Dv626.1.RF

From InterSciWiki
Jump to: navigation, search

Dv626.1.RF Random forest version .Dow-Eff Functions - DEf -- .Dv626.1 edit version -- http://SocSciCompute.ss.uci.edu/ use option DEF2 LOCAL DEF2 DEF2 bio.5,v149,v205,v21,v53,v665,v670,v7 for Visual_Manual here: riv<-c("v149", "v51sq","v625","v64","v676","v154") v149, v51sq,v625,v64,v676,v154 // v149, v625,v64,v676,v154 //sq51

Intro

setwd("/Users/drwhite") # default  Data Quality v720: All Females ethnographers -> more reporting of Female Gender Variables p=.126 Need for instrumental variables
#the indented library items are for the latest installation of R 2.15.0 Patched (2012-04-20 r59126) -- "Easter Beagle" The others are an earlier build. "Systemfit" is for path analysis
Comments40 request to Anthon

Tips

Test your models against h[3] or h[4], not the myOutput.csv. There are two different versions of the restricted model ("Rmodel" and "RmodelRobust"), the first is the restricted model since the second should be used when the errors are heteroskedastic." Heteroskedastic Breusch-Pagan test. H0: residuals homoskedastic --- You can tell from h[5] See Dow & Eff Functions1 Model 1.1

  • try to "match" your riv<- with totry
  • Follow the "to try" -- these are computed from vars in evm -- and variables not in codebook
  • some "totry" vars will not conform to your model
  • Be careful of high VIF (variable inflation factors) such as v158.1 and the 10 individual complexity measures
  • even with all "totry" vars in your model you may find other variables that fit the model
  • It seems like "to try" comes from entire database not just evm
So when you add variables from totry make sure they are in evm<- and iv<- lists
  • Newly created variables will not appear in evm and so will not be in to try
  • Dont put _ inside addesc variable names
Avoid certain combinations of variables when they overlap in meaning
  • If you have significant variables in the range v149-v158 or the summary variable v158.1, dont mix them, either those within the first and not v158.a or vice versa and check the VIFs.
  • I.e., Dont add variables in a Guttman scale and the Guttman scale itself or v158.1 and the 10 individual complexity measures
  • As above, If one variable is a composite of another (v675 and v674 in v676; any Guttman Scale, e.g. Sanday and other) DONT USE THEM IN THE SAME iv<- indepvar
Distribute evm, iv, riv variables properly and name them correctly
  • The same variable is not allowed twice in evm, iv, riv. All riv <in iv <in evm. Once you get a good DvModel, try to improve at DvModel.1 DvModel.2 etc.
  • Every variable in riv must also be in iv; Every variable in iv must also be in evm.
  • Use your search for a given variable number to verify (1) it is present in the proper riv, iv, evm sequences? (2) does it have " " quote marks of the type made within R rather than in word? (3) is it separated by a single comma?
  • Make sure to put "v" where required before your variable numbers e.g. "v777" not "777"

Background

Table 4: http://intersci.ss.uci.edu/wiki/pdf/WileyCh5CCRNetsofVarsModels2blackDRW.pdf

TO SAVE YOUR RESULTS open, RENAME and save MyOutput (after rename), add a new first, empty column to the left of text, enter two blanks in THAT COLUMN and fill it by dragging the two columns down to the bottom.

  • Then log in, copy and paste from the *.csv that and the other columns in your results into the bottom of this page with ==at least four headers== like those shown here

DEf R Script Dv626.1 improved with Random forest Paul Rodriguez and "totry"

