cannot scale data.
## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition2RRAe' and 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran'
## and 'RoofMatlMetal' and 'Exterior1stCBlock' and 'Exterior1stStone' and
## 'ExterCondPo' and 'HeatingFloor' and 'HeatingQCPo' and 'ElectricalMix' and
## 'GarageCondEx' constant. Cannot scale data.
## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition2RRAe' and 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran'
## and 'RoofMatlMetal' and 'Exterior1stCBlock' and 'Exterior1stStone' and
## 'ExterCondPo' and 'HeatingFloor' and 'HeatingQCPo' and 'ElectricalMix' and
## 'GarageCondEx' constant. Cannot scale data.## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear", cost =
## param$cost, : Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and
## 'Condition2RRAe' and 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran'
## and 'RoofMatlMetal' and 'Exterior1stCBlock' and 'Exterior1stStone' and
## 'ExterCondPo' and 'HeatingFloor' and 'HeatingQCPo' and 'ElectricalMix'
## constant. Cannot scale data.# Validation predictions and metrics
score_val <- mean(svr_cv$results$RMSE)
baselines_rmse$svr <- score_val
# Test predictions and metrics
svr <- e1071::svm(SalePrice ~ ., data = cbind(X_train_val, SalePrice = y_train_val))## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'UtilitiesAllPub' and 'UtilitiesNoSeWa' and 'Condition2RRAe' and
## 'Condition2RRAn' and 'RoofStyleShed' and 'RoofMatlMembran' and 'RoofMatlMetal'
## and 'Exterior1stCBlock' and 'Exterior1stStone' and 'ExterCondPo' and
## 'HeatingFloor' and 'HeatingQCPo' and 'ElectricalMix' constant. Cannot scale
## data.y_pred_test <- predict(svr, newdata = X_test)
score_test <- rmse(y_pred_test, y_test)
baselines_rmse_test$svr <- score_test
actual_metrics_test$svr <- metrics_fusion(y_pred_test, y_test)
# Display scores
score_val## [1] 0.146061score_test## [1] 0.1069807Decision Tree
Decision Tree regression applied to the data.
Calculates the RMSE for validation and test sets, and stores the results.
Additional performance metrics computed and displays the RMSE values.
dt_tc <- trainControl(method = "cv", number = 5)
dt_cv <- caret::train(
SalePrice ~ .,
data = cbind(X_train_val, SalePrice = y_train_val),
trControl = dt_tc,
method = "rpart"
)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.# Validation predictions and metrics
score_val <- mean(dt_cv$results$RMSE, na.rm = TRUE)
baselines_rmse$decision_tree <- score_val
# Test predictions and metrics
dt <- caret::train(x = X_train_val, y = y_train_val, method = "rpart")## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.y_pred_test <- predict(dt, newdata = X_test)
score_test <- rmse(y_pred_test, y_test)
baselines_rmse_test$decision_tree <- score_test
actual_metrics_test$decision_tree <- metrics_fusion(y_pred_test, y_test)
# Display scores
score_val## [1] 0.3029599score_test## [1] 0.2648677Ensemble Learning
Empty lists initialized that will be used to store the RMSE values and actual performance metrics for ensemble learning models.
Empty lists will be populated with values in subsequent steps of the code.
ensemble_rmse <- list()
ensemble_actual_metrics <- list()
ensemble_rmse_test <- list()
ensemble_actual_metrics_test <- list()Random Forest
Random forest algorithm implemented for regression tasks using ensemble learning.
Rrandom forest model trained on the training and validation data.
Its performance is evaluated using root mean squared error (RMSE) on the validation and test datasets.
Calculate feature importance using the random forest model and creates a bar plot to visualize the top 30 important features.
