rm(list=ls())
ls()
## character(0)
getwd()
## [1] "/Users/mac/bigdata"
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, union
data(iris)
library(caret)
## Loading required package: ggplot2
## Loading required package: lattice
idx<-createDataPartition(iris$Species, p=0.8)
table(iris[idx$Resample1, "Species"])
##
## setosa versicolor virginica
## 40 40 40
table(iris[-idx$Resample1, "Species"])
##
## setosa versicolor virginica
## 10 10 10
idx<-createDataPartition(iris$Species, p=0.8, list=FALSE)
train<-iris[idx,]
test<-iris[-idx,]
nrow(train)
## [1] 120
nrow(test)
## [1] 30
library(caret)
set.seed(2)
data("iris")
idx<-createDataPartition(iris$Species,p=0.6,list=FALSE)
train<-iris[idx,]
valid_test<-iris[-idx,]
idx2<-createDataPartition(valid_test$Species,p=0.5,list=FALSE)
valid<-valid_test[idx2,]
test<-valid_test[-idx2,]
nrow(train)/nrow(iris)
## [1] 0.6
nrow(valid)/nrow(iris)
## [1] 0.2
nrow(test)/nrow(iris)
## [1] 0.2
library(caret)
data(iris)
zs_iris<-preProcess(iris,method = c("center","scale"))
zs_iris
## Created from 150 samples and 5 variables
##
## Pre-processing:
## - centered (4)
## - ignored (1)
## - scaled (4)
zs_iris_p<-predict(zs_iris,iris)
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
describe(zs_iris_p[,1:4])
## vars n mean sd median trimmed mad min max range skew
## Sepal.Length 1 150 0 1 -0.05 -0.04 1.25 -1.86 2.48 4.35 0.31
## Sepal.Width 2 150 0 1 -0.13 -0.03 1.02 -2.43 3.08 5.51 0.31
## Petal.Length 3 150 0 1 0.34 0.00 1.05 -1.56 1.78 3.34 -0.27
## Petal.Width 4 150 0 1 0.13 -0.02 1.36 -1.44 1.71 3.15 -0.10
## kurtosis se
## Sepal.Length -0.61 0.08
## Sepal.Width 0.14 0.08
## Petal.Length -1.42 0.08
## Petal.Width -1.36 0.08
library(caret)
data(iris)
sca_iris<-preProcess(iris,method="range")
sca_iris
## Created from 150 samples and 5 variables
##
## Pre-processing:
## - ignored (1)
## - re-scaling to [0, 1] (4)
sca_iris_p<-predict(sca_iris,iris)
head(sca_iris_p)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 0.22222222 0.6250000 0.06779661 0.04166667 setosa
## 2 0.16666667 0.4166667 0.06779661 0.04166667 setosa
## 3 0.11111111 0.5000000 0.05084746 0.04166667 setosa
## 4 0.08333333 0.4583333 0.08474576 0.04166667 setosa
## 5 0.19444444 0.6666667 0.06779661 0.04166667 setosa
## 6 0.30555556 0.7916667 0.11864407 0.12500000 setosa
library(psych)
describe(sca_iris_p)
## vars n mean sd median trimmed mad min max range skew
## Sepal.Length 1 150 0.43 0.23 0.42 0.42 0.29 0 1 1 0.31
## Sepal.Width 2 150 0.44 0.18 0.42 0.43 0.19 0 1 1 0.31
## Petal.Length 3 150 0.47 0.30 0.57 0.47 0.31 0 1 1 -0.27
## Petal.Width 4 150 0.46 0.32 0.50 0.45 0.43 0 1 1 -0.10
## Species* 5 150 2.00 0.82 2.00 2.00 1.48 1 3 2 0.00
## kurtosis se
## Sepal.Length -0.61 0.02
## Sepal.Width 0.14 0.01
## Petal.Length -1.42 0.02
## Petal.Width -1.36 0.03
## Species* -1.52 0.07
library(caret)
data(iris)
bc_iris=preProcess(iris,method="BoxCox")
bc_iris
## Created from 150 samples and 5 variables
##
## Pre-processing:
## - Box-Cox transformation (4)
## - ignored (1)
##
## Lambda estimates for Box-Cox transformation:
## -0.1, 0.3, 0.9, 0.6
bc_iris_t=predict(bc_iris,iris)
head(bc_iris_t)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 1.629241 1.520660 1.4 -1.0321154 setosa
## 2 1.589235 1.301297 1.4 -1.0321154 setosa
## 3 1.547563 1.391905 1.3 -1.0321154 setosa
## 4 1.526056 1.347113 1.5 -1.0321154 setosa
## 5 1.609438 1.561856 1.4 -1.0321154 setosa
## 6 1.686399 1.680826 1.7 -0.7048667 setosa
par(mfrow=c(1,2))
plot(density(x=iris$Sepal.Width))
plot(density(x=bc_iris_t$Sepal.Width))
library(caret)
data(iris)
pca_iris<-preProcess(iris,method="pca")
pca_iris
## Created from 150 samples and 5 variables
##
## Pre-processing:
## - centered (4)
## - ignored (1)
## - principal component signal extraction (4)
## - scaled (4)
##
## PCA needed 2 components to capture 95 percent of the variance
pca_iris_p<-predict(pca_iris,iris)
head(pca_iris_p)
## Species PC1 PC2
## 1 setosa -2.257141 -0.4784238
## 2 setosa -2.074013 0.6718827
## 3 setosa -2.356335 0.3407664
## 4 setosa -2.291707 0.5953999
## 5 setosa -2.381863 -0.6446757
## 6 setosa -2.068701 -1.4842053
library(earth)
## Loading required package: Formula
## Loading required package: plotmo
## Loading required package: plotrix
##
## Attaching package: 'plotrix'
## The following object is masked from 'package:psych':
##
## rescale
## Loading required package: TeachingDemos
data("etitanic")
library(dplyr)
glimpse(etitanic)
## Rows: 1,046
## Columns: 6
## $ pclass <fct> 1st, 1st, 1st, 1st, 1st, 1st, 1st, 1st, 1st, 1st, 1st, 1st, 1…
## $ survived <int> 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1…
## $ sex <fct> female, male, female, male, female, male, female, male, femal…
## $ age <dbl> 29.0000, 0.9167, 2.0000, 30.0000, 25.0000, 48.0000, 63.0000, …
## $ sibsp <int> 0, 1, 1, 1, 1, 0, 1, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1…
## $ parch <int> 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1…
dummies<-dummyVars(survived~.,data = etitanic)