Latest DEf01c MiEff877.0 Latest version 
There is no effect of Data Quality v720: Female ethnographers
setwd("/Users/drwhite") # default
#the indented library items are for the latest installation of R 2.15.0 Patched (2012-04-20 r59126) -- "Easter Beagle" The others are an earlier build. "Systemfit" is for path analysis
 setwd("/Users/drwhite") # default
# the indented library items are for the latest installation of R 2.15.0 Patched (2012-04-20 r59126) 
# "Systemfit" is for path analysis
library(mice)
library(foreign)
library(stringr)
library(AER)
library(spdep)
library(psych)
library(geosphere)
library(relaimpo)
library(linprog)
library(dismo)
library(forward)
library(pastecs)
library(classInt)
library(maps)
library(dismo)
library(plyr)
library(aod)
library(reshape)
library(RColorBrewer)
   install.packages("systemfit", repos="http://R-Forge.R-project.org" )  #aa variables for SUR 
#Add vars from RF    femalefieldwkr	langfieldwk	v621	v626	v625	largeg	malefieldyear	v150	v150.1	v61	v21.1	v5
# ===bring in functions and data===
#load(url("http://dl.dropbox.com/u/9256203/DE6.Rdata"),.GlobalEnv)
#ls() #-can see the objects contained in DE6.Rdata
#load(url("http://dl.dropbox.com/u/9256203/DE7.Rdata"),.GlobalEnv)
load(url("http://dl.dropbox.com/u/9256203/DEf01c.Rdata"),.GlobalEnv) #New Oct 18 2013 from MiEff877.0 Latest version 
ls() #-can see the objects contained in DE7.Rdata
#NEW
# ========================= SCCS =========================
# ======Ignore these residuals from an earlier model with scale construction ======
setDS("SCCS")


# ===list and modify variables for use in model===
# --make new variables—changed from sccsA
dx$femaleEquality=dx$v626              ##1 user add depvar name e.g. “femaleEquality“ and number e.g. – 626 CANNOT BE IN evm
 dx$v51sq=dx$v51^2
 addesc("v51sq","FaHelperInfant")
# --create descriptions for new variables-- 
addesc("femaleEquality","Beliefs in Women's Inferiority")  # Beliefs in Women's Inferiority
#addesc("NoPlowCplx","NoPlowCmplx") 
# --create new dummy variables--
mkdummy("v233",4) #tree
mkdummy("v233",5) #roots
mkdummy("v244",2) #pigs 
mkdummy("v244",7) #bovines
mkdummy("v245",2) #milk
mkdummy("v899",1) #himilexp
#mkdummy("v243">1) #plow 
dx$plow<-(dx$v243>1)*1
dx$NoPlowCplx<-(1-dx$plow)*dx$v158.1
addesc("NoPlowCplx","NoPlowCmplx") 
addesc("plow","Plow") 
# --identify variables to keep for model building--

evm<-c("socname","mht.name","femaleEquality","bio.1","bio.11","bio.6","bio.9","v1257","v720","v854","v5","v621","v51sq","v676","v149","v155","v204", "v991", "NoPlowCplx","v1122", "v158.1","v152","v154","v52","v53","v54","v369","v371", "v625","v628","v664", "v1845","v159","v160","v161","v162","v163","v164", "v165","v166", "v167", "v168","v169", "v170", "v171", "v172","v173","v174","v175", "v64","plow") ##,"v989","v990","v614") ### user omitted depvar "valchild" if present and added new depvar "v626" which must be deleted here.