rf_tc <- trainControl(method = "cv", number = 5)
rf_cv <- caret::train(
SalePrice ~ .,
data = cbind(X_train_val, SalePrice = y_train_val),
trControl = rf_tc,
method = "rf"
)
# Validation predictions and metrics
score_val <- mean(rf_cv$results$RMSE)
ensemble_rmse$random_forest <- score_val
# Test predictions and metrics
rf <- randomForest(x = X_train_val, y = y_train_val, proximity = T)
y_pred_test_rf <- predict(rf, newdata = X_test)
score_test <- rmse(y_pred_test_rf, y_test)
ensemble_rmse_test$random_forest <- score_test
ensemble_actual_metrics_test$random_forest <- metrics_fusion(y_pred_test_rf, y_test)
y_pred_train_rf <- predict(rf, newdata = X_train_val)
# Display scores
score_val## [1] 0.167545score_test## [1] 0.1239416rf_df <- data.frame(rf$importance) %>%
mutate(Feature = rownames(rf$importance)) %>%
arrange(desc(IncNodePurity)) %>%
head(30)
rf_df## IncNodePurity Feature
## OverallQual 39.3317144 OverallQual
## GrLivArea 23.3829345 GrLivArea
## YearBuilt 18.0851631 YearBuilt
## GarageArea 7.7513679 GarageArea
## TotalBsmtSF 5.2122830 TotalBsmtSF
## FullBath 5.1360600 FullBath
## X1stFlrSF 5.1002968 X1stFlrSF
## YearRemodAdd 4.5115344 YearRemodAdd
## ExterQualTA 3.0327954 ExterQualTA
## GarageFinishUnf 2.8712788 GarageFinishUnf
## LotArea 2.7641380 LotArea
## X2ndFlrSF 2.6220777 X2ndFlrSF
## CentralAirN 2.3953955 CentralAirN
## KitchenQualTA 2.3324030 KitchenQualTA
## BsmtFinSF1 2.2961851 BsmtFinSF1
## CentralAirY 2.2914090 CentralAirY
## Fireplaces 2.2350229 Fireplaces
## LotFrontage 1.9015351 LotFrontage
## BsmtQualTA 1.5372494 BsmtQualTA
## FoundationPConc 1.2024432 FoundationPConc
## BsmtQualEx 1.1726103 BsmtQualEx
## KitchenQualEx 0.9373911 KitchenQualEx
## OverallCond 0.9236530 OverallCond
## BsmtUnfSF 0.9024310 BsmtUnfSF
## OpenPorchSF 0.8647029 OpenPorchSF
## BsmtQualGd 0.7743535 BsmtQualGd
## MasVnrArea 0.6959345 MasVnrArea
## BedroomAbvGr 0.6883436 BedroomAbvGr
## WoodDeckSF 0.6112699 WoodDeckSF
## KitchenQualGd 0.5909569 KitchenQualGdggplot(data = rf_df, aes(x = reorder(Feature, IncNodePurity), y = IncNodePurity)) +
geom_bar(stat = "identity") +
coord_flip()
Gradient-Boosted Trees
Gradient-Boosted Trees (GBT) using XGBoost, LightGBM, and CatBoost libraries implemented for regression tasks.
The models are trained on the training and validation data, and their performance is evaluated using RMSE on the validation and test datasets.
The best hyperparameters are determined through cross-validation, and the models are then used to make predictions on the test data.
The RMSE scores and other evaluation metrics are recorded for each model.
The feature importance of the GBT model is visualized using a bar chart.
Code snippet showcases the application of ensemble learning techniques for regression tasks using boosting algorithms.
dtrain_val <- xgb.DMatrix(data = as.matrix(X_train_val), label = y_train_val)
dtest <- xgb.DMatrix(data = as.matrix(X_test), label = y_test)
xgb_params = list(
eta = 0.01,
gamma = 0.0468,
max_depth = 6,
min_child_weight = 1.41,
subsample = 0.769,
colsample_bytree = 0.283
)
xgb_cv <- xgb.cv(
params = xgb_params,
data = dtrain_val,
nround = 10000,
nfold = 5,
prediction = F,
showsd = T,
metrics = "rmse",
verbose = 1,
print_every_n = 500,
early_stopping_rounds = 25
)## [1] train-rmse:11.415094+0.004943 test-rmse:11.415006+0.019759
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 25 rounds.