dummies
## Dummy Variable Object
##
## Formula: survived ~ .
## 6 variables, 2 factors
## Variables and levels will be separated by '.'
## A less than full rank encoding is used
head(predict(dummies,newdata = etitanic))
## pclass.1st pclass.2nd pclass.3rd sex.female sex.male age sibsp parch
## 1 1 0 0 1 0 29.0000 0 0
## 2 1 0 0 0 1 0.9167 1 2
## 3 1 0 0 1 0 2.0000 1 2
## 4 1 0 0 0 1 30.0000 1 2
## 5 1 0 0 1 0 25.0000 1 2
## 6 1 0 0 0 1 48.0000 0 0
data("airquality")
colSums(is.na(airquality))
## Ozone Solar.R Wind Temp Month Day
## 37 7 0 0 0 0
library(RANN)
head(airquality)
## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
knn_im<-preProcess(airquality[1:4],method = "knnImpute")
head(predict(knn_im,airquality))
## Ozone Solar.R Wind Temp Month Day
## 1 -0.03423409 0.04517615 -0.7259482 -1.1497140 5 1
## 2 -0.18580489 -0.75430487 -0.5556388 -0.6214670 5 2
## 3 -0.91334473 -0.41008388 0.7500660 -0.4101682 5 3
## 4 -0.73145977 1.41095624 0.4378323 -1.6779609 5 4
## 5 -0.69508278 -0.92308420 1.2326091 -2.3118573 5 5
## 6 -0.42831817 -0.28349938 1.4029185 -1.2553634 5 6
knn_im_p<-predict(knn_im,airquality)
colSums(is.na(knn_im_p))
## Ozone Solar.R Wind Temp Month Day
## 0 0 0 0 0 0
describe(knn_im_p)
## vars n mean sd median trimmed mad min max range skew
## Ozone 1 153 -0.04 0.92 -0.31 -0.16 0.72 -1.25 3.82 5.06 1.30
## Solar.R 2 153 -0.01 0.99 0.17 0.03 1.05 -1.99 1.64 3.63 -0.39
## Wind 3 153 0.00 1.00 -0.07 -0.02 0.97 -2.34 3.05 5.39 0.34
## Temp 4 153 0.00 1.00 0.12 0.04 0.94 -2.31 2.02 4.33 -0.37
## Month 5 153 6.99 1.42 7.00 6.99 1.48 5.00 9.00 4.00 0.00
## Day 6 153 15.80 8.86 16.00 15.80 11.86 1.00 31.00 30.00 0.00
## kurtosis se
## Ozone 1.62 0.07
## Solar.R -0.99 0.08
## Wind 0.03 0.08
## Temp -0.46 0.08
## Month -1.32 0.11
## Day -1.22 0.72
median_im<-preProcess(airquality[1:4],method = "medianImpute")
head(predict(median_im,airquality))
## Ozone Solar.R Wind Temp Month Day
## 1 41.0 190 7.4 67 5 1
## 2 36.0 118 8.0 72 5 2
## 3 12.0 149 12.6 74 5 3
## 4 18.0 313 11.5 62 5 4
## 5 31.5 205 14.3 56 5 5
## 6 28.0 205 14.9 66 5 6
library(mlbench)
data("BostonHousing")
x=BostonHousing[,c("age","lstat","tax")]
y=BostonHousing$medv
featurePlot(x,
y=BostonHousing$medv,
plot="scatter",
layout=c(3,1))
library(mlbench)
library(caret)
library(dplyr)
data("PimaIndiansDiabetes")
PimaIndiansDiabetes %>% count(diabetes)
## diabetes n
## 1 neg 500
## 2 pos 268
glimpse(PimaIndiansDiabetes)
## Rows: 768
## Columns: 9
## $ pregnant <dbl> 6, 1, 8, 1, 0, 5, 3, 10, 2, 8, 4, 10, 10, 1, 5, 7, 0, 7, 1, 1…
## $ glucose <dbl> 148, 85, 183, 89, 137, 116, 78, 115, 197, 125, 110, 168, 139,…
## $ pressure <dbl> 72, 66, 64, 66, 40, 74, 50, 0, 70, 96, 92, 74, 80, 60, 72, 0,…
## $ triceps <dbl> 35, 29, 0, 23, 35, 0, 32, 0, 45, 0, 0, 0, 0, 23, 19, 0, 47, 0…
## $ insulin <dbl> 0, 0, 0, 94, 168, 0, 88, 0, 543, 0, 0, 0, 0, 846, 175, 0, 230…
## $ mass <dbl> 33.6, 26.6, 23.3, 28.1, 43.1, 25.6, 31.0, 35.3, 30.5, 0.0, 37…
## $ pedigree <dbl> 0.627, 0.351, 0.672, 0.167, 2.288, 0.201, 0.248, 0.134, 0.158…
## $ age <dbl> 50, 31, 32, 21, 33, 30, 26, 29, 53, 54, 30, 34, 57, 59, 51, 3…
## $ diabetes <fct> pos, neg, pos, neg, pos, neg, pos, neg, pos, pos, neg, pos, n…
control<-trainControl(method = "repeatedcv",number = 5,repeats = 3)
model<-train(diabetes~.,data=PimaIndiansDiabetes, method="rf",
preProcess=c("center","scale"),trControl=control)
importance<-varImp(model,scale=FALSE)
print(importance)
## rf variable importance
##
## Overall
## glucose 89.13
## mass 57.97
## age 47.66
## pedigree 42.77
## pressure 30.95
## pregnant 28.55
## insulin 25.55
## triceps 24.00
plot(importance)
library(caret)
library(mlbench)
data(Sonar)
Sonar<-na.omit(Sonar)
set.seed(998)
inTraining<-createDataPartition(y=Sonar$Class, p=.75, list=FALSE)
training<-Sonar[inTraining]
testing<-Sonar[-inTraining,]
set.seed(825)
fitControl<-trainControl(method="repeatedcv",
number = 10,
repeats = 10,
classProbs = TRUE,
summaryFunction = twoClassSummary)
library(dplyr)
library(caret)
library(caTools)
df<-read.csv("diagnosis.csv")
glimpse(df)
## Rows: 569
## Columns: 32
## $ id <int> 842302, 842517, 84300903, 84348301, 84358402, …
## $ diagnosis <chr> "M", "M", "M", "M", "M", "M", "M", "M", "M", "…
## $ radius_mean <dbl> 17.990, 20.570, 19.690, 11.420, 20.290, 12.450…
## $ texture_mean <dbl> 10.38, 17.77, 21.25, 20.38, 14.34, 15.70, 19.9…
## $ perimeter_mean <dbl> 122.80, 132.90, 130.00, 77.58, 135.10, 82.57, …
## $ area_mean <dbl> 1001.0, 1326.0, 1203.0, 386.1, 1297.0, 477.1, …
## $ smoothness_mean <dbl> 0.11840, 0.08474, 0.10960, 0.14250, 0.10030, 0…
## $ compactness_mean <dbl> 0.27760, 0.07864, 0.15990, 0.28390, 0.13280, 0…
## $ concavity_mean <dbl> 0.30010, 0.08690, 0.19740, 0.24140, 0.19800, 0…
## $ concave.points_mean <dbl> 0.14710, 0.07017, 0.12790, 0.10520, 0.10430, 0…
## $ symmetry_mean <dbl> 0.2419, 0.1812, 0.2069, 0.2597, 0.1809, 0.2087…
## $ fractal_dimension_mean <dbl> 0.07871, 0.05667, 0.05999, 0.09744, 0.05883, 0…
## $ radius_se <dbl> 1.0950, 0.5435, 0.7456, 0.4956, 0.7572, 0.3345…
## $ texture_se <dbl> 0.9053, 0.7339, 0.7869, 1.1560, 0.7813, 0.8902…
## $ perimeter_se <dbl> 8.589, 3.398, 4.585, 3.445, 5.438, 2.217, 3.18…
## $ area_se <dbl> 153.40, 74.08, 94.03, 27.23, 94.44, 27.19, 53.