Lavaan
Sith
2023-03-30
R Libraries
Minimum set of libraries
require(car)## Loading required package: car## Warning: package 'car' was built under R version 4.3.2## Loading required package: carData## Warning: package 'carData' was built under R version 4.3.2require(CauseAndCorrelation)## Loading required package: CauseAndCorrelation## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
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## intersect, setdiff, setequal, unionrequire(factoextra)## Loading required package: factoextra## Warning: package 'factoextra' was built under R version 4.3.2## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.3.2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBarequire(ggcorrplot)## Loading required package: ggcorrplot## Warning: package 'ggcorrplot' was built under R version 4.3.2require(kableExtra)## Loading required package: kableExtra## Warning: package 'kableExtra' was built under R version 4.3.2## Error: package or namespace load failed for 'kableExtra':
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## lavaan is FREE software! Please report any bugs.require(lavaanPlot)## Loading required package: lavaanPlot## Warning: package 'lavaanPlot' was built under R version 4.3.2require(magrittr)## Loading required package: magrittr## Warning: package 'magrittr' was built under R version 4.3.2require(psych)## Loading required package: psych## Warning: package 'psych' was built under R version 4.3.2##
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## predict.MxModel OpenMxrequire(tidyverse)## Loading required package: tidyverse## Warning: package 'tidyverse' was built under R version 4.3.2## Warning: package 'tibble' was built under R version 4.3.2## Warning: package 'tidyr' was built under R version 4.3.2## Warning: package 'readr' was built under R version 4.3.2## Warning: package 'purrr' was built under R version 4.3.2## Warning: package 'stringr' was built under R version 4.3.2## Warning: package 'forcats' was built under R version 4.3.2## Warning: package 'lubridate' was built under R version 4.3.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsrequire(performance)## Loading required package: performance## Warning: package 'performance' was built under R version 4.3.2mydata=read.csv('jasp4.csv')R Functions
Correlation, printing, and citing
corfunction=function(d,m='pearson',sz=2){
mycorr=cor(d[, 1:ncol(d)], method=m); p.mat=ggcorrplot::cor_pmat(d[,1:ncol(d)])
myplot=ggcorrplot(mycorr, hc.order=T,type="lower",
colors=c("red", "white","green"),tl.cex = 8,
tl.col = "black", lab=TRUE, lab_size=sz, p.mat=p.mat,sig.level=0.01,
insig="pch", pch=4)
print(myplot)}
myprint=function(x){print(x) }
mycite=function(x){citation(x)}Descriptives
Descriptives prior to normalizing
myprint(describe(mydata))## vars n mean sd median trimmed mad min
## GeoName* 1 382 191.50 110.42 191.50 191.50 141.59 1.00
## New_MEFI_Score 2 382 6.77 0.75 6.79 6.79 0.76 4.00
## MEFI_Score 3 382 6.74 0.87 6.76 6.77 0.91 3.82
## Govt_Consumption 4 382 6.93 1.69 7.18 7.08 1.28 0.00
## Govt_Xfers_Subsidies 5 382 8.84 0.74 9.08 8.97 0.46 5.56
## Govt_Retirement 6 382 4.24 2.02 4.53 4.33 2.12 0.00
## Income_Payroll_Tax 7 382 4.81 2.81 4.28 4.69 1.89 0.00
## Sales_Tax 8 382 5.39 1.71 5.44 5.43 1.24 0.00
## Property_Tax 9 382 8.27 1.13 8.40 8.36 0.83 0.00
## Minimum_Wage_Over_Income 10 382 7.45 1.23 7.59 7.53 1.10 2.98
## Percent_Govt_Employment 11 382 8.35 0.91 8.48 8.43 0.71 0.00
## Percent_Private_Union_Density 12 382 6.37 2.76 7.04 6.59 3.02 0.00
## Minimum_Wage_Over_10._Income 13 382 6.68 1.79 7.14 6.91 1.50 0.84
## max range skew kurtosis se
## GeoName* 382.00 381.00 0.00 -1.21 5.65
## New_MEFI_Score 8.62 4.62 -0.37 0.33 0.04
## MEFI_Score 8.81 4.99 -0.29 -0.19 0.04
## Govt_Consumption 10.00 10.00 -1.47 4.21 0.09
## Govt_Xfers_Subsidies 9.74 4.17 -1.81 3.23 0.04
## Govt_Retirement 8.12 8.12 -0.42 -0.56 0.10
## Income_Payroll_Tax 10.00 10.00 0.50 -0.53 0.14
## Sales_Tax 9.81 9.81 -0.28 1.30 0.09
## Property_Tax 10.00 10.00 -3.27 20.97 0.06
## Minimum_Wage_Over_Income 10.00 7.02 -0.58 0.31 0.06
## Percent_Govt_Employment 10.00 10.00 -2.52 18.23 0.05
## Percent_Private_Union_Density 10.00 10.00 -0.54 -0.81 0.14
## Minimum_Wage_Over_10._Income 9.28 8.44 -1.08 0.51 0.09Correlations
#colnames(mydata)[4:13]=c('X1A','X1B','X1C','X2A','X2B','X2C','X3A', 'X3B','X3C', 'New3A')
corfunction(mydata[,-c(1:3)], ,3)
KDE Pairs
KDE Pairs of data
kdepairs(mydata[, 2:3])## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
kdepairs(mydata[,4:6])## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
kdepairs(mydata[,7:9])## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
kdepairs(mydata[,10:13])## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
Normalize
Z-scores to adjust for magnitudes
tempname=mydata$GeoName
mydata[,4:13]=as.data.frame(apply(mydata[, 4:13], 2, 'scale'))
mydata$GeoName=tempname
myprint(describe(mydata))## vars n mean sd median trimmed mad
## GeoName* 1 382 191.50 110.42 191.50 191.50 141.59
## New_MEFI_Score 2 382 6.77 0.75 6.79 6.79 0.76
## MEFI_Score 3 382 6.74 0.87 6.76 6.77 0.91
## Govt_Consumption 4 382 0.00 1.00 0.15 0.09 0.76
## Govt_Xfers_Subsidies 5 382 0.00 1.00 0.32 0.18 0.62
## Govt_Retirement 6 382 0.00 1.00 0.15 0.05 1.05
## Income_Payroll_Tax 7 382 0.00 1.00 -0.19 -0.04 0.67
## Sales_Tax 8 382 0.00 1.00 0.03 0.02 0.72
## Property_Tax 9 382 0.00 1.00 0.12 0.08 0.74
## Minimum_Wage_Over_Income 10 382 0.00 1.00 0.11 0.06 0.90
## Percent_Govt_Employment 11 382 0.00 1.00 0.15 0.09 0.78
## Percent_Private_Union_Density 12 382 0.00 1.00 0.24 0.08 1.09
## Minimum_Wage_Over_10._Income 13 382 0.00 1.00 0.25 0.13 0.84
## min max range skew kurtosis se
## GeoName* 1.00 382.00 381.00 0.00 -1.21 5.65
## New_MEFI_Score 4.00 8.62 4.62 -0.37 0.33 0.04
## MEFI_Score 3.82 8.81 4.99 -0.29 -0.19 0.04
## Govt_Consumption -4.10 1.82 5.92 -1.47 4.21 0.05
## Govt_Xfers_Subsidies -4.40 1.20 5.60 -1.81 3.23 0.05
## Govt_Retirement -2.09 1.92 4.01 -0.42 -0.56 0.05
## Income_Payroll_Tax -1.71 1.85 3.56 0.50 -0.53 0.05
## Sales_Tax -3.15 2.58 5.73 -0.28 1.30 0.05
## Property_Tax -7.33 1.53 8.87 -3.27 20.97 0.05
## Minimum_Wage_Over_Income -3.64 2.07 5.72 -0.58 0.31 0.05
## Percent_Govt_Employment -9.22 1.82 11.05 -2.52 18.23 0.05
## Percent_Private_Union_Density -2.31 1.32 3.63 -0.54 -0.81 0.05
## Minimum_Wage_Over_10._Income -3.26 1.45 4.71 -1.08 0.51 0.05Correlations
Not hierarchically clustered
corfunction(mydata[,4:13])
## Vanishing Tetrads
#print("Tetrad Testing, M1")
#vanishing.tetrads(mydata[, 4:12])
#print("Tetrad Testing, M2")
#vanishing.tetrads(mydata[,4:11,13])M1 Base with Cov (Using Original Scores w/o Cov Structure)
Baseline model
Estimate of negative variance exists for the forced additive measure of Economic Freedom (P2 and P3).
colnames(mydata)[4:13]=c('X1A','X1B','X1C','X2A','X2B','X2C','X3A', 'X3B','X3C', 'New3A')
mod='
Economic_Freedom=~1/3*Government_Spending+1/3*Taxation+1/3*Labor_Market_Freedom
Government_Spending=~1/3*X1A+1/3*X1B+1/3*X1C
Taxation=~1/3*X2A+1/3*X2B+1/3*X2C
Labor_Market_Freedom=~1/3*X3A+1/3*X3B+1/3*X3C
'
fit=cfa(mod, data=mydata, do.fit=T, estimator='DWLS')## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negative(mys1=summary(fit, standardized=T,fit.measures = TRUE))## lavaan 0.6.16 ended normally after 50 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 13
##
## Number of observations 382
##
## Model Test User Model:
##
## Test statistic 463.355
## Degrees of freedom 32
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 819.555
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.449
## Tucker-Lewis Index (TLI) 0.381
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.188
## 90 Percent confidence interval - lower 0.173
## 90 Percent confidence interval - upper 0.203
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.172
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Economic_Freedom =~
## Gvrnmnt_Spndng 0.333 0.830 0.830
## Taxation 0.333 NA NA
## Labr_Mrkt_Frdm 0.333 0.839 0.839
## Government_Spending =~
## X1A 0.333 0.480 0.480
## X1B 0.333 0.480 0.480
## X1C 0.333 0.480 0.480
## Taxation =~
## X2A 0.333 NA NA
## X2B 0.333 NA NA
## X2C 0.333 NA NA
## Labor_Market_Freedom =~
## X3A 0.333 0.474 0.474
## X3B 0.333 0.474 0.474
## X3C 0.333 0.474 0.474
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .X1A 0.770 0.132 5.846 0.000 0.770 0.770
## .X1B 0.770 0.122 6.334 0.000 0.770 0.770
## .X1C 0.770 0.070 11.075 0.000 0.770 0.770
## .X2A 1.176 0.071 16.598 0.000 1.176 1.176
## .X2B 1.176 0.099 11.869 0.000 1.176 1.176
## .X2C 1.176 0.248 4.744 0.000 1.176 1.176
## .X3A 0.775 0.084 9.232 0.000 0.775 0.775
## .X3B 0.775 0.233 3.334 0.001 0.775 0.775
## .X3C 0.775 0.064 12.080 0.000 0.775 0.775
## Economic_Fredm 12.820 0.851 15.057 0.000 1.000 1.000
## .Gvrnmnt_Spndng 0.646 0.304 2.122 0.034 0.312 0.312
## .Taxation -3.006 0.319 -9.437 0.000 NA NA
## .Labr_Mrkt_Frdm 0.597 0.298 2.003 0.045 0.295 0.295lavaanPlot(model = fit, edge_options = list(color = "grey"),coefs = TRUE, covs=F)temp2=lavPredict(fit)[,1]## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negative#mydata$ScoreM1=10*(temp2-min(temp2))/(max(temp2)-min(temp2)) #scale between 0 and 10M2 Sub with Cov (using Original Scores w/o Cov Structure)
Estimate of negative variance exists for the forced additive measure of Economic Freedom (P2 and P3).
mod='
Economic_Freedom=~1/3*Government_Spending+1/3*Taxation+1/3*Labor_Market_Freedom
Government_Spending=~1/3*X1A+1/3*X1B+1/3*X1C
Taxation=~1/3*X2A+1/3*X2B+1/3*X2C
Labor_Market_Freedom=~1/3*New3A+1/3*X3B+1/3*X3C
'
fit=cfa(mod, data=mydata, do.fit=T, estimator='DWLS')## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negative(mys2=summary(fit, standardized=T,fit.measures = TRUE))## lavaan 0.6.16 ended normally after 50 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 13
##
## Number of observations 382
##
## Model Test User Model:
##
## Test statistic 591.511
## Degrees of freedom 32
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 983.750
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.410
## Tucker-Lewis Index (TLI) 0.336
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.214
## 90 Percent confidence interval - lower 0.199
## 90 Percent confidence interval - upper 0.230
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.194
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Economic_Freedom =~
## Gvrnmnt_Spndng 0.333 0.856 0.856
## Taxation 0.333 NA NA
## Labr_Mrkt_Frdm 0.333 0.951 0.951
## Government_Spending =~
## X1A 0.333 0.480 0.480
## X1B 0.333 0.480 0.480
## X1C 0.333 0.480 0.480
## Taxation =~
## X2A 0.333 NA NA
## X2B 0.333 NA NA
## X2C 0.333 NA NA
## Labor_Market_Freedom =~
## New3A 0.333 0.432 0.432
## X3B 0.333 0.432 0.432
## X3C 0.333 0.432 0.432
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .X1A 0.770 0.132 5.846 0.000 0.770 0.770
## .X1B 0.770 0.122 6.334 0.000 0.770 0.770
## .X1C 0.770 0.070 11.075 0.000 0.770 0.770
## .X2A 1.176 0.071 16.598 0.000 1.176 1.176
## .X2B 1.176 0.099 11.869 0.000 1.176 1.176
## .X2C 1.176 0.248 4.744 0.000 1.176 1.176
## .New3A 0.814 0.086 9.447 0.000 0.814 0.814
## .X3B 0.814 0.232 3.503 0.000 0.814 0.814
## .X3C 0.814 0.063 12.938 0.000 0.814 0.814
## Economic_Fredm 13.666 0.829 16.480 0.000 1.000 1.000
## .Gvrnmnt_Spndng 0.551 0.303 1.818 0.069 0.266 0.266
## .Taxation -3.100 0.318 -9.755 0.000 NA NA
## .Labr_Mrkt_Frdm 0.159 0.274 0.580 0.562 0.095 0.095lavaanPlot(model = fit, edge_options = list(color = "grey"),coefs = TRUE,covs=T)temp2=lavPredict(fit)[,1]## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negative#mydata$ScoreM2=10*(temp2-min(temp2))/(max(temp2)-min(temp2)) #scale between 0 and 10M3 Non-Fixed Weights
Statistically significant estimate of negative variance exists for the forced additive measure of Economic Freedom (P1).
