Civil Cases of New Hampshire Businesses
Daniel Lee
June 9, 2018
Data
The data represents all business cases in the Belknap County during 2015-2016 and business and commercial dispute cases in Concord Superior during 2012-2016.
Explore
- There were 409 cases.
# Load package
library(GoodmanKruskal)
library(tidyverse)
# Import data
data <- read.csv("data/NH.csv", skip = 1) # 1st row is the description of variables
# Convert to factors: some are imported as integers
data <-
data %>%
mutate_if(is.integer, factor)
# Basic descriptive statistics of data
summary(data)
## County DoNo CaseType FileDate
## BCDD : 58 211-2015-CV-00001: 1 2 :113 42492 : 8
## Belknap:351 211-2015-CV-00003: 1 37 : 38 42060 : 7
## 211-2015-CV-00004: 1 38 : 37 42510 : 6
## 211-2015-CV-00012: 1 4 : 29 42138 : 5
## 211-2015-CV-00013: 1 35 : 24 42620 : 5
## 211-2015-CV-00014: 1 47 : 22 42639 : 5
## (Other) :403 (Other):146 (Other):373
## PNames
## Discover Bank : 14
## Paugus Bay Plaza Condominium Association: 8
## Barcklay Bank Delaware : 7
## American Express Centurion Bank : 6
## Concord Hospital : 6
## Bank of New Hampshire : 5
## (Other) :363
## DNames ProSe
## Town of Gilford : 6 Both : 23
## Barry K. Meyers; Brenda M. Stowe: 2 Defendant:131
## Barry Myers : 2 Neither :216
## Brazilian Resources, Inc. : 2 Plaintiff: 19
## City of Laconia : 2 Some DF : 20
## LRG Healthcare : 2
## (Other) :393
## PLaw PFirm
## 9 : 42 9 : 42
## Robert L. O'Brien : 24 Schlee & Stillman, LLC : 24
## Michael J. Fontaine: 9 Welts, White, & Fontaine, P.C.: 16
## Benjamin R. Roberge: 8 Zwicker & Associates, P.C. : 16
## Edward D. Philpot : 8 Donais Law Offices, PLLC : 8
## Harvey J. Garod : 6 Martin, Lord, & Osman, P.A. : 8
## (Other) :312 (Other) :295
## EqReq MonReq LoC DLaw
## : 1 Min. : 0 0 :332 9 :156
## 9 : 2 1st Qu.: 1 15 : 7 Joseph D. Becher : 5
## No : 55 Median : 1 3 : 6 Daniel J. Orroth : 4
## Unknown: 4 Mean : 11819 8 : 6 Gary M. Burt : 4
## Yes :346 3rd Qu.: 1670 10 : 6 Laura Spector-Morgan: 4
## NA's : 1 Max. :1118000 11 : 6 (Other) :235
## (Other): 46 NA's : 1
## DFirm
## 9 :156
## Getman, Schulthess, Steere, & Poulin, P.A.: 11
## Mitchell Municipal Group, P.A. : 8
## Primmer, Piper, Eggleston, & Cramer, P.C. : 8
## Wescott Law, P.A. : 8
## (Other) :217
## NA's : 1
## X3d X3dLaw
## 0 :392 0 :397
## Andrew Howe; Martina Howe : 4 Ethan G. Wood : 4
## Donald A. Kennedy : 3 Donald A. Kennedy : 3
## Angela Pearl : 2 David W. Johnston : 1
## Belknap County Superior Court: 1 Frank P. Spinella, Jr.: 1
## Brenda Stowe : 1 Gregg M. Charest : 1
## (Other) : 6 (Other) : 2
## X3dFirm CtrCl CrsCl
## 0 :397 0:369 0:404
## Patrick Wood Law Office, PLLC : 4 1: 39 1: 4
## Law Office of Donald A. Kennedy: 2 2: 1 2: 1
## Devine, Millimet & Branch, P.A.: 1
## Frasca & Frasca Law Offices : 1
## Law Office of Gregg M. Charest : 1
## (Other) : 3
## Rec
## Ignatius : 4
## MacLeod; Borenstein: 1
## Na : 1
## O'Neill : 5
## NA's :398
##
##
## Rem
## HRC : 1
## Made the appeal and then withdrew, so NA (thought you'd like to know<U+0085> the guy sued NH and won!): 1
## Removed : 13
## Transfer : 4
## Transferred : 2
## NA's :388
##
## Judge OthMot
## O'Neill :260 None :125
## McNamara : 32 Motion for Entry of Final Judgment: 24
## O'Neill; Fauver : 5 Motion for Voluntary Nonsuit : 16
## Anderson; McNamara: 4 Assented to Motion to Continue : 10
## Colburn; McNamara : 4 Motion to Strike : 6
## (Other) : 52 Motion for Entry of Final Decree : 5
## NA's : 52 (Other) :223
## MTime MTA MTD DSJ PSJ DMIL
## 0 :284 0 :353 0 :342 0 :378 0 :384 0 :400
## 1 : 18 1 : 51 1 : 22 1 : 4 1 : 13 1; 10: 1
## 1; 2: 5 2 : 4 3 : 16 10 : 17 1;3: 1 1; 3 : 1
## 1;2 : 1 NA's: 1 10 : 11 2 : 1 10 : 4 10 : 4
## 2 :101 2 : 6 2; 4: 1 3 : 6 2; 4 : 1
## 1; 3 : 5 3 : 7 6 : 1 3; 10: 1
## (Other): 7 4 : 1 4 : 1
## PMIL Stp Arb TrialType DISP JMent
## 0 :403 0:236 No : 66 0: 14 3 :184 1 : 93
## 6 : 1 1:131 Yes : 55 1: 1 8 : 74 2 : 64
## 10: 5 2: 42 NA's:288 2: 4 7 : 42 9 : 15
## 8: 1 1 : 38 0 : 3
## 9:389 6 : 37 1; NA : 1
## (Other): 33 (Other): 2
## NA's : 1 NA's :231
## NOJ Award Eq TermDate Enforce
## 0 :260 Min. : 0 0 : 73 Open : 19 0 :377
## 1 : 73 1st Qu.: 0 1 : 3 42093 : 5 1 : 11
## 1; 0: 1 Median : 1 2 : 2 42263 : 5 1; 2: 3
## 2 : 3 Mean : 11170 3 : 6 42619 : 5 1; 3: 1
## 3 : 8 3rd Qu.: 1 9 :324 42579 : 4 2 : 4
## 9 : 62 Max. :1700000 34000: 1 (Other):370 3 : 13
## NA's: 2 NA's : 1
# Sturecture of data
str(data)
## 'data.frame': 409 obs. of 40 variables:
## $ County : Factor w/ 2 levels "BCDD","Belknap": 2 2 2 2 2 2 2 2 2 2 ...
## $ DoNo : Factor w/ 409 levels "211-2015-CV-00001",..: 136 137 138 139 140 141 142 143 144 145 ...
## $ CaseType : Factor w/ 41 levels "1","10","10; 26",..: 9 13 28 28 22 11 11 11 11 11 ...
## $ FileDate : Factor w/ 269 levels "2015-07031","41073",..: 133 133 134 134 135 136 136 137 138 138 ...
## $ PNames : Factor w/ 344 levels "A.O. Phaneuf & Son Funeral Home and Crematorium, Inc.; Cremation Society of New Hampshire, Inc.; Arthur Phaneuf",..: 21 236 259 259 15 71 72 157 207 12 ...
## $ DNames : Factor w/ 398 levels "223 D.W. Highway, LLC",..: 347 374 208 239 123 358 149 285 1 66 ...
## $ ProSe : Factor w/ 5 levels "Both","Defendant",..: 3 3 2 2 2 3 2 3 3 3 ...
## $ PLaw : Factor w/ 228 levels "9","A. Gerard O'Neil, Jr.",..: 209 118 16 16 84 151 151 62 202 190 ...
## $ PFirm : Factor w/ 171 levels "9","Abrahamsen Ratchford, P.C.",..: 156 94 45 45 121 168 168 123 83 136 ...
## $ EqReq : Factor w/ 5 levels "","9","No","Unknown",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ MonReq : num 1 1 1436 2159 4928 ...
## $ LoC : Factor w/ 25 levels "0","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ DLaw : Factor w/ 212 levels "9","Allison M. Ambrose",..: 107 110 1 1 1 172 1 156 1 1 ...
## $ DFirm : Factor w/ 162 levels "9","Azarian Law Office, PLLC",..: 132 99 1 1 1 81 1 148 1 1 ...
## $ X3d : Factor w/ 12 levels "0","Andrew Howe; Martina Howe",..: 1 2 1 1 1 1 1 1 1 1 ...
## $ X3dLaw : Factor w/ 8 levels "0","David W. Johnston",..: 1 4 1 1 1 1 1 1 1 1 ...
## $ X3dFirm : Factor w/ 9 levels "0","Devine, Millimet & Branch, P.A.",..: 1 7 1 1 1 1 1 1 1 1 ...
## $ CtrCl : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ CrsCl : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ Rec : Factor w/ 4 levels "Ignatius","MacLeod; Borenstein",..: NA NA NA NA NA NA NA NA NA NA ...
## $ Rem : Factor w/ 5 levels "HRC","Made the appeal and then withdrew, so NA (thought you'd like to know<U+0085> the guy sued NH and won!)",..: NA NA NA NA NA NA NA NA NA NA ...
## $ Judge : Factor w/ 37 levels "Abramson; Brown; McNamara",..: NA 26 NA 26 26 26 36 26 26 NA ...
## $ OthMot : Factor w/ 216 levels "Assented Motion to Continue",..: 216 172 216 216 13 44 45 139 216 216 ...
## $ MTime : Factor w/ 5 levels "0","1","1; 2",..: 1 1 1 2 1 1 1 5 1 1 ...
## $ MTA : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 2 2 1 2 1 ...
## $ MTD : Factor w/ 11 levels "0","1","1; 3",..: 1 2 1 1 2 1 1 2 1 1 ...
## $ DSJ : Factor w/ 7 levels "0","1","10","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ PSJ : Factor w/ 6 levels "0","1","1;3",..: 1 1 1 1 1 2 1 1 1 1 ...
## $ DMIL : Factor w/ 7 levels "0","1; 10","1; 3",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ PMIL : Factor w/ 3 levels "0","6","10": 1 1 1 1 1 1 1 1 1 1 ...