##
## [501] train-rmse:0.123204+0.000852 test-rmse:0.162023+0.009912
## [1001] train-rmse:0.067810+0.000528 test-rmse:0.129432+0.014106
## [1501] train-rmse:0.064470+0.000336 test-rmse:0.127999+0.013950
## [2001] train-rmse:0.062999+0.000382 test-rmse:0.127396+0.013698
## [2501] train-rmse:0.061982+0.000391 test-rmse:0.126990+0.013605
## [3001] train-rmse:0.061281+0.000436 test-rmse:0.126734+0.013585
## Stopping. Best iteration:
## [3301] train-rmse:0.060918+0.000425 test-rmse:0.126586+0.013508# Validation predictions and metrics
score_val <- xgb_cv$evaluation_log$test_rmse_mean %>% min
ensemble_rmse$xgboost <- score_val
# Test predictions and metrics
xgb <- xgboost(
params = xgb_params,
data = dtrain_val,
nround = 10000,
eval_metric = "rmse",
verbose = 1,
print_every_n = 500,
early_stopping_rounds = 25
)## [1] train-rmse:11.415130
## Will train until train_rmse hasn't improved in 25 rounds.
##
## [501] train-rmse:0.122812
## [1001] train-rmse:0.067480
## [1501] train-rmse:0.063925
## [2001] train-rmse:0.062216
## Stopping. Best iteration:
## [2208] train-rmse:0.061725y_pred_test_xgb <- predict(xgb, newdata = dtest)
score_test <- rmse(y_pred_test_xgb, y_test)
ensemble_rmse_test$xgboost_test <- score_test
ensemble_actual_metrics_test$xgboost_test <- metrics_fusion(y_pred_test_xgb, y_test)
y_pred_train_xgb <- predict(xgb, newdata = dtrain_val)
# Display scores
score_val## [1] 0.1265857score_test## [1] 0.1099328xgb_df <- xgb.importance(model = xgb) %>% head(30)
xgb_df## Feature Gain Cover Frequency
## 1: OverallQual 0.223583835 0.058366696 0.043129861
## 2: GrLivArea 0.192673044 0.091216449 0.073650719
## 3: YearBuilt 0.061567751 0.033700748 0.030638699
## 4: TotalBsmtSF 0.057968033 0.038530505 0.045133160
## 5: GarageArea 0.053090442 0.036053993 0.038298374
## 6: X1stFlrSF 0.036595006 0.028218689 0.041715767
## 7: Fireplaces 0.031446071 0.012856771 0.009309451
## 8: YearRemodAdd 0.027022009 0.023839746 0.023686071
## 9: GarageFinishUnf 0.023343008 0.003431918 0.002238982
## 10: LotArea 0.022658833 0.049547352 0.051850106
## 11: BsmtFinSF1 0.021328304 0.038846878 0.034291775
## 12: FullBath 0.018354795 0.014619173 0.008248881
## 13: CentralAirN 0.016944106 0.008585731 0.007659675
## 14: OverallCond 0.014003635 0.055172582 0.042069291
## 15: X2ndFlrSF 0.012836399 0.026386796 0.023450389
## 16: BsmtQualEx 0.011919525 0.004003206 0.003063870
## 17: LotFrontage 0.007554254 0.018685584 0.020740042
## 18: CentralAirY 0.007187775 0.005697863 0.004949328
## 19: KitchenQualTA 0.006882377 0.003711625 0.005774216
## 20: WoodDeckSF 0.006688944 0.020476970 0.018147537
## 21: BsmtExposureNo 0.005776640 0.012996450 0.009073769
## 22: BsmtUnfSF 0.005677273 0.023804477 0.030049493
## 23: BsmtQualTA 0.005616715 0.001527392 0.001178412
## 24: MSZoningC..all. 0.005542325 0.010853073 0.010487862
## 25: KitchenQualEx 0.005470795 0.003679848 0.002710346
## 26: BsmtFullBath 0.004639231 0.014215850 0.010252180
## 27: SaleConditionFamily 0.004021150 0.008337800 0.010134339
## 28: OpenPorchSF 0.003669641 0.014007728 0.017440490
## 29: ExterQualTA 0.003612649 0.001287493 0.001767617
## 30: BsmtFinType1Unf 0.003359587 0.004583573 0.005656375
## Feature Gain Cover Frequencyggplot(data = xgb_df, aes(x = reorder(Feature, Gain), y = Gain)) +
geom_bar(stat = "identity") +
coord_flip()
objective_fn <- makeSingleObjectiveFunction(
fn = function(x) {
params = list(
booster = "gbtree",
eta = x["eta"],
gamma = x["gamma"],
max_depth = x["max_depth"],
min_child_weight = x["min_child_weight"],
subsample = x["subsample"],
colsample_bytree = x["colsample_bytree"],
max_delta_step = x["max_delta_step"]
)
cv <- xgb.cv(
params = params,
data = dtrain_val,
nround = 10000,
nfold = 5,
prediction = F,
showsd = T,
metrics = "rmse",
verbose = 1,
print_every_n = 500,
early_stopping_rounds = 25
)
cv$evaluation_log$test_rmse_mean %>% min
},
par.set = makeParamSet(
makeNumericParam("eta", lower = 0.005, upper = 0.01),
makeNumericParam("gamma", lower = 0.01, upper = 5),
makeIntegerParam("max_depth", lower = 2, upper = 10),
makeIntegerParam("min_child_weight", lower = 1, upper = 2000),
makeNumericParam("subsample", lower = 0.20, upper = 0.8),
makeNumericParam("colsample_bytree", lower = 0.20, upper = 0.8),
makeNumericParam("max_delta_step", lower = 0, upper = 5)
),
minimize = TRUE
)
#Train model
design <- generateDesign(n = 1000, par.set = getParamSet(objective_fn), fun = lhs::randomLHS)
control <- makeMBOControl() %>% setMBOControlTermination(., iters = 10)
#run <- mbo(
# fun = objective_fn,
# design = design,
# learner = makeLearner("regr.km", predict.type = "se", covtype = "matern3_2", control = list(trace = FALSE)),
# control = control,
# show.info = TRUE
#)# Best parameters
#run$xlgb_train_val <- lgb.Dataset(data = as.matrix(X_train_val), label = y_train_val)
lgb_test <- lgb.Dataset(data = as.matrix(X_test), label = y_test)
params <- list(
objective = "regression",
metric = "rmse",
boosting_type = "gbdt",
num_boost_round = 100,
num_leaves = 15,
learning_rate = 0.1,
feature_fraction = 0.9,
bagging_fraction = 0.8,
bagging_freq = 5
)
lgb_cv <- lgb.cv(
params = params,
data = lgb_train_val,
early_stopping_rounds = 25,
verbose = 0
)## [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000693 seconds.