lavOptions("verbose")## $verbose
## [1] FALSEmod='
Economic_Freedom=~Government_Spending+Taxation+Labor_Market_Freedom
Government_Spending=~X1A+X1B+X1C
Taxation=~X2A+X2B+X2C
Labor_Market_Freedom=~X3A+X3B+X3C
'
fit=cfa(mod, data=mydata, do.fit=T, estimator='DWLS')## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negative(mys3=summary(fit, standardized=T, fit.measures = TRUE))## lavaan 0.6.16 ended normally after 77 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 382
##
## Model Test User Model:
##
## Test statistic 217.474
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 819.555
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.753
## Tucker-Lewis Index (TLI) 0.630
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.145
## 90 Percent confidence interval - lower 0.128
## 90 Percent confidence interval - upper 0.163
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.147
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Economic_Freedom =~
## Gvrnmnt_Spndng 1.000 1.172 1.172
## Taxation 1.501 0.302 4.979 0.000 0.357 0.357
## Labr_Mrkt_Frdm 0.765 0.185 4.139 0.000 0.845 0.845
## Government_Spending =~
## X1A 1.000 0.336 0.336
## X1B 0.951 0.164 5.813 0.000 0.319 0.319
## X1C 2.090 0.308 6.795 0.000 0.702 0.702
## Taxation =~
## X2A 1.000 1.657 1.657
## X2B -0.192 0.074 -2.603 0.009 -0.318 -0.318
## X2C 0.045 0.024 1.867 0.062 0.075 0.075
## Labor_Market_Freedom =~
## X3A 1.000 0.356 0.356
## X3B 0.789 0.170 4.640 0.000 0.281 0.281
## X3C 1.892 0.303 6.237 0.000 0.674 0.674
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .X1A 0.887 0.131 6.761 0.000 0.887 0.887
## .X1B 0.898 0.120 7.491 0.000 0.898 0.898
## .X1C 0.507 0.115 4.413 0.000 0.507 0.507
## .X2A -1.745 1.092 -1.597 0.110 -1.745 -1.745
## .X2B 0.899 0.102 8.852 0.000 0.899 0.899
## .X2C 0.994 0.246 4.049 0.000 0.994 0.994
## .X3A 0.873 0.085 10.251 0.000 0.873 0.873
## .X3B 0.921 0.232 3.969 0.000 0.921 0.921
## .X3C 0.545 0.117 4.652 0.000 0.545 0.545
## Economic_Fredm 0.155 0.047 3.271 0.001 1.000 1.000
## .Gvrnmnt_Spndng -0.042 0.036 -1.171 0.242 -0.374 -0.374
## .Taxation 2.395 1.083 2.212 0.027 0.873 0.873
## .Labr_Mrkt_Frdm 0.036 0.028 1.301 0.193 0.286 0.286lavaanPlot(model = fit, edge_options = list(color = "grey"),coefs = TRUE, covs=F)temp2=lavPredict(fit)[,1]## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative
## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativemydata$ScoreM3=10*(temp2-min(temp2))/(max(temp2)-min(temp2)) #scale between 0 and 10M4 Sub Weights Free
Statistically significant estimate of negative variance exists for the forced additive measure of Economic Freedom (P2).
mod='
Economic_Freedom=~Government_Spending+Taxation+Labor_Market_Freedom
Government_Spending=~X1A+X1B+X1C
Taxation=~X2A+X2B+X2C
Labor_Market_Freedom=~New3A+X3B+X3C
'
fit=cfa(mod, data=mydata, do.fit=T, estimator='DWLS')
(mys4=summary(fit, standardized=T, fit.measures=TRUE))## lavaan 0.6.16 ended normally after 82 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 382
##
## Model Test User Model:
##
## Test statistic 198.480
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 983.750
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.816
## Tucker-Lewis Index (TLI) 0.724
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.138
## 90 Percent confidence interval - lower 0.121
## 90 Percent confidence interval - upper 0.156
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.145
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Economic_Freedom =~
## Gvrnmnt_Spndng 1.000 0.728 0.728
## Taxation 3.418 0.769 4.446 0.000 0.732 0.732
## Labr_Mrkt_Frdm 2.690 0.663 4.057 0.000 0.743 0.743
## Government_Spending =~
## X1A 1.000 0.259 0.259
## X1B 0.820 0.194 4.215 0.000 0.212 0.212
## X1C 3.661 0.759 4.825 0.000 0.949 0.949
## Taxation =~
## X2A 1.000 0.881 0.881
## X2B -0.497 0.077 -6.469 0.000 -0.438 -0.438
## X2C 0.331 0.056 5.897 0.000 0.292 0.292
## Labor_Market_Freedom =~
## New3A 1.000 0.683 0.683
## X3B 0.076 0.053 1.432 0.152 0.052 0.052
## X3C 1.463 0.172 8.488 0.000 1.000 1.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .X1A 0.933 0.130 7.197 0.000 0.933 0.933
## .X1B 0.955 0.118 8.073 0.000 0.955 0.955
## .X1C 0.100 0.228 0.437 0.662 0.100 0.100
## .X2A 0.224 0.155 1.447 0.148 0.224 0.224
## .X2B 0.808 0.102 7.895 0.000 0.808 0.808
## .X2C 0.915 0.247 3.709 0.000 0.915 0.915
## .New3A 0.533 0.105 5.090 0.000 0.533 0.533
## .X3B 0.997 0.230 4.327 0.000 0.997 0.997
## .X3C 0.000 0.152 0.003 0.997 0.000 0.000
## Economic_Fredm 0.036 0.015 2.433 0.015 1.000 1.000
## .Gvrnmnt_Spndng 0.032 0.014 2.227 0.026 0.470 0.470
## .Taxation 0.360 0.133 2.714 0.007 0.464 0.464
## .Labr_Mrkt_Frdm 0.209 0.052 3.990 0.000 0.449 0.449lavaanPlot(model = fit, edge_options = list(color = "grey"),coefs = TRUE,covs=F)temp2=lavPredict(fit)[,1]
mydata$ScoreM4=10*(temp2-min(temp2))/(max(temp2)-min(temp2)) #scale between 0 and 10EFA 1
fit=efa(data=mydata[,c(4:6,10:12)], do.fit=T, estimator='DWLS', rotation='varimax', nfactors=2, sample.cov=cor(mydata[,c(4:6,10:12)]), rotation.args = list(geomin.epsilon = 0.05, rstarts = 1, orthogonal=T))
summary(fit, standardized=T, fit.measures = TRUE, cutoff=0.4)## This is lavaan 0.6.16 -- running exploratory factor analysis
##
## Estimator DWLS
## Rotation method VARIMAX ORTHOGONAL
## Rotation algorithm (rstarts) GPA (1)
## Standardized metric TRUE
## Row weights Kaiser
##
## Number of observations 382
##
## Fit measures:
## chisq df pvalue cfi rmsea
## nfactors = 2 5.581 4 0.233 0.996 0.032
##
## Eigenvalues correlation matrix:
##
## ev1 ev2 ev3 ev4 ev5 ev6
## 2.370 1.356 0.847 0.690 0.400 0.338
##
## Standardized loadings: (* = significant at 1% level)
##
## f1 f2 unique.var communalities
## X1A 0.774* .* 0.390 0.610
## X1B .* .* 0.832 0.168
## X1C .* 0.528* 0.687 0.313
## X3A 0.455* .* 0.753 0.247
## X3B 0.851* 0.276 0.724
## X3C 0.999* 0.000 1.000
##
## f1 f2 total
## Sum of sq (ortho) loadings 1.723 1.338 3.061
## Proportion of total 0.563 0.437 1.000
## Proportion var 0.287 0.223 0.510
## Cumulative var 0.287 0.510 0.510M5 Base, EFA-Based with Covariance Structure
Model fits. (Estimate of negative variance statistically insignificant)
mod='
#Score~EF
Economic_Freedom=~F1+F2+F3
F1=~X1A+X3A+X3B
F2=~X2A+X2B+X2C
F3=~X1C+X3C
'
#optimal covariance structure
fit=sem(mod, data=mydata, do.fit=T, estimator='DWLS')## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negative(mys5=summary(fit, standardized=T, fit.measures=T))## lavaan 0.6.16 ended normally after 86 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 19
##
## Number of observations 382
##
## Model Test User Model:
##
## Test statistic 71.100
## Degrees of freedom 17
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 688.904
## Degrees of freedom 28
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.918
## Tucker-Lewis Index (TLI) 0.865
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.091
## 90 Percent confidence interval - lower 0.070
## 90 Percent confidence interval - upper 0.114
## P-value H_0: RMSEA <= 0.050 0.001
## P-value H_0: RMSEA >= 0.080 0.819
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.088
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Economic_Freedom =~
## F1 1.000 0.216 0.216
## F2 2.821 0.551 5.119 0.000 0.425 0.425
## F3 6.575 3.325 1.978 0.048 1.347 1.347
## F1 =~
## X1A 1.000 0.734 0.734
## X3A 0.766 0.166 4.612 0.000 0.562 0.562
## X3B 0.982 0.173 5.672 0.000 0.721 0.721
## F2 =~
## X2A 1.000 1.054 1.054
## X2B -0.436 0.088 -4.973 0.000 -0.459 -0.459
## X2C 0.173 0.048 3.607 0.000 0.183 0.183
## F3 =~
## X1C 1.000 0.774 0.774
## X3C 0.896 0.125 7.195 0.000 0.694 0.694
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .X1A 0.462 0.189 2.443 0.015 0.462 0.462
## .X3A 0.684 0.112 6.113 0.000 0.684 0.684
## .X3B 0.481 0.267 1.802 0.072 0.481 0.481
## .X2A -0.110 0.257 -0.429 0.668 -0.110 -0.110
## .X2B 0.789 0.106 7.457 0.000 0.789 0.789
## .X2C 0.967 0.246 3.929 0.000 0.967 0.967
## .X1C 0.401 0.112 3.577 0.000 0.401 0.401
## .X3C 0.519 0.094 5.542 0.000 0.519 0.519
## Economic_Fredm 0.025 0.014 1.824 0.068 1.000 1.000
## .F1 0.513 0.136 3.780 0.000 0.953 0.953
## .F2 0.910 0.260 3.502 0.000 0.820 0.820
## .F3 -0.489 0.566 -0.863 0.388 -0.815 -0.815lavaanPlot(model = fit, edge_options = list(color = "grey"),coefs = TRUE, covs=F)temp2=lavPredict(fit)[,1]## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative
## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativemydata$ScoreM5=10*(temp2-min(temp2))/(max(temp2)-min(temp2)) #scale between 0 and 10EFA 2
fit=efa(data=mydata[, c(4:6, 11,12,13)], do.fit=T, estimator='DWLS', rotation='varimax', nfactors=2, sample.cov=cor(mydata[, c(4:6, 11,12,13)]), rotation.args = list(geomin.epsilon = 0.05, rstarts = 1, orthogonal=T))
summary(fit, standardized=T, fit.measures = TRUE, cutoff=.4)## This is lavaan 0.6.16 -- running exploratory factor analysis
##
## Estimator DWLS
## Rotation method VARIMAX ORTHOGONAL
## Rotation algorithm (rstarts) GPA (1)
## Standardized metric TRUE
## Row weights Kaiser
##
## Number of observations 382
##
## Fit measures:
## chisq df pvalue cfi rmsea
## nfactors = 2 2.651 4 0.618 1 0
##
## Eigenvalues correlation matrix:
##
## ev1 ev2 ev3 ev4 ev5 ev6
## 2.238 1.756 0.769 0.621 0.361 0.254
##
## Standardized loadings: (* = significant at 1% level)
##
## f1 f2 unique.var communalities
## X1A 0.824* * 0.312 0.688
## X1B 0.440* 0.803 0.197
## X1C .* 0.523* 0.671 0.329
## X3B 0.710* 0.496 0.504
## X3C 0.999* 0.000 1.000
## New3A 0.690* 0.520 0.480
##
## f2 f1 total
## Sum of sq (ortho) loadings 1.759 1.441 3.199
## Proportion of total 0.550 0.450 1.000
## Proportion var 0.293 0.240 0.533
## Cumulative var 0.293 0.533 0.533M6 Sub, EFA-Based, with Covariance Structure
Model fits. (Estimate of negative variance statistically insignificant)
mod='
Economic_Freedom=~F1+F2+F3
F1=~X1A+X1B+X3B
F2=~X2A+X2B+X2C
F3=~X1C+New3A+X3C
'
#optimal covariance structure for fit
fit=sem(mod, data=mydata, do.fit=T, estimator='DWLS')
(mys6=summary(fit, standardized=T, fit.measures=T))## lavaan 0.6.16 ended normally after 76 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 382
##
## Model Test User Model:
##
## Test statistic 159.957
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 983.750
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.857
## Tucker-Lewis Index (TLI) 0.785
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.122
## 90 Percent confidence interval - lower 0.104
## 90 Percent confidence interval - upper 0.140
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.103
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Economic_Freedom =~
## F1 1.000 0.232 0.232
## F2 4.170 1.005 4.148 0.000 0.945 0.945
## F3 2.332 0.625 3.731 0.000 0.712 0.712
## F1 =~
## X1A 1.000 0.827 0.827
## X1B 0.611 0.152 4.007 0.000 0.505 0.505
## X3B 0.705 0.161 4.368 0.000 0.583 0.583
## F2 =~
## X2A 1.000 0.845 0.845
## X2B -0.507 0.079 -6.444 0.000 -0.428 -0.428
## X2C 0.371 0.059 6.270 0.000 0.314 0.314
## F3 =~
## X1C 1.000 0.627 0.627
## New3A 1.064 0.101 10.552 0.000 0.667 0.667
## X3C 1.408 0.146 9.651 0.000 0.883 0.883
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .X1A 0.316 0.236 1.340 0.180 0.316 0.316
## .X1B 0.745 0.140 5.321 0.000 0.745 0.745
## .X3B 0.660 0.256 2.580 0.010 0.660 0.660
## .X2A 0.286 0.144 1.978 0.048 0.286 0.286
## .X2B 0.816 0.102 7.993 0.000 0.816 0.816
## .X2C 0.902 0.247 3.653 0.000 0.902 0.902
## .X1C 0.607 0.079 7.708 0.000 0.607 0.607
## .New3A 0.555 0.101 5.483 0.000 0.555 0.555
## .X3C 0.221 0.108 2.046 0.041 0.221 0.221
## Economic_Fredm 0.037 0.014 2.679 0.007 1.000 1.000
## .F1 0.647 0.192 3.366 0.001 0.946 0.946
## .F2 0.076 0.225 0.339 0.735 0.107 0.107
## .F3 0.194 0.069 2.813 0.005 0.492 0.492lavaanPlot(model = fit, edge_options = list(color = "grey"),coefs = TRUE, covs=F)temp2=lavPredict(fit)[,1]
mydata$ScoreM6=10*(temp2-min(temp2))/(max(temp2)-min(temp2)) #scale between 0 and 10EFA 3
fit=efa(data=mydata[,c(4:12)], do.fit=T, estimator='DWLS', rotation='varimax', nfactors=3, sample.cov=cor(mydata[,4:12]), rotation.args = list(geomin.epsilon = 0.05, rstarts = 1, orthogonal=T))
summary(fit, standardized=T, fit.measures = TRUE, cutoff=0.4)## This is lavaan 0.6.16 -- running exploratory factor analysis
##
## Estimator DWLS
## Rotation method VARIMAX ORTHOGONAL
## Rotation algorithm (rstarts) GPA (1)
## Standardized metric TRUE
## Row weights Kaiser
##
## Number of observations 382
##
## Fit measures:
## chisq df pvalue cfi rmsea
## nfactors = 3 20.664 12 0.056 0.989 0.044
##
## Eigenvalues correlation matrix:
##
## ev1 ev2 ev3 ev4 ev5 ev6 ev7 ev8 ev9
## 2.644 1.877 1.249 0.945 0.763 0.599 0.364 0.312 0.247
##
## Standardized loadings: (* = significant at 1% level)
##
## f1 f2 f3 unique.var communalities
## X1A 0.695* 0.516 0.484
## X1B .* 0.497* 0.710 0.290
## X1C .* .* 0.433* 0.631 0.369
## X2A 0.955* .* .* 0.000 1.000
## X2B -0.556* .* 0.650 0.350
## X2C . .* 0.826 0.174
## X3A . 0.514* .* 0.700 0.300
## X3B 0.762* 0.408 0.592
## X3C .* .* 0.966* 0.000 1.000
##
## f2 f1 f3 total
## Sum of sq (ortho) loadings 1.787 1.424 1.349 4.560
## Proportion of total 0.392 0.312 0.296 1.000
## Proportion var 0.199 0.158 0.150 0.507
## Cumulative var 0.199 0.357 0.507 0.507M7 Sub, EFA-Based, with Covariance Structure
Model fits. (Estimate of negative variance statistically insignificant)
mod='
Economic_Freedom=~F1+F2+F3
F1=~X2A+X2B
F2=~X1A+X1B+X3A+X3B
F3=~X1C+X3C
'
#optimal covariance structure for fit
fit=sem(mod, data=mydata, do.fit=T, estimator='DWLS')## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative(mys7=summary(fit, standardized=T, fit.measures=T))## lavaan 0.6.16 ended normally after 61 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 19
##
## Number of observations 382
##
## Model Test User Model:
##
## Test statistic 57.094
## Degrees of freedom 17
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 748.582
## Degrees of freedom 28
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.944
## Tucker-Lewis Index (TLI) 0.908
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.079
## 90 Percent confidence interval - lower 0.057
## 90 Percent confidence interval - upper 0.102
## P-value H_0: RMSEA <= 0.050 0.017
## P-value H_0: RMSEA >= 0.080 0.488
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.076
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Economic_Freedom =~
## F1 1.000 0.403 0.403
## F2 0.381 0.066 5.751 0.000 0.378 0.378
## F3 0.972 0.214 4.532 0.000 0.846 0.846
## F1 =~
## X2A 1.000 1.761 1.761
## X2B -0.173 0.079 -2.204 0.028 -0.305 -0.305
## F2 =~
## X1A 1.000 0.715 0.715
## X1B 0.737 0.122 6.066 0.000 0.527 0.527
## X3A 0.732 0.125 5.868 0.000 0.523 0.523
## X3B 0.914 0.137 6.686 0.000 0.654 0.654
## F3 =~
## X1C 1.000 0.815 0.815
## X3C 0.808 0.116 6.981 0.000 0.659 0.659
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .X2A -2.101 1.446 -1.453 0.146 -2.101 -2.101
## .X2B 0.907 0.103 8.828 0.000 0.907 0.907
## .X1A 0.488 0.167 2.917 0.004 0.488 0.488
## .X1B 0.722 0.132 5.465 0.000 0.722 0.722
## .X3A 0.726 0.098 7.416 0.000 0.726 0.726
## .X3B 0.572 0.249 2.295 0.022 0.572 0.572
## .X1C 0.335 0.123 2.732 0.006 0.335 0.335
## .X3C 0.566 0.089 6.352 0.000 0.566 0.566
## Economic_Fredm 0.504 0.115 4.370 0.000 1.000 1.000
## .F1 2.597 1.437 1.808 0.071 0.838 0.838
## .F2 0.439 0.098 4.470 0.000 0.857 0.857
## .F3 0.189 0.125 1.514 0.130 0.285 0.285lavaanPlot(model = fit, edge_options = list(color = "grey"),coefs = TRUE, covs=F)temp2=lavPredict(fit)[,1]## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negativemydata$ScoreM7=10*(temp2-min(temp2))/(max(temp2)-min(temp2)) #scale between 0 and 10EFA 4
mod='
Economic_Freedom=~.