## $ Stp : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ Arb : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
## $ TrialType: Factor w/ 5 levels "0","1","2","8",..: 5 5 5 5 3 5 5 5 5 5 ...
## $ DISP : Factor w/ 12 levels "1","10","2","2; 3",..: 5 1 10 10 1 2 9 1 10 10 ...
## $ JMent : Factor w/ 7 levels "0","1","1; NA",..: NA 4 NA NA NA NA 2 4 NA NA ...
## $ NOJ : Factor w/ 6 levels "0","1","1; 0",..: 6 6 6 6 6 6 4 6 6 6 ...
## $ Award : num 1 0 0 0 0 0 34000 0 0 0 ...
## $ Eq : Factor w/ 6 levels "0","1","2","3",..: 5 5 5 5 5 5 6 5 5 5 ...
## $ TermDate : Factor w/ 296 levels "41186","41402",..: 122 122 103 111 138 296 296 151 130 105 ...
## $ Enforce : Factor w/ 6 levels "0","1","1; 2",..: 1 1 1 1 1 1 2 1 1 1 ...Find association between variables
The choice of association measures depends on the type of variables. All variables in our dataset are categorical variables with no natural order, or nominal variables. Goodman and Kruskal recently published a study that introduced the Goodman and Kruskal’s (GK) tau as a new association meausre for categorical variables, along with the GoodmanKruskal R package that is very usueful in exploratory data analysis becuase it computes the GK tau measures in a systematic way (analoguous to correlogram of numerica variables) when dealing with a large number of variables. The authors also have a good general discussion on what association measures to use depending on the type of variables we have, which can be summarized as:
- Numerical variables (i.e., GDP and jobs): Pearson’s product-moment correlation, Spearman’s rank correlation, Kendall’s Tau (can be computed by the cor function in the base R package)
- Ordinal variables (i.e., education attainment, income levels): Spearman’s rank correlation, Kendall’s Tau (can be computed by the cor function in the base R package)
- Nominal (unordered) variables or categorical variables (i.e., nature of suits, races, counties): chi-squared, Cramer’s V (can be computed by the assocstats function in the vcd package); Goodman and Kruskal’s tau (can be computed by the GKtauDataframe function in the GoodmanKruskal package)
The GK tau measure differs from chi-squared and Cramer’s V, more popular measures for nominal variables, in that it is asymmetric: the forward association between variables x and y is generally not the same as the backward association between y and x. The GK tau not only indicates the strength of the association but also the direction of the association.
The GK tau measures the fraction of variability in the categorical variable y that can be explained by the categorical variable x. The diagonal elements represent the numbers of the levels for each variable, while the off-diagonal elements are GK tau values. The GK tau values represent the association measure tau(x,y) from the variable x indicated in the row name to the variable y indicated in the column name.
Examples using a subset of dataset
Since it’s difficult to visualize the association of all 38 variables in a single screen, subsets of the data set was taken as examples to explain how to interpret GK tau measures.
Example using variables of many levels
The subset includes variables that have more than 100 levels (unique values). There are 11 such variables.
# Compute GK tau values for a reduced number of variables
data_reduced <-
data %>%
select_if(function(col) length(unique(col)) > 100)
str(data_reduced)
## 'data.frame': 409 obs. of 11 variables:
## $ DoNo : Factor w/ 409 levels "211-2015-CV-00001",..: 136 137 138 139 140 141 142 143 144 145 ...
## $ FileDate: Factor w/ 269 levels "2015-07031","41073",..: 133 133 134 134 135 136 136 137 138 138 ...
## $ PNames : Factor w/ 344 levels "A.O. Phaneuf & Son Funeral Home and Crematorium, Inc.; Cremation Society of New Hampshire, Inc.; Arthur Phaneuf",..: 21 236 259 259 15 71 72 157 207 12 ...
## $ DNames : Factor w/ 398 levels "223 D.W. Highway, LLC",..: 347 374 208 239 123 358 149 285 1 66 ...
## $ PLaw : Factor w/ 228 levels "9","A. Gerard O'Neil, Jr.",..: 209 118 16 16 84 151 151 62 202 190 ...
## $ PFirm : Factor w/ 171 levels "9","Abrahamsen Ratchford, P.C.",..: 156 94 45 45 121 168 168 123 83 136 ...
## $ MonReq : num 1 1 1436 2159 4928 ...
## $ DLaw : Factor w/ 212 levels "9","Allison M. Ambrose",..: 107 110 1 1 1 172 1 156 1 1 ...
## $ DFirm : Factor w/ 162 levels "9","Azarian Law Office, PLLC",..: 132 99 1 1 1 81 1 148 1 1 ...
## $ OthMot : Factor w/ 216 levels "Assented Motion to Continue",..: 216 172 216 216 13 44 45 139 216 216 ...
## $ TermDate: Factor w/ 296 levels "41186","41402",..: 122 122 103 111 138 296 296 151 130 105 ...
GKmatrix1 <- GKtauDataframe(data_reduced)
GKmatrix1
## DoNo FileDate PNames DNames PLaw PFirm MonReq DLaw
## DoNo 409.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## FileDate 0.657 269.000 0.674 0.660 0.692 0.692 0.667 0.725
## PNames 0.841 0.851 344.000 0.848 0.929 0.958 0.813 0.949
## DNames 0.973 0.976 0.978 398.000 0.978 0.980 0.985 0.980
## PLaw 0.556 0.572 0.621 0.562 228.000 0.956 0.633 0.678
## PFirm 0.417 0.431 0.486 0.423 0.767 171.000 0.518 0.550
## MonReq 0.262 0.262 0.261 0.262 0.278 0.278 108.000 0.200
## DLaw 0.520 0.526 0.528 0.530 0.541 0.545 0.450 213.000
## DFirm 0.397 0.404 0.410 0.408 0.421 0.428 0.386 0.814
## OthMot 0.527 0.533 0.540 0.527 0.559 0.561 0.490 0.646
## TermDate 0.725 0.735 0.733 0.725 0.743 0.740 0.744 0.748
## DFirm OthMot TermDate
## DoNo 1.000 1.000 1.000
## FileDate 0.727 0.693 0.667
## PNames 0.961 0.859 0.846
## DNames 0.987 0.973 0.973
## PLaw 0.686 0.634 0.561
## PFirm 0.561 0.484 0.423
## MonReq 0.202 0.258 0.263
## DLaw 0.983 0.556 0.522
## DFirm 163.000 0.427 0.398
## OthMot 0.646 216.000 0.532
## TermDate 0.750 0.738 297.000
## attr(,"class")
## [1] "GKtauMatrix"
plot(GKmatrix1)
Interpretation
- The GK tau measures in Row of DoNo is all 1. It suggest that all variables are perfectly predictable by the docket number. It makes sense because each case has its unique docket number. However, the same is not true for the backward association with the docket number: the docket number is not perfectly predictable by other variables. Column of DoNo doesn’t exhibit 1, although very high.
- In fact, tau measures are all very in this case. This makes sense because they all have a large number of levels (unique values). Their tau measures are high for the same reason that the docket number is perfectly predictive of all other variables in the dataset: DoNo has 409 levels out of 409 observations; DNames has 398 levels; PNames 344 levels; and so on.
- Note that Cramer’s V would return 1, perfect association, for a pair of DoNo and any other variable in the dataset, correctly measuring the strength of the relationship but with no information on its directionality. See below.
Cramer’s V This section takes the docket number (DoNo) and the case type (CaseType) as an example. Cramer’s V returns 1, correctly indicating the strength of the association: the case type is perfectly predictable by the docket number. However, it lacks the association’s directionality. We know from GK tau measures that the case type, or any other variables in the data for that matter, is perfectly predictable by the docket number, while the case type gives us little information about the docket number.
library(vcd)
# assocstats function takes a table as input
assocstats(table(data$DoNo, data$CaseType))
## X^2 df P(> X^2)
## Likelihood Ratio 2291.5 16320 1.00000
## Pearson 16360.0 16320 0.41102
##
## Phi-Coefficient : NA
## Contingency Coeff.: 0.988
## Cramer's V : 1Example using variables of a few levels
The display below is another example using a set of variables that have less than five levels (unique values). There are six such variables in the dataset.
# Compute GK tau values for a reduced number of variables
length(unique(data$PNames))
## [1] 344
data_reduced <-
data %>%
select_if(function(col) length(unique(col)) < 5)
str(data_reduced)
## 'data.frame': 409 obs. of 7 variables:
## $ County: Factor w/ 2 levels "BCDD","Belknap": 2 2 2 2 2 2 2 2 2 2 ...
## $ CtrCl : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ CrsCl : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ MTA : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 2 2 1 2 1 ...
## $ PMIL : Factor w/ 3 levels "0","6","10": 1 1 1 1 1 1 1 1 1 1 ...
## $ Stp : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ Arb : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
GKmatrix1 <- GKtauDataframe(data_reduced)
GKmatrix1
## County CtrCl CrsCl MTA PMIL Stp Arb
## County 2.000 0.072 0.002 0.005 0.005 0.031 0.392
## CtrCl 0.075 3.000 0.002 0.021 0.001 0.033 0.069
## CrsCl 0.002 0.003 3.000 0.017 0.000 0.004 0.007
## MTA 0.075 0.044 0.003 4.000 0.007 0.012 0.034
## PMIL 0.007 0.002 0.000 0.008 3.000 0.005 0.021
## Stp 0.121 0.058 0.002 0.006 0.003 3.000 0.095
## Arb 0.728 0.092 0.003 0.006 0.026 0.083 3.000
## attr(,"class")
## [1] "GKtauMatrix"
plot(GKmatrix1)
Interpretation
- In contrast to variables of a large number of levels, none of the variables shows high associations.
Example using variables of a moderate number of levels
The display below is third example using a set of variables that have greater than 5 levels but less than 10 levels. There are 11 such variables in the dataset.
# Compute GK tau values for a reduced number of variables
length(unique(data$PNames))
## [1] 344
data_reduced <-
data %>%
select_if(function(col) length(unique(col)) > 5 & length(unique(col)) < 10)
str(data_reduced)
## 'data.frame': 409 obs. of 11 variables:
## $ EqReq : Factor w/ 5 levels "","9","No","Unknown",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ X3dLaw : Factor w/ 8 levels "0","David W. Johnston",..: 1 4 1 1 1 1 1 1 1 1 ...