## You can set `force_row_wise=true` to remove the overhead.
## And if memory is not enough, you can set `force_col_wise=true`.
## [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000526 seconds.
## You can set `force_row_wise=true` to remove the overhead.
## And if memory is not enough, you can set `force_col_wise=true`.
## [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000568 seconds.
## You can set `force_row_wise=true` to remove the overhead.
## And if memory is not enough, you can set `force_col_wise=true`.
## [LightGBM] [Info] Start training from score 12.014045
## [LightGBM] [Info] Start training from score 12.014641
## [LightGBM] [Info] Start training from score 12.040380# Validation predictions and metrics
score_val <- min(unlist(lgb_cv$record_evals$valid$rmse$eval))
ensemble_rmse$lightgbm <- score_val
# Test predictions and metrics
lgb <- lgb.train(
params = params,
data = lgb_train_val,
verbose = 0
)## [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000722 seconds.
## You can set `force_row_wise=true` to remove the overhead.
## And if memory is not enough, you can set `force_col_wise=true`.y_pred_test_lgb <- predict(lgb, data = as.matrix(X_test))
score_test <- rmse(y_pred_test_lgb, y_test)
ensemble_rmse_test$lightgbm <- score_test
ensemble_actual_metrics_test$lightgbm <- metrics_fusion(y_pred_test_lgb, y_test)
y_pred_train_lgb <- predict(lgb, data = as.matrix(X_train_val))
# Display scores
score_val## [1] 0.1317233score_test## [1] 0.1145342lgb_df <- lgb.importance(model = lgb) %>% head(30)
lgb_df## Feature Gain Cover Frequency
## 1: OverallQual 0.437537456 0.055313343 0.038571429
## 2: GrLivArea 0.168184420 0.081907389 0.087142857
## 3: YearBuilt 0.054298849 0.049860419 0.055000000
## 4: GarageArea 0.048860844 0.062384908 0.067142857
## 5: KitchenQualTA 0.026934104 0.003939391 0.007142857
## 6: CentralAirN 0.025805810 0.018320062 0.012857143
## 7: TotalBsmtSF 0.024949278 0.046557973 0.055000000
## 8: X1stFlrSF 0.024066406 0.049152001 0.053571429
## 9: YearRemodAdd 0.022061462 0.020411681 0.026428571
## 10: LotArea 0.017418772 0.064524877 0.068571429
## 11: BsmtFinSF1 0.014189495 0.023373582 0.030000000
## 12: OverallCond 0.011982742 0.035288454 0.033571429
## 13: GarageFinishUnf 0.009883550 0.002123151 0.002857143
## 14: BsmtQualEx 0.007786983 0.001076290 0.002142857
## 15: Fireplaces 0.007671615 0.007124118 0.007857143
## 16: FullBath 0.007012259 0.012698967 0.007142857
## 17: BsmtFullBath 0.006989644 0.012415179 0.010714286
## 18: OpenPorchSF 0.005697021 0.060564464 0.047142857
## 19: WoodDeckSF 0.004939344 0.018595441 0.021428571
## 20: LotFrontage 0.004691219 0.025301235 0.027142857
## 21: X2ndFlrSF 0.004547048 0.019526684 0.015000000
## 22: BsmtUnfSF 0.004206325 0.018750999 0.030000000
## 23: BsmtExposureNo 0.004176088 0.009364989 0.008571429
## 24: MasVnrArea 0.002984632 0.017073499 0.016428571
## 25: PavedDriveN 0.002671038 0.001744768 0.001428571
## 26: PavedDriveY 0.002601930 0.006251734 0.005000000
## 27: BsmtFinType1Unf 0.002592272 0.001444163 0.004285714
## 28: MSZoningRM 0.002434767 0.002287117 0.003571429
## 29: LandContourLvl 0.002431043 0.001774198 0.003571429
## 30: SaleConditionAbnorml 0.001954281 0.016508026 0.009285714
## Feature Gain Cover Frequencyggplot(data = xgb_df, aes(x = reorder(Feature, Gain), y = Gain)) +
geom_bar(stat = "identity") +
coord_flip()
train_val_pool <- catboost.load_pool(data = X_train_val, label = y_train_val)
test_pool <- catboost.load_pool(data = X_test, label = y_test)
params <- list(
loss_function = "RMSE",
iterations = 10000,
learning_rate = 0.01,
metric_period = 1000
)
catb_cv <- catboost.cv(
train_val_pool,
params = params,
fold_count = 5,
early_stopping_rounds = 25
)## Warning: Overfitting detector is active, thus evaluation metric is calculated on every iteration. 'metric_period' is ignored for evaluation metric.