…
## $ smoothness_se <dbl> 0.006399, 0.005225, 0.006150, 0.009110, 0.0114…
## $ compactness_se <dbl> 0.049040, 0.013080, 0.040060, 0.074580, 0.0246…
## $ concavity_se <dbl> 0.05373, 0.01860, 0.03832, 0.05661, 0.05688, 0…
## $ concave.points_se <dbl> 0.015870, 0.013400, 0.020580, 0.018670, 0.0188…
## $ symmetry_se <dbl> 0.03003, 0.01389, 0.02250, 0.05963, 0.01756, 0…
## $ fractal_dimension_se <dbl> 0.006193, 0.003532, 0.004571, 0.009208, 0.0051…
## $ radius_worst <dbl> 25.38, 24.99, 23.57, 14.91, 22.54, 15.47, 22.8…
## $ texture_worst <dbl> 17.33, 23.41, 25.53, 26.50, 16.67, 23.75, 27.6…
## $ perimeter_worst <dbl> 184.60, 158.80, 152.50, 98.87, 152.20, 103.40,…
## $ area_worst <dbl> 2019.0, 1956.0, 1709.0, 567.7, 1575.0, 741.6, …
## $ smoothness_worst <dbl> 0.1622, 0.1238, 0.1444, 0.2098, 0.1374, 0.1791…
## $ compactness_worst <dbl> 0.6656, 0.1866, 0.4245, 0.8663, 0.2050, 0.5249…
## $ concavity_worst <dbl> 0.71190, 0.24160, 0.45040, 0.68690, 0.40000, 0…
## $ concave.points_worst <dbl> 0.26540, 0.18600, 0.24300, 0.25750, 0.16250, 0…
## $ symmetry_worst <dbl> 0.4601, 0.2750, 0.3613, 0.6638, 0.2364, 0.3985…
## $ fractal_dimension_worst <dbl> 0.11890, 0.08902, 0.08758, 0.17300, 0.07678, 0…
df<-df %>%
select(-id)
df$diagnosis<-as.factor(df$diagnosis)
prop.table(table(df$diagnosis))
##
## B M
## 0.6274165 0.3725835
any(is.na(df))
## [1] FALSE
set.seed(3)
inTraining<-createDataPartition(y=df$diagnosis,p=.75,list=FALSE)
training<-df[inTraining,]
testing<-df[-inTraining,]
my_trainControl<-trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
classProbs = TRUE,
summaryFunction = twoClassSummary)
glmnetfit<-train(diagnosis~., data=training,
method="glmnet",
tuneLength=20,
trControl=fitControl,
verbose=FALSE,
metric="ROC")
glmnetfit
## glmnet
##
## 427 samples
## 30 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 10 times)
## Summary of sample sizes: 386, 384, 384, 384, 384, 384, ...
## Resampling results across tuning parameters:
##
## alpha lambda ROC Sens Spec
## 0.1000000 0.0001199795 0.9924250 0.9817521 0.9529167
## 0.1000000 0.0001860300 0.9924250 0.9817521 0.9529167
## 0.1000000 0.0002884425 0.9924250 0.9817521 0.9529167
## 0.1000000 0.0004472347 0.9925432 0.9821225 0.9529167
## 0.1000000 0.0006934444 0.9932239 0.9839744 0.9541250
## 0.1000000 0.0010751965 0.9935754 0.9873219 0.9529167
## 0.1000000 0.0016671092 0.9938571 0.9925356 0.9529583
## 0.1000000 0.0025848791 0.9941358 0.9954986 0.9498333
## 0.1000000 0.0040078959 0.9944206 0.9969943 0.9504167
## 0.1000000 0.0062143058 0.9944436 0.9984900 0.9466667
## 0.1000000 0.0096353793 0.9943732 0.9992450 0.9453750
## 0.1000000 0.0149398076 0.9942559 0.9992450 0.9447500
## 0.1000000 0.0231644074 0.9942085 0.9996296 0.9397500
## 0.1000000 0.0359167792 0.9941391 0.9996296 0.9328333
## 0.1000000 0.0556895330 0.9939249 0.9996296 0.9240417
## 0.1000000 0.0863474996 0.9932652 0.9988604 0.9090000
## 0.1000000 0.1338831607 0.9923208 0.9977493 0.8882500
## 0.1000000 0.2075879534 0.9913428 0.9962393 0.8813750
## 0.1000000 0.3218683974 0.9908304 0.9962393 0.8569167
## 0.1000000 0.4990620292 0.9901276 0.9962393 0.8278333
## 0.1473684 0.0001199795 0.9923291 0.9806268 0.9516250
## 0.1473684 0.0001860300 0.9923291 0.9806268 0.9516250
## 0.1473684 0.0002884425 0.9924019 0.9809972 0.9522500
## 0.1473684 0.0004472347 0.9925432 0.9821225 0.9529167
## 0.1473684 0.0006934444 0.9931999 0.9839744 0.9541250
## 0.1473684 0.0010751965 0.9935994 0.9873219 0.9522917
## 0.1473684 0.0016671092 0.9939275 0.9932764 0.9529583
## 0.1473684 0.0025848791 0.9941373 0.9954986 0.9498333
## 0.1473684 0.0040078959 0.9943975 0.9973789 0.9504167
## 0.1473684 0.0062143058 0.9943964 0.9984900 0.9460417
## 0.1473684 0.0096353793 0.9943510 0.9992450 0.9460000
## 0.1473684 0.0149398076 0.9942087 0.9996296 0.9460000
## 0.1473684 0.0231644074 0.9941640 0.9996296 0.9391250
## 0.1473684 0.0359167792 0.9941631 0.9996296 0.9309583
## 0.1473684 0.0556895330 0.9936416 0.9992450 0.9184167
## 0.1473684 0.0863474996 0.9932179 0.9984900 0.9052500
## 0.1473684 0.1338831607 0.9922001 0.9962393 0.8857500
## 0.1473684 0.2075879534 0.9914834 0.9962393 0.8775833
## 0.1473684 0.3218683974 0.9905740 0.9962393 0.8473750
## 0.1473684 0.4990620292 0.9896537 0.9962393 0.7944167
## 0.1947368 0.0001199795 0.9920726 0.9795014 0.9503750
## 0.1947368 0.0001860300 0.9920726 0.9795014 0.9503750
## 0.1947368 0.0002884425 0.9923994 0.9809972 0.9522500
## 0.1947368 0.0004472347 0.9925201 0.9821225 0.9529167
## 0.1947368 0.0006934444 0.9932239 0.9839744 0.9547500
## 0.1947368 0.0010751965 0.9936010 0.9877066 0.9522917
## 0.1947368 0.0016671092 0.9939043 0.9929060 0.9529167
## 0.1947368 0.0025848791 0.9941389 0.9943875 0.9498333