'
fit=efa(data=mydata[,c(4:9,11:13)], do.fit=T, estimator='DWLS', rotation='varimax', nfactors=3, sample.cov=cor(mydata[,c(4:9,11:13)]), rotation.args = list(geomin.epsilon = 0.05, rstarts = 1, orthogonal=T))
summary(fit, standardized=T, fit.measures = TRUE, cutoff=0.4)## This is lavaan 0.6.16 -- running exploratory factor analysis
##
## Estimator DWLS
## Rotation method VARIMAX ORTHOGONAL
## Rotation algorithm (rstarts) GPA (1)
## Standardized metric TRUE
## Row weights Kaiser
##
## Number of observations 382
##
## Fit measures:
## chisq df pvalue cfi rmsea
## nfactors = 3 16.988 12 0.15 0.995 0.033
##
## Eigenvalues correlation matrix:
##
## ev1 ev2 ev3 ev4 ev5 ev6 ev7 ev8 ev9
## 2.759 1.869 1.333 1.001 0.631 0.522 0.334 0.317 0.233
##
## Standardized loadings: (* = significant at 1% level)
##
## f1 f2 f3 unique.var communalities
## X1A 0.687* 0.523 0.477
## X1B 0.557* .* 0.664 0.336
## X1C .* .* 0.460* 0.623 0.377
## X2A .* 0.926* .* 0.000 1.000
## X2B .* -0.599* 0.606 0.394
## X2C . .* 0.832 0.168
## X3B 0.650* . 0.565 0.435
## X3C .* . 0.978* 0.003 0.997
## New3A . 0.690* 0.511 0.489
##
## f3 f1 f2 total
## Sum of sq (ortho) loadings 1.858 1.451 1.364 4.672
## Proportion of total 0.398 0.311 0.292 1.000
## Proportion var 0.206 0.161 0.152 0.519
## Cumulative var 0.206 0.368 0.519 0.519M8 Sub, EFA-Based, with Covariance Structure
Model fits. (Estimate of negative variance statistically insignificant)
mod='
Economic_Freedom=~F1+F2+F3
F1=~X2A+X2B
F2=~X1A+X1B+X3B
F3=~X1C+X3C+New3A
'
#optimal covariance structure for fit
fit=sem(mod, data=mydata, do.fit=T, estimator='DWLS')## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative(mys8=summary(fit, standardized=T, fit.measures=T))## lavaan 0.6.16 ended normally after 88 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 19
##
## Number of observations 382
##
## Model Test User Model:
##
## Test statistic 74.800
## Degrees of freedom 17
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 857.848
## Degrees of freedom 28
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.930
## Tucker-Lewis Index (TLI) 0.885
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.094
## 90 Percent confidence interval - lower 0.073
## 90 Percent confidence interval - upper 0.117
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.873
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.093
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Economic_Freedom =~
## F1 1.000 0.603 0.603
## F2 0.224 0.055 4.110 0.000 0.337 0.337
## F3 0.360 0.094 3.839 0.000 0.553 0.553
## F1 =~
## X2A 1.000 1.647 1.647
## X2B -0.198 0.076 -2.615 0.009 -0.326 -0.326
## F2 =~
## X1A 1.000 0.661 0.661
## X1B 0.975 0.216 4.516 0.000 0.645 0.645
## X3B 0.730 0.158 4.628 0.000 0.482 0.482
## F3 =~
## X1C 1.000 0.646 0.646
## X3C 1.370 0.155 8.818 0.000 0.885 0.885
## New3A 0.976 0.099 9.824 0.000 0.630 0.630
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .X2A -1.713 1.078 -1.589 0.112 -1.713 -1.713
## .X2B 0.894 0.102 8.740 0.000 0.894 0.894
## .X1A 0.563 0.176 3.196 0.001 0.563 0.563
## .X1B 0.585 0.159 3.665 0.000 0.585 0.585
## .X3B 0.767 0.244 3.144 0.002 0.767 0.767
## .X1C 0.583 0.083 7.051 0.000 0.583 0.583
## .X3C 0.218 0.114 1.901 0.057 0.218 0.218
## .New3A 0.603 0.100 6.010 0.000 0.603 0.603
## Economic_Fredm 0.986 0.260 3.798 0.000 1.000 1.000
## .F1 1.727 1.081 1.597 0.110 0.636 0.636
## .F2 0.388 0.112 3.474 0.001 0.887 0.887
## .F3 0.289 0.051 5.643 0.000 0.694 0.694lavaanPlot(model = fit, edge_options = list(color = "grey"),coefs = TRUE, covs=F)temp2=lavPredict(fit)[,1]## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negativemydata$ScoreM8=10*(temp2-min(temp2))/(max(temp2)-min(temp2)) #scale between 0 and 10Top 10
newdata=subset(mydata, select=c('GeoName','MEFI_Score', 'New_MEFI_Score', 'ScoreM3','ScoreM4','ScoreM5','ScoreM6', 'ScoreM7','ScoreM8'))
MEFI=newdata[order(-newdata$MEFI_Score),]
MEFI$RankOriginal=seq(1:nrow(MEFI))
MEFI=MEFI[order(-MEFI$New_MEFI_Score),]
MEFI$RankNewScore=seq(1:nrow(MEFI))
MEFI=MEFI[order(-MEFI$ScoreM3),]
MEFI$RankM3=seq(1:nrow(MEFI))
MEFI=MEFI[order(-MEFI$ScoreM4),]
MEFI$RankM4=seq(1:nrow(MEFI))
MEFI=MEFI[order(-MEFI$ScoreM5),]
MEFI$RankM5=seq(1:nrow(MEFI))
MEFI=MEFI[order(-MEFI$ScoreM6),]
MEFI$RankM6=seq(1:nrow(MEFI))
MEFI=MEFI[order(-MEFI$ScoreM7),]
MEFI$RankM7=seq(1:nrow(MEFI))
MEFI=MEFI[order(-MEFI$ScoreM8),]
MEFI$RankM8=seq(1:nrow(MEFI))
write.csv(MEFI,'MEFI.csv', row.names=F)Rank Correlation
cor(MEFI[, c(10:17)], method='spearman')## RankOriginal RankNewScore RankM3 RankM4 RankM5 RankM6
## RankOriginal 1.0000000 0.9691024 0.9317558 0.8712406 0.7772446 0.7342913
## RankNewScore 0.9691024 1.0000000 0.8979671 0.8000575 0.6998202 0.6906990
## RankM3 0.9317558 0.8979671 1.0000000 0.9027356 0.8459990 0.7387254
## RankM4 0.8712406 0.8000575 0.9027356 1.0000000 0.8936045 0.8814783
## RankM5 0.7772446 0.6998202 0.8459990 0.8936045 1.0000000 0.6101163
## RankM6 0.7342913 0.6906990 0.7387254 0.8814783 0.6101163 1.0000000
## RankM7 0.9001536 0.8686890 0.9342997 0.9033216 0.7542611 0.8370515
## RankM8 0.6004661 0.6321198 0.6123308 0.6052751 0.3042064 0.7437503
## RankM7 RankM8
## RankOriginal 0.9001536 0.6004661
## RankNewScore 0.8686890 0.6321198
## RankM3 0.9342997 0.6123308
## RankM4 0.9033216 0.6052751
## RankM5 0.7542611 0.3042064
## RankM6 0.8370515 0.7437503
## RankM7 1.0000000 0.8028731
## RankM8 0.8028731 1.0000000corfunction(MEFI[,c(10:17)], 'spearman', 3)
Model Performance
require(tidyverse)
mydf=as.data.frame(round(rbind(mys1$fit, mys2$fit, mys3$fit,mys4$fit,mys5$fit,mys6$fit, mys7$fit,mys8$fit),5))
rownames(mydf)=c('M1 w/Cov','M2 w/Cov', 'M3','M4','M5','M6', 'M7', 'M8')
mydf$Model=rownames(mydf)
print(mydf[,6:8])## baseline.chisq baseline.df baseline.pvalue
## M1 w/Cov 819.5549 36 0
## M2 w/Cov 983.7500 36 0
## M3 819.5549 36 0
## M4 983.7500 36 0
## M5 688.9045 28 0
## M6 983.7500 36 0
## M7 748.5824 28 0
## M8 857.8481 28 0mydf[,c(11:13)]## rmsea rmsea.ci.lower rmsea.ci.upper
## M1 w/Cov 0.18810 0.17313 0.20346
## M2 w/Cov 0.21422 0.19930 0.22951
## M3 0.14546 0.12808 0.16349
## M4 0.13814 0.12071 0.15624
## M5 0.09139 0.07002 0.11388
## M6 0.12194 0.10438 0.14022
## M7 0.07868 0.05674 0.10166
## M8 0.09447 0.07319 0.11686mysub=mydf[,c(9,10)]%>%pivot_longer(everything(), names_to='metric', values_to='value')
mysub$Model=c(rep('M1',2),rep('M2',2),rep('M3',2),rep('M4',2),rep('M5',2),rep('M6',2), rep('M7',2), rep('M8',2))
myplot=ggplot(mysub, aes(x=metric, y=value, fill=Model))+geom_bar(stat='identity', alpha=.5, position='dodge')+
geom_text(aes(label = round(value, 3)),
position = position_dodge(.9),
color="black",vjust = .5,hjust =1, angle = 90)+xlab("")+ylab("Metric Value")
myplot
path1 <- data.frame(x=c(.5,1.5),y=c(.95,.95), Model=c('Benchmark'))
path2 <- data.frame(x=c(1.5,2.5),y=c(.9,.9), Model=c('Benchmark'))
q <- myplot + geom_path(data=path1,aes(x=x, y=y, col=Model))
q <- q + geom_path(data=path2,aes(x=x, y=y, col=Model))+ylim(0, 1.2)
q
Top k
k=5
BaseTop=MEFI[MEFI$RankOriginal<=k,]
BaseTop=select(BaseTop, c('GeoName','RankOriginal'))
BaseTop=BaseTop[order(BaseTop$RankOriginal),"GeoName"]
SubTop=MEFI[MEFI$RankNewScore<=k,]
SubTop=select(SubTop, c('GeoName','RankNewScore'))
SubTop=SubTop[order(SubTop$RankNewScore),"GeoName"]
M3Top=MEFI[MEFI$RankM3<=k,]
M3Top=select(M3Top, c('GeoName','RankM3'))
M3Top=M3Top[order(M3Top$RankM3),"GeoName"]
M4Top=MEFI[MEFI$RankM4<=k,]
M4Top=select(M4Top, c('GeoName','RankM4'))
M4Top=M4Top[order(M4Top$RankM4),"GeoName"]
M5Top=MEFI[MEFI$RankM5<=k,]
M5Top=select(M5Top, c('GeoName','RankM5'))
M5Top=M5Top[order(M5Top$RankM5),"GeoName"]
M6Top=MEFI[MEFI$RankM6<=k,]
M6Top=select(M6Top, c('GeoName','RankM6'))
M6Top=M6Top[order(M6Top$RankM6),"GeoName"]
M7Top=MEFI[MEFI$RankM7<=k,]
M7Top=select(M7Top, c('GeoName','RankM7'))
M7Top=M7Top[order(M7Top$RankM7),"GeoName"]
M8Top=MEFI[MEFI$RankM8<=k,]
M8Top=select(M8Top, c('GeoName','RankM8'))
M8Top=M8Top[order(M8Top$RankM8),"GeoName"]
newdf=as.data.frame(cbind(BaseTop, SubTop, M3Top, M4Top, M5Top,M6Top, M7Top, M8Top))
myprint(newdf)## BaseTop SubTop
## 1 Naples-Immokalee-Marco Island, FL MSA Naples-Immokalee-Marco Island, FL MSA
## 2 Sebastian-Vero Beach, FL MSA Sebastian-Vero Beach, FL MSA
## 3 Midland, TX MSA The Villages, FL MSA
## 4 The Villages, FL MSA Midland, TX MSA
## 5 Port St. Lucie, FL MSA Tyler, TX MSA
## M3Top
## 1 Naples-Immokalee-Marco Island, FL MSA
## 2 The Villages, FL MSA
## 3 Sebastian-Vero Beach, FL MSA
## 4 Homosassa Springs, FL MSA
## 5 Crestview-Fort Walton Beach-Destin, FL MSA
## M4Top
## 1 The Villages, FL MSA
## 2 Homosassa Springs, FL MSA
## 3 Naples-Immokalee-Marco Island, FL MSA
## 4 Punta Gorda, FL MSA
## 5 Crestview-Fort Walton Beach-Destin, FL MSA
## M5Top M6Top
## 1 Grand Island, NE MSA Tyler, TX MSA
## 2 Blacksburg-Christiansburg-Radford, VA MSA Abilene, TX MSA
## 3 Lynchburg, VA MSA San Angelo, TX MSA
## 4 Harrisonburg, VA MSA Midland, TX MSA
## 5 Staunton-Waynesboro, VA MSA Killeen-Temple, TX MSA
## M7Top M8Top
## 1 Naples-Immokalee-Marco Island, FL MSA Fairbanks, AK MSA
## 2 Manchester-Nashua, NH MSA Anchorage, AK MSA
## 3 The Villages, FL MSA Manchester-Nashua, NH MSA
## 4 Sebastian-Vero Beach, FL MSA Killeen-Temple, TX MSA
## 5 Homosassa Springs, FL MSA Sherman-Denison, TX MSAmyt=table(as.matrix(newdf))
myprint(myt[order(myt, decreasing=T)])##
## Naples-Immokalee-Marco Island, FL MSA
## 5
## The Villages, FL MSA
## 5
## Sebastian-Vero Beach, FL MSA
## 4
## Homosassa Springs, FL MSA
## 3
## Midland, TX MSA
## 3
## Crestview-Fort Walton Beach-Destin, FL MSA
## 2
## Killeen-Temple, TX MSA
## 2
## Manchester-Nashua, NH MSA
## 2
## Tyler, TX MSA
## 2
## Abilene, TX MSA
## 1
## Anchorage, AK MSA
## 1
## Blacksburg-Christiansburg-Radford, VA MSA
## 1
## Fairbanks, AK MSA
## 1
## Grand Island, NE MSA
## 1
## Harrisonburg, VA MSA
## 1
## Lynchburg, VA MSA
## 1
## Port St. Lucie, FL MSA
## 1
## Punta Gorda, FL MSA
## 1
## San Angelo, TX MSA
## 1
## Sherman-Denison, TX MSA
## 1
## Staunton-Waynesboro, VA MSA
## 1Bottom k
k=nrow(MEFI)-5
BaseBottom=MEFI[MEFI$RankOriginal>k,]
BaseBottom=select(BaseBottom, c('GeoName','RankOriginal'))
BaseBottom=BaseBottom[order(BaseBottom$RankOriginal),"GeoName"]
SubBottom=MEFI[MEFI$RankNewScore>k,]
SubBottom=select(SubBottom, c('GeoName','RankNewScore'))
SubBottom=SubBottom[order(SubBottom$RankNewScore),"GeoName"]