## $ X3dFirm: Factor w/ 9 levels "0","Devine, Millimet & Branch, P.A.",..: 1 7 1 1 1 1 1 1 1 1 ...
## $ Rem : Factor w/ 5 levels "HRC","Made the appeal and then withdrew, so NA (thought you'd like to know<U+0085> the guy sued NH and won!)",..: NA NA NA NA NA NA NA NA NA NA ...
## $ DSJ : Factor w/ 7 levels "0","1","10","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ PSJ : Factor w/ 6 levels "0","1","1;3",..: 1 1 1 1 1 2 1 1 1 1 ...
## $ DMIL : Factor w/ 7 levels "0","1; 10","1; 3",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ JMent : Factor w/ 7 levels "0","1","1; NA",..: NA 4 NA NA NA NA 2 4 NA NA ...
## $ NOJ : Factor w/ 6 levels "0","1","1; 0",..: 6 6 6 6 6 6 4 6 6 6 ...
## $ Eq : Factor w/ 6 levels "0","1","2","3",..: 5 5 5 5 5 5 6 5 5 5 ...
## $ Enforce: Factor w/ 6 levels "0","1","1; 2",..: 1 1 1 1 1 1 2 1 1 1 ...
GKmatrix1 <- GKtauDataframe(data_reduced)
GKmatrix1
## EqReq X3dLaw X3dFirm Rem DSJ PSJ DMIL JMent NOJ Eq
## EqReq 6.000 0.001 0.002 0.079 0.097 0.005 0.003 0.017 0.032 0.035
## X3dLaw 0.005 8.000 0.943 0.001 0.002 0.001 0.000 0.023 0.031 0.027
## X3dFirm 0.009 1.000 9.000 0.001 0.002 0.001 0.000 0.027 0.031 0.027
## Rem 0.045 0.001 0.001 6.000 0.054 0.009 0.001 0.020 0.042 0.036
## DSJ 0.017 0.001 0.001 0.065 7.000 0.126 0.159 0.024 0.014 0.064
## PSJ 0.008 0.001 0.001 0.027 0.147 6.000 0.080 0.024 0.039 0.015
## DMIL 0.004 0.000 0.000 0.001 0.099 0.048 7.000 0.013 0.007 0.021
## JMent 0.040 0.012 0.012 0.031 0.041 0.065 0.028 8.000 0.440 0.074
## NOJ 0.063 0.019 0.019 0.140 0.022 0.063 0.006 0.375 7.000 0.136
## Eq 0.007 0.014 0.014 0.021 0.044 0.018 0.004 0.056 0.089 6.000
## Enforce 0.017 0.002 0.001 0.003 0.009 0.021 0.001 0.074 0.097 0.017
## Enforce
## EqReq 0.004
## X3dLaw 0.002
## X3dFirm 0.002
## Rem 0.003
## DSJ 0.003
## PSJ 0.018
## DMIL 0.001
## JMent 0.117
## NOJ 0.129
## Eq 0.034
## Enforce 6.000
## attr(,"class")
## [1] "GKtauMatrix"
plot(GKmatrix1)
Interpretation
- It displays a pair of variables with a strong connection - X3dLaw and X3dFirm. Both the forward and backward association between the two variables are strong: the GK tau measure from X3d to X3dFirm is 0.94, while the GK tau measure from X3dFirm to X3d is 1. However, the strong connection between the third party lawyer and the third party law firm is a rather obvious one.
- There is another pair of variables that show significant connection - JMent and NOJ. Both the forward and backward association between the two variables are noticible: the GK tau measure from JMent to NOJ is 0.44, while the GK tau measure from NOJ to JMent is 0.38. Although not as strong, this association may be more useful and interesting than the previous one.
JMent versus NOJ
The variables are defined as:
- JMent takes “1” if the court entered judgment for the plaintiff; enter “2” if the court entered judgment for the defendant; enter “3” if the court entered judgment in part for both parties; enter “NA” if the judgment is a settlment; enter “9” if the case is still open.
- NOJ takes “1” if the court awarded a monetary award only; enter “2” if the court awarded a monetary award and equity; enter “3” if the court awarded just equity (i.e., an injunction or specific performance); enter “0” if neither money nor equity was awarded.
# JMent versus NOJ
table(data$JMent, data$NOJ)
##
## 0 1 1; 0 2 3 9
## 0 3 0 0 0 0 0
## 1 13 71 0 2 6 1
## 1; NA 0 0 1 0 0 0
## 2 56 0 0 0 1 7
## 3 1 0 0 0 0 0
## 9 0 0 0 0 0 15
## NA; 1 0 1 0 0 0 0Interpretation
- There are a few undefined values (i.e., 1; NA in JMent). I decided not to change the values without knowing for sure whether they are typos.
- There were 71 cases (out of 409) where the court awarded a monetary award to the plaintiff. A monetary award is always given to the plaintiff, with only one exception (undetermined?).
GK tau measures of all variables
The display below shows GK tau measures of all variables.
One of the easiest and intuitive variables for laymen is JUDGE. It was interesting to see how much we would know about a case by knowing the judge assigned to the case. For example, JUDGE explains 46.1% of the variation in LoC; 50.2% in Rec; 42.9% in Arb; 61.1% in Eq.
# Compute GK tau values for all variables
GKmatrix1 <- GKtauDataframe(data)
GKmatrix1
## County DoNo CaseType FileDate PNames DNames ProSe PLaw
## County 2.000 0.002 0.010 0.003 0.003 0.003 0.064 0.006
## DoNo 1.000 409.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.479 0.098 41.000 0.105 0.117 0.102 0.296 0.169
## FileDate 0.930 0.657 0.714 269.000 0.674 0.660 0.726 0.692
## PNames 1.000 0.841 0.974 0.851 344.000 0.848 0.953 0.929
## DNames 1.000 0.973 0.980 0.976 0.978 398.000 0.985 0.978
## ProSe 0.106 0.010 0.098 0.012 0.014 0.010 5.000 0.106
## PLaw 0.976 0.556 0.731 0.572 0.621 0.562 0.770 228.000
## PFirm 0.925 0.417 0.631 0.431 0.486 0.423 0.694 0.767
## EqReq 0.038 0.012 0.061 0.015 0.013 0.012 0.051 0.036
## MonReq 0.098 0.262 0.254 0.262 0.261 0.262 0.356 0.278
## LoC 0.866 0.059 0.071 0.060 0.059 0.059 0.083 0.063
## DLaw 0.970 0.520 0.608 0.526 0.528 0.530 0.787 0.541
## DFirm 0.864 0.397 0.505 0.404 0.410 0.408 0.743 0.421
## X3d 0.021 0.027 0.037 0.027 0.034 0.034 0.040 0.033
## X3dLaw 0.019 0.017 0.024 0.017 0.025 0.024 0.024 0.022
## X3dFirm 0.019 0.020 0.025 0.020 0.027 0.027 0.024 0.024
## CtrCl 0.075 0.005 0.007 0.005 0.005 0.005 0.044 0.006
## CrsCl 0.002 0.005 0.003 0.005 0.005 0.005 0.008 0.005
## Rec 0.019 0.010 0.020 0.010 0.010 0.010 0.014 0.012
## Rem 0.049 0.012 0.021 0.012 0.012 0.012 0.017 0.013
## Judge 1.000 0.091 0.110 0.093 0.093 0.091 0.135 0.098
## OthMot 0.885 0.527 0.598 0.533 0.540 0.527 0.597 0.559
## MTime 0.084 0.010 0.022 0.011 0.010 0.010 0.112 0.015
## MTA 0.075 0.007 0.023 0.007 0.009 0.007 0.008 0.012
## MTD 0.158 0.025 0.030 0.025 0.025 0.025 0.061 0.025
## DSJ 0.051 0.015 0.019 0.015 0.015 0.015 0.038 0.016
## PSJ 0.024 0.012 0.013 0.013 0.013 0.012 0.013 0.014
## DMIL 0.017 0.015 0.016 0.015 0.015 0.015 0.016 0.015
## PMIL 0.007 0.005 0.006 0.005 0.005 0.005 0.008 0.005
## Stp 0.121 0.005 0.024 0.006 0.006 0.005 0.174 0.011
## Arb 0.728 0.005 0.013 0.006 0.006 0.005 0.096 0.009
## TrialType 0.036 0.010 0.015 0.010 0.010 0.010 0.014 0.011
## DISP 0.100 0.029 0.075 0.032 0.034 0.029 0.292 0.054
## JMent 0.072 0.017 0.053 0.018 0.019 0.017 0.250 0.026
## NOJ 0.063 0.015 0.068 0.016 0.017 0.015 0.178 0.020
## Award 0.126 0.169 0.152 0.169 0.168 0.169 0.247 0.179
## Eq 0.694 0.012 0.021 0.013 0.013 0.012 0.069 0.015
## TermDate 0.922 0.725 0.752 0.735 0.733 0.725 0.796 0.743
## Enforce 0.014 0.012 0.021 0.014 0.013 0.012 0.068 0.014
## PFirm EqReq MonReq LoC DLaw DFirm X3d X3dLaw
## County 0.010 0.005 0.010 0.343 0.029 0.030 0.002 0.002
## DoNo 