## 0.1947368 0.0040078959 0.9943726 0.9973789 0.9504167
## 0.1947368 0.0062143058 0.9943260 0.9984900 0.9453750
## 0.1947368 0.0096353793 0.9942070 0.9992450 0.9466250
## 0.1947368 0.0149398076 0.9940912 0.9996296 0.9447500
## 0.1947368 0.0231644074 0.9940681 0.9996296 0.9353750
## 0.1947368 0.0359167792 0.9940465 0.9996296 0.9278333
## 0.1947368 0.0556895330 0.9935000 0.9988746 0.9177917
## 0.1947368 0.0863474996 0.9930081 0.9977493 0.8995833
## 0.1947368 0.1338831607 0.9923444 0.9962393 0.8826250
## 0.1947368 0.2075879534 0.9914389 0.9962393 0.8725833
## 0.1947368 0.3218683974 0.9901741 0.9962393 0.8367083
## 0.1947368 0.4990620292 0.9885821 0.9962393 0.7498750
## 0.2421053 0.0001199795 0.9918610 0.9787607 0.9485000
## 0.2421053 0.0001860300 0.9919560 0.9791311 0.9497500
## 0.2421053 0.0002884425 0.9923762 0.9802422 0.9522500
## 0.2421053 0.0004472347 0.9925432 0.9821225 0.9529167
## 0.2421053 0.0006934444 0.9931776 0.9843447 0.9547500
## 0.2421053 0.0010751965 0.9936010 0.9877066 0.9516250
## 0.2421053 0.0016671092 0.9939043 0.9925356 0.9510417
## 0.2421053 0.0025848791 0.9941157 0.9947721 0.9498333
## 0.2421053 0.0040078959 0.9943973 0.9981197 0.9497917
## 0.2421053 0.0062143058 0.9943260 0.9988746 0.9466250
## 0.2421053 0.0096353793 0.9941607 0.9992450 0.9472500
## 0.2421053 0.0149398076 0.9940449 0.9996296 0.9447500
## 0.2421053 0.0231644074 0.9940440 0.9996296 0.9366250
## 0.2421053 0.0359167792 0.9939022 0.9992450 0.9278333
## 0.2421053 0.0556895330 0.9933139 0.9984900 0.9177917
## 0.2421053 0.0863474996 0.9928890 0.9977493 0.8958333
## 0.2421053 0.1338831607 0.9922527 0.9962393 0.8795000
## 0.2421053 0.2075879534 0.9912057 0.9962393 0.8630000
## 0.2421053 0.3218683974 0.9895884 0.9962393 0.8095833
## 0.2421053 0.4990620292 0.9875280 0.9966097 0.7216250
## 0.2894737 0.0001199795 0.9915533 0.9776496 0.9459583
## 0.2894737 0.0001860300 0.9919560 0.9787464 0.9497500
## 0.2894737 0.0002884425 0.9923762 0.9798860 0.9522500
## 0.2894737 0.0004472347 0.9925672 0.9821225 0.9529167
## 0.2894737 0.0006934444 0.9931536 0.9847151 0.9547500
## 0.2894737 0.0010751965 0.9936257 0.9880769 0.9516250
## 0.2894737 0.0016671092 0.9939275 0.9914245 0.9504167
## 0.2894737 0.0025848791 0.9941645 0.9951425 0.9498333
## 0.2894737 0.0040078959 0.9943501 0.9981197 0.9497917
## 0.2894737 0.0062143058 0.9942319 0.9988746 0.9472500
## 0.2894737 0.0096353793 0.9941838 0.9992450 0.9479167
## 0.2894737 0.0149398076 0.9940681 0.9996296 0.9447917
## 0.2894737 0.0231644074 0.9939969 0.9996296 0.9360000
## 0.2894737 0.0359167792 0.9937342 0.9992450 0.9240417
## 0.2894737 0.0556895330 0.9932429 0.9981054 0.9152917
## 0.2894737 0.0863474996 0.9927474 0.9977493 0.8889167
## 0.2894737 0.1338831607 0.9920212 0.9962393 0.8775833
## 0.2894737 0.2075879534 0.9908545 0.9962393 0.8561250
## 0.2894737 0.3218683974 0.9885128 0.9962393 0.7932083
## 0.2894737 0.4990620292 0.9872676 0.9988604 0.6989583
## 0.3368421 0.0001199795 0.9913672 0.9776496 0.9447083
## 0.3368421 0.0001860300 0.9919792 0.9787464 0.9491250
## 0.3368421 0.0002884425 0.9923291 0.9798860 0.9522500
## 0.3368421 0.0004472347 0.9925672 0.9825071 0.9529167
## 0.3368421 0.0006934444 0.9931305 0.9847293 0.9547917
## 0.3368421 0.0010751965 0.9936951 0.9877066 0.9522500
## 0.3368421 0.0016671092 0.9939506 0.9906838 0.9510417
## 0.3368421 0.0025848791 0.9940926 0.9955128 0.9504583
## 0.3368421 0.0040078959 0.9942797 0.9981197 0.9497917
## 0.3368421 0.0062143058 0.9942782 0.9988746 0.9466250
## 0.3368421 0.0096353793 0.9941375 0.9992450 0.9472917
## 0.3368421 0.0149398076 0.9941384 0.9992593 0.9454167
## 0.3368421 0.0231644074 0.9939488 0.9992593 0.9341250
## 0.3368421 0.0359167792 0.9936175 0.9988604 0.9227917
## 0.3368421 0.0556895330 0.9931726 0.9981054 0.9146667
## 0.3368421 0.0863474996 0.9926308 0.9977493 0.8851667
## 0.3368421 0.1338831607 0.9917185 0.9962393 0.8744167
## 0.3368421 0.2075879534 0.9900821 0.9962393 0.8423750
## 0.3368421 0.3218683974 0.9876909 0.9962393 0.7674583
## 0.3368421 0.4990620292 0.9875538 1.0000000 0.6592917
## 0.3842105 0.0001199795 0.9913441 0.9772792 0.9447083
## 0.3842105 0.0001860300 0.9920023 0.9783761 0.9491250
## 0.3842105 0.0002884425 0.9922596 0.9798860 0.9522500
## 0.3842105 0.0004472347 0.9925672 0.9821225 0.9529167
## 0.3842105 0.0006934444 0.9931305 0.9851140 0.9547917
## 0.3842105 0.0010751965 0.9936232 0.9877066 0.9510000
## 0.3842105 0.0016671092 0.9939506 0.9906838 0.9510417
## 0.3842105 0.0025848791 0.9940917 0.9958974 0.9504583
## 0.3842105 0.0040078959 0.9941841 0.9977493 0.9497917
## 0.3842105 0.0062143058 0.9942782 0.9988746 0.9472917
## 0.3842105 0.0096353793 0.9941607 0.9992450 0.9485417
## 0.3842105 0.0149398076 0.9940921 0.9992593 0.9447917
## 0.3842105 0.0231644074 0.9939506 0.9992593 0.9334583
## 0.3842105 0.0359167792 0.9934297 0.9984900 0.9215417
## 0.3842105 0.0556895330 0.9931726 0.9981054 0.9127917
## 0.3842105 0.0863474996 0.9924430 0.9977493 0.8813750
## 0.3842105 0.1338831607 