# ===make imputed data===
smi<-doMI(evm,nimp=10,maxit=7);dim(smi)
aa<-aggregate(smi[,sapply(smi,function(x) is.numeric(x))],list(smi$.id),mean) #will list imputed variables from evm
########
## --dependent variable--
#dpV<-"v877" #Polygyny Guttman Scale I: Co-wife Autonomy Constructed from 854-852
##--independent variables in UNrestricted model--
#UiV<-c("bio.13","bio.16","bio.18","bio.2","v1256","v1259","v1708", "v79","v826","v860", "v861","v862","v872","v887", "femecon.mean","path.mean","v1196", "v1197", "v1198", "v1201")
##--independent variables in restricted model (all must be in UiV above)--
#RiV<-c("v79","v860","v862","v872")
########
# --dependent variable--
dpV<-"v626" #female quality
# ===identify role of variables in model===
#-independent variables in UNrestricted model--
#####iv
UiV<-c("bio.1","bio.11","bio.6","bio.9","v1257","v720","v1122","v149","v155","v51sq","v54","v625","v621","v64","v676","v991","v854",   "v204", "NoPlowCplx","v5","v158.1","v152","v154","v52","v53","v369","v371") ##,"v989","v990","v614") 
#--independent variables in restricted model--
#riv<-c("v5","v621","v51sq","v676","v149","v204","NoPlowCplx","v1122")   #before "totry"
#riv<-c("bio.11","v720","v1122","v149","v155","v51sq","v625","v64","v676","v991") #v720 no effect
#riv<-c("bio.11","v1122","v149","v155","v51sq","v625","v64","v676")  # ,"v991","v854") 
#riv<-c("bio.11"                       ,"v1257","v149","v152","v154","v155",          "v204","v51sq",          "v54","v625","v64","v676","v991" )  #,"v1122"
#riv<-c("bio.11"                       ,"v1257",                              "v158.1",                      "v51sq","v52","v54","v625","v64","v676","v991")  #,"v1122" R2=51%
#riv<-c("bio.11"                       ,"v1257","v149","v152","v154","v155",                      "v51sq","v52","v54","v625","v64","v676","v991")  #,"v1122" R2=63%
#riv<-c("bio.11"                       ,"v1257","v149","v152","v154","v155",                      "v51sq","v625","v64","v676") #Table 7, Chapter 5
RiV<-c("v149", "v51sq","v625","v64","v676","v154") #Chapter 6-->  "v154 added" 
#$totry   $didwell "v1122"     "v1257"  "v149"   "v154"   "v155"   "v158.1" "v204"   "v51sq","v54","v625","v64","v676","v991"
# ===estimate regression model===
#Try "Careful" with ecology autcor slimmed down stepW=FALSE, relimp=FALSE, doboot=0, -- relimp is VERY time consuming, so is doboot
h <- doOLS(smi, depvar = dpV, indpv = UiV, rindpv = RiV, othexog = NULL, dw = TRUE, lw = TRUE, ew = TRUE, stepW = FALSE, boxcox = FALSE, getismat = FALSE, relimp=FALSE, slmtests = FALSE, haustest = NULL, mean.data = TRUE, doboot = 0) #Works with DEf01c 