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.178 0.568 0.313 0.295 0.189 0.203 0.246 0.308
## FileDate 0.692 0.736 0.667 0.911 0.725 0.727 0.702 0.666
## PNames 0.958 0.972 0.813 0.960 0.949 0.961 0.910 0.915
## DNames 0.980 0.982 0.985 0.986 0.980 0.987 0.890 0.844
## ProSe 0.110 0.261 0.138 0.049 0.273 0.276 0.011 0.007
## PLaw 0.956 0.714 0.633 0.803 0.678 0.686 0.655 0.720
## PFirm 171.000 0.618 0.518 0.652 0.550 0.561 0.551 0.574
## EqReq 0.037 6.000 0.250 0.033 0.016 0.016 0.002 0.001
## MonReq 0.278 0.614 108.000 0.095 0.200 0.202 0.012 0.012
## LoC 0.067 0.080 0.061 25.000 0.095 0.096 0.033 0.044
## DLaw 0.545 0.503 0.450 0.872 213.000 0.983 0.557 0.458
## DFirm 0.428 0.392 0.386 0.694 0.814 163.000 0.442 0.352
## X3d 0.039 0.007 0.026 0.016 0.034 0.040 12.000 1.000
## X3dLaw 0.027 0.005 0.021 0.015 0.023 0.028 0.701 8.000
## X3dFirm 0.028 0.009 0.021 0.015 0.023 0.028 0.701 1.000
## CtrCl 0.006 0.014 0.010 0.037 0.022 0.022 0.002 0.003
## CrsCl 0.005 0.004 0.002 0.013 0.006 0.006 0.013 0.000
## Rec 0.012 0.019 0.014 0.015 0.014 0.015 0.056 0.000
## Rem 0.013 0.045 0.023 0.037 0.023 0.021 0.003 0.001
## Judge 0.102 0.147 0.098 0.461 0.126 0.127 0.239 0.108
## OthMot 0.561 0.543 0.490 0.830 0.646 0.646 0.955 1.000
## MTime 0.016 0.007 0.019 0.039 0.051 0.052 0.015 0.004
## MTA 0.015 0.014 0.013 0.043 0.010 0.010 0.004 0.003
## MTD 0.026 0.012 0.016 0.091 0.052 0.054 0.030 0.033
## DSJ 0.016 0.017 0.010 0.063 0.031 0.031 0.002 0.001
## PSJ 0.014 0.008 0.016 0.018 0.020 0.020 0.002 0.001
## DMIL 0.015 0.004 0.011 0.026 0.021 0.021 0.001 0.000
## PMIL 0.005 0.003 0.002 0.010 0.008 0.008 0.000 0.000
## Stp 0.012 0.027 0.061 0.050 0.081 0.082 0.007 0.005
## Arb 0.012 0.015 0.029 0.412 0.047 0.049 0.004 0.004
## TrialType 0.011 0.004 0.006 0.029 0.016 0.015 0.003 0.001
## DISP 0.056 0.112 0.112 0.082 0.125 0.126 0.025 0.020
## JMent 0.028 0.040 0.068 0.058 0.108 0.109 0.011 0.012
## NOJ 0.023 0.063 0.049 0.043 0.075 0.075 0.019 0.019
## Award 0.179 0.177 0.341 0.120 0.138 0.140 0.125 0.010
## Eq 0.019 0.007 0.032 0.463 0.034 0.034 0.011 0.014
## TermDate 0.740 0.778 0.744 0.868 0.748 0.750 0.548 0.604
## Enforce 0.014 0.017 0.019 0.016 0.029 0.029 0.013 0.002
## X3dFirm CtrCl CrsCl Rec Rem Judge OthMot MTime MTA
## County 0.001 0.072 0.002 0.002 0.007 0.190 0.012 0.059 0.005
## DoNo 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.304 0.177 0.022 0.219 0.174 0.218 0.147 0.208 0.165
## FileDate 0.666 0.804 0.798 0.769 0.757 0.770 0.693 0.767 0.697
## PNames 0.915 0.945 1.000 0.884 0.975 0.904 0.859 0.907 0.965
## DNames 0.845 0.986 1.000 1.000 0.975 0.983 0.973 0.970 0.990
## ProSe 0.006 0.068 0.033 0.011 0.056 0.039 0.042 0.140 0.008
## PLaw 0.720 0.713 0.491 0.757 0.532 0.696 0.634 0.743 0.739
## PFirm 0.546 0.595 0.550 0.599 0.384 0.603 0.484 0.584 0.643
## EqReq 0.002 0.004 0.048 0.087 0.079 0.026 0.017 0.018 0.015
## MonReq 0.010 0.102 0.201 0.100 0.039 0.197 0.258 0.185 0.456
## LoC 0.043 0.232 0.033 0.017 0.111 0.239 0.076 0.141 0.090
## DLaw 0.457 0.874 0.648 0.777 0.864 0.672 0.556 0.766 0.561
## DFirm 0.351 0.750 0.521 0.514 0.575 0.537 0.427 0.639 0.442
## X3d 0.943 0.049 0.198 0.180 0.047 0.038 0.035 0.041 0.006
## X3dLaw 0.943 0.048 0.000 0.001 0.001 0.013 0.022 0.023 0.004
## X3dFirm 9.000 0.048 0.000 0.001 0.001 0.019 0.025 0.023 0.004
## CtrCl 0.003 3.000 0.002 0.004 0.002 0.016 0.014 0.114 0.021
## CrsCl 0.000 0.003 3.000 0.020 0.010 0.002 0.005 0.001 0.017
## Rec 0.000 0.014 0.037 5.000 0.080 0.035 0.011 0.023 0.004
## Rem 0.001 0.029 0.047 0.092 6.000 0.039 0.012 0.017 0.025
## Judge 0.109 0.263 0.050 0.502 0.347 38.000 0.140 0.225 0.201
## OthMot 1.000 0.872 0.665 0.613 0.546 0.588 216.000 0.767 0.567
## MTime 0.004 0.142 0.001 0.019 0.009 0.023 0.033 5.000 0.013
## MTA 0.003 0.044 0.003 0.003 0.053 0.021 0.008 0.017 4.000
## MTD 0.032 0.051 0.016 0.007 0.071 0.050 0.038 0.124 0.012
## DSJ 0.001 0.050 0.001 0.043 0.065 0.019 0.022 0.053 0.012
## PSJ 0.001 0.036 0.001 0.014 0.027 0.007 0.016 0.019 0.028
## DMIL 0.000 0.049 0.000 0.090 0.001 0.016 0.019 0.029 0.013
## PMIL 0.000 0.002 0.000 0.090 0.001 0.008 0.007 0.034 0.008
## Stp 0.005 0.058 0.002 0.006 0.011 0.021 0.017 0.089 0.006
## Arb 0.003 0.092 0.003 0.005 0.018 0.152 0.021 0.081 0.006
## TrialType 0.001 0.031 0.001 0.076 0.058 0.017 0.015 0.063 0.004
## DISP 0.018 0.098 0.209 0.084 0.182 0.071 0.057 0.137 0.082
## JMent 0.012 0.075 0.201 0.072 0.031 0.056 0.037 0.101 0.036
## NOJ 0.019 0.020 0.102 0.193 0.140 0.043 0.036 0.049 0.042
## Award 0.009 0.131 0.400 0.187 0.089 0.121 0.190 0.125 0.229
## Eq 0.014 0.078 0.002 0.003 0.021 0.143 0.027 0.075 0.023
## TermDate 0.604 0.706 0.226 0.443 0.867 0.798 0.738 0.767 0.776
## Enforce 0.001 0.014 0.080 0.002 0.003 0.013 0.021 0.016 0.016
## MTD DSJ PSJ DMIL PMIL Stp Arb TrialType DISP
## County 0.020 0.020 0.012 0.002 0.005 0.031 0.392 0.003 0.017
## DoNo 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.162 0.157 0.119 0.060 0.067 0.239 0.264 0.150 0.218
## FileDate 0.732 0.802 0.757 0.793 0.916 0.754 0.832 0.779 0.661
## PNames 0.937 0.966 0.863 1.000 1.000 0.922 0.962 1.000 0.856
## DNames 0.945 0.983 0.979 1.000 0.916 0.984 0.989 1.000 0.976
## ProSe 0.052 0.038 0.014 0.013 0.011 0.206 0.108 0.019 0.156
## PLaw 0.694 0.775 0.693 0.831 1.000 0.705 0.763 0.693 0.625
## PFirm 0.527 0.588 0.513 0.553 0.687 0.571 0.643 0.492 0.482
## EqReq 0.030 0.097 0.005 0.003 0.040 0.031 0.032 0.011 0.033
## MonReq 0.137 0.023 0.244 0.009 0.005 0.257 0.163 0.063 0.364
## LoC 0.123 0.182 0.137 0.088 0.212 0.108 0.523 0.036 0.055
## DLaw 0.809 0.892 0.702 0.887 0.873 0.811 0.843 0.806 0.523
## DFirm 0.631 0.593 0.561 0.672 0.402 0.691 0.725 0.552 0.423
## X3d 0.063 0.002 0.002 0.001 0.001 0.037 0.030 0.023 0.033
## X3dLaw 0.048 0.002 0.001 0.000 0.000 0.024 0.020 0.001 0.022
## X3dFirm 0.048 0.002 0.001 0.000 0.000 0.024 0.020 0.001 0.023
## CtrCl 0.006 0.014 0.017 0.018 0.001 0.033 0.069 0.023 0.022
## CrsCl 0.007 0.001 0.001 0.000 0.000 0.004 0.007 0.000 0.007
## Rec 0.017 0.071 0.040 0.111 0.167 0.015 0.015 0.113 0.023
## Rem 0.024 0.054 0.009 0.001 0.001 0.020 0.029 0.074 0.060
## Judge 0.183 0.156 0.163 0.172 0.373 0.120 0.429 0.272 0.134
## OthMot 0.845 0.899 0.828 0.906 1.000 0.675 0.759 0.828 0.612
## MTime 0.086 0.045 0.017 0.015 0.039 0.063 0.071 0.057 0.035
## MTA 0.017 0.039 0.017 0.004 0.007 0.012 0.034 0.050 0.012
## MTD 11.000 0.134 0.141 0.189 0.070 0.052 0.092 0.020 0.047
## DSJ 0.072 7.000 0.126 0.159 0.061 0.045 0.045 0.110 0.021
## PSJ 0.023 0.147 6.000 0.080 0.009 0.025 0.024 0.029 0.032
## DMIL 0.067 0.099 0.048 7.000 0.205 0.022 0.041 0.094 0.015
## PMIL 0.026 0.036 0.005 0.130 3.000 0.005 0.021 0.025 0.008
## Stp 0.033 0.039 0.021 0.015 0.003 3.000 0.095 0.025 0.097
## Arb 0.022 0.033 0.012 