## 0: learn: 11.9131669 test: 11.9135090 best: 11.9135090 (0) total: 109ms remaining: 18m 7s
## 1000: learn: 0.1300238 test: 0.2351916 best: 0.2351916 (1000) total: 34.5s remaining: 5m 10s
## 2000: learn: 0.0718702 test: 0.2058939 best: 0.2058939 (2000) total: 1m 9s remaining: 4m 39s
## 3000: learn: 0.0477858 test: 0.1985011 best: 0.1985011 (3000) total: 1m 43s remaining: 4m 2s
## 4000: learn: 0.0333253 test: 0.1953407 best: 0.1953407 (4000) total: 2m 18s remaining: 3m 27s
## 5000: learn: 0.0237993 test: 0.1939864 best: 0.1939864 (5000) total: 2m 52s remaining: 2m 52s
## 6000: learn: 0.0174452 test: 0.1934132 best: 0.1934092 (5976) total: 3m 27s remaining: 2m 18s
## Stopped by overfitting detector (25 iterations wait)# Validation predictions and metrics
score_val <- min(catb_cv$test.RMSE.mean)
ensemble_rmse$catboost <- score_val
# Test predictions and metrics
catb <- catboost.train(
params = params,
learn_pool = train_val_pool
)## 0: learn: 0.4014370 total: 4.85ms remaining: 48.5s
## 1000: learn: 0.0823546 total: 4.6s remaining: 41.4s
## 2000: learn: 0.0523925 total: 8.94s remaining: 35.7s
## 3000: learn: 0.0371214 total: 12.8s remaining: 29.9s
## 4000: learn: 0.0268016 total: 16.3s remaining: 24.5s
## 5000: learn: 0.0197163 total: 19.9s remaining: 19.9s
## 6000: learn: 0.0148433 total: 23.4s remaining: 15.6s
## 7000: learn: 0.0112215 total: 27s remaining: 11.5s
## 8000: learn: 0.0086462 total: 30.5s remaining: 7.62s
## 9000: learn: 0.0068226 total: 34s remaining: 3.78s
## 9999: learn: 0.0054538 total: 37.5s remaining: 0usy_pred_test_catboost <- catboost.predict(catb, test_pool)
score_test <- rmse(y_pred_test_catboost, y_test)
ensemble_rmse_test$catboost <- score_test
ensemble_actual_metrics_test$catboost <- metrics_fusion(y_pred_test_catboost, y_test)
y_pred_train_catboost <- catboost.predict(catb, train_val_pool)
# Display scores
score_val## [1] 0.1934132score_test## [1] 0.1075237catb_df <- data.frame(catboost.get_feature_importance(catb))
catb_df <- catb_df %>%
mutate(Feature = rownames(catb_df)) %>%
rename(Importance = catboost.get_feature_importance.catb.) %>%
arrange(desc(Importance)) %>%
head(30)
catb_df## Importance Feature
## OverallQual 20.8412958 OverallQual
## GrLivArea 13.7476338 GrLivArea
## YearBuilt 5.8871821 YearBuilt
## TotalBsmtSF 4.1368143 TotalBsmtSF
## GarageArea 4.0947282 GarageArea
## X1stFlrSF 3.6570384 X1stFlrSF
## Fireplaces 2.8957613 Fireplaces
## YearRemodAdd 2.8781681 YearRemodAdd
## LotArea 2.8516231 LotArea
## BsmtFinSF1 2.2897609 BsmtFinSF1
## OverallCond 2.0943099 OverallCond
## CentralAirY 1.8612928 CentralAirY
## FullBath 1.4684400 FullBath
## X2ndFlrSF 1.4440066 X2ndFlrSF
## CentralAirN 1.3363753 CentralAirN
## LotFrontage 1.2350676 LotFrontage
## BsmtFullBath 1.2289655 BsmtFullBath
## GarageFinishUnf 1.0894208 GarageFinishUnf
## BsmtQualGd 1.0699817 BsmtQualGd
## KitchenQualTA 0.8872915 KitchenQualTA
## WoodDeckSF 0.8509923 WoodDeckSF
## OpenPorchSF 0.8400538 OpenPorchSF
## BsmtExposureNo 0.8054760 BsmtExposureNo
## BsmtUnfSF 0.8007778 BsmtUnfSF
## KitchenQualGd 0.6979207 KitchenQualGd
## BedroomAbvGr 0.6597603 BedroomAbvGr
## BsmtFinType1Unf 0.6596745 BsmtFinType1Unf
## BsmtQualEx 0.6432281 BsmtQualEx
## HalfBath 0.6094491 HalfBath
## BsmtQualTA 0.5995891 BsmtQualTAggplot(data = catb_df, aes(x = reorder(Feature, Importance), y = Importance)) +
geom_bar(stat = "identity") +
coord_flip()
Association Rule Mining on Housing Characteristics
Code snippet performs association rule mining on housing characteristics using the Apriori algorithm.
Converts categorical data into transactions and applies the Apriori algorithm to find frequent itemsets.
Generate association rules using given support and confidence thresholds.
The code includes a dictionary to map column names and values to their corresponding descriptions for better rule interpretation.