M3Bottom=MEFI[MEFI$RankM3>k,]
M3Bottom=select(M3Bottom, c('GeoName','RankM3'))
M3Bottom=M3Bottom[order(M3Bottom$RankM3),"GeoName"]
M4Bottom=MEFI[MEFI$RankM4>k,]
M4Bottom=select(M4Bottom, c('GeoName','RankM4'))
M4Bottom=M4Bottom[order(M4Bottom$RankM4),"GeoName"]
M5Bottom=MEFI[MEFI$RankM5>k,]
M5Bottom=select(M5Bottom, c('GeoName','RankM5'))
M5Bottom=M5Bottom[order(M5Bottom$RankM5),"GeoName"]
M6Bottom=MEFI[MEFI$RankM6>k,]
M6Bottom=select(M6Bottom, c('GeoName','RankM6'))
M6Bottom=M6Bottom[order(M6Bottom$RankM6),"GeoName"]
M7Bottom=MEFI[MEFI$RankM7>k,]
M7Bottom=select(M7Bottom, c('GeoName','RankM7'))
M7Bottom=M7Bottom[order(M7Bottom$RankM7),"GeoName"]
M8Bottom=MEFI[MEFI$RankM8>k,]
M8Bottom=select(M8Bottom, c('GeoName','RankM8'))
M8Bottom=M8Bottom[order(M8Bottom$RankM8),"GeoName"]
newdf=as.data.frame(cbind(BaseBottom, SubBottom, M3Bottom, M4Bottom, M5Bottom,M6Bottom, M7Bottom, M8Bottom))
myprint(newdf)## BaseBottom SubBottom
## 1 Merced, CA MSA Merced, CA MSA
## 2 Visalia-Porterville, CA MSA Visalia-Porterville, CA MSA
## 3 Rapid City, SD MSA Bakersfield, CA MSA
## 4 Bakersfield, CA MSA Rapid City, SD MSA
## 5 El Centro, CA MSA El Centro, CA MSA
## M3Bottom M4Bottom
## 1 Stockton-Lodi, CA MSA Eugene, OR MSA
## 2 Visalia-Porterville, CA MSA Los Angeles-Long Beach-Anaheim, CA MSA
## 3 Merced, CA MSA Fresno, CA MSA
## 4 Bakersfield, CA MSA Bakersfield, CA MSA
## 5 El Centro, CA MSA New York-Newark-Jersey City, NY-NJ-PA MSA
## M5Bottom
## 1 Providence-Warwick-Pawtucket, RI MSA
## 2 Chicago-Naperville-Elgin, IL-IN-WI MSA
## 3 New York-Newark-Jersey City, NY-NJ-PA MSA
## 4 Anchorage, AK MSA
## 5 Fairbanks, AK MSA
## M6Bottom
## 1 Bend-Redmond, OR MSA
## 2 Salem, OR MSA
## 3 Eugene, OR MSA
## 4 Albany, OR MSA
## 5 New York-Newark-Jersey City, NY-NJ-PA MSA
## M7Bottom M8Bottom
## 1 Merced, CA MSA Mankato-North Mankato, MN MSA
## 2 Visalia-Porterville, CA MSA Rochester, MN MSA
## 3 New York-Newark-Jersey City, NY-NJ-PA MSA Kahului-Wailuku-Lahaina, HI MSA
## 4 Bakersfield, CA MSA Lexington-Fayette, KY MSA
## 5 El Centro, CA MSA Urban Honolulu, HI MSAmyt=table(as.matrix(newdf))
myprint(myt[order(myt, decreasing=T)])##
## Bakersfield, CA MSA
## 5
## El Centro, CA MSA
## 4
## Merced, CA MSA
## 4
## New York-Newark-Jersey City, NY-NJ-PA MSA
## 4
## Visalia-Porterville, CA MSA
## 4
## Eugene, OR MSA
## 2
## Rapid City, SD MSA
## 2
## Albany, OR MSA
## 1
## Anchorage, AK MSA
## 1
## Bend-Redmond, OR MSA
## 1
## Chicago-Naperville-Elgin, IL-IN-WI MSA
## 1
## Fairbanks, AK MSA
## 1
## Fresno, CA MSA
## 1
## Kahului-Wailuku-Lahaina, HI MSA
## 1
## Lexington-Fayette, KY MSA
## 1
## Los Angeles-Long Beach-Anaheim, CA MSA
## 1
## Mankato-North Mankato, MN MSA
## 1
## Providence-Warwick-Pawtucket, RI MSA
## 1
## Rochester, MN MSA
## 1
## Salem, OR MSA
## 1
## Stockton-Lodi, CA MSA
## 1
## Urban Honolulu, HI MSA
## 1Best Fit Model
tmp=data.frame(MEFI[order(MEFI$RankM6, decreasing=F),])
tmp## GeoName
## 352 Tyler, TX MSA
## 1 Abilene, TX MSA
## 308 San Angelo, TX MSA
## 229 Midland, TX MSA
## 183 Killeen-Temple, TX MSA
## 372 Wichita Falls, TX MSA
## 10 Amarillo, TX MSA
## 209 Longview, TX MSA
## 361 Waco, TX MSA
## 345 The Villages, FL MSA
## 30 Beaumont-Port Arthur, TX MSA
## 324 Sherman-Denison, TX MSA
## 268 Pensacola-Ferry Pass-Brent, FL MSA
## 87 Dallas-Fort Worth-Arlington, TX MSA
## 76 College Station-Bryan, TX MSA
## 83 Corpus Christi, TX MSA
## 213 Lubbock, TX MSA
## 328 Sioux Falls, SD MSA
## 48 Brownsville-Harlingen, TX MSA
## 309 San Antonio-New Braunfels, TX MSA
## 357 Victoria, TX MSA
## 321 Sebastian-Vero Beach, FL MSA
## 160 Houston-The Woodlands-Sugar Land, TX MSA
## 110 El Paso, TX MSA
## 256 Odessa, TX MSA
## 222 McAllen-Edinburg-Mission, TX MSA
## 322 Sebring, FL MSA
## 85 Crestview-Fort Walton Beach-Destin, FL MSA
## 245 Naples-Immokalee-Marco Island, FL MSA
## 254 Ocala, FL MSA
## 193 Lakeland-Winter Haven, FL MSA
## 342 Tampa-St. Petersburg-Clearwater, FL MSA
## 157 Homosassa Springs, FL MSA
## 261 Orlando-Kissimmee-Sanford, FL MSA
## 265 Palm Bay-Melbourne-Titusville, FL MSA
## 22 Austin-Round Rock, TX MSA
## 196 Laredo, TX MSA
## 283 Punta Gorda, FL MSA
## 170 Jacksonville, FL MSA
## 266 Panama City, FL MSA
## 95 Deltona-Daytona Beach-Ormond Beach, FL MSA
## 198 Las Vegas-Henderson-Paradise, NV MSA
## 252 North Port-Sarasota-Bradenton, FL MSA
## 278 Port St. Lucie, FL MSA
## 289 Reno, NV MSA
## 130 Gainesville, FL MSA
## 341 Tallahassee, FL MSA
## 226 Miami-Fort Lauderdale-West Palm Beach, FL MSA
## 58 Carson City, NV MSA
## 55 Cape Coral-Fort Myers, FL MSA
## 73 Cleveland, TN MSA
## 174 Johnson City, TN MSA
## 246 Nashville-Davidson?\x9b\x83?\xaa\x83?\x9dMurfreesboro?\x9b\x83?\xaa\x83?\x9dFranklin, TN MSA
## 186 Knoxville, TN MSA
## 239 Morristown, TN MSA
## 344 Texarkana, TX-AR MSA
## 169 Jackson, TN MSA
## 148 Hammond, LA MSA
## 235 Monroe, LA MSA
## 191 Lake Charles, LA MSA
## 224 Memphis, TN-MS-AR MSA
## 27 Baton Rouge, LA MSA
## 189 Lafayette, LA MSA
## 7 Alexandria, LA MSA
## 67 Chattanooga, TN-GA MSA
## 184 Kingsport-Bristol-Bristol, TN-VA MSA
## 249 New Orleans-Metairie, LA MSA
## 325 Shreveport-Bossier City, LA MSA
## 120 Flagstaff, AZ MSA
## 286 Rapid City, SD MSA
## 72 Clarksville, TN-KY MSA
## 378 Yakima, WA MSA
## 37 Bismarck, ND MSA
## 279 Prescott, AZ MSA
## 32 Bellingham, WA MSA
## 331 Spokane-Spokane Valley, WA MSA
## 320 Seattle-Tacoma-Bellevue, WA MSA
## 46 Bremerton-Silverdale, WA MSA
## 159 Houma-Thibodaux, LA MSA
## 259 Olympia-Tumwater, WA MSA
## 271 Phoenix-Mesa-Scottsdale, AZ MSA
## 362 Walla Walla, WA MSA
## 182 Kennewick-Richland-Pasco, WA MSA
## 369 Wenatchee, WA MSA
## 192 Lake Havasu City-Kingman, AZ MSA
## 349 Tucson, AZ MSA
## 326 Sierra Vista-Douglas, AZ MSA
## 382 Yuma, AZ MSA
## 210 Longview, WA MSA
## 240 Mount Vernon-Anacortes, WA MSA
## 111 Enid, OK MSA
## 197 Las Cruces, NM MSA
## 350 Tulsa, OK MSA
## 258 Oklahoma City, OK MSA
## 200 Lawton, OK MSA
## 219 Manhattan, KS MSA
## 6 Albuquerque, NM MSA
## 168 Jackson, MS MSA
## 315 Santa Fe, NM MSA
## 339 Sumter, SC MSA
## 126 Fort Smith, AR-OK MSA
## 122 Florence, SC MSA
## 158 Hot Springs, AR MSA
## 199 Lawrence, KS MSA
## 371 Wichita, KS MSA
## 233 Mobile, AL MSA
## 116 Fargo, ND-MN MSA
## 64 Charleston-North Charleston, SC MSA
## 202 Lewiston, ID-WA MSA
## 347 Topeka, KS MSA
## 90 Daphne-Fairhope-Foley, AL MSA
## 218 Manchester-Nashua, NH MSA
## 119 Fayetteville-Springdale-Rogers, AR-MO MSA
## 14 Anniston-Oxford-Jacksonville, AL MSA
## 237 Montgomery, AL MSA
## 93 Decatur, AL MSA
## 153 Hattiesburg, MS MSA
## 207 Little Rock-North Little Rock-Conway, AR MSA
## 21 Augusta-Richmond County, GA-SC MSA
## 99 Dothan, AL MSA
## 272 Pine Bluff, AR MSA
## 123 Florence-Muscle Shoals, AL MSA
## 190 Lafayette-West Lafayette, IN MSA
## 363 Warner Robins, GA MSA
## 155 Hilton Head Island-Bluffton-Beaufort, SC MSA
## 40 Bloomington, IN MSA
## 146 Gulfport-Biloxi-Pascagoula, MS MSA
## 176 Jonesboro, AR MSA
## 187 Kokomo, IN MSA
## 243 Myrtle Beach-Conway-North Myrtle Beach, SC-NC MSA
## 117 Farmington, NM MSA
## 145 Greenville-Anderson-Mauldin, SC MSA
## 131 Gainesville, GA MSA
## 162 Huntsville, AL MSA
## 227 Michigan City-La Porte, IN MSA
## 241 Muncie, IN MSA
## 171 Jacksonville, NC MSA
## 79 Columbia, SC MSA
## 36 Birmingham-Hoover, AL MSA
## 80 Columbus, GA-AL MSA
## 351 Tuscaloosa, AL MSA
## 297 Rome, GA MSA
## 135 Grand Forks, ND-MN MSA
## 88 Dalton, GA MSA
## 163 Idaho Falls, ID MSA
## 215 Macon, GA MSA
## 156 Hinesville, GA MSA
## 114 Evansville, IN-KY MSA
## 318 Savannah, GA MSA
## 18 Atlanta-Sandy Springs-Roswell, GA MSA
## 355 Valdosta, GA MSA
## 108 Elkhart-Goshen, IN MSA
## 20 Auburn-Opelika, AL MSA
## 330 Spartanburg, SC MSA
## 81 Columbus, IN MSA
## 49 Brunswick, GA MSA
## 42 Boise City-Nampa, ID MSA
## 17 Athens-Clarke County, GA MSA
## 3 Albany, GA MSA
## 134 Goldsboro, NC MSA
## 164 Indianapolis-Carmel-Anderson, IN MSA
## 275 Pocatello, ID MSA
## 177 Joplin, MO MSA
## 129 Gadsden, AL MSA
## 51 Burlington, NC MSA
## 376 Winston-Salem, NC MSA
## 118 Fayetteville, NC MSA
## 56 Cape Girardeau, MO-IL MSA
## 143 Greensboro-High Point, NC MSA
## 343 Terre Haute, IN MSA
## 285 Raleigh, NC MSA
## 173 Jefferson City, MO MSA
## 16 Asheville, NC MSA
## 65 Charlotte-Concord-Gastonia, NC-SC MSA
## 154 Hickory-Lenoir-Morganton, NC MSA
## 127 Fort Wayne, IN MSA
## 334 Springfield, MO MSA
## 208 Logan, UT-ID MSA
## 302 St. Joseph, MO-KS MSA
## 103 Durham-Chapel Hill, NC MSA
## 247 New Bern, NC MSA
## 281 Provo-Orem, UT MSA
## 296 Rocky Mount, NC MSA
## 301 St. George, UT MSA
## 257 Ogden-Clearfield, UT MSA
## 181 Kansas City, MO-KS MSA
## 78 Columbia, MO MSA
## 75 Coeur d'Alene, ID MSA
## 307 Salt Lake City, UT MSA
## 374 Wilmington, NC MSA
## 115 Fairbanks, AK MSA
## 12 Anchorage, AK MSA
## 96 Denver-Aurora-Lakewood, CO MSA
## 144 Greenville, NC MSA
## 337 Staunton-Waynesboro, VA MSA
## 214 Lynchburg, VA MSA
## 327 Sioux City, IA-NE-SD MSA
## 329 South Bend-Mishawaka, IN-MI MSA
## 77 Colorado Springs, CO MSA
## 206 Lincoln, NE MSA
## 282 Pueblo, CO MSA
## 137 Grand Junction, CO MSA
## 303 St. Louis, MO-IL MSA
## 125 Fort Collins-Loveland, CO MSA
## 141 Greeley, CO MSA
## 151 Harrisonburg, VA MSA
## 290 Richmond, VA MSA
## 38 Blacksburg-Christiansburg-Radford, VA MSA
## 101 Dubuque, IA MSA
## 205 Lima, OH MSA
## 359 Virginia Beach-Norfolk-Newport News, VA-NC MSA
## 375 Winchester, VA-WV MSA
## 66 Charlottesville, VA MSA
## 54 Canton-Massillon, OH MSA
## 370 Wheeling, WV-OH MSA
## 44 Boulder, CO MSA
## 292 Roanoke, VA MSA
## 260 Omaha-Council Bluffs, NE-IA MSA
## 136 Grand Island, NE MSA
## 335 Springfield, OH MSA
## 365 Waterloo-Cedar Falls, IA MSA
## 380 Youngstown-Warren-Boardman, OH-PA MSA
## 60 Cedar Rapids, IA MSA
## 165 Iowa City, IA MSA
## 11 Ames, IA MSA
## 97 Des Moines-West Des Moines, IA MSA
## 91 Davenport-Moline-Rock Island, IA-IL MSA
## 9 Altoona, PA MSA
## 161 Huntington-Ashland, WV-KY-OH MSA
## 238 Morgantown, WV MSA
## 175 Johnstown, PA MSA
## 57 Carbondale-Marion, IL MSA
## 267 Parkersburg-Vienna, WV MSA
## 368 Weirton-Steubenville, WV-OH MSA
## 31 Beckley, WV MSA
## 194 Lancaster, PA MSA
## 61 Chambersburg-Waynesboro, PA MSA
## 228 Midland, MI MSA
## 71 Cincinnati-Middleton, OH-KY-IN MSA
## 63 Charleston, WV MSA
## 112 Erie, PA MSA
## 105 Eau Claire, WI MSA
## 94 Decatur, IL MSA
## 39 Bloomington, IL MSA
## 25 