0.029 0.026 0.083 3.000 0.039 0.050
## TrialType 0.022 0.069 0.008 0.137 0.047 0.030 0.036 5.000 0.065
## DISP 0.093 0.053 0.296 0.030 0.354 0.235 0.141 0.704 13.000
## JMent 0.058 0.041 0.065 0.028 0.180 0.153 0.103 0.644 0.425
## NOJ 0.047 0.022 0.063 0.006 0.167 0.062 0.049 0.141 0.185
## Award 0.080 0.072 0.315 0.004 0.007 0.215 0.158 0.091 0.428
## Eq 0.024 0.044 0.018 0.004 0.012 0.031 0.472 0.005 0.035
## TermDate 0.767 0.739 0.661 0.686 0.840 0.766 0.781 0.809 0.801
## Enforce 0.007 0.009 0.021 0.001 0.001 0.022 0.016 0.003 0.036
## JMent NOJ Award Eq TermDate Enforce
## County 0.031 0.025 0.008 0.580 0.003 0.004
## DoNo 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.242 0.215 0.252 0.316 0.103 0.112
## FileDate 0.640 0.709 0.645 0.915 0.667 0.652
## PNames 0.869 0.855 0.819 0.975 0.846 0.727
## DNames 0.969 0.977 0.982 1.000 0.973 0.984
## ProSe 0.209 0.129 0.110 0.077 0.011 0.059
## PLaw 0.611 0.622 0.647 0.833 0.561 0.575
## PFirm 0.466 0.499 0.521 0.727 0.423 0.449
## EqReq 0.017 0.032 0.105 0.035 0.014 0.004
## MonReq 0.359 0.381 0.473 0.147 0.263 0.523
## LoC 0.071 0.057 0.057 0.830 0.059 0.023
## DLaw 0.531 0.501 0.520 0.796 0.522 0.296
## DFirm 0.443 0.402 0.426 0.656 0.398 0.247
## X3d 0.034 0.048 0.036 0.029 0.027 0.061
## X3dLaw 0.023 0.031 0.024 0.027 0.017 0.002
## X3dFirm 0.027 0.031 0.024 0.027 0.020 0.002
## CtrCl 0.019 0.009 0.019 0.064 0.006 0.002
## CrsCl 0.006 0.007 0.007 0.012 0.005 0.036
## Rec 0.016 0.026 0.012 0.015 0.014 0.001
## Rem 0.020 0.042 0.043 0.036 0.014 0.003
## Judge 0.168 0.142 0.110 0.611 0.095 0.091
## OthMot 0.659 0.676 0.558 0.817 0.532 0.794
## MTime 0.027 0.019 0.024 0.072 0.012 0.008
## MTA 0.005 0.019 0.011 0.049 0.008 0.007
## MTD 0.050 0.026 0.026 0.102 0.025 0.009
## DSJ 0.024 0.014 0.016 0.064 0.016 0.003
## PSJ 0.024 0.039 0.020 0.015 0.013 0.018
## DMIL 0.013 0.007 0.010 0.021 0.016 0.001
## PMIL 0.007 0.003 0.006 0.013 0.005 0.001
## Stp 0.084 0.045 0.065 0.080 0.006 0.009
## Arb 0.063 0.036 0.029 0.694 0.007 0.005
## TrialType 0.080 0.078 0.035 0.027 0.040 0.003
## DISP 0.721 0.429 0.424 0.083 0.066 0.134
## JMent 8.000 0.440 0.300 0.074 0.050 0.117
## NOJ 0.375 7.000 0.123 0.136 0.025 0.129
## Award 0.432 0.371 70.000 0.136 0.171 0.711
## Eq 0.056 0.089 0.033 6.000 0.013 0.034
## TermDate 0.785 0.790 0.746 0.847 297.000 0.741
## Enforce 0.074 0.097 0.039 0.017 0.013 6.000
## attr(,"class")
## [1] "GKtauMatrix"Belknap County Court
# Import data
data_belknap <- read.csv("data/belknap.csv", skip = 1) # 1st row is the description of variables
GKmatrix_belknap <- GKtauDataframe(data_belknap)
GKmatrix_belknap
## County DoNo CaseType FileDate PNames DNames ProSe PLaw
## County 1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DoNo -Inf 351.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType NaN 0.077 28.000 0.084 0.102 0.082 0.312 0.160
## FileDate -Inf 0.623 0.683 219.000 0.642 0.627 0.703 0.663
## PNames -Inf 0.826 0.982 0.838 290.000 0.834 0.952 0.922
## DNames -Inf 0.971 0.976 0.975 0.977 341.000 0.984 0.977
## ProSe NaN 0.011 0.116 0.013 0.016 0.012 5.000 0.122
## PLaw NaN 0.520 0.717 0.537 0.591 0.527 0.754 183.000
## PFirm NaN 0.397 0.644 0.414 0.472 0.404 0.684 0.792
## EqReq NaN 0.011 0.068 0.012 0.012 0.011 0.058 0.041
## MonReq NaN 0.297 0.300 0.297 0.295 0.297 0.359 0.320
## LoC NaN 0.023 0.028 0.024 0.023 0.023 0.017 0.024
## DLaw NaN 0.454 0.549 0.461 0.463 0.464 0.764 0.476
## DFirm NaN 0.351 0.471 0.360 0.360 0.362 0.718 0.374
## X3d NaN 0.029 0.040 0.029 0.037 0.037 0.043 0.036
## X3dLaw NaN 0.017 0.025 0.017 0.026 0.025 0.025 0.022
## X3dFirm NaN 0.020 0.026 0.020 0.029 0.028 0.025 0.025
## CtrCl NaN 0.006 0.012 0.006 0.006 0.006 0.034 0.007
## CrsCl NaN 0.006 0.004 0.006 0.006 0.006 0.008 0.005
## Rec NaN 0.009 0.020 0.009 0.009 0.009 0.013 0.011
## Rem NaN 0.009 0.020 0.009 0.009 0.009 0.015 0.010
## Judge NaN 0.057 0.070 0.058 0.058 0.057 0.070 0.061
## OthMot NaN 0.469 0.549 0.475 0.483 0.468 0.561 0.505
## MTime NaN 0.011 0.027 0.012 0.012 0.011 0.093 0.016
## MTA NaN 0.003 0.024 0.003 0.005 0.003 0.002 0.007
## MTD NaN 0.023 0.025 0.023 0.024 0.023 0.061 0.023
## DSJ NaN 0.011 0.017 0.011 0.012 0.011 0.034 0.012
## PSJ NaN 0.014 0.015 0.016 0.015 0.014 0.016 0.017
## DMIL NaN 0.014 0.015 0.014 0.014 0.014 0.017 0.015
## PMIL NaN 0.006 0.007 0.006 0.006 0.006 0.008 0.006
## Stp NaN 0.006 0.031 0.007 0.007 0.006 0.180 0.013
## Arb NaN 0.006 0.008 0.008 0.006 0.006 0.052 0.007
## TrialType NaN 0.006 0.013 0.006 0.006 0.006 0.018 0.007
## DISP NaN 0.026 0.078 0.029 0.031 0.026 0.281 0.055
## JMent NaN 0.017 0.054 0.018 0.020 0.017 0.244 0.027
## NOJ NaN 0.014 0.072 0.016 0.017 0.014 0.186 0.021
## Award NaN 0.180 0.168 0.180 0.179 0.180 0.234 0.193
## Eq NaN 0.014 0.018 0.015 0.015 0.015 0.021 0.015
## TermDate -Inf 0.711 0.745 0.722 0.718 0.711 0.788 0.728
## Enforce NaN 0.014 0.023 0.016 0.015 0.014 0.064 0.016
## PFirm EqReq MonReq LoC DLaw DFirm X3d X3dLaw
## County 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DoNo 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.171 0.590 0.321 0.072 0.178 0.195 0.198 0.244
## FileDate 0.662 0.704 0.637 0.909 0.688 0.690 0.683 0.635
## PNames 0.957 0.979 0.793 0.959 0.951 0.958 0.936 0.954
## DNames 0.979 0.979 0.988 0.973 0.975 0.983 0.882 0.830
## ProSe 0.127 0.289 0.142 0.010 0.307 0.310 0.012 0.008
## PLaw 0.967 0.683 0.607 0.634 0.648 0.654 0.684 0.768
## PFirm 140.000 0.607 0.494 0.390 0.540 0.545 0.554 0.581
## EqReq 0.042 5.000 0.244 0.054 0.015 0.015 0.003 0.002
## MonReq 0.319 0.673 105.000 0.116 0.219 0.221 0.016 0.015
## LoC 0.024 0.042 0.023 9.000 0.030 0.030 0.002 0.001
## DLaw 0.481 0.444 0.399 0.680 160.000 0.979 0.529 0.408
## DFirm 0.379 0.349 0.344 0.443 0.822 124.000 0.406 0.292
## X3d 0.042 0.008 0.029 0.002 0.038 0.045 11.000 1.000
## X3dLaw 0.029 0.006 0.023 0.001 0.024 0.031 0.682 7.000
## X3dFirm 0.030 0.009 0.023 0.001 0.024 0.031 0.682 1.000
## CtrCl 0.007 0.015 0.006 0.007 0.021 0.021 0.002 0.002
## CrsCl 0.006 0.004 0.002 0.051 0.007 0.007 0.013 0.000
## Rec 0.011 0.020 0.012 0.001 0.014 0.016 0.059 0.001
## Rem 0.010 0.051 0.020 0.011 0.021 0.019 0.005 0.001
## Judge 0.062 0.125 0.062 0.016 0.064 0.063 0.189 0.025
## OthMot 0.506 0.493 0.450 0.694 0.591 0.591 0.952 1.000
## MTime 0.019 0.004 0.013 0.016 0.052 0.053 0.017 0.005
## MTA 0.012 0.013 0.008 0.002 0.002 0.002 0.004 0.003
## MTD 0.024 0.028 0.018 0.055 0.054 0.055 0.034 0.039
## DSJ 0.012 0.016 0.009 0.084 0.029 0.028 0.002 0.001
## PSJ 0.016 0.007 0.012 0.012 0.023 0.023 0.001 0.001
## DMIL 0.015 0.003 0.011 0.052 0.023 0.023 0.001 0.000
## PMIL 0.006 0.005 0.002 0.015 0.010 0.010 0.000 0.000
## Stp 0.014 0.024 0.060 0.006 0.098 0.100 0.008 0.006
## Arb 0.008 0.015 0.023 0.384 0.031 