0.9914639 0.9962393 0.8655833
## 0.3842105 0.2075879534 0.9894688 0.9962393 0.8305000
## 0.3842105 0.3218683974 0.9875721 0.9973647 0.7555000
## 0.3842105 0.4990620292 0.9882447 1.0000000 0.5995833
## 0.4315789 0.0001199795 0.9913432 0.9772792 0.9447083
## 0.4315789 0.0001860300 0.9919551 0.9783761 0.9478333
## 0.4315789 0.0002884425 0.9922356 0.9798860 0.9510000
## 0.4315789 0.0004472347 0.9925672 0.9821225 0.9522500
## 0.4315789 0.0006934444 0.9930833 0.9851140 0.9541667
## 0.4315789 0.0010751965 0.9936214 0.9877066 0.9510000
## 0.4315789 0.0016671092 0.9939025 0.9910684 0.9516667
## 0.4315789 0.0025848791 0.9940677 0.9962678 0.9504583
## 0.4315789 0.0040078959 0.9940906 0.9977493 0.9497917
## 0.4315789 0.0062143058 0.9943022 0.9988746 0.9466667
## 0.4315789 0.0096353793 0.9942301 0.9988746 0.9472917
## 0.4315789 0.0149398076 0.9941162 0.9992593 0.9454583
## 0.4315789 0.0231644074 0.9938313 0.9988746 0.9315833
## 0.4315789 0.0359167792 0.9933346 0.9984900 0.9202917
## 0.4315789 0.0556895330 0.9930791 0.9981054 0.9083750
## 0.4315789 0.0863474996 0.9923014 0.9973789 0.8782083
## 0.4315789 0.1338831607 0.9912299 0.9962393 0.8617500
## 0.4315789 0.2075879534 0.9887004 0.9962393 0.8235417
## 0.4315789 0.3218683974 0.9878830 0.9988604 0.7316667
## 0.4315789 0.4990620292 0.9889977 1.0000000 0.5262083
## 0.4789474 0.0001199795 0.9912720 0.9769088 0.9447083
## 0.4789474 0.0001860300 0.9917691 0.9776353 0.9478333
## 0.4789474 0.0002884425 0.9921884 0.9795157 0.9510000
## 0.4789474 0.0004472347 0.9925432 0.9821225 0.9522500
## 0.4789474 0.0006934444 0.9930601 0.9851140 0.9547917
## 0.4789474 0.0010751965 0.9935033 0.9877208 0.9522500
## 0.4789474 0.0016671092 0.9938313 0.9910684 0.9516667
## 0.4789474 0.0025848791 0.9940436 0.9962678 0.9510833
## 0.4789474 0.0040078959 0.9940196 0.9981197 0.9491667
## 0.4789474 0.0062143058 0.9942559 0.9985043 0.9466667
## 0.4789474 0.0096353793 0.9942533 0.9988889 0.9479167
## 0.4789474 0.0149398076 0.9941384 0.9992593 0.9435000
## 0.4789474 0.0231644074 0.9938544 0.9988746 0.9310000
## 0.4789474 0.0359167792 0.9934735 0.9988746 0.9202917
## 0.4789474 0.0556895330 0.9930800 0.9981054 0.9026667
## 0.4789474 0.0863474996 0.9923014 0.9973789 0.8762917
## 0.4789474 0.1338831607 0.9907828 0.9962393 0.8517500
## 0.4789474 0.2075879534 0.9882523 0.9962393 0.8122083
## 0.4789474 0.3218683974 0.9884982 0.9996154 0.7077917
## 0.4789474 0.4990620292 0.9900808 1.0000000 0.4545833
## 0.5263158 0.0001199795 0.9912007 0.9765385 0.9447083
## 0.5263158 0.0001860300 0.9917228 0.9768946 0.9478333
## 0.5263158 0.0002884425 0.9921644 0.9791453 0.9503750
## 0.5263158 0.0004472347 0.9925201 0.9821225 0.9522500
## 0.5263158 0.0006934444 0.9930370 0.9847436 0.9541667
## 0.5263158 0.0010751965 0.9934338 0.9877208 0.9528750
## 0.5263158 0.0016671092 0.9938051 0.9910684 0.9516667
## 0.5263158 0.0025848791 0.9940205 0.9955128 0.9510833
## 0.5263158 0.0040078959 0.9940899 0.9977493 0.9491667
## 0.5263158 0.0062143058 0.9942791 0.9981339 0.9472917
## 0.5263158 0.0096353793 0.9942764 0.9985185 0.9454167
## 0.5263158 0.0149398076 0.9941616 0.9988889 0.9422500
## 0.5263158 0.0231644074 0.9938767 0.9988746 0.9297500
## 0.5263158 0.0359167792 0.9935448 0.9985043 0.9177917
## 0.5263158 0.0556895330 0.9930560 0.9984900 0.8970000
## 0.5263158 0.0863474996 0.9920922 0.9969943 0.8731667
## 0.5263158 0.1338831607 0.9902929 0.9962393 0.8442500
## 0.5263158 0.2075879534 0.9881862 0.9966097 0.7969583
## 0.5263158 0.3218683974 0.9888074 1.0000000 0.6820000
## 0.5263158 0.4990620292 0.9902673 1.0000000 0.3318750
## 0.5736842 0.0001199795 0.9910156 0.9761681 0.9447083
## 0.5736842 0.0001860300 0.9916268 0.9765242 0.9459583
## 0.5736842 0.0002884425 0.9920940 0.9780199 0.9491250
## 0.5736842 0.0004472347 0.9924259 0.9821225 0.9516250
## 0.5736842 0.0006934444 0.9929898 0.9843732 0.9541667
## 0.5736842 0.0010751965 0.9933635 0.9877208 0.9528750
## 0.5736842 0.0016671092 0.9937347 0.9910684 0.9522917
## 0.5736842 0.0025848791 0.9939239 0.9951425 0.9504167
## 0.5736842 0.0040078959 0.9940899 0.9970085 0.9497917
## 0.5736842 0.0062143058 0.9943247 0.9981339 0.9466667
## 0.5736842 0.0096353793 0.9942533 0.9985185 0.9454167
## 0.5736842 0.0149398076 0.9940663 0.9988889 0.9416250
## 0.5736842 0.0231644074 0.9939238 0.9988746 0.9285000
## 0.5736842 0.0359167792 0.9935465 0.9985043 0.9115000
## 0.5736842 0.0556895330 0.9931005 0.9984900 0.8944583
## 0.5736842 0.0863474996 0.9920459 0.9973789 0.8662083
## 0.5736842 0.1338831607 0.9898475 0.9962393 0.8405000
## 0.5736842 0.2075879534 0.9884037 0.9984900 0.7868750
## 0.5736842 0.3218683974 0.9895355 1.0000000 0.6687500
## 0.5736842 0.4990620292 0.9900557 1.0000000 0.2154583
## 0.6210526 0.0001199795 0.9908767 0.9754274 0.9447083
## 0.6210526 0.0001860300 0.9915077 0.9757835 0.9459583
## 0.6210526 0.0002884425 0.9920477 0.9780199 0.9478333
## 0.6210526 0.0004472347 0.9924250 0.9810114 0.9503750
## 0.6210526 0.0006934444 0.9929179 0.9836182 