Bangor, ME MSA
## 269 Peoria, IL MSA
## 221 Mansfield, OH MSA
## 284 Racine, WI MSA
## 62 Champaign--Urbana, IL MSA
## 201 Lebanon, PA MSA
## 379 York-Hanover, PA MSA
## 92 Dayton, OH MSA
## 236 Monroe, MI MSA
## 142 Green Bay, WI MSA
## 179 Kalamazoo-Portage, MI MSA
## 295 Rockford, IL MSA
## 89 Danville, IL MSA
## 15 Appleton, WI MSA
## 13 Ann Arbor, MI MSA
## 336 State College, PA MSA
## 273 Pittsburgh, PA MSA
## 124 Fond du Lac, WI MSA
## 107 Elizabethtown-Fort Knox, KY MSA
## 172 Janesville-Beloit, WI MSA
## 69 Chicago-Naperville-Elgin, IL-IN-WI MSA
## 276 Portland-South Portland, ME MSA
## 251 Niles-Benton Harbor, MI MSA
## 332 Springfield, IL MSA
## 203 Lewiston-Auburn, ME MSA
## 180 Kankakee, IL MSA
## 299 Saginaw, MI MSA
## 59 Casper, WY MSA
## 138 Grand Rapids-Wyoming, MI MSA
## 2 Akron, OH MSA
## 367 Wausau, WI MSA
## 242 Muskegon, MI MSA
## 319 Scranton--Wilkes-Barre--Hazleton, PA MSA
## 98 Detroit-Warren-Dearborn, MI MSA
## 132 Gettysburg, PA MSA
## 121 Flint, MI MSA
## 41 Bloomsburg-Berwick, PA MSA
## 217 Madison, WI MSA
## 346 Toledo, OH MSA
## 167 Jackson, MI MSA
## 230 Milwaukee-Waukesha-West Allis, WI MSA
## 74 Cleveland-Elyria, OH MSA
## 323 Sheboygan, WI MSA
## 373 Williamsport, PA MSA
## 104 East Stroudsburg, PA MSA
## 52 Burlington-South Burlington, VT MSA
## 287 Reading, PA MSA
## 263 Owensboro, KY MSA
## 188 La Crosse-Onalaska, WI-MN MSA
## 29 Bay City, MI MSA
## 195 Lansing-East Lansing, MI MSA
## 8 Allentown-Bethlehem-Easton, PA-NJ MSA
## 68 Cheyenne, WY MSA
## 82 Columbus, OH MSA
## 150 Harrisburg-Carlisle, PA MSA
## 212 Louisville-Jefferson County, KY-IN MSA
## 140 Great Falls, MT MSA
## 232 Missoula, MT MSA
## 34 Billings, MT MSA
## 262 Oshkosh-Neenah, WI MSA
## 353 Urban Honolulu, HI MSA
## 28 Battle Creek, MI MSA
## 147 Hagerstown-Martinsburg, MD-WV MSA
## 86 Cumberland, MD-WV MSA
## 280 Providence-Warwick-Pawtucket, RI MSA
## 45 Bowling Green, KY MSA
## 270 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA
## 100 Dover, DE MSA
## 364 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA
## 306 Salisbury, MD-DE MSA
## 178 Kahului-Wailuku-Lahaina, HI MSA
## 348 Trenton, NJ MSA
## 358 Vineland-Bridgeton, NJ MSA
## 204 Lexington-Fayette, KY MSA
## 53 California-Lexington Park, MD MSA
## 43 Boston-Cambridge-Quincy, MA-NH MSA
## 24 Baltimore-Columbia-Towson, MD MSA
## 102 Duluth, MN-WI MSA
## 47 Bridgeport-Stamford-Norwalk, CT MSA
## 220 Mankato-North Mankato, MN MSA
## 231 Minneapolis-St. Paul-Bloomington, MN-WI MSA
## 377 Worcester, MA-CT MSA
## 293 Rochester, MN MSA
## 300 St. Cloud, MN MSA
## 253 Norwich-New London, CT MSA
## 333 Springfield, MA MSA
## 26 Barnstable Town, MA MSA
## 152 Hartford-West Hartford-East Hartford, CT MSA
## 19 Atlantic City-Hammonton, NJ MSA
## 248 New Haven-Milford, CT MSA
## 274 Pittsfield, MA MSA
## 366 Watertown-Fort Drum, NY MSA
## 50 Buffalo-Cheektowaga-Niagara Falls, NY MSA
## 255 Ocean City, NJ MSA
## 314 Santa Cruz-Watsonville, CA MSA
## 109 Elmira, NY MSA
## 305 Salinas, CA MSA
## 356 Vallejo-Fairfield, CA MSA
## 354 Utica-Rome, NY MSA
## 216 Madera, CA MSA
## 70 Chico, CA MSA
## 5 Albany-Schenectady-Troy, NY MSA
## 294 Rochester, NY MSA
## 340 Syracuse, NY MSA
## 310 San Diego-Carlsbad, CA MSA
## 244 Napa, CA MSA
## 312 San Jose-Sunnyvale-Santa Clara, CA MSA
## 298 Sacramento--Roseville--Arden-Arcadee, CA MSA
## 149 Hanford-Corcoran, CA MSA
## 211 Los Angeles-Long Beach-Anaheim, CA MSA
## 288 Redding, CA MSA
## 313 San Luis Obispo-Paso Robles-Arroyo Grande, CA MSA
## 264 Oxnard-Thousand Oaks-Ventura, CA MSA
## 277 Portland-Vancouver-Hillsboro, OR-WA MSA
## 381 Yuba City, CA MSA
## 128 Fresno, CA MSA
## 317 Santa Rosa, CA MSA
## 338 Stockton-Lodi, CA MSA
## 291 Riverside-San Bernardino-Ontario, CA MSA
## 234 Modesto, CA MSA
## 311 San Francisco-Oakland-Fremont, CA MSA
## 35 Binghamton, NY MSA
## 360 Visalia-Porterville, CA MSA
## 316 Santa Maria-Santa Barbara, CA MSA
## 133 Glens Falls, NY MSA
## 166 Ithaca, NY MSA
## 225 Merced, CA MSA
## 185 Kingston, NY MSA
## 106 El Centro, CA MSA
## 23 Bakersfield, CA MSA
## 139 Grants Pass, OR MSA
## 223 Medford-Ashland, OR MSA
## 84 Corvallis, OR MSA
## 33 Bend-Redmond, OR MSA
## 304 Salem, OR MSA
## 113 Eugene, OR MSA
## 4 Albany, OR MSA
## 250 New York-Newark-Jersey City, NY-NJ-PA MSA
## MEFI_Score New_MEFI_Score ScoreM3 ScoreM4 ScoreM5 ScoreM6
## 352 8.305701 8.249855 8.8169675 9.605915 8.0295216 10.0000000
## 1 7.966254 7.882337 8.4378697 9.529896 7.9729599 9.9106501
## 308 8.094744 8.001471 8.5919262 9.524295 7.9402623 9.8958049
## 229 8.593456 8.277960 9.0462172 9.637316 7.9633258 9.8597869
## 183 7.970140 7.872188 8.4803114 9.603775 8.2020497 9.8255029
## 372 7.992037 7.933851 8.4783624 9.517987 8.0418599 9.8011050
## 10 7.939000 7.801282 8.3660239 9.465303 7.8898248 9.7916322
## 209 7.811227 7.722632 8.3821291 9.532117 8.0703701 9.7831672
## 361 7.794591 7.670908 8.4345047 9.579712 8.2086325 9.6706482
## 345 8.562153 8.462706 9.8408763 10.000000 9.1539059 9.6620731
## 30 7.839124 7.727138 8.4430064 9.458057 7.9946525 9.6250584
## 324 7.879510 7.774540 8.5280410 9.541875 8.1779413 9.6193289
## 268 8.285605 8.153410 9.2775364 9.801192 8.8446957 9.6098726
## 87 8.092567 7.891692 8.4661499 9.293119 7.5731458 9.6081200
## 76 7.715802 7.643581 8.4128431 9.568855 8.2544570 9.6043629
## 83 7.725414 7.629802 8.3421223 9.471140 8.0617954 9.5957705
## 213 7.565643 7.491084 7.8433627 9.407342 8.0163168 9.5935859
## 328 8.053074 7.727225 8.2856424 9.179330 7.4300388 9.5683316
## 48 7.098762 7.122448 7.6641970 9.528908 8.4494991 9.5644675
## 309 7.801722 7.649969 8.1699916 9.321718 7.7651678 9.5488043
## 357 7.552875 7.445567 8.1832518 9.595903 8.3898413 9.5440952
## 321 8.742002 8.595771 9.7995043 9.806301 8.7902206 9.5299947
## 160 8.089738 7.891201 8.4803587 9.230276 7.5043745 9.5255691
## 110 7.244049 7.239665 7.1327054 8.801946 6.9966193 9.5237929
## 256 7.517312 7.204155 7.9093748 9.467928 8.0224183 9.5127544
## 222 6.980315 6.996983 7.6109631 9.517907 8.4855844 9.4961099
## 322 7.818680 7.709877 8.9531221 9.829109 9.0967824 9.4745711
## 85 8.301959 8.153389 9.4771277 9.859433 9.0062463 9.4658703
## 245 8.806686 8.618350 10.0000000 9.889110 8.9727438 9.4272348
## 254 8.074176 7.956015 9.1047807 9.752038 8.9336395 9.4174787
## 193 7.723405 7.572496 8.4422712 9.497465 8.4837310 9.4163595
## 342 8.192180 8.029575 9.1738228 9.689031 8.7252372 9.4137653
## 157 8.212575 8.099092 9.5786658 9.955464 9.2929209 9.4132799
## 261 7.938190 7.782602 8.8478082 9.607937 8.6096418 9.3966189
## 265 8.161420 7.996476 9.1451228 9.684214 8.7511754 9.3958518
## 22 8.016775 7.729349 8.3377149 9.139422 7.3735593 9.3624559
## 196 6.978413 6.983385 7.5954426 9.334419 8.1538284 9.3582145
## 283 8.078356 7.934464 9.4155082 9.879077 9.1508928 9.3528419
## 170 8.155603 7.964928 8.5178575 9.106970 7.6501914 9.3226264
## 266 7.779503 7.649647 8.6344418 9.446215 8.4215793 9.3017655
## 95 7.915611 7.799790 8.8499748 9.568722 8.6480039 9.2966998
## 198 6.932798 7.250402 6.5924561 7.004919 4.6762689 9.2614451
## 252 8.228521 8.058899 9.2260800 9.606275 8.6459639 9.2527789
## 278 8.324451 8.166524 9.4499947 9.695085 8.8237023 9.2388088
## 289 7.718917 8.003773 7.1804757 6.977018 4.5624374 9.2289906
## 130 7.867612 7.754130 8.3518076 9.190605 7.9883037 9.2160281
## 341 7.587567 7.443676 7.3714542 8.604388 6.9077336 9.1988332
## 226 8.086403 7.933155 8.8697989 9.354494 8.2408754 9.1489837
## 58 7.596674 7.743518 7.1011238 6.982716 4.5785168 9.1143784
## 55 7.943821 7.776538 8.9697120 9.654496 8.9398723 9.0568215
## 73 7.905506 7.891662 8.7630618 9.760474 9.5691502 9.0080856
## 174 7.849410 7.868315 8.7211893 9.738767 9.5901045 8.9515518
## 246 8.066807 7.933665 8.6009103 9.429750 8.8300831 8.9324620
## 186 7.802294 7.762720 8.3079041 9.396805 8.8888512 8.9309932
## 239 7.810368 7.789507 8.6269128 9.731205 9.6086430 8.8927608
## 344 7.233473 7.128243 7.3414382 8.686876 8.0051134 8.3883335
## 169 6.607818 6.596313 6.5852696 9.168001 9.1269097 8.3106927
## 148 6.202778 6.184857 4.6284888 7.412008 6.2408859 8.1905450
## 235 6.646153 6.602501 5.2302040 7.374610 6.0545651 8.1694663
## 191 6.592190 6.508772 5.2335216 7.381058 6.0416081 8.1294076
## 224 7.253817 7.173124 6.5624798 7.870140 6.7034864 8.0824890
## 27 6.892378 6.794925 5.1397498 6.970307 5.3015936 7.9825214
## 189 6.951486 6.877424 5.5112711 7.292596 5.9896571 7.9296025
## 7 6.872442 6.810076 5.1546791 7.050042 5.5864223 7.8971416
## 67 7.523486 7.439364 7.5742006 8.717233 8.5516156 7.8483367
## 184 7.682901 7.639456 8.4796069 9.247439 9.7075533 7.8415829
## 249 7.048577 6.967499 5.3355304 6.931469 5.3040540 7.8268096
## 325 6.712994 6.667003 5.0871084 6.923638 5.3949493 7.7892870
## 120 7.365965 6.905044 7.3939824 8.719880 8.4489461 7.7673040
## 286 4.436836 4.183502 5.9437270 9.133106 9.0388655 7.7279668
## 72 7.408867 7.481892 7.3163546 8.154799 8.0198634 7.6641551
## 378 6.175702 6.270471 6.0979250 5.766091 4.0005145 7.6182303
## 37 7.562230 6.973945 7.1797683 8.134681 7.0742852 7.6139010
## 279 7.170851 6.709930 7.2372366 8.630278 8.4309287 7.6075521
## 32 6.369341 6.433701 6.4427167 5.747701 3.8722507 7.5978760
## 331 6.190919 6.276331 5.9948373 5.559240 3.5910603 7.5716426
## 320 6.600002 6.535707 6.4112918 5.617284 3.5203671 7.5432922
## 46 6.553676 6.606339 6.5366785 5.743180 3.8812094 7.5362462
## 159 6.632943 6.526133 5.1705159 7.193895 6.0952777 7.5217971
## 259 6.370216 6.420541 6.4281487 5.737548 3.9024469 7.5128622
## 271 7.303761 6.842840 7.0219247 8.340880 7.9019731 7.5054591
## 362 6.136793 6.189456 6.1810355 5.734746 3.9617059 7.4928324
## 182 5.802344 5.880739 5.5852480 5.747863 4.1102416 7.4582339
## 369 5.854285 5.949054 5.7269918 5.724270 4.0886102 7.3964208
## 192 6.965140 6.504219 7.1436970 8.611692 8.5888978 7.3934781
## 349 7.277831 6.816910 7.1276013 8.319624 7.9426663 7.3902996
## 326 7.067884 6.606963 7.1879376 8.612949 8.6198351 7.3415221
## 382 6.732408 6.271487 7.0367785 8.594513 8.6534238 7.2576126
## 210 5.821972 5.905045 5.6507910 5.663217 4.0849636 7.2339022
## 240 5.688774 5.741437 5.5801086 5.685091 4.1721874 7.1657518
## 111 7.546786 7.506764 7.1815118 7.816980 7.7336026 7.1528174
## 197 5.776511 5.828062 3.2210508 5.509174 3.7690958 7.0068152
## 350 7.723071 7.636266 7.3057655 7.708789 7.5543779 6.9899409
## 258 7.670083 7.578599 7.3263666 