0.032 0.003 0.003
## TrialType 0.008 0.005 0.007 0.002 0.013 0.013 0.003 0.001
## DISP 0.057 0.132 0.116 0.064 0.130 0.132 0.028 0.024
## JMent 0.029 0.046 0.069 0.061 0.113 0.115 0.012 0.014
## NOJ 0.023 0.074 0.050 0.011 0.081 0.082 0.022 0.025
## Award 0.193 0.211 0.366 0.113 0.137 0.139 0.134 0.013
## Eq 0.015 0.006 0.025 0.544 0.012 0.012 0.010 0.014
## TermDate 0.725 0.785 0.745 0.823 0.728 0.731 0.582 0.660
## Enforce 0.016 0.025 0.020 0.027 0.030 0.030 0.014 0.002
## X3dFirm CtrCl CrsCl Rec Rem Judge OthMot MTime MTA MTD
## County 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DoNo 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.240 0.187 0.023 0.197 0.121 0.104 0.128 0.185 0.166 0.108
## FileDate 0.635 0.780 0.798 0.796 0.730 0.695 0.658 0.716 0.647 0.663
## PNames 0.954 0.954 1.000 0.872 0.966 0.871 0.844 0.900 0.969 0.926
## DNames 0.830 1.000 1.000 1.000 0.966 0.980 0.971 0.968 1.000 0.937
## ProSe 0.008 0.056 0.037 0.016 0.070 0.017 0.046 0.118 0.012 0.057
## PLaw 0.768 0.613 0.490 0.732 0.394 0.587 0.602 0.710 0.717 0.626
## PFirm 0.550 0.484 0.549 0.694 0.285 0.496 0.468 0.586 0.610 0.491
## EqReq 0.002 0.008 0.047 0.096 0.086 0.019 0.016 0.003 0.015 0.004
## MonReq 0.013 0.105 0.201 0.110 0.038 0.253 0.298 0.201 0.498 0.161
## LoC 0.001 0.060 0.038 0.001 0.050 0.023 0.027 0.032 0.007 0.008
## DLaw 0.407 0.819 0.647 0.754 0.811 0.522 0.484 0.720 0.470 0.768
## DFirm 0.291 0.737 0.521 0.478 0.581 0.398 0.375 0.590 0.390 0.630
## X3d 0.938 0.043 0.198 0.198 0.066 0.050 0.039 0.047 0.007 0.086
## X3dLaw 0.938 0.043 0.000 0.001 0.001 0.008 0.024 0.023 0.005 0.066
## X3dFirm 8.000 0.043 0.000 0.001 0.001 0.016 0.027 0.023 0.005 0.066
## CtrCl 0.002 3.000 0.005 0.012 0.002 0.004 0.012 0.098 0.026 0.011
## CrsCl 0.000 0.007 3.000 0.022 0.014 0.003 0.006 0.001 0.021 0.010
## Rec 0.001 0.027 0.036 4.000 0.049 0.054 0.011 0.032 0.004 0.022
## Rem 0.001 0.009 0.046 0.095 4.000 0.041 0.010 0.014 0.007 0.002
## Judge 0.026 0.114 0.049 0.550 0.208 21.000 0.100 0.115 0.107 0.069
## OthMot 1.000 0.808 0.664 0.573 0.433 0.417 165.000 0.719 0.482 0.791
## MTime 0.004 0.121 0.001 0.026 0.005 0.032 0.032 5.000 0.010 0.092
## MTA 0.003 0.009 0.003 0.003 0.005 0.004 0.002 0.006 2.000 0.001
## MTD 0.039 0.041 0.016 0.008 0.004 0.021 0.036 0.097 0.024 9.000
## DSJ 0.001 0.028 0.001 0.039 0.005 0.009 0.017 0.037 0.009 0.037
## PSJ 0.001 0.037 0.001 0.001 0.002 0.011 0.017 0.023 0.027 0.033
## DMIL 0.000 0.052 0.000 0.100 0.001 0.015 0.019 0.044 0.024 0.080
## PMIL 0.000 0.001 0.000 0.099 0.000 0.012 0.008 0.034 0.002 0.023
## Stp 0.006 0.041 0.006 0.010 0.015 0.006 0.017 0.051 0.000 0.043
## Arb 0.002 0.051 0.015 0.004 0.019 0.017 0.014 0.039 0.004 0.015
## TrialType 0.001 0.058 0.001 0.082 0.002 0.010 0.014 0.082 0.001 0.006
## DISP 0.022 0.115 0.209 0.088 0.120 0.066 0.063 0.141 0.071 0.096
## JMent 0.014 0.114 0.200 0.079 0.047 0.046 0.044 0.116 0.028 0.045
## NOJ 0.024 0.043 0.102 0.213 0.200 0.045 0.044 0.067 0.034 0.043
## Award 0.012 0.020 0.400 0.204 0.041 0.111 0.208 0.108 0.245 0.084
## Eq 0.014 0.011 0.009 0.002 0.008 0.018 0.020 0.024 0.026 0.012
## TermDate 0.660 0.578 0.252 0.440 0.851 0.753 0.722 0.747 0.750 0.722
## Enforce 0.002 0.015 0.113 0.002 0.003 0.011 0.026 0.014 0.012 0.007
## DSJ PSJ DMIL PMIL Stp Arb TrialType DISP JMent NOJ
## County 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DoNo 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.107 0.112 0.057 0.080 0.243 0.096 0.160 0.216 0.226 0.216
## FileDate 0.766 0.716 0.735 1.000 0.732 0.770 0.801 0.639 0.625 0.703
## PNames 0.974 0.800 1.000 1.000 0.919 0.934 1.000 0.836 0.855 0.840
## DNames 0.974 1.000 1.000 0.874 0.985 0.981 1.000 0.976 0.970 0.975
## ProSe 0.037 0.012 0.017 0.011 0.254 0.071 0.028 0.154 0.200 0.130
## PLaw 0.734 0.598 0.855 1.000 0.672 0.615 0.709 0.604 0.592 0.610
## PFirm 0.581 0.483 0.623 0.531 0.571 0.458 0.550 0.477 0.461 0.492
## EqReq 0.060 0.004 0.002 0.060 0.033 0.028 0.013 0.037 0.018 0.036
## MonReq 0.021 0.295 0.009 0.004 0.302 0.184 0.075 0.398 0.374 0.398
## LoC 0.086 0.013 0.046 0.046 0.031 0.221 0.002 0.019 0.021 0.015
## DLaw 0.835 0.606 0.855 0.811 0.783 0.749 0.771 0.467 0.489 0.459
## DFirm 0.560 0.489 0.711 0.474 0.694 0.611 0.620 0.388 0.419 0.386
## X3d 0.002 0.002 0.001 0.000 0.039 0.033 0.027 0.034 0.034 0.049
## X3dLaw 0.001 0.001 0.000 0.000 0.023 0.018 0.001 0.022 0.023 0.032
## X3dFirm 0.001 0.001 0.000 0.000 0.023 0.018 0.001 0.024 0.027 0.032
## CtrCl 0.008 0.010 0.020 0.001 0.035 0.052 0.048 0.018 0.022 0.013
## CrsCl 0.001 0.000 0.000 0.000 0.006 0.016 0.001 0.007 0.006 0.007
## Rec 0.064 0.001 0.142 0.250 0.017 0.012 0.132 0.022 0.016 0.025
## Rem 0.010 0.002 0.001 0.000 0.016 0.007 0.002 0.048 0.021 0.044
## Judge 0.069 0.012 0.213 0.252 0.054 0.039 0.200 0.084 0.111 0.091
## OthMot 0.846 0.750 0.879 1.000 0.619 0.668 0.859 0.571 0.628 0.655
## MTime 0.039 0.007 0.026 0.038 0.056 0.045 0.089 0.031 0.025 0.028
## MTA 0.006 0.009 0.007 0.001 0.000 0.002 0.000 0.008 0.002 0.016
## MTD 0.092 0.126 0.233 0.041 0.051 0.059 0.016 0.045 0.043 0.021
## DSJ 5.000 0.139 0.082 0.128 0.043 0.040 0.078 0.017 0.027 0.009
## PSJ 0.180 6.000 0.070 0.000 0.032 0.032 0.053 0.032 0.022 0.035
## DMIL 0.098 0.058 6.000 0.330 0.024 0.068 0.072 0.016 0.015 0.010
## PMIL 0.063 0.000 0.187 3.000 0.005 0.040 0.053 0.008 0.010 0.002
## Stp 0.031 0.016 0.021 0.002 3.000 0.092 0.043 0.108 0.097 0.048
## Arb 0.031 0.010 0.040 0.036 0.082 3.000 0.050 0.042 0.038 0.017
## TrialType 0.053 0.012 0.035 0.095 0.036 0.047 3.000 0.066 0.085 0.080
## DISP 0.086 0.310 0.037 0.289 0.288 0.155 0.772 10.000 0.722 0.411
## JMent 0.062 0.032 0.035 0.026 0.190 0.102 0.761 0.438 7.000 0.428
## NOJ 0.030 0.023 0.007 0.003 0.074 0.035 0.170 0.182 0.370 6.000
## Award 0.060 0.348 0.004 0.008 0.248 0.167 0.053 0.434 0.434 0.358
## Eq 0.031 0.003 0.006 0.010 0.010 0.194 0.004 0.021 0.031 0.049
## TermDate 0.629 0.505 0.596 0.761 0.744 0.686 0.774 0.798 0.783 0.786
## Enforce 0.004 0.017 0.001 0.001 0.031 0.013 0.004 0.037 0.075 0.096
## Award Eq TermDate Enforce
## County 0.000 0.000 0.000 0.000
## DoNo 1.000 1.000 1.000 1.000
## CaseType 0.263 0.081 0.083 0.091
## FileDate 0.625 0.889 0.635 0.627
## PNames 0.801 0.934 0.829 0.707
## DNames 0.984 1.000 0.971 0.982
## ProSe 0.112 0.011 0.013 0.061
## PLaw 0.627 0.658 0.524 0.588
## PFirm 0.509 0.472 0.402 0.468
## EqReq 0.116 0.035 0.013 0.005
## MonReq 0.541 0.197 0.298 0.525
## LoC 0.026 0.635 0.023 0.012
## DLaw 0.459 0.513 0.456 0.245
## DFirm 0.388 0.354 0.352 0.192
## X3d 0.038 0.037 0.028 0.065
## X3dLaw 0.024 0.036 0.017 0.002
## X3dFirm 0.024 0.036 0.020 0.002
## CtrCl 0.008 0.005 0.008 0.002
## CrsCl 0.007 0.034 0.005 0.038
## Rec 0.011 0.002 0.013 0.002
## Rem 0.034 0.008 0.010 0.003
## Judge 0.068 0.044 0.061 0.059
## OthMot 0.510 0.725 0.473 0.779
## MTime 0.013 0.024 0.015 0.010
## MTA 0.007 0.003 0.003 0.001
## MTD 0.026 0.043 0.023 0.007
## DSJ 0.013 0.052 0.013 0.004
## PSJ 0.016 0.008 0.016 0.018
## DMIL 0.010 0.034 0.016 0.001
## PMIL 0.009 0.008 0.005 0.001
## Stp 0.075 0.003 0.008 0.009
## Arb 0.025 0.406 0.007 0.006
## TrialType 0.033 0.003 0.041 0.003
## DISP 0.432 0.049 0.068 0.140
## JMent 0.314 0.048 0.056 0.120
## NOJ 0.120 0.181 0.027 0.133
## Award 64.000 0.150 0.182 0.725
## Eq 0.021 6.000 0.014 0.034
## TermDate 0.732 0.744 250.000 0.722
## Enforce 0.046 0.018 0.016 6.000
## attr(,"class")
## [1] "GKtauMatrix"BCDD Court
# Import data
data_BCDD <- read.csv("data/BCDD.csv", skip = 1) # 1st row is the description of variables