CSVwrite(h, "MiEff877new0.ew", FALSE) # DEf01b SCCS DEf01c SCCS ------------------------- ew = TRUE
#Try "Careful" no ecology autcor slimmed down stepW=FALSE, relimp=FALSE, doboot=0, 
h <- doOLS(smi, depvar = dpV, indpv = UiV, rindpv = RiV, othexog = NULL, dw = TRUE, lw = FALSE, ew = TRUE, stepW = FALSE, boxcox = FALSE, getismat = FALSE, relimp=FALSE, slmtests = FALSE, haustest = NULL, mean.data = TRUE, doboot = 0) #Works with DEf01c 
CSVwrite(h, "MiEff877new0.ewNull", FALSE) # DEf01b SCCS DEf01c SCCS ------------------------- ew = TRUE
######################Dont go beyond
#This is Dv626.1
h<-doOLS(smi,depvar="femaleEquality",indpv=iv,rindpv=riv,othexog=NULL,  #MUST HAVE INDEPVAR
        dw=TRUE,lw=TRUE,rw=FALSE,ew=FALSE,
        stepW=TRUE,relimp=FALSE,slmtests=FALSE)
# ===estimate regression model===
                  #h <- doOLS(smi, depvar = dpV, indpv = UiV, rindpv = RiV, othexog = oxog, dw = TRUE,    lw = TRUE, ew = TRUE, stepW = TRUE, boxcox = FALSE, getismat = FALSE, relimp = TRUE, slmtests = FALSE, haustest = c("v213.d3"), mean.data = TRUE, doboot = 500)
                  #CSVwrite(h, "olsresults", FALSE)
                  #h <- doOLS(smi, depvar = dpV, indpv = UiV, rindpv = RiV, othexog = oxog, dw = TRUE, lw = TRUE, ew = FALSE, stepW = TRUE, boxcox = FALSE, getismat = FALSE, relimp = TRUE, slmtests = FALSE, haustest = c("v213.d3"), mean.data = TRUE, doboot = 500)
                  #CSVwrite(h, "olsresultsNo.ew", FALSE)
#deactivate ecological autocorrelation with  ew = FALSE
h<-doOLS(smi,depvar="femaleEquality",indpv=iv,rindpv=riv,othexog=NULL,  #MUST HAVE INDEPVAR
        dw=TRUE,lw=TRUE,rw=FALSE,ew=FALSE,
        stepW=TRUE,relimp=FALSE,slmtests=FALSE)
#h <- doOLS(smi, depvar = dpV, indpv = UiV, rindpv = RiV, othexog = oxog, dw = TRUE,  lw = TRUE, ew = TRUE, stepW = TRUE, relimp = TRUE, slmtests = FALSE)
CSVwrite(h, "Dv626.1olsresultsNo.ew", FALSE)
#activate ecological autocorrelation with  ew = TRUE
h<-doOLS(smi,depvar="femaleEquality",indpv=iv,rindpv=riv,othexog=NULL,  #MUST HAVE INDEPVAR
        dw=TRUE,lw=TRUE,rw=FALSE,ew=TRUE,
        stepW=TRUE,relimp=FALSE,slmtests=FALSE)
#h <- doOLS(smi, depvar = dpV, indpv = UiV, rindpv = RiV, othexog = oxog, dw = TRUE,  lw = TRUE, ew = FALSE, stepW = TRUE, relimp = TRUE, slmtests = FALSE)
CSVwrite(h, "Dv626.1olsresults.ew", FALSE)
###Error in `[.data.frame`(fff, , indpv) : undefined columns selected
print(h)
h[1]
h[5]
h[4]
h[6]
# --print output to csv file--
CSVwrite(h,"myOutput",FALSE)