7.776253 7.6960748 6.9761112
## 200 7.156816 7.144865 6.5608304 7.689242 7.8170144 6.8452681
## 219 7.308970 7.453041 7.7732238 7.684109 8.0180153 6.8320093
## 6 6.154302 6.100590 3.6268447 5.442972 3.6317847 6.7764822
## 168 6.729378 6.672535 4.9281665 6.235111 5.1171696 6.6663133
## 315 6.263269 6.087920 3.7564132 5.431582 3.5995715 6.6398555
## 339 7.367744 7.222715 6.5759201 7.857361 8.0076245 6.6343326
## 126 6.782059 6.653091 6.3229574 7.695918 8.1309449 6.6306239
## 122 7.405011 7.276356 6.5988930 7.845038 7.9967128 6.6169820
## 158 6.744399 6.612255 6.2560518 7.689805 8.2326505 6.5903450
## 199 6.978300 7.089622 7.3035178 7.634358 8.1260055 6.5756102
## 371 7.364078 7.374815 7.3416386 7.270526 7.3573619 6.5138769
## 233 6.447193 6.506158 4.9682004 6.814923 6.7735799 6.5058715
## 116 7.150823 6.901942 6.5703450 7.026671 6.6731752 6.4887157
## 64 7.505090 7.289885 6.8160063 7.820303 7.9143512 6.4743693
## 202 6.990795 7.041929 6.7922527 6.712772 6.4759589 6.4410444
## 347 7.081999 7.071684 7.4895940 7.630584 8.1245894 6.4391652
## 90 6.763181 6.880626 5.3177168 6.899996 6.9973993 6.4275398
## 218 8.087754 8.099079 9.2285924 7.551349 7.4900963 6.4197659
## 119 7.321780 7.135468 6.7585882 7.544553 7.9597287 6.3834523
## 14 6.149303 6.280783 4.5515319 6.837688 7.0453079 6.3806441
## 237 6.609036 6.707768 4.6628547 6.518859 6.3109570 6.3720678
## 93 6.507599 6.625044 5.0790707 6.883238 7.0470711 6.3619843
## 153 5.907741 5.932769 3.9514419 6.168383 5.4249319 6.3417187
## 207 6.853000 6.674072 6.2764146 7.508870 7.9910841 6.3325023
## 21 7.620578 7.485244 7.3357569 7.901038 8.4647382 6.3050560
## 99 6.245529 6.386366 4.6039622 6.890635 7.1989868 6.3037954
## 272 6.396622 6.231729 5.8086555 7.439469 7.9955480 6.2896591
## 123 6.422316 6.549117 4.6898706 6.648297 6.6775398 6.2887631
## 190 7.298110 7.420080 7.6836085 7.628549 8.5286839 6.2877655
## 363 7.589523 7.477140 7.6067833 8.047294 8.9154907 6.2760875
## 155 7.611129 7.438030 7.0165162 7.740170 7.8958852 6.2751267
## 40 7.311130 7.468188 7.8526434 7.848324 8.9822729 6.2746570
## 146 5.948761 5.919988 4.0957921 6.147233 5.3579653 6.2685553
## 176 6.558954 6.410435 6.0238806 7.552482 8.2157434 6.2554072
## 187 7.260012 7.421748 7.8715052 7.851516 9.0157854 6.2478443
## 243 7.187469 7.068171 6.7734289 7.865137 8.3804418 6.2373448
## 117 5.433575 5.450039 2.5864824 5.301303 3.9311783 6.2210427
## 145 7.326075 7.110870 6.3533979 7.741203 8.0225432 6.2140559
## 131 7.680129 7.516284 7.5690924 7.863066 8.5515523 6.1844354
## 162 6.540676 6.601981 4.8722839 6.856287 7.0801753 6.1837076
## 227 7.097847 7.250226 7.6668320 7.803213 9.0117980 6.1737328
## 241 6.837356 7.024823 7.1105278 7.802525 9.1227184 6.1390673
## 171 7.622307 7.529634 7.5743742 8.093371 9.1995898 6.1389076
## 79 7.253703 7.040838 6.4990917 7.725889 8.0261437 6.1231009
## 36 6.755798 6.784354 4.9119487 6.314314 6.0791974 6.1118523
## 80 7.378606 7.347126 6.8249803 7.399779 7.8773329 6.1024174
## 351 6.365196 6.459249 4.5494831 6.510758 6.5389764 6.0907900
## 297 7.299187 7.186804 7.3711785 7.992613 8.9698615 6.0736940
## 135 6.823402 6.689542 6.1491889 6.670340 6.5205213 6.0721817
## 88 7.218244 7.080130 6.9167878 7.776445 8.5828342 6.0556832
## 163 7.576593 7.512783 7.2919384 7.481487 8.1068057 6.0475114
## 215 7.623472 7.529802 6.9504608 7.296237 7.6367220 6.0322597
## 156 7.392166 7.291479 7.3811751 7.969358 8.9834295 6.0309752
## 114 7.269004 7.355961 7.1549767 7.170887 7.9020268 6.0101486
## 318 7.326483 7.211761 6.9404427 7.570526 8.1669031 5.9997753
## 18 7.790038 7.595783 7.3881075 7.520347 7.9715828 5.9802230
## 355 7.061460 6.998200 6.9753632 7.963204 9.0877388 5.9650420
## 108 7.489145 7.384214 7.9611881 7.788262 8.9501290 5.9567501
## 20 6.279730 6.425245 4.8456716 6.730852 7.1723844 5.9533507
## 330 6.955649 6.703018 5.9297810 7.693565 8.1512370 5.9524764
## 81 7.070936 7.043198 7.3045958 7.815205 9.0845229 5.9433397
## 49 7.083883 7.015945 6.7605199 7.708234 8.5665420 5.9429261
## 42 7.604201 7.460859 7.4032567 7.462121 8.0607678 5.9344076
## 17 7.361497 7.244435 7.0250133 7.682792 8.4698337 5.9116917
## 3 7.033448 6.956153 6.4824464 7.390737 7.9977068 5.9088359
## 134 7.268940 7.092057 7.0748958 8.058615 9.3394948 5.8799805
## 164 7.412696 7.471508 7.7317115 7.684106 9.0199120 5.8764163
## 275 7.299106 7.254010 7.1052396 7.387061 8.0771879 5.8698030
## 177 7.127078 7.240190 6.5412970 6.420371 6.5951084 5.8690828
## 129 6.568715 6.730604 5.1116629 6.627358 7.1549715 5.8610638
## 51 7.373712 7.236595 7.2664047 8.026170 9.2635386 5.8584959
## 376 7.499331 7.313091 7.3367198 7.978903 9.1263619 5.8481215
## 118 7.187639 7.045844 7.2358803 8.046794 9.3661660 5.8427328
## 56 7.211811 7.364237 6.7282146 6.363701 6.5406310 5.8340497
## 143 7.432923 7.251362 7.2873982 8.020783 9.2234569 5.8198950
## 343 7.013298 7.196087 7.5083777 7.627970 9.1019498 5.8155224
## 285 7.678090 7.428693 7.5727862 8.031737 9.1700172 5.7884839
## 173 7.417551 7.465166 6.9380106 6.474234 6.6372358 5.7866799
## 16 7.279983 7.037603 7.0226809 8.028909 9.2751311 5.7518589
## 65 7.281840 7.041293 6.8922328 7.944825 9.0456541 5.7463026
## 154 7.164166 6.954535 6.8947315 8.019578 9.3429651 5.7305127
## 127 7.402672 7.501250 7.8481194 7.577777 9.0216872 5.7249897
## 334 7.099889 7.203644 6.3667049 6.268031 6.3942452 5.7124761
## 208 7.103819 6.943920 6.6524872 7.811368 9.0940865 5.7049456
## 302 6.939632 6.988984 6.5052197 6.485760 6.7774245 5.7011389
## 103 7.734209 7.356157 7.6452778 8.030337 9.1361785 5.6859720
## 247 7.043723 6.901928 6.6299664 8.006268 9.4424431 5.6842461
## 281 7.128484 6.902427 6.6742379 7.851727 9.1528010 5.6514063
## 296 6.943090 6.805973 6.5962407 7.774894 9.0328425 5.6381851
## 301 6.958899 6.760912 6.5552383 7.843058 9.1733913 5.6350948
## 257 7.222522 6.982430 6.7904879 7.840845 9.1443543 5.6010470
## 181 7.089413 7.056098 6.3997235 6.308019 6.4303556 5.5901863
## 78 6.939395 7.029115 6.0227356 6.249604 6.4411874 5.5812969
## 75 7.008933 6.863252 6.6832695 7.400160 8.3072065 5.5793057
## 307 7.229756 6.858669 6.7189436 7.806189 9.0180083 5.5083162
## 374 6.714652 6.556483 6.4247127 7.969653 9.4742189 5.5002658
## 115 6.633907 6.675872 4.1100068 2.960524 0.0000000 5.4201213
## 12 6.553053 6.683907 4.0481406 2.942521 0.0311488 5.4156198
## 96 7.647274 7.515615 6.8807179 5.998371 5.9676367 5.3718361
## 144 6.551182 6.388334 5.9808691 7.938537 9.5766848 5.3370609
## 337 7.806334 7.664884 8.3143315 8.088411 9.8620346 5.3238177
## 214 7.663058 7.584765 8.2172133 8.070524 9.8881387 5.3197747
## 327 6.832335 6.701831 6.5370740 6.951669 7.7561176 5.3196990
## 329 7.133708 7.276348 7.3647611 6.964655 8.2953864 5.3138410
## 77 7.695630 7.659878 7.0403064 6.036438 6.1752632 5.2948309
## 206 7.328316 7.219256 8.3871476 7.868629 9.6248002 5.2891032
## 282 7.354399 7.349056 6.7838956 6.039713 6.2921534 5.2872941
## 137 7.590941 7.562206 6.9653803 6.031327 6.2137224 5.2710119
## 303 7.469524 7.568053 6.4246223 5.476601 5.3282074 5.2601653
## 125 7.740603 7.674441 7.0735867 6.035463 6.1751383 5.2498542
## 141 7.584194 7.515693 6.9544434 6.031790 6.2284518 5.2173906
## 151 7.603876 7.443712 8.0872629 8.059861 9.8848080 5.2116438
## 290 7.970823 7.780250 8.3584158 7.945264 9.5957062 5.1701606
## 38 7.511367 7.407343 7.9158041 8.022605 9.9059102 5.1654722
## 101 6.996048 6.953533 6.3852160 6.487641 7.1214716 5.1553691
## 205 6.153852 6.440600 3.5601990 3.461709 1.8863935 5.1285007
## 359 7.485843 7.354490 7.5632718 7.584211 9.0618401 5.0848347
## 375 7.406812 7.246985 7.5403349 7.514078 9.0401519 5.0532865
## 66 8.071121 7.761250 8.3693120 7.858686 9.4099244 5.0527513
## 54 6.279347 6.573113 3.7277832 3.411463 1.8846487 5.0417703
## 370 5.888629 6.095377 3.4702107 3.964973 2.8487492 5.0413470
## 44 7.267555 7.032972 6.6661086 5.937655 5.9856667 5.0398711
## 292 7.496248 7.350120 7.5807317 7.586647 9.0950778 5.0327585
## 260 7.137609 6.972976 6.9409512 6.640754 7.4315786 5.0005367
## 136 6.775247 6.668526 7.8743419 7.910797 10.0000000 4.9592842
## 335 6.032381 6.288720 3.4438220 3.403177 1.9797969 4.9521033
## 365 6.638892 6.572986 5.9690110 6.433178 7.2329672 4.9254877
## 380 6.125149 6.431378 3.6579093 3.515931 2.2051582 4.9232910
## 60 6.849836 6.734807 6.2217612 6.438713 7.2043524 4.9110006
## 165 7.167849 7.050481 6.5678658 6.394216 7.0876167 4.8842461
## 11 6.623880 6.562652 5.8906743 6.398113 7.2397634 4.8416761
## 97 6.987147 6.792586 6.3662965 6.425930 7.0991926 4.8376531
## 91 6.588592 6.779391 5.5519841 4.899369 4.8921813 4.6525374
## 9 6.753290 7.126639 5.2157706 4.609996 4.6422474 4.6288642
## 161 5.864151 6.028671 3.5415614 4.341300 4.0541810 4.6130217
## 238 6.062568 6.188066 3.8891173 4.616148 4.5607769 4.6016232
## 175 6.433054 6.813420 4.8000112 4.462383 4.3812541 4.5917756
## 57 6.370872 6.779001 5.0753784 3.806258 3.3084878 4.5328257
## 267 5.804791 5.960698 3.5256607 4.538188 4.5501431 4.5295198
## 368 5.436184 5.668967 2.7690621 3.759779 3.0530644 4.5258224
## 31 5.673704 5.831950 3.5525712 4.637828 4.7640411 4.5207050
## 194 6.870730 7.096710 5.7605079 4.893648 5.0780736 4.4922644
## 61 6.861595 7.099271 5.6931397 4.836213 5.0540848 4.4823218
## 228 6.764919 7.162695 5.8402333 4.234621 4.1519380 4.4807809
## 71 6.348105 6.549410 3.6303482 3.288315 2.1043235 4.4523024
## 63 5.784584 5.919438 3.6799828 4.638668 4.7442197 4.4507526
## 112 6.493901 6.855554 4.9517294 4.484904 4.4950472 4.4480001
## 105 6.909050 6.950145 6.0892990 5.678907 6.4283596 4.4106078
## 94 6.390476 6.782231 4.8106513 3.520721 2.8102860 4.4040322
## 39 6.373566 6.741930 5.1847955 3.739309 3.1783103 4.4013934
## 25 6.418001 6.532698 6.2100081 5.606810 6.5332312 4.3910126
## 269 6.436965 6.812346 5.0940205 3.644803 3.0377855 4.3814898
## 221 5.834736 6.114466 3.2011703 3.145327 2.1032724 4.3715567
## 284 6.961440 7.037623 6.1575318 5.655445 6.4301891 4.3663876
## 62 6.411556 6.782259 5.2111830 3.766975 3.2766497 4.3639398
## 201 6.634026 6.909129 5.0054294 4.281248 4.0964957 4.3559906
## 379 6.700844 6.945538 5.5943175 4.773165 4.9845570 4.3378494
## 92 6.014905 6.264226 3.4352963 3.132095 2.0189006 4.3143720
## 236 6.313106 6.724917 5.2463908 4.015816 3.9469518 4.3128962
## 142 6.975720 6.979388 6.1246366 5.658026 6.4280083 4.3039610
## 179 6.378409 6.776185 5.5287169 4.216862 4.2997830 4.2971578
## 295 6.301404 6.669768 