0.9535417
## 0.6210526 0.0010751965 0.9933394 0.9873504 0.9528750
## 0.6210526 0.0016671092 0.9936395 0.9914387 0.9522917
## 0.6210526 0.0025848791 0.9939230 0.9947721 0.9504167
## 0.6210526 0.0040078959 0.9941578 0.9970085 0.9510833
## 0.6210526 0.0062143058 0.9943007 0.9977635 0.9454167
## 0.6210526 0.0096353793 0.9942764 0.9985185 0.9453750
## 0.6210526 0.0149398076 0.9940654 0.9988889 0.9403750
## 0.6210526 0.0231644074 0.9939247 0.9985043 0.9266250
## 0.6210526 0.0359167792 0.9936641 0.9985043 0.9095833
## 0.6210526 0.0556895330 0.9931219 0.9984900 0.8919583
## 0.6210526 0.0863474996 0.9918120 0.9977493 0.8630833
## 0.6210526 0.1338831607 0.9895455 0.9962393 0.8380000
## 0.6210526 0.2075879534 0.9889396 0.9988604 0.7793750
## 0.6210526 0.3218683974 0.9898658 1.0000000 0.6423750
## 0.6210526 0.4990620292 0.9895595 1.0000000 0.1121667
## 0.6684211 0.0001199795 0.9907823 0.9750570 0.9447083
## 0.6684211 0.0001860300 0.9914598 0.9761681 0.9453333
## 0.6684211 0.0002884425 0.9920014 0.9776496 0.9472083
## 0.6684211 0.0004472347 0.9923547 0.9806410 0.9503750
## 0.6684211 0.0006934444 0.9928003 0.9836182 0.9528750
## 0.6684211 0.0010751965 0.9932932 0.9865954 0.9528750
## 0.6684211 0.0016671092 0.9935685 0.9910684 0.9522917
## 0.6684211 0.0025848791 0.9938278 0.9936610 0.9516667
## 0.6684211 0.0040078959 0.9940610 0.9962678 0.9517083
## 0.6684211 0.0062143058 0.9942773 0.9977778 0.9441667
## 0.6684211 0.0096353793 0.9942292 0.9985185 0.9447500
## 0.6684211 0.0149398076 0.9940191 0.9988889 0.9372500
## 0.6684211 0.0231644074 0.9939247 0.9985043 0.9253750
## 0.6684211 0.0359167792 0.9937139 0.9985043 0.9070833
## 0.6684211 0.0556895330 0.9930740 0.9984900 0.8894583
## 0.6684211 0.0863474996 0.9916572 0.9977493 0.8587083
## 0.6684211 0.1338831607 0.9895001 0.9981197 0.8355000
## 0.6684211 0.2075879534 0.9892931 1.0000000 0.7737500
## 0.6684211 0.3218683974 0.9901492 1.0000000 0.6103750
## 0.6684211 0.4990620292 0.9882585 1.0000000 0.0213750
## 0.7157895 0.0001199795 0.9906425 0.9739316 0.9440833
## 0.7157895 0.0001860300 0.9913200 0.9750570 0.9447083
## 0.7157895 0.0002884425 0.9918601 0.9776496 0.9465833
## 0.7157895 0.0004472347 0.9922374 0.9798860 0.9497500
## 0.7157895 0.0006934444 0.9926614 0.9836182 0.9522500
## 0.7157895 0.0010751965 0.9932451 0.9862393 0.9541250
## 0.7157895 0.0016671092 0.9935213 0.9907123 0.9522917
## 0.7157895 0.0025848791 0.9938262 0.9932906 0.9522917
## 0.7157895 0.0040078959 0.9940138 0.9966382 0.9492083
## 0.7157895 0.0062143058 0.9943005 0.9973932 0.9447500
## 0.7157895 0.0096353793 0.9941598 0.9985185 0.9428750
## 0.7157895 0.0149398076 0.9940191 0.9985043 0.9366250
## 0.7157895 0.0231644074 0.9939710 0.9985043 0.9234167
## 0.7157895 0.0359167792 0.9937355 0.9985043 0.9051667
## 0.7157895 0.0556895330 0.9928187 0.9984900 0.8807083
## 0.7157895 0.0863474996 0.9917064 0.9977493 0.8549583
## 0.7157895 0.1338831607 0.9892170 0.9988604 0.8329583
## 0.7157895 0.2075879534 0.9897416 1.0000000 0.7731667
## 0.7157895 0.3218683974 0.9898448 1.0000000 0.5671250
## 0.7157895 0.4990620292 0.9846020 1.0000000 0.0000000
## 0.7631579 0.0001199795 0.9905036 0.9731766 0.9440833
## 0.7631579 0.0001860300 0.9911349 0.9743162 0.9440833
## 0.7631579 0.0002884425 0.9917428 0.9761681 0.9459583
## 0.7631579 0.0004472347 0.9921911 0.9787749 0.9503750
## 0.7631579 0.0006934444 0.9925920 0.9821225 0.9522500
## 0.7631579 0.0010751965 0.9931500 0.9851282 0.9528750
## 0.7631579 0.0016671092 0.9934741 0.9896011 0.9522917
## 0.7631579 0.0025848791 0.9936855 0.9925356 0.9535417
## 0.7631579 0.0040078959 0.9940138 0.9966239 0.9473333
## 0.7631579 0.0062143058 0.9943708 0.9970228 0.9441250
## 0.7631579 0.0096353793 0.9942533 0.9985185 0.9410000
## 0.7631579 0.0149398076 0.9941357 0.9988889 0.9353750
## 0.7631579 0.0231644074 0.9940648 0.9985043 0.9209167
## 0.7631579 0.0359167792 0.9938299 0.9985043 0.9013750
## 0.7631579 0.0556895330 0.9928245 0.9984900 0.8757083
## 0.7631579 0.0863474996 0.9915173 0.9977493 0.8512083
## 0.7631579 0.1338831607 0.9895273 0.9988604 0.8310833
## 0.7631579 0.2075879534 0.9898166 1.0000000 0.7694167
## 0.7631579 0.3218683974 0.9895340 1.0000000 0.5130417
## 0.7631579 0.4990620292 0.9835697 1.0000000 0.0000000
## 0.8105263 0.0001199795 0.9904095 0.9728205 0.9447083
## 0.8105263 0.0001860300 0.9909951 0.9735755 0.9440833
## 0.8105263 0.0002884425 0.9915336 0.9754274 0.9459583
## 0.8105263 0.0004472347 0.9920738 0.9773077 0.9490833
## 0.8105263 0.0006934444 0.9924738 0.9802707 0.9510000
## 0.8105263 0.0010751965 0.9930791 0.9832621 0.9522500
## 0.8105263 0.0016671092 0.9933806 0.9881054 0.9529167
## 0.8105263 0.0025848791 0.9936615 0.9921652 0.9529583
## 0.8105263 0.0040078959 0.9940379 0.9966239 0.9473333
## 0.8105263 0.0062143058 0.9943236 0.9970085 0.9441250
## 0.8105263 0.0096353793 0.9943699 0.9981339 0.9397500
## 0.8105263 0.0149398076 0.9941582 0.9988889 0.9347500
## 0.8105263 0.0231644074 0.9941582 0.9981339 0.9190417
## 0.8105263 