GKmatrix_BCDD <- GKtauDataframe(data_BCDD)
GKmatrix_BCDD
## County DoNo CaseType FileDate PNames DNames ProSe PLaw
## County 1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DoNo NaN 58.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType NaN 0.351 21.000 0.368 0.349 0.354 0.342 0.358
## FileDate NaN 0.965 0.981 56.000 0.965 0.965 1.000 0.965
## PNames NaN 0.930 0.924 0.930 54.000 0.930 0.893 0.965
## DNames NaN 0.982 1.000 0.982 0.982 57.000 1.000 0.982
## ProSe NaN 0.035 0.031 0.035 0.035 0.035 3.000 0.034
## PLaw NaN 0.789 0.813 0.789 0.824 0.789 0.786 46.000
## PFirm NaN 0.632 0.639 0.631 0.666 0.631 0.543 0.715
## EqReq NaN 0.035 0.064 0.052 0.035 0.035 0.037 0.037
## MonReq NaN 0.070 0.071 0.073 0.073 0.070 0.018 0.070
## LoC NaN 0.386 0.394 0.385 0.384 0.386 0.308 0.380
## DLaw NaN 0.947 0.956 0.947 0.947 0.959 1.000 0.947
## DFirm NaN 0.789 0.816 0.789 0.824 0.801 1.000 0.805
## X3d NaN 0.018 0.021 0.018 0.017 0.018 0.001 0.017
## X3dLaw NaN 0.018 0.021 0.018 0.017 0.018 0.001 0.017
## X3dFirm NaN 0.018 0.021 0.018 0.017 0.018 0.001 0.017
## CtrCl NaN 0.018 0.034 0.017 0.017 0.017 0.005 0.018
## CrsCl NaN 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## Rec NaN 0.018 0.020 0.018 0.018 0.018 0.189 0.018
## Rem NaN 0.053 0.052 0.052 0.053 0.053 0.051 0.053
## Judge NaN 0.281 0.291 0.289 0.288 0.280 0.224 0.281
## OthMot NaN 0.912 0.928 0.912 0.912 0.912 1.000 0.918
## MTime NaN 0.053 0.065 0.053 0.051 0.052 0.106 0.055
## MTA NaN 0.053 0.062 0.053 0.053 0.052 0.020 0.064
## MTD NaN 0.105 0.129 0.109 0.106 0.105 0.065 0.107
## DSJ NaN 0.088 0.102 0.093 0.088 0.088 0.199 0.089
## PSJ NaN 0.053 0.047 0.053 0.053 0.052 0.088 0.053
## DMIL NaN 0.035 0.034 0.035 0.035 0.035 0.003 0.035
## PMIL NaN 0.018 0.015 0.018 0.018 0.018 0.003 0.018
## Stp NaN 0.035 0.024 0.034 0.034 0.035 0.047 0.038
## Arb NaN 0.018 0.025 0.018 0.018 0.018 0.005 0.018
## TrialType NaN 0.053 0.049 0.053 0.053 0.053 0.004 0.052
## DISP NaN 0.140 0.130 0.141 0.147 0.140 0.310 0.136
## JMent Inf 0.053 0.097 0.053 0.053 0.052 0.040 0.049
## NOJ NaN 0.070 0.106 0.070 0.071 0.070 0.039 0.066
## Award NaN 0.123 0.103 0.123 0.123 0.122 0.233 0.120
## Eq NaN 0.035 0.032 0.035 0.035 0.035 0.004 0.033
## TermDate NaN 0.912 0.905 0.912 0.930 0.912 0.893 0.929
## Enforce NaN 0.035 0.034 0.035 0.035 0.035 0.003 0.033
## PFirm EqReq MonReq LoC DLaw DFirm X3d X3dLaw X3dFirm
## County 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DoNo 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.364 0.789 0.473 0.358 0.353 0.360 1.000 1.000 1.000
## FileDate 0.964 1.000 1.000 0.963 0.965 0.965 1.000 1.000 1.000
## PNames 0.964 0.909 1.000 0.926 0.930 0.965 0.491 0.491 0.491
## DNames 0.982 1.000 0.958 0.982 1.000 1.000 1.000 1.000 1.000
## ProSe 0.033 0.068 0.016 0.033 0.035 0.036 0.002 0.002 0.002
## PLaw 0.924 1.000 0.873 0.779 0.789 0.805 0.491 0.491 0.491
## PFirm 37.000 0.849 0.830 0.635 0.631 0.651 0.491 0.491 0.491
## EqReq 0.037 3.000 0.335 0.034 0.035 0.036 0.002 0.002 0.002
## MonReq 0.080 0.335 5.000 0.076 0.070 0.072 0.006 0.006 0.006
## LoC 0.393 0.483 0.458 23.000 0.384 0.387 0.491 0.491 0.491
## DLaw 0.946 1.000 0.943 0.945 55.000 1.000 1.000 1.000 1.000
## DFirm 0.819 0.909 0.845 0.797 0.842 46.000 1.000 1.000 1.000
## X3d 0.018 0.002 0.005 0.019 0.018 0.018 2.000 1.000 1.000
## X3dLaw 0.018 0.002 0.005 0.019 0.018 0.018 1.000 2.000 1.000
## X3dFirm 0.018 0.002 0.005 0.019 0.018 0.018 1.000 1.000 2.000
## CtrCl 0.019 0.007 0.045 0.020 0.018 0.016 0.042 0.042 0.042
## CrsCl 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## Rec 0.019 0.002 0.051 0.018 0.018 0.018 0.000 0.000 0.000
## Rem 0.053 0.076 0.069 0.052 0.053 0.051 0.002 0.002 0.002
## Judge 0.290 0.298 0.372 0.291 0.280 0.287 1.000 1.000 1.000
## OthMot 0.917 1.000 0.932 0.908 0.912 0.912 1.000 1.000 1.000
## MTime 0.047 0.021 0.061 0.058 0.051 0.055 0.015 0.015 0.015
## MTA 0.061 0.020 0.115 0.061 0.057 0.055 0.004 0.004 0.004
## MTD 0.104 0.089 0.084 0.100 0.104 0.101 0.008 0.008 0.008
## DSJ 0.084 0.095 0.124 0.088 0.088 0.087 0.004 0.004 0.004
## PSJ 0.052 0.014 0.127 0.054 0.052 0.051 0.003 0.003 0.003
## DMIL 0.033 0.003 0.010 0.036 0.035 0.036 0.001 0.001 0.001
## PMIL 0.019 0.003 0.010 0.016 0.018 0.016 0.001 0.001 0.001
## Stp 0.029 0.033 0.074 0.046 0.034 0.039 0.031 0.031 0.031
## Arb 0.017 0.006 0.020 0.017 0.018 0.018 0.001 0.001 0.001
## TrialType 0.051 0.005 0.015 0.053 0.053 0.052 0.001 0.001 0.001
## DISP 0.138 0.031 0.099 0.133 0.140 0.137 0.009 0.009 0.009
## JMent 0.053 0.016 0.043 0.050 0.052 0.050 0.003 0.003 0.003
## NOJ 0.084 0.014 0.039 0.068 0.071 0.069 0.003 0.003 0.003
## Award 0.130 0.017 0.114 0.122 0.123 0.121 0.012 0.012 0.012
## Eq 0.050 0.005 0.017 0.034 0.035 0.034 0.001 0.001 0.001
## TermDate 0.928 0.819 0.830 0.908 0.912 0.912 0.491 0.491 0.491
## Enforce 0.030 0.003 0.072 0.033 0.035 0.036 0.001 0.001 0.001
## CtrCl CrsCl Rec Rem Judge OthMot MTime MTA MTD DSJ
## County 0.000 NaN 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DoNo 1.000 NaN 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.426 NaN 0.491 0.384 0.411 0.364 0.455 0.270 0.463 0.352
## FileDate 0.958 NaN 1.000 0.910 0.975 0.965 0.968 1.000 0.966 1.000
## PNames 0.917 NaN 1.000 1.000 0.924 0.929 0.905 0.947 0.966 0.948
## DNames 0.958 NaN 1.000 1.000 0.975 0.982 0.968 0.947 0.966 1.000
## ProSe 0.015 NaN 0.322 0.040 0.023 0.036 0.056 0.018 0.020 0.027
## PLaw 0.854 NaN 1.000 0.880 0.818 0.800 0.825 0.825 0.879 0.844
## PFirm 0.751 NaN 1.000 0.731 0.729 0.634 0.556 0.775 0.720 0.645
## EqReq 0.016 NaN 0.002 0.075 0.084 0.044 0.050 0.018 0.105 0.165
## MonReq 0.111 NaN 0.067 0.052 0.095 0.072 0.093 0.283 0.070 0.033
## LoC 0.465 NaN 0.322 0.333 0.437 0.382 0.401 0.453 0.435 0.438
## DLaw 0.945 NaN 1.000 1.000 0.941 0.947 0.926 0.930 0.920 1.000
## DFirm 0.764 NaN 1.000 0.701 0.788 0.788 0.851 0.719 0.713 0.826
## X3d 0.042 NaN 0.000 0.001 0.033 0.018 0.013 0.003 0.004 