Top ten association rules with their corresponding explanations displayed, including the antecedent (IF) and consequent (THEN) parts along with their descriptions and confidence percentages.
transactions <- transactions(categorical_data)## Warning: Column(s) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
## 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37
## not logical or factor. Applying default discretization (see '? discretizeDF').summary(transactions)## transactions as itemMatrix in sparse format with
## 1460 rows (elements/itemsets/transactions) and
## 218 columns (items) and a density of 0.1697248
##
## most frequent items:
## Utilities=AllPub Street=Pave Condition2=Norm RoofMatl=CompShg
## 1459 1454 1445 1434
## Heating=GasA (Other)
## 1428 46800
##
## element (itemset/transaction) length distribution:
## sizes
## 37
## 1460
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 37 37 37 37 37 37
##
## includes extended item information - examples:
## labels variables levels
## 1 MSZoning=C (all) MSZoning C (all)
## 2 MSZoning=FV MSZoning FV
## 3 MSZoning=RH MSZoning RH
##
## includes extended transaction information - examples:
## transactionID
## 1 1
## 2 2
## 3 3inspect(head(transactions, n = 1))## items transactionID
## [1] {MSZoning=RL,
## Street=Pave,
## LotShape=Reg,
## LandContour=Lvl,
## Utilities=AllPub,
## LotConfig=Inside,
## LandSlope=Gtl,
## Neighborhood=CollgCr,
## Condition1=Norm,
## Condition2=Norm,
## BldgType=1Fam,
## HouseStyle=2Story,
## RoofStyle=Gable,
## RoofMatl=CompShg,
## Exterior1st=VinylSd,
## MasVnrType=BrkFace,
## ExterQual=Gd,
## ExterCond=TA,
## Foundation=PConc,
## BsmtQual=Gd,
## BsmtCond=TA,
## BsmtExposure=No,
## BsmtFinType1=GLQ,
## BsmtFinType2=Unf,
## Heating=GasA,
## HeatingQC=Ex,
## CentralAir=Y,
## Electrical=SBrkr,
## KitchenQual=Gd,
## Functional=Typ,
## GarageType=Attchd,
## GarageFinish=RFn,
## GarageQual=TA,
## GarageCond=TA,
## PavedDrive=Y,
## SaleType=WD,
## SaleCondition=Normal} 1rules <- apriori(transactions, parameter = list(support = 0.95, confidence = 0.95))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.95 0.1 1 none FALSE TRUE 5 0.95 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 1387
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[218 item(s), 1460 transaction(s)] done [0.00s].
## sorting and recoding items ... [7 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [94 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(rules)## set of 94 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4
## 7 28 39 20
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.766 3.000 4.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.9500 Min. :0.9534 Min. :0.9507 Min. :0.9996
## 1st Qu.:0.9562 1st Qu.:0.9793 1st Qu.:0.9678 1st Qu.:0.9998
## Median :0.9637 Median :0.9895 Median :0.9781 Median :1.0000
## Mean :0.9664 Mean :0.9871 Mean :0.9791 Mean :1.0000
## 3rd Qu.:0.9740 3rd Qu.:0.9958 3rd Qu.:0.9897 3rd Qu.:1.0000
## Max. :0.9993 Max. :0.9993 Max. :1.0000 Max. :1.0010
## count
## Min. :1387
## 1st Qu.:1396
## Median :1407
## Mean :1411
## 3rd Qu.:1422
## Max. :1459
##
## mining info:
## data ntransactions support confidence
## transactions 1460 0.95 0.95
## call
## apriori(data = transactions, parameter = list(support = 0.95, confidence = 0.95))con <- file("data_description.txt", open = "r")
column_dictionary <- list()
value_dictionary <- list()
repeat {
line <- readLines(con, n = 1)
if (length(line) == 0) {
break
}
first_character <- substr(line, 1, 1)
if (first_character == "") {
next
}
if (first_character != " ") {
column_name <- sub(":.*", "", line)
column_description <- trimws(sub(".*:", "", line))
column_dictionary[[column_name]] <- column_description
value_dictionary[[column_name]] <- list()
} else {
pairs <- unlist(strsplit(line, "\t"))
key <- trimws(pairs[1])
value <- trimws(pairs[2])
value_dictionary[[column_name]][[key]] <- value
}
}
close(con)rules_top_ten_df <- data.frame(
lhs = labels(lhs(rules)),
rhs = labels(rhs(rules)),
rules@quality
) %>% arrange(desc(lift)) %>% head(n = 20)for (i in 1:nrow(rules_top_ten_df)) {
row <- rules_top_ten_df[i, ]
explanation <- ""
lhs <- unlist(strsplit(gsub('^.|.$', '', row["lhs"]), ","))
for (i in 1:length(lhs)) {
pair <- unlist(strsplit(lhs[i], "="))
key <- pair[1]
value <- pair[2]
key_t <- column_dictionary[[key]]
value_t <- value_dictionary[[key]][[value]]
if (i == 1) {
explanation <- paste("IF", key_t, "=", value_t)
} else {
explanation <- paste(explanation, "AND", key_t, "=", value_t)
}
}
rhs <- unlist(strsplit(gsub('^.|.$', '', row["rhs"]), "="))
key <- rhs[1]
value <- rhs[2]
key_t <- column_dictionary[[key]]
value_t <- value_dictionary[[key]][[value]]