0.0359167792 0.9938521 0.9985043 0.8944583
## 0.8105263 0.0556895330 0.9927139 0.9981197 0.8719583
## 0.8105263 0.0863474996 0.9915432 0.9984900 0.8505833
## 0.8105263 0.1338831607 0.9898329 1.0000000 0.8291667
## 0.8105263 0.2075879534 0.9898382 1.0000000 0.7625417
## 0.8105263 0.3218683974 0.9892488 1.0000000 0.4746250
## 0.8105263 0.4990620292 0.5000000 1.0000000 0.0000000
## 0.8578947 0.0001199795 0.9901061 0.9720798 0.9440833
## 0.8578947 0.0001860300 0.9908538 0.9724644 0.9440833
## 0.8578947 0.0002884425 0.9913929 0.9732051 0.9459583
## 0.8578947 0.0004472347 0.9918637 0.9765670 0.9490833
## 0.8578947 0.0006934444 0.9924275 0.9791453 0.9503750
## 0.8578947 0.0010751965 0.9930078 0.9836325 0.9516250
## 0.8578947 0.0016671092 0.9932880 0.9884758 0.9529167
## 0.8578947 0.0025848791 0.9936615 0.9917806 0.9517083
## 0.8578947 0.0040078959 0.9940619 0.9958689 0.9460417
## 0.8578947 0.0062143058 0.9942061 0.9970085 0.9447500
## 0.8578947 0.0096353793 0.9942971 0.9973789 0.9397500
## 0.8578947 0.0149398076 0.9941119 0.9977635 0.9341250
## 0.8578947 0.0231644074 0.9942045 0.9977493 0.9171667
## 0.8578947 0.0359167792 0.9935512 0.9977635 0.8944583
## 0.8578947 0.0556895330 0.9925737 0.9981197 0.8693750
## 0.8578947 0.0863474996 0.9912346 0.9984900 0.8493333
## 0.8578947 0.1338831607 0.9898578 1.0000000 0.8259167
## 0.8578947 0.2075879534 0.9895050 1.0000000 0.7555833
## 0.8578947 0.3218683974 0.9882327 1.0000000 0.3805000
## 0.8578947 0.4990620292 0.5000000 1.0000000 0.0000000
## 0.9052632 0.0001199795 0.9897571 0.9705840 0.9434583
## 0.9052632 0.0001860300 0.9906412 0.9702279 0.9434583
## 0.9052632 0.0002884425 0.9910646 0.9728348 0.9459583
## 0.9052632 0.0004472347 0.9917455 0.9750712 0.9478333
## 0.9052632 0.0006934444 0.9922381 0.9784046 0.9497500
## 0.9052632 0.0010751965 0.9928921 0.9828775 0.9516250
## 0.9052632 0.0016671092 0.9931921 0.9877066 0.9516667
## 0.9052632 0.0025848791 0.9935680 0.9917806 0.9530000
## 0.9052632 0.0040078959 0.9939444 0.9951425 0.9454167
## 0.9052632 0.0062143058 0.9941582 0.9958832 0.9435000
## 0.9052632 0.0096353793 0.9942740 0.9962678 0.9385000
## 0.9052632 0.0149398076 0.9941823 0.9966382 0.9277917
## 0.9052632 0.0231644074 0.9940176 0.9962536 0.9127917
## 0.9052632 0.0359167792 0.9933881 0.9966239 0.8938333
## 0.9052632 0.0556895330 0.9925303 0.9981197 0.8681667
## 0.9052632 0.0863474996 0.9906048 0.9984900 0.8493333
## 0.9052632 0.1338831607 0.9897216 1.0000000 0.8227500
## 0.9052632 0.2075879534 0.9890837 1.0000000 0.7418333
## 0.9052632 0.3218683974 0.9867765 1.0000000 0.2872917
## 0.9052632 0.4990620292 0.5000000 1.0000000 0.0000000
## 0.9526316 0.0001199795 0.9894529 0.9687322 0.9434583
## 0.9526316 0.0001860300 0.9903848 0.9690883 0.9428333
## 0.9526316 0.0002884425 0.9909696 0.9713248 0.9447083
## 0.9526316 0.0004472347 0.9916049 0.9735755 0.9478333
## 0.9526316 0.0006934444 0.9921206 0.9780199 0.9491250
## 0.9526316 0.0010751965 0.9926588 0.9810114 0.9509583
## 0.9526316 0.0016671092 0.9930770 0.9869516 0.9516667
## 0.9526316 0.0025848791 0.9934282 0.9895442 0.9530000
## 0.9526316 0.0040078959 0.9938278 0.9947436 0.9460417
## 0.9526316 0.0062143058 0.9939944 0.9951140 0.9428750
## 0.9526316 0.0096353793 0.9942045 0.9951140 0.9360000
## 0.9526316 0.0149398076 0.9942286 0.9951140 0.9271667
## 0.9526316 0.0231644074 0.9940191 0.9958689 0.9115417
## 0.9526316 0.0359167792 0.9933422 0.9958832 0.8944583
## 0.9526316 0.0556895330 0.9923433 0.9962536 0.8706667
## 0.9526316 0.0863474996 0.9898787 0.9973932 0.8518750
## 0.9526316 0.1338831607 0.9893381 0.9996296 0.8233750
## 0.9526316 0.2075879534 0.9884410 1.0000000 0.7205417
## 0.9526316 0.3218683974 0.9846057 1.0000000 0.2029583
## 0.9526316 0.4990620292 0.5000000 1.0000000 0.0000000
## 1.0000000 0.0001199795 0.9892668 0.9672222 0.9434583
## 1.0000000 0.0001860300 0.9900340 0.9687179 0.9428333
## 1.0000000 0.0002884425 0.9907362 0.9709687 0.9434583
## 1.0000000 0.0004472347 0.9913500 0.9713248 0.9465833
## 1.0000000 0.0006934444 0.9919824 0.9769231 0.9485000
## 1.0000000 0.0010751965 0.9924460 0.9799003 0.9509583
## 1.0000000 0.0016671092 0.9927726 0.9858405 0.9510000
## 1.0000000 0.0025848791 0.9932397 0.9888034 0.9511250
## 1.0000000 0.0040078959 0.9937861 0.9936182 0.9460417
## 1.0000000 0.0062143058 0.9940674 0.9951140 0.9397500
## 1.0000000 0.0096353793 0.9942989 0.9951140 0.9341250
## 1.0000000 0.0149398076 0.9943942 0.9951140 0.9246667
## 1.0000000 0.0231644074 0.9941841 0.9951140 0.9159167
## 1.0000000 0.0359167792 0.9934367 0.9951140 0.8945417
## 1.0000000 0.0556895330 0.9921541 0.9958832 0.8731250
## 1.0000000 0.0863474996 0.9901894 0.9951425 0.8600000
## 1.0000000 0.1338831607 0.9890839 0.9996296 0.8233750
## 1.0000000 0.2075879534 0.9871237 1.0000000 0.7162083
## 1.0000000 0.3218683974 0.9798371 1.0000000 0.1179583
## 1.0000000 0.4990620292 0.5000000 1.0000000 0.0000000
##
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were alpha = 0.1 and lambda = 0.006214306.