4.9913485 3.653655 3.1194087 4.2929270
## 89 6.162570 6.554326 4.7029267 3.616943 3.1526350 4.2908373
## 15 6.962810 6.910338 6.0658728 5.673085 6.4523067 4.2824457
## 13 6.661356 6.995974 5.6943398 4.083631 3.9349730 4.2778641
## 336 6.856906 7.108617 5.6501095 4.690089 4.9919818 4.2737694
## 273 6.660654 6.912365 5.0908659 4.420074 4.4470306 4.2704640
## 124 6.790153 6.770430 5.9921424 5.660443 6.4859676 4.2487648
## 107 6.005776 6.180997 3.5923427 4.431232 4.5754459 4.2475426
## 172 6.595526 6.676388 5.8104973 5.637073 6.5425943 4.2445721
## 69 6.033901 6.306980 3.7886397 2.800542 1.4028252 4.2425351
## 276 6.642859 6.647614 6.4676867 5.580557 6.4475056 4.2215070
## 251 6.273036 6.680169 5.3934490 4.164334 4.2779311 4.2063089
## 332 6.114006 6.477691 4.4872491 3.526539 2.9776560 4.1920517
## 203 6.232408 6.314356 6.1156386 5.558025 6.5720267 4.1742119
## 180 6.245016 6.622736 4.9953208 3.659177 3.2493517 4.1708595
## 299 6.169910 6.584061 5.2558901 4.278542 4.6977212 4.1562480
## 59 6.780837 6.611406 5.4793355 5.692803 6.5630409 4.1493806
## 138 6.563286 6.918957 5.6162788 4.164462 4.3687337 4.1294922
## 2 6.131943 6.388282 3.4977074 3.026825 2.0282229 4.1291036
## 367 6.691408 6.613206 5.7349201 5.644817 6.5152682 4.1255912
## 242 6.129050 6.526826 5.5925715 4.546919 5.2105379 4.1235425
## 319 6.553293 6.823717 5.2016255 4.481763 4.7340399 4.1109566
## 98 6.416473 6.795535 4.8928172 3.555662 3.2061933 4.1074584
## 132 6.521408 6.817563 5.5529575 4.889159 5.5989130 4.1026340
## 121 5.981201 6.397691 4.8542080 4.087924 4.3604429 4.0971788
## 41 6.731552 6.941158 5.5591113 4.700236 5.1792512 4.0913880
## 217 6.953194 6.793120 6.1597462 5.639513 6.3952176 4.0884173
## 346 5.970134 6.240507 3.3122673 3.006678 2.0798870 4.0777149
## 167 6.063030 6.470163 4.8926169 3.891340 4.0059775 4.0455654
## 230 6.647278 6.615859 4.5909892 4.432448 4.2469775 4.0414070
## 74 5.920469 6.153415 3.3035865 3.025413 2.0824201 4.0364827
## 323 6.493014 6.520074 5.3825771 5.597347 6.6006490 4.0239890
## 373 6.435623 6.736457 4.9087073 4.309715 4.6007551 3.9799721
## 104 6.189913 6.532853 5.5636215 4.794580 5.3264267 3.9780580
## 52 6.323417 6.011487 6.2264729 6.145862 7.5135654 3.9185630
## 287 6.376678 6.602659 5.0731416 4.444214 4.7460552 3.9174554
## 263 5.951527 6.180549 3.5042232 4.208220 4.5335154 3.9124766
## 188 6.524156 6.608377 5.5620963 5.343989 6.3548842 3.9115640
## 29 5.620588 6.053451 3.7991323 3.182879 2.7434421 3.9031089
## 195 6.047758 6.443195 5.0691436 4.002109 4.3085200 3.8920976
## 8 6.394809 6.626505 4.9968687 4.276104 4.4601088 3.8834456
## 68 6.043216 5.925247 4.5591120 5.635332 6.8139760 3.8709157
## 82 6.013636 6.216173 3.3336313 2.928741 2.0723223 3.8523811
## 150 6.615699 6.799574 5.4043686 4.585619 5.0933583 3.8515019
## 212 6.552958 6.673021 4.7758563 4.744686 5.5417924 3.8028758
## 140 6.909168 7.120665 5.8078097 4.423592 4.6749947 3.7835884
## 232 6.982083 7.219312 5.9211725 4.401991 4.6192347 3.7698370
## 34 7.001037 7.128324 5.8727983 4.414581 4.6292544 3.6972254
## 262 6.166611 6.079051 4.8091998 5.530570 6.6672323 3.6785285
## 353 5.525477 6.119629 4.6245096 2.839308 3.2326860 3.6771544
## 28 5.990731 6.372133 5.0975836 4.007823 4.5701759 3.6617035
## 147 6.305562 6.404367 5.3264324 5.231515 6.8010173 3.5516532
## 86 6.318744 6.445084 5.7103646 5.531781 7.4543171 3.5007612
## 280 5.914227 6.028910 3.7142454 2.471361 1.5330905 3.3541289
## 45 6.046448 6.252078 3.6126087 3.937622 4.6257706 3.3320351
## 270 6.483800 6.656987 4.8901355 3.870022 4.2569004 3.3204851
## 100 6.434728 6.548083 5.7200978 5.392932 6.9733289 3.2274238
## 364 6.952040 6.570023 6.7608863 6.143913 8.1817593 3.1242445
## 306 6.531438 6.646768 6.2719130 5.550881 7.5413022 3.0760717
## 178 4.898941 5.394848 3.6050417 2.650884 3.3002051 3.0618062
## 348 6.413820 6.612714 5.1402711 3.030706 3.0456560 2.8636523
## 358 5.501018 5.915117 3.9891298 2.992980 3.3703000 2.8184642
## 204 6.065324 6.238206 3.1997544 3.273890 3.8186374 2.8042571
## 53 6.965968 6.967988 6.7865624 5.535530 8.1736413 2.6634737
## 43 7.025329 6.639788 6.4497437 4.576952 5.8078196 2.4886419
## 24 6.674288 6.723092 5.7440133 4.663361 6.7170698 2.3971520
## 102 5.555191 5.765296 4.5159299 3.909988 5.7311198 2.3179027
## 47 6.732983 6.853189 5.7412555 2.971331 3.7285144 2.2686723
## 220 5.783158 6.058402 4.8455430 3.643901 5.4650936 2.2015978
## 231 6.227190 6.321124 5.2113101 3.636817 5.2200840 2.1781667
## 377 6.551832 6.300919 6.1228585 4.418325 6.1022064 2.1732654
## 293 5.915336 6.080638 4.8708685 3.653822 5.4345342 2.1300423
## 300 5.732325 5.921019 4.3765380 3.599304 5.4150460 2.1030482
## 253 6.130708 6.402960 5.3185761 2.928461 3.9451288 2.0936459
## 333 6.324013 6.066677 5.5156219 4.202382 5.7418076 2.0909381
## 26 6.551317 6.279947 5.8465786 4.056513 5.4161552 1.9649097
## 152 6.228898 6.342086 5.1287271 2.585068 3.2486775 1.9499337
## 19 5.178263 5.620433 4.7638335 2.670168 3.2122434 1.8301616
## 248 5.902484 6.053099 4.5808071 2.293074 2.8602528 1.8004857
## 274 5.993028 5.766102 3.5218273 2.291376 2.3003808 1.7658176
## 366 5.256052 5.904005 4.5258761 2.444300 4.2702602 1.7563884
## 50 5.354570 5.983810 4.5095790 2.401527 4.1883094 1.6782098
## 255 5.054998 5.436349 4.4760573 2.649806 3.2815095 1.6563184
## 314 6.075319 6.118485 4.3938427 2.817650 4.1448534 1.6121760
## 109 5.209421 5.862053 4.5750702 2.391547 4.2897348 1.5860154
## 305 5.535437 5.632404 3.6602796 2.800438 4.2881581 1.5503930
## 356 5.763455 5.818317 4.1256131 2.792160 4.2162666 1.5311621
## 354 5.142093 5.783028 4.4485889 2.367013 4.2895406 1.5123095
## 216 5.330176 5.438840 3.2517146 2.757708 4.3284794 1.5107253
## 70 5.383730 5.522803 3.2001213 2.742834 4.3156069 1.4974701
## 5 5.708030 6.215632 5.0911949 2.367015 4.0989304 1.4681757
## 294 5.359463 5.934902 4.6547762 2.358059 4.2099975 1.4551039
## 340 5.335126 5.940974 4.6296657 2.349033 4.2253136 1.4480810
## 310 5.759855 5.796004 3.1645807 1.816586 2.3569623 1.4445262
## 244 6.089994 6.074681 4.6608339 2.778606 4.0711360 1.4416432
## 312 6.369464 6.197426 4.4925447 2.464412 3.3664594 1.4158327
## 298 5.670024 5.720208 3.1471325 2.097176 2.9691259 1.4058025
## 149 5.206877 5.315541 2.9720446 2.724192 4.3780575 1.4008426
## 211 5.512085 5.583322 2.4884141 1.452078 1.7511868 1.3907372
## 288 5.354283 5.472303 3.1902065 2.724008 4.3429057 1.3839613
## 313 5.760169 5.836084 3.4458080 1.899691 2.5640092 1.3627219
## 264 5.830031 5.905946 3.3457166 1.863484 2.5390511 1.3456130
## 277 5.904558 5.889136 3.7767637 2.012999 2.5507809 1.3228021
## 381 5.201145 5.298113 3.1000838 2.720491 4.4161568 1.3072745
## 128 5.060974 5.202385 2.1220924 1.434632 1.9408548 1.2996139
## 317 5.718838 5.703525 3.1924030 1.838259 2.4605175 1.2977098
## 338 5.027410 5.131395 1.9726887 1.654859 2.3551283 1.2964070
## 291 4.925027 5.031352 2.3621631 2.224550 3.4989965 1.2555543
## 234 5.109908 5.223250 2.3500509 1.644438 2.3801617 1.2493026
## 311 6.007986 5.826591 3.2672639 1.734234 2.1485884 1.2152527
## 35 4.676305 5.328936 3.4742813 2.248841 4.3474377 1.2148004
## 360 4.550569 4.696659 1.3062610 1.858782 3.0109760 1.1578584
## 316 5.362190 5.424070 2.7211042 1.725531 2.4695509 1.1533124
## 133 5.019526 5.594965 4.6668602 2.286413 4.2882560 1.1504010
## 166 5.181647 5.679893 4.7412284 2.267447 4.1925934 1.1005246
## 225 4.597295 4.724671 1.2511273 1.532265 2.4488534 1.0244122
## 185 5.056695 5.627455 4.7060735 2.185087 4.2196415 0.8832531
## 106 3.819789 4.000967 0.0000000 1.615822 2.9718686 0.7773095
## 23 4.201715 4.345466 0.4509558 1.229730 2.1180902 0.6796507
## 139 5.763283 5.893996 3.3057397 1.593385 2.7790038 0.5290082
## 223 5.875402 5.975706 3.4300045 1.541953 2.6360606 0.4695739
## 84 5.981392 6.053625 3.6773525 1.568544 2.6552389 0.4523523
## 33 5.966449 6.048040 3.6095699 1.540800 2.6562329 0.3475306
## 304 5.622567 5.704158 3.1463211 1.512940 2.7315471 0.3218229
## 113 5.624758 5.713366 3.1293772 1.492802 2.7169344 0.2631538
## 4 5.490305 5.578913 3.1069057 1.535527 2.8536597 0.2396920
## 250 5.176069 5.751756 2.7523837 0.000000 0.8922214 0.0000000
## ScoreM7 ScoreM8 RankOriginal RankNewScore RankM3 RankM4 RankM5 RankM6
## 352 9.1441598 7.0131427 6 5 20 20 110 1
## 1 8.8086323 6.8303646 28 25 36 28 125 2
## 308 8.9512090 6.9233040 14 14 26 30 130 3
## 229 9.3556066 7.0864254 3 4 14 17 127 4
## 183 8.9842125 7.1669431 27 26 30 21 94 5
## 372 9.0262323 7.1396728 25 19 32 31 109 6
## 10 8.7441320 6.8455688 30 28 42 36 135 7
## 209 8.7486679 6.8179200 39 42 40 27 106 8
## 361 8.8701502 6.9161581 44 45 37 23 93 9
## 345 9.7785055 6.8147767 4 3 2 1 26 10
## 30 8.8650614 6.9742023 37 41 34 37 122 11
## 324 9.0406773 7.1637326 34 34 27 26 96 12
## 268 9.3092118 6.6703209 8 7 8 8 60 13
## 87 8.8802863 6.9634512 15 22 33 45 144 14
## 76 8.9161890 6.9361614 53 50 38 24 89 15
## 83 8.7526736 6.8850339 49 53 45 34 107 16
## 213 8.5729230 6.9842903 73 68 61 40 116 17
## 328 8.5132090 6.5697398 23 40 49 49 152 18
## 48 8.4690362 7.1052608 139 124 66 29 81 19
## 309 8.7200304 6.9945142 43 48 52 44 138 20
## 357 8.6267114 6.9066829 75 77 51 22 85 21
## 321 9.7621049 6.9011298 2 2 3 7 63 22
## 160 8.9349836 7.0859304 16 24 29 47 148 23
## 110 7.9408403 6.9559731 120 107 107 54 169 24
## 256 8.2678645 6.5433957 78 113 56 35 113 25
## 222 8.4336823 7.1216772 158 149 68 32 77 26
## 322 9.0632988 6.6727335 38 43 16 6 35 27
## 85 9.4046922 6.6558365 7 8 5 5 50 28
## 245 10.0000000 7.0562004 1 1 1 3 53 29
## 254 9.3726013 6.9716059 20 17 13 10 57 30
## 193 8.6397743 6.5602027 50 57 35 33 78 31
## 342 9.2436396 6.6875029 11 12 11 14 65 32
## 157 9.7059724 7.0337659 10 9 4 2 19 33
## 261 8.8786588 6.3993400 31 31 19 18 70 34
## 265 9.2496282 6.7987464 12 15 12 15 64 35
## 22 8.8290644 7.0497630 24 39 46 51 153 36
## 196 8.2469592 6.8898861 159 152 70 43 99 37
## 283 9.3804264 6.6842946 19 18 7 4 28 38
## 170 8.8084457 6.7409647 13 16 28 53 142 39
## 266 8.6980857 6.5257174 46 49 23 38 84 40
## 95 9.0170559 6.7152898 32 29 18 25 67 41
## 198 6.2753036 5.3537773 174 102 150 158 251 42
## 252 9.2978834 6.8203525 9 11 10 19 68 43
## 278 9.5727565 7.0215598 5 6 6 13 62 44
## 289 7.3704729 6.5272527 52 13 103 160 261 45
## 130 8.7937961 6.8633837 35 37 44 48 124 46
## 341 7.9608432 6.6206097 70 79 84 61 171 47
## 226 9.0534226 6.7704879 18 21 17 42 90 48
## 58 7.2608264 6.4408198 