0.003
## X3dLaw 0.042 NaN 0.000 0.001 0.033 0.018 0.013 0.003 0.004 0.003
## X3dFirm 0.042 NaN 0.000 0.001 0.033 0.018 0.013 0.003 0.004 0.003
## CtrCl 2.000 NaN 0.007 0.016 0.027 0.020 0.066 0.010 0.021 0.013
## CrsCl 0.000 1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## Rec 0.007 NaN 2.000 0.156 0.006 0.018 0.020 0.003 0.004 0.087
## Rem 0.078 NaN 0.322 4.000 0.095 0.051 0.027 0.101 0.122 0.190
## Judge 0.372 NaN 0.014 0.691 17.000 0.288 0.453 0.571 0.464 0.278
## OthMot 0.958 NaN 1.000 0.856 0.929 53.000 0.917 1.000 1.000 1.000
## MTime 0.094 NaN 0.025 0.070 0.145 0.055 4.000 0.072 0.125 0.053
## MTA 0.065 NaN 0.004 0.175 0.107 0.055 0.075 4.000 0.073 0.112
## MTD 0.131 NaN 0.008 0.366 0.154 0.109 0.195 0.112 7.000 0.237
## DSJ 0.106 NaN 1.000 0.413 0.074 0.090 0.109 0.065 0.246 6.000
## PSJ 0.116 NaN 0.491 0.321 0.054 0.054 0.084 0.068 0.049 0.159
## DMIL 0.049 NaN 0.001 0.003 0.012 0.036 0.032 0.006 0.048 0.165
## PMIL 0.015 NaN 0.001 0.003 0.039 0.018 0.026 0.160 0.058 0.005
## Stp 0.028 NaN 0.025 0.014 0.061 0.033 0.130 0.107 0.018 0.060
## Arb 0.075 NaN 0.001 0.006 0.025 0.016 0.010 0.012 0.018 0.006
## TrialType 0.056 NaN 0.001 0.324 0.072 0.051 0.045 0.093 0.096 0.175
## DISP 0.135 NaN 0.237 0.690 0.176 0.141 0.178 0.234 0.211 0.145
## JMent 0.105 Inf 0.003 0.056 0.076 0.054 0.040 0.165 0.175 0.032
## NOJ 0.099 NaN 0.003 0.100 0.080 0.069 0.075 0.129 0.080 0.056
## Award 0.332 NaN 0.039 0.237 0.157 0.123 0.163 0.204 0.083 0.131
## Eq 0.023 NaN 0.001 0.068 0.019 0.033 0.055 0.009 0.024 0.039
## TermDate 0.875 NaN 1.000 1.000 0.898 0.912 0.905 0.895 0.966 1.000
## Enforce 0.049 NaN 0.001 0.003 0.039 0.036 0.026 0.085 0.009 0.080
## PSJ DMIL PMIL Stp Arb TrialType DISP JMent NOJ Award
## County 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DoNo 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## CaseType 0.272 0.132 0.125 0.317 0.203 0.160 0.298 0.365 0.226 0.285
## FileDate 1.000 1.000 1.000 0.947 1.000 1.000 1.000 1.000 1.000 0.969
## PNames 1.000 1.000 1.000 0.920 1.000 1.000 1.000 1.000 1.000 0.938
## DNames 0.931 1.000 1.000 0.973 1.000 1.000 0.967 0.938 1.000 0.969
## ProSe 0.032 0.003 0.003 0.040 0.007 0.003 0.085 0.046 0.050 0.060
## PLaw 0.896 0.743 1.000 0.836 0.866 0.597 0.760 0.699 0.644 0.774
## PFirm 0.724 0.303 1.000 0.616 0.770 0.474 0.634 0.565 0.637 0.696
## EqReq 0.012 0.003 0.004 0.032 0.009 0.004 0.014 0.014 0.012 0.022
## MonReq 0.140 0.009 0.012 0.108 0.026 0.012 0.104 0.210 0.124 0.106
## LoC 0.498 0.316 0.556 0.447 0.591 0.263 0.321 0.457 0.401 0.372
## DLaw 0.908 1.000 1.000 0.938 1.000 1.000 0.956 0.917 1.000 0.959
## DFirm 0.747 1.000 0.396 0.849 1.000 0.655 0.728 0.647 0.702 0.763
## X3d 0.002 0.000 0.001 0.017 0.001 0.001 0.004 0.002 0.002 0.008
## X3dLaw 0.002 0.000 0.001 0.017 0.001 0.001 0.004 0.002 0.002 0.008
## X3dFirm 0.002 0.000 0.001 0.017 0.001 0.001 0.004 0.002 0.002 0.008
## CtrCl 0.033 0.016 0.015 0.017 0.075 0.010 0.032 0.026 0.025 0.101
## CrsCl 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## Rec 0.118 0.000 0.001 0.014 0.001 0.001 0.044 0.002 0.002 0.026
## Rem 0.154 0.003 0.004 0.038 0.009 0.489 0.208 0.125 0.145 0.129
## Judge 0.474 0.022 0.612 0.279 0.359 0.666 0.425 0.570 0.521 0.348
## OthMot 1.000 1.000 1.000 0.899 0.785 0.724 0.948 1.000 0.890 0.926
## MTime 0.075 0.011 0.031 0.140 0.015 0.012 0.076 0.049 0.059 0.129
## MTA 0.049 0.006 0.310 0.137 0.017 0.332 0.113 0.190 0.143 0.084
## MTD 0.196 0.060 0.168 0.075 0.033 0.109 0.187 0.321 0.132 0.117
## DSJ 0.256 0.572 0.008 0.111 0.022 0.489 0.137 0.155 0.175 0.133
## PSJ 4.000 0.240 0.104 0.070 0.041 0.006 0.162 0.205 0.238 0.095
## DMIL 0.120 3.000 0.001 0.031 0.003 0.328 0.009 0.004 0.004 0.017
## PMIL 0.046 0.001 2.000 0.015 0.003 0.001 0.058 0.047 0.053 0.003
## Stp 0.077 0.019 0.029 3.000 0.131 0.024 0.025 0.024 0.021 0.031
## Arb 0.014 0.002 0.003 0.072 2.000 0.003 0.009 0.013 0.014 0.028
## TrialType 0.006 0.496 0.002 0.045 0.004 4.000 0.096 0.104 0.116 0.078
## DISP 0.380 0.013 0.568 0.104 0.077 0.405 9.000 0.761 0.602 0.406
## JMent 0.237 0.005 0.493 0.032 0.042 0.072 0.277 4.000 0.630 0.205
## NOJ 0.271 0.004 0.492 0.054 0.041 0.071 0.225 0.586 5.000 0.195
## Award 0.251 0.019 0.008 0.111 0.495 0.339 0.374 0.491 0.621 8.000
## Eq 0.052 0.001 0.002 0.028 0.004 0.002 0.064 0.042 0.364 0.097
## TermDate 1.000 1.000 1.000 0.920 1.000 1.000 1.000 1.000 1.000 1.000
## Enforce 0.115 0.001 0.001 0.031 0.003 0.001 0.050 0.102 0.114 0.053
## Eq TermDate Enforce
## County 0.000 0.000 0.000
## DoNo 1.000 1.000 1.000
## CaseType 0.181 0.349 0.533
## FileDate 1.000 0.965 1.000
## PNames 1.000 0.947 1.000
## DNames 1.000 0.982 1.000
## ProSe 0.004 0.035 0.003
## PLaw 0.508 0.806 0.358
## PFirm 0.537 0.648 0.132
## EqReq 0.005 0.034 0.003
## MonReq 0.011 0.068 0.499
## LoC 0.276 0.384 0.162
## DLaw 1.000 0.947 1.000
## DFirm 0.595 0.789 1.000
## X3d 0.001 0.017 0.000
## X3dLaw 0.001 0.017 0.000
## X3dFirm 0.001 0.017 0.000
## CtrCl 0.017 0.016 0.016
## CrsCl 0.000 0.000 0.000
## Rec 0.001 0.018 0.000
## Rem 0.094 0.053 0.003
## Judge 0.338 0.287 0.503
## OthMot 0.722 0.912 1.000
## MTime 0.175 0.052 0.023
## MTA 0.010 0.052 0.113
## MTD 0.158 0.106 0.012
## DSJ 0.328 0.089 0.070
## PSJ 0.153 0.053 0.112
## DMIL 0.001 0.035 0.001
## PMIL 0.001 0.018 0.001
## Stp 0.016 0.036 0.019
## Arb 0.003 0.018 0.002
## TrialType 0.002 0.053 0.001
## DISP 0.401 0.147 0.158
## JMent 0.066 0.053 0.112
## NOJ 1.000 0.071 0.112
## Award 0.383 0.127 0.502
## Eq 3.000 0.035 0.001
## TermDate 1.000 53.000 1.000
## Enforce 0.001 0.035 3.000
## attr(,"class")
## [1] "GKtauMatrix"Comparing Judge between the courts
It compares how much of the variation in other variables Judge explains per County (county of record).
Judge_Belknap <- GKmatrix_belknap["Judge", ]
Judge_BCDD <- GKmatrix_BCDD["Judge", ]
Judge_Counties <- data.frame(Judge_Belknap, Judge_BCDD)
Judge_Counties
## Judge_Belknap Judge_BCDD
## County NaN NaN
## DoNo 0.057 0.281
## CaseType 0.070 0.291
## FileDate 0.058 0.289
## PNames 0.058 0.288
## DNames 0.057 0.280
## ProSe 0.070 0.224
## PLaw 0.061 0.281
## PFirm 0.062 0.290
## EqReq 0.125 0.298
## MonReq 0.062 0.372
## LoC 0.016 0.291
## DLaw 0.064 0.280
## DFirm 0.063 0.287
## X3d 0.189 1.000
## X3dLaw 0.025 1.000
## X3dFirm 0.026 1.000
## CtrCl 0.114 0.372
## CrsCl 0.049 NaN
## Rec 0.550 0.014
## Rem 0.208 0.691
## Judge 21.000 17.000
## OthMot 0.100 0.288
## MTime 0.115 0.453
## MTA 0.107 0.571
## MTD 0.069 0.464
## DSJ 0.069 0.278
## PSJ 0.012 0.474
## DMIL 0.213 0.022
## PMIL 0.252 0.612
## Stp 0.054 0.279
## Arb 0.039 0.359
## TrialType 0.200 0.666
## DISP 0.084 0.425
## JMent 0.111 0.570
## NOJ 0.091 0.521
## Award 0.068 0.348
## Eq 0.044 0.338
## TermDate 0.061 0.287
## Enforce 0.059 0.503