confidence_pct <- format(round(row["confidence"] * 100, 2), 2)
explanation <- paste(explanation, "THEN", key_t, "=", value_t, "(Confidence:", paste0(confidence_pct, "%)"))
print(explanation)
cat("\n")
}## [1] "IF Proximity to various conditions (if more than one is present) = Normal THEN Garage condition = Typical/Average (Confidence: 96.47%)"
##
## [1] "IF Garage condition = Typical/Average THEN Proximity to various conditions (if more than one is present) = Normal (Confidence: 99.08%)"
##
## [1] "IF Type of utilities available = All public Utilities (E,G,W,& S) AND Garage condition = Typical/Average THEN Proximity to various conditions (if more than one is present) = Normal (Confidence: 99.08%)"
##
## [1] "IF Type of utilities available = All public Utilities (E,G,W,& S) AND Proximity to various conditions (if more than one is present) = Normal THEN Garage condition = Typical/Average (Confidence: 96.47%)"
##
## [1] "IF Type of road access to property = Paved AND Garage condition = Typical/Average THEN Proximity to various conditions (if more than one is present) = Normal (Confidence: 99.07%)"
##
## [1] "IF Type of road access to property = Paved AND Type of utilities available = All public Utilities (E,G,W,& S) AND Garage condition = Typical/Average THEN Proximity to various conditions (if more than one is present) = Normal (Confidence: 99.07%)"
##
## [1] "IF Type of road access to property = Paved AND Proximity to various conditions (if more than one is present) = Normal THEN Garage condition = Typical/Average (Confidence: 96.46%)"
##
## [1] "IF Type of road access to property = Paved AND Type of utilities available = All public Utilities (E,G,W,& S) AND Proximity to various conditions (if more than one is present) = Normal THEN Garage condition = Typical/Average (Confidence: 96.45%)"
##
## [1] "IF Type of heating = Gas forced warm air furnace THEN Roof material = Standard (Composite) Shingle (Confidence: 98.25%)"
##
## [1] "IF Roof material = Standard (Composite) Shingle THEN Type of heating = Gas forced warm air furnace (Confidence: 97.84%)"
##
## [1] "IF Type of utilities available = All public Utilities (E,G,W,& S) AND Type of heating = Gas forced warm air furnace THEN Roof material = Standard (Composite) Shingle (Confidence: 98.25%)"
##
## [1] "IF Type of utilities available = All public Utilities (E,G,W,& S) AND Roof material = Standard (Composite) Shingle THEN Type of heating = Gas forced warm air furnace (Confidence: 97.84%)"
##
## [1] "IF Type of road access to property = Paved AND Type of heating = Gas forced warm air furnace THEN Roof material = Standard (Composite) Shingle (Confidence: 98.24%)"
##
## [1] "IF Type of road access to property = Paved AND Type of utilities available = All public Utilities (E,G,W,& S) AND Type of heating = Gas forced warm air furnace THEN Roof material = Standard (Composite) Shingle (Confidence: 98.24%)"
##
## [1] "IF Type of road access to property = Paved AND Roof material = Standard (Composite) Shingle THEN Type of heating = Gas forced warm air furnace (Confidence: 97.83%)"
##
## [1] "IF Type of road access to property = Paved AND Type of utilities available = All public Utilities (E,G,W,& S) AND Roof material = Standard (Composite) Shingle THEN Type of heating = Gas forced warm air furnace (Confidence: 97.83%)"
##
## [1] "IF Proximity to various conditions (if more than one is present) = Normal AND Type of heating = Gas forced warm air furnace THEN Roof material = Standard (Composite) Shingle (Confidence: 98.23%)"
##
## [1] "IF Type of utilities available = All public Utilities (E,G,W,& S) AND Proximity to various conditions (if more than one is present) = Normal AND Type of heating = Gas forced warm air furnace THEN Roof material = Standard (Composite) Shingle (Confidence: 98.23%)"
##
## [1] "IF Proximity to various conditions (if more than one is present) = Normal AND Roof material = Standard (Composite) Shingle THEN Type of heating = Gas forced warm air furnace (Confidence: 97.82%)"
##
## [1] "IF Type of utilities available = All public Utilities (E,G,W,& S) AND Proximity to various conditions (if more than one is present) = Normal AND Roof material = Standard (Composite) Shingle THEN Type of heating = Gas forced warm air furnace (Confidence: 97.81%)"Stacking
Stacking is performed by combining the predictions from different models (Random Forest, XGBoost, LightGBM, CatBoost) into a new dataset.
Linear regression model is trained on the stacked dataset to learn the relationship between ensemble predictions and the actual target values.
Model’s predictions on the test dataset are evaluated using root mean squared error (RMSE).