67 38 111 159 258 49
## 55 9.2472918 7.0258416 29 33 15 16 56 50
## 73 8.7855400 6.0366010 33 23 21 9 12 51
## 174 8.7707794 6.0791320 36 27 22 11 10 52
## 246 8.4362479 5.7152423 22 20 25 39 61 53
## 186 8.2920473 5.7705586 42 35 48 41 59 54
## 239 8.8182161 6.2101194 40 30 24 12 8 55
## 344 7.0681650 5.1289176 121 122 88 57 118 56
## 169 7.3246795 5.9641268 217 241 151 50 31 57
## 148 3.4985830 2.2153860 277 300 296 144 206 58
## 235 4.0747576 2.4861705 208 239 244 150 214 59
## 191 3.8486815 2.1497637 220 258 243 149 215 60
## 224 6.6394232 5.1421305 118 117 155 92 177 61
## 27 4.2106346 2.8306027 177 190 253 161 235 62
## 189 4.7210819 3.2170347 170 175 232 152 216 63
## 7 4.5163977 3.3384713 178 186 251 156 223 64
## 67 7.6104022 5.4207483 77 80 73 56 74 65
## 184 7.9638451 5.0415181 55 51 31 46 6 66
## 249 4.6034986 3.2356276 147 158 237 164 234 67
## 325 4.2617098 3.0458375 201 221 260 165 230 68
## 120 6.4526487 3.7755058 97 170 81 55 82 69
## 286 5.6712245 5.1901857 380 381 199 52 43 70
## 72 6.8466159 4.5448344 89 70 92 65 114 71
## 378 6.1601032 6.3545407 280 289 190 206 304 72
## 37 7.2299114 5.6510938 74 155 104 66 165 73
## 279 6.4694952 4.0466140 128 207 99 58 83 74
## 32 6.2629974 6.1751292 259 267 166 208 312 75
## 331 6.0034130 6.2277196 278 287 195 229 318 76
## 320 6.1270775 6.0337989 218 252 170 225 319 77
## 46 6.4995814 6.4684341 225 237 160 209 311 78
## 159 4.6770505 3.5270493 213 256 250 154 212 79
## 259 6.3018019 6.2819480 257 270 167 210 310 80
## 271 6.4754538 4.2230129 107 180 118 63 133 81
## 362 6.0886931 6.1970506 287 298 183 211 305 82
## 182 5.7225071 6.1054144 329 339 223 207 296 83
## 369 5.7746002 6.1216149 324 325 213 212 299 84
## 192 6.6712668 4.4721116 163 260 106 60 71 85
## 349 6.6224942 4.4398326 113 183 108 64 129 86
## 326 6.6836655 4.5679586 145 236 101 59 69 87
## 382 6.3904047 4.2290271 197 288 115 62 66 88
## 210 6.0144591 6.5094943 327 335 218 217 300 89
## 240 5.7063387 6.2787953 340 351 224 214 293 90
## 111 6.3187421 3.9568577 76 66 102 105 140 91
## 197 2.8220583 2.9689502 332 343 357 235 314 92
## 350 6.5451872 4.1994852 51 52 93 118 145 93
## 258 6.5888357 4.1904654 58 56 91 113 141 94
## 200 6.1781121 4.4031814 131 119 156 123 137 95
## 219 6.2314936 3.5353983 106 76 62 124 115 96
## 6 3.3864995 3.4022630 284 307 332 237 316 97
## 168 4.2556147 3.1630486 199 218 271 194 239 98
## 315 3.5321862 3.5317899 271 309 324 238 317 99
## 339 6.3936790 4.2775612 96 109 152 97 117 100
## 126 4.9207663 2.4715593 188 223 176 120 101 101
## 122 6.4635818 4.3477780 91 98 148 101 120 102
## 158 4.4007995 1.5980759 195 233 179 122 91 103
## 199 6.0565199 3.6609281 160 131 95 127 102 104
## 371 6.2403763 4.0811052 98 87 87 153 154 105
## 233 4.5673898 3.0554280 240 259 269 171 175 106
## 116 5.8273435 4.0674952 132 172 153 157 179 107
## 64 6.4691303 4.1541288 80 97 132 104 131 108
## 202 6.0397334 4.4530440 155 139 133 173 193 109
## 347 6.1984050 3.8490506 143 133 79 128 103 110
## 90 5.1712328 3.6665732 192 174 240 166 168 111
## 218 9.8660913 8.1890414 17 10 9 136 149 112
## 119 5.0627386 2.2494662 104 120 140 137 128 113
## 14 4.4673922 3.3425657 286 285 303 170 167 114
## 237 4.7710831 3.6071067 216 208 292 178 204 115
## 93 5.0059458 3.5931817 236 228 261 168 166 116
## 153 3.6248481 3.2040730 318 328 319 195 227 117
## 207 4.6050286 1.9592582 182 216 177 140 123 118
## 21 6.4646439 3.7429901 63 69 90 91 80 119
## 99 4.7053448 3.6214229 272 277 298 167 158 120
## 272 4.2131831 1.8699066 250 294 207 143 121 121
## 123 4.8569071 3.7534662 245 250 290 175 178 122
## 190 6.4664830 3.6121448 110 84 64 129 76 123
## 363 6.2389351 3.1815988 69 71 69 72 58 124
## 155 6.8701326 4.6588215 64 81 119 116 134 125
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## RankM7 RankM8
## 352 16 16
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## 7 232 201
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## 250 380 377Rank Difference Boxplots
hist=read.csv('c:/users/lfult/documents/minimum wage/for sir/for histograms.csv')
boxplot(hist$Difference~hist$Model, horizontal = F, notch=TRUE, col=rainbow(6), ylab='Difference in Ranking', xlab='Model Number')
Python Libraries
#Basic Operating System Stuff
import os
import gc #garbage collector
import random #random seed generator
#Basic dataframe, array, and math stuff
import pandas as pd #data frame
import math #math functions
import numpy as np #numerical package
#TensorFlow
import tensorflow as tf
from tensorflow.python.client import device_lib #GPU Check
import tensorflow.keras #keras
from tensorflow.keras import layers
from tensorflow.keras import Sequential,Input,Model
from tensorflow.keras.layers import Dense, Dropout, Flatten, Input, Add, Activation, ZeroPadding2D,GlobalAveragePooling2D
from tensorflow.keras.layers import BatchNormalization,Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D, LeakyReLU
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint #use for early stopping and reduction on level-out
from tensorflow.keras.initializers import glorot_uniform, he_uniform #to initialize random weights for filters
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image as image_utils
from tensorflow.keras.applications.imagenet_utils import preprocess_input, decode_predictions, preprocess_input
from tensorflow.keras.models import Model, load_model #Can't do much without a model
from tensorflow.keras import utils
from tensorflow.keras.utils import get_file, plot_model, to_categorical, model_to_dot
from tensorflow.keras.metrics import AUC
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications import ResNet50V2
from tensorflow.keras.optimizers import Adam
import tensorflow.keras.backend as K #let's write our own metrics and loss functions
#Graphing
import seaborn as sns
from IPython.display import SVG #Same here
import matplotlib.pyplot as plt #plotting
import matplotlib #image save
from matplotlib.pyplot import imshow #Show images
from PIL import Image #Another image utility
import seaborn as sns
import pydotEncoder/Decoder
full=r.mydata
train=full.iloc[:, [3,4,5,6,7,8,9,10,11]]
train## X1A X1B X1C ... X3A X3B X3C
## 0 0.876454 0.679524 0.821950 ... 0.296081 0.409132 0.978251
## 1 0.469000 0.301275 -2.092300 ... 0.427759 0.556290 -0.567498
## 2 -0.306983 0.587005 0.216335 ... -0.457682 -0.620902 1.116972
## 3 -0.908877 0.688564 -1.661572 ... -1.557264 -1.178275 -1.043113
## 4 0.401644 0.230071 0.146937 ... 0.352563 0.304395 -2.311420
## .. ... ... ... ... ... ... ...
## 377 0.016846 0.356482 0.291466 ... -2.454380 -0.190875 -1.796170
## 378 0.269602 -0.274356 -0.357070 ... 0.988170 1.117096 -0.468412
## 379 0.300280 0.191226 -1.955005 ... -0.298687 -0.309723 -0.547100
## 380 -0.960661 -2.092283 -0.660316 ... -1.793695 -1.872533 -1.162017
## 381 0.210762 0.225109 0.727711 ... -3.195241 -0.956891 1.116972
##
## [382 rows x 9 columns]train2=full.iloc[:, [3,4,5,6,7,8,9,12,11]]
autoencoder=tf.keras.Sequential()
autoencoder = Sequential()
autoencoder.add(Input(shape=(9,)))
autoencoder.add(Dense(3, activation='elu', name='weights'))
autoencoder.add(Dense(1, activation='linear', name="bottleneck"))
autoencoder.compile(loss='mean_squared_error', optimizer = Adam())
autoencoder.summary()## Model: "sequential_1"
## ┌─────────────────────────────────┬───────────────────────────┬────────────┐
## │ Layer (type) │ Output Shape │ Param # │
## ├─────────────────────────────────┼───────────────────────────┼────────────┤
## │ weights (Dense) │ (None, 3) │ 30 │
## ├─────────────────────────────────┼───────────────────────────┼────────────┤
## │ bottleneck (Dense) │ (None, 1) │ 4 │
## └─────────────────────────────────┴───────────────────────────┴────────────┘
## Total params: 34 (136.00 B)
## Trainable params: 34 (136.00 B)
## Non-trainable params: 0 (0.00 B)plot_model(autoencoder)
autoencoder2=tf.keras.Sequential()
autoencoder2 = Sequential()
autoencoder2.add(Input(shape=(9,)))
autoencoder2.add(Dense(3, activation='elu', name='weights'))
autoencoder2.add(Dense(1, activation='linear', name="bottleneck"))
autoencoder2.compile(loss='mean_squared_error', optimizer = Adam())
Models
mod10 = autoencoder.fit(train,train, batch_size=128, epochs=1000, verbose=0, validation_data=(train,train))
autoencoder.save("c:/users/lfult/documents/minimum wage/encoded.h5")## WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.mod11 = autoencoder2.fit(train2,train2, batch_size=128, epochs=1000, verbose=0, validation_data=(train2,train2))
autoencoder.save("c:/users/lfult/documents/minimum wage/encoded2.h5")## WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.Scores to Rankings
rating=pd.DataFrame(np.round(autoencoder.predict(train),2), columns=['Rating'])##
[1m 1/12[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 25ms/step
[1m12/12[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/steprating2=pd.DataFrame(np.round(autoencoder2.predict(train2),2), columns=['Rating'])##
[1m 1/12[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 24ms/step
[1m12/12[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step
rating['Geo']=full.GeoName
rating=rating.sort_values(by='Rating', ascending=False)
rating['Ranking']=np.arange(1,383)
rating2['Geo']=full.GeoName
rating2=rating.sort_values(by='Rating', ascending=False)
rating2['Ranking']=np.arange(1,383)library(reticulate)## Warning: package 'reticulate' was built under R version 4.3.2py$rating[373:382,]## Rating Geo Ranking
## 177 -1.02 Kahului-Wailuku-Lahaina, HI MSA 373
## 290 -1.09 Riverside-San Bernardino-Ontario, CA MSA 374
## 18 -1.09 Atlantic City-Hammonton, NJ MSA 375
## 254 -1.24 Ocean City, NJ MSA 376
## 34 -1.34 Binghamton, NY MSA 377
## 224 -1.43 Merced, CA MSA 378
## 359 -1.44 Visalia-Porterville, CA MSA 379
## 22 -1.60 Bakersfield, CA MSA 380
## 105 -2.09 El Centro, CA MSA 381
## 285 -2.36 Rapid City, SD MSA 382Rankings with New Variable
py$rating2[373:382,]## Rating Geo Ranking
## 177 -1.02 Kahului-Wailuku-Lahaina, HI MSA 373
## 290 -1.09 Riverside-San Bernardino-Ontario, CA MSA 374
## 18 -1.09 Atlantic City-Hammonton, NJ MSA 375
## 254 -1.24 Ocean City, NJ MSA 376
## 34 -1.34 Binghamton, NY MSA 377
## 224 -1.43 Merced, CA MSA 378
## 359 -1.44 Visalia-Porterville, CA MSA 379
## 22 -1.60 Bakersfield, CA MSA 380
## 105 -2.09 El Centro, CA MSA 381
## 285 -2.36 Rapid City, SD MSA 382