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(dplyr)
library(nycflights13)
data("flights")
glimpse(flights)
## Rows: 336,776
## Columns: 19
## $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
## $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ day <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ dep_time <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
## $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
## $ dep_delay <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
## $ arr_time <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
## $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
## $ arr_delay <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
## $ carrier <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
## $ flight <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
## $ tailnum <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
## $ origin <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
## $ dest <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
## $ air_time <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
## $ distance <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
## $ hour <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
## $ minute <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
## $ time_hour <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…
today()
## [1] "2024-01-31"
now()
## [1] "2024-01-31 15:59:45 KST"
ymd("2023-01-31")
## [1] "2023-01-31"
mdy("january 1st, 2023")
## [1] "2023-01-01"
dmy("31-jan-2023")
## [1] "2023-01-31"
ymd(20230131)
## [1] "2023-01-31"
ymd_hms("2017-01-31 20:11:59")
## [1] "2017-01-31 20:11:59 UTC"
mdy_hm("01/31/2017 08:01")
## [1] "2017-01-31 08:01:00 UTC"
library(dplyr)
library(nycflights13)
data("flights")
glimpse(flights)
## Rows: 336,776
## Columns: 19
## $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
## $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ day <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ dep_time <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
## $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
## $ dep_delay <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
## $ arr_time <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
## $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
## $ arr_delay <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
## $ carrier <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
## $ flight <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
## $ tailnum <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
## $ origin <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
## $ dest <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
## $ air_time <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
## $ distance <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
## $ hour <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
## $ minute <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
## $ time_hour <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…
flights %>% select(year,month,day,hour,minute)
## # A tibble: 336,776 × 5
## year month day hour minute
## <int> <int> <int> <dbl> <dbl>
## 1 2013 1 1 5 15
## 2 2013 1 1 5 29
## 3 2013 1 1 5 40
## 4 2013 1 1 5 45
## 5 2013 1 1 6 0
## 6 2013 1 1 5 58
## 7 2013 1 1 6 0
## 8 2013 1 1 6 0
## 9 2013 1 1 6 0
## 10 2013 1 1 6 0
## # ℹ 336,766 more rows
flights %>%
select(year,month,day,hour,minute) %>%
mutate(departure=make_datetime(year,month,day,hour,minute))
## # A tibble: 336,776 × 6
## year month day hour minute departure
## <int> <int> <int> <dbl> <dbl> <dttm>
## 1 2013 1 1 5 15 2013-01-01 05:15:00
## 2 2013 1 1 5 29 2013-01-01 05:29:00
## 3 2013 1 1 5 40 2013-01-01 05:40:00
## 4 2013 1 1 5 45 2013-01-01 05:45:00
## 5 2013 1 1 6 0 2013-01-01 06:00:00
## 6 2013 1 1 5 58 2013-01-01 05:58:00
## 7 2013 1 1 6 0 2013-01-01 06:00:00
## 8 2013 1 1 6 0 2013-01-01 06:00:00
## 9 2013 1 1 6 0 2013-01-01 06:00:00
## 10 2013 1 1 6 0 2013-01-01 06:00:00
## # ℹ 336,766 more rows
library(ggplot2)
data("economics")
glimpse(economics)
## Rows: 574
## Columns: 6
## $ date <date> 1967-07-01, 1967-08-01, 1967-09-01, 1967-10-01, 1967-11-01, …
## $ pce <dbl> 506.7, 509.8, 515.6, 512.2, 517.4, 525.1, 530.9, 533.6, 544.3…
## $ pop <dbl> 198712, 198911, 199113, 199311, 199498, 199657, 199808, 19992…
## $ psavert <dbl> 12.6, 12.6, 11.9, 12.9, 12.8, 11.8, 11.7, 12.3, 11.7, 12.3, 1…
## $ uempmed <dbl> 4.5, 4.7, 4.6, 4.9, 4.7, 4.8, 5.1, 4.5, 4.1, 4.6, 4.4, 4.4, 4…
## $ unemploy <dbl> 2944, 2945, 2958, 3143, 3066, 3018, 2878, 3001, 2877, 2709, 2…
library(lubridate)
df<-read.csv("netflix.csv")
glimpse(df)
## Rows: 8,807
## Columns: 11
## $ show_id <chr> "s1", "s2", "s3", "s4", "s5", "s6", "s7", "s8", "s9", "s1…
## $ type <chr> "Movie", "TV Show", "TV Show", "TV Show", "TV Show", "TV …
## $ title <chr> "Dick Johnson Is Dead", "Blood & Water", "Ganglands", "Ja…
## $ director <chr> "Kirsten Johnson", "", "Julien Leclercq", "", "", "Mike F…
## $ cast <chr> "", "Ama Qamata, Khosi Ngema, Gail Mabalane, Thabang Mola…
## $ country <chr> "United States", "South Africa", "", "", "India", "", "",…
## $ date_added <chr> "25-Sep-21", "24-Sep-21", "24-Sep-21", "24-Sep-21", "24-S…
## $ release_year <int> 2020, 2021, 2021, 2021, 2021, 2021, 2021, 1993, 2021, 202…
## $ rating <chr> "PG-13", "TV-MA", "TV-MA", "TV-MA", "TV-MA", "TV-MA", "PG…
## $ duration <chr> "90 min", "2 Seasons", "1 Season", "1 Season", "2 Seasons…
## $ listed_in <chr> "Documentaries", "International TV Shows, TV Dramas, TV M…
df$y2<-parse_date_time(df$date_added,orders=c("mdy","dmy"),train=F)
df %>% filter(country=="United Kingdom") %>%
filter(y2>='2018-01-01'&y2<='2018-01-31')
## show_id type title director
## 1 s5058 TV Show Retribution
## 2 s5075 Movie Bad Day for the Cut Chris Baugh
## 3 s5098 TV Show Lovesick
## 4 s7399 Movie Manolo: The Boy Who Made Shoes for Lizards Michael Roberts
## 5 s8358 TV Show The Inbetweeners
## 6 s8620 Movie Treasures from the Wreck of the Unbelievable Sam Hobkinson
## cast
## 1 Georgina Campbell, Joe Dempsie, Adrian Edmondson, Steve Evets, Laura Fraser, Julie Graham, John Lynch, Gary Lewis, Juliet Stevenson, Joanna Vanderham
## 2 Nigel O'Neill, Susan Lynch, J?zef Pawlowski, Stuart Graham, David Pearse, Brian Milligan, Anna Prochniak, Stella McCusker, Ian McElhinney, Lalor Roddy
## 3 Johnny Flynn, Antonia Thomas, Daniel Ings, Hannah Britland, Joshua McGuire, Richard Thomson, Jessica Ellerby
## 4 Manolo Blahnik, Anna Wintour, Andr? Leon Talley, Paloma Picasso, Candace Bushnell, Iman, Rihanna, Naomi Campbell, Isaac Mizrahi, Rupert Everett, Sofia Coppola, John Galliano
## 5 Simon Bird, James Buckley, Blake Harrison, Joe Thomas, Greg Davies, Emily Head, Martin Trenaman, Belinda Stewart-Wilson, Robin Weaver, Henry Lloyd-Hughes
## 6
## country date_added release_year rating duration
## 1 United Kingdom 30-Jan-18 2016 TV-MA 1 Season
## 2 United Kingdom 18-Jan-18 2017 TV-MA 99 min
## 3 United Kingdom 01-Jan-18 2018 TV-MA 3 Seasons
## 4 United Kingdom 15-Jan-18 2017 TV-MA 89 min
## 5 United Kingdom January 1, 2018 2010 TV-MA 3 Seasons
## 6 United Kingdom 01-Jan-18 2017 TV-G 82 min
## listed_in y2
## 1 British TV Shows, Crime TV Shows, International TV Shows 2018-01-30
## 2 Independent Movies, International Movies, Thrillers 2018-01-18
## 3 British TV Shows, International TV Shows, Romantic TV Shows 2018-01-01
## 4 Documentaries, International Movies 2018-01-15
## 5 British TV Shows, TV Comedies 2018-01-01
## 6 Documentaries, Dramas, International Movies 2018-01-01