Metrics fusion function is applied to assess the performance of the stacking ensemble.
stacked_data <- data.frame(y = y_train_val, prediction_rf = y_pred_train_rf, prediction_xgb = y_pred_train_xgb, prediction_lgb = y_pred_train_lgb, prediction_catboost = y_pred_train_catboost)
stacked_data_test <- data.frame(y = y_test, prediction_rf = y_pred_test_rf, prediction_xgb = y_pred_test_xgb, prediction_lgb = y_pred_test_lgb, prediction_catboost = y_pred_test_catboost)
model_meta <- caret::train(y ~ ., data = stacked_data, method = "lm")
predictions_meta <- predict(model_meta, newdata = stacked_data_test)
ensemble_rmse_test$stacking_score <- rmse(predictions_meta, y_test)
ensemble_actual_metrics_test$stacking <- metrics_fusion(predictions_meta, y_test)Comparison
Compares the performance of different models and ensembles using the root mean squared error (RMSE) metric.
Bar charts created to display the RMSE values, with the x-axis representing the models and the y-axis representing the RMSE.
First set of plots focuses on the baseline models on both the training and test sets.
Second set focuses on the ensemble models on both the training and test sets.
Purpose is to compare the models’ performance and determine which ones have the lowest RMSE, indicating better predictive accuracy.
# Plot RMSE for baseline models
df <- data.frame(models = names(baselines_rmse), rmse = unlist(baselines_rmse))
df## models rmse
## linear_regression linear_regression 0.1549031
## lasso lasso 0.1712791
## ridge ridge 0.2462979
## elasticnet elasticnet 0.1711617
## knn knn 0.1832919
## svr svr 0.1460610
## decision_tree decision_tree 0.3029599ggplot(df, aes(x = models, y = rmse)) +
geom_bar(stat = "identity", fill = "steelblue") +
xlab("Models") +
ylab("RMSE") +
ylim(0, 0.35) +
ggtitle("Baseline RMSE") +
theme_minimal()
# Plot RMSE for ensemble models
df <- data.frame(models = names(ensemble_rmse), rmse = unlist(ensemble_rmse))
df## models rmse
## random_forest random_forest 0.1675450
## xgboost xgboost 0.1265857
## lightgbm lightgbm 0.1317233
## catboost catboost 0.1934132ggplot(df, aes(x = models, y = rmse)) +
geom_bar(stat = "identity", fill = "steelblue") +
xlab("Models") +
ylab("RMSE") +
ylim(0, 0.35) +
ggtitle("Ensemble RMSE") +
theme_minimal()
# Plot RMSE for baseline models on the test set
df <- data.frame(models = names(baselines_rmse_test), rmse = unlist(baselines_rmse_test))
df## models rmse
## linear_regression linear_regression 0.1218889
## lasso lasso 0.1148247
## ridge ridge 0.1265604
## elasticnet elasticnet 0.1173051
## knn knn 0.1792911
## svr svr 0.1069807
## decision_tree decision_tree 0.2648677ggplot(df, aes(x = models, y = rmse)) +
geom_bar(stat = "identity", fill = "steelblue") +
xlab("Models") +
ylab("RMSE") +
ylim(0, 0.35) +
ggtitle("Baseline RMSE") +
theme_minimal()
# Plot RMSE for ensemble models on the test set
df <- data.frame(models = names(ensemble_rmse_test), rmse = unlist(ensemble_rmse_test))
df## models rmse
## random_forest random_forest 0.1239416
## xgboost_test xgboost_test 0.1099328
## lightgbm lightgbm 0.1145342
## catboost catboost 0.1075237
## stacking_score stacking_score 0.1078134ggplot(df, aes(x = models, y = rmse)) +
geom_bar(stat = "identity", fill = "steelblue") +
xlab("Models") +
ylab("RMSE") +
ylim(0, 0.35) +
ggtitle("Ensemble RMSE") +
theme_minimal()
data.frame(t(data.frame(actual_metrics_test))) %>% arrange(desc(r2))## mae mape rmse mse r2
## linear_regression 14935.65 0.08766533 22326.23 498460523 0.9208717
## lasso 15956.16 0.08582267 26423.02 698175905 0.9041092
## svr 15056.32 0.08159073 25228.70 636487525 0.9019092
## elasticnet 16344.68 0.08756502 27331.17 746992779 0.8986378
## ridge 18051.14 0.09831870 28817.58 830453105 0.8920696
## knn 24675.74 0.14031078 38013.83 1445051148 0.7907291
## decision_tree 37197.20 0.20696292 53865.51 2901493541 0.5424929data.frame(t(data.frame(ensemble_actual_metrics_test))) %>% arrange(desc(r2))## mae mape rmse mse r2
## catboost 14067.55 0.07714256 23360.30 545703544 0.9138881
## stacking 14087.02 0.07722054 23453.79 550080445 0.9132716
## xgboost_test 14927.04 0.08141034 23843.04 568490734 0.9109508
## lightgbm 15367.22 0.08351224 25701.52 660568315 0.8963502
## random_forest 16655.08 0.09036259 27900.36 778430138 0.8875507save(list=ls(), file="assignment_model")