Screen-shot Results using h[1] h[2] h[5] h[3] or h[4] h[6] to h[10]

Use these R functions after your model is complete to have a print results as text. The myOutput.csv results are saved on your computer as a file that you must rename, save, and delete before your next run. New results do not write over it.

h[1] #$DependVarb - Variable name and asc------
h[2] #$URmodel - Unrestricted model 
h[5] #$Diagnostics - e.g.,                          Fstat   df      pvalue star
# RESET test. H0: model has correct functional form 6.222   45.033  0.016   ** p<.05
# Wald test. H0: appropriate variables dropped      6.417  118.134  0.013   **
# Breusch-Pagan test. H0: residuals homoskedastic   0.518  411.501  0.472     h[4] Robust
# Shapiro-Wilkes test. H0: residuals normal         0.504   41.602  0.482 <- non-signif. so use h[4] results else h[3] 
# Hausman test. H0: Wy exogenous                    0.004 1836.000  0.947     
# Hausman test. H0: bio.11 exogenous                1.413 1010.000  0.235     
# Hausman test. H0: v1122 exogenous                 4.046  441.000  0.045   **
# Hausman test. H0: v149 exogenous                  0.469 1072.000  0.494     
# Hausman test. H0: v155 exogenous                  1.237  997.000  0.266     
# Hausman test. H0: v51sq exogenous                 0.742   93.000  0.391     
# Hausman test. H0: v625 exogenous                  0.017   90.000  0.897     
# Hausman test. H0: v64 exogenous                   3.277  299.000  0.071    * p<.10  (if *** p<.01)
# Hausman test. H0: v676 exogenous                  0.030   66.000  0.863     
# Hausman test. H0: v991 exogenous                  0.035   40.000  0.852     
# SELECT TO PRINT EITHER h[3] or h[4]
h[3] #$Rmodel - restricted model Independent variables - corrected if heteroscedascity significant p < .10
h[4] #$URmodelRobust - restricted model Independent variables - uncorrected if heteroscedascity insignificant p > .10
h[6] #$OtherStats - R2.IV.composite. R2.final.model R2.UR.model nimp nobs 
h[7] #$DescripStats - names of variables, number of cases, mean values
h[8] #dfbetas - (what are the integers in col 1?) dfbeta variable     observation   dataValue depvarValue
h[9] #$totry -e.g., [1] "v1122" "v149" "v154" "v158.1" "v162" "v204" "v51sq" "v52" "v53"  "v54" "v621" "v625" "v64" "v676"   "v991"  
h[10] #$didwell -e.g.,[1] "v1122" "v149" "v154" "v158.1" "v204" "v51sq" "v52" "v53"  "v54" "v621" "v625" "v64" "v676"   "v991" 
h[1]
$DependVarb
[1] "Dependent variable='femaleEquality': Beliefs in Women's Inferiority"
h[2]
$URmodel
                                                                                desc          coef
(Intercept)                                                                     <NA>  1.1892523648
Wy                                                                              <NA> -0.0859191796
bio.1                                                 BIO1 = Annual Mean Temperature  0.0005482262
bio.11                                   BIO11 = Mean Temperature of Coldest Quarter -0.0007414730
bio.6                                        BIO6 = Min Temperature of Coldest Month  0.0006683380
bio.9                                      BIO9 = Mean Temperature of Driest Quarter  0.0001968808
v1122                                                      Log10 of Total Population -0.0908939225
v149                                                    Scale 1- Writing And Records -0.1056789035
v155                                                                  Scale 7- Money  0.0319214590
v51sq                                                                 FaHelperInfant  0.0226187725
v54                                                  Role of Father, Early Childhood -0.1442637220
v625        High Value Placed on Males Being Aggressive, Strong, And Sexually Potent  0.1844169117
v621                    an Explicit View That Men Should And Do Dominate Their Wives  0.1060389242
v64                                                               Population Density -0.1373807675
v676          Creation Stories (composite of 675 And 656, plus Additional Societies) -0.1044291496
v991         Importance of Fathers for Both Boys And Girls, without Regard to Gender  0.0509695828
v854         Niche Temperature (approximate) Adapted from William Goode, World Atlas  0.0102893597
v204                                                           Dependence on Hunting  0.0635976371
NoPlowCplx                                                               NoPlowCmplx  0.0026382738
v5                                     Animal Husbandry- Contribution to Food Supply -0.0018336410
v158.1                                                    Sum of Complexity measures  0.0337044804
v154                                                         Scale 6- Land Transport -0.1026106329
v52                                      Non-maternal Relationships, Early Childhood  0.0727572750
v53                                                          Role of Father, Infancy  0.1122311035
v369                                           Sex of Parental Caretakers: Early Boy -0.0274018760
v371                                            Sex of Parental Caretakers: Late Boy  0.0228673809
            stdcoef pvalue star     VIF stepkept
(Intercept)      NA  0.100           NA       10
Wy           -0.041  0.687        1.960        0
bio.1         0.114  0.786       33.631        1
bio.11       -0.223  0.804      172.232        0
bio.6         0.210  0.756       93.727        2
bio.9         0.053  0.787        8.013        2
v1122        -0.300  0.025   **   3.552       10
v149         -0.351  0.009  ***   3.832       10
v155          0.105  0.375        3.032        3
v51sq         0.267  0.008  ***   1.593       10
v54          -0.285  0.090    *   3.208        8
v625          0.316  0.023   **   2.053       10
v621          0.137  0.427        1.817        8
v64          -0.605  0.004  ***   5.933       10
v676         -0.172  0.078    *   1.724        9
v991          0.239  0.140        1.768        7
v854          0.043  0.796        5.956        0
v204          0.256  0.034   **   3.060       10
NoPlowCplx    0.068  0.591        2.623        1
v5           -0.006  0.949        1.992        0
v158.1        0.789  0.007  ***  16.569       10
v154         -0.276  0.070    *   4.673        9
v52           0.120  0.147        1.419        9
v53           0.211  0.309        3.843        8
v369         -0.053  0.709        3.321        1
v371          0.066  0.638        2.762        1
  h[4]
$RmodelRobust
                                                                                desc          coef
(Intercept)                                                                     <NA>  1.8420023991
Wy                                                                              <NA> -0.0412653274
bio.11                                   BIO11 = Mean Temperature of Coldest Quarter  0.0006719153
v1122                                                      Log10 of Total Population -0.0640755534
v149                                                    Scale 1- Writing And Records -0.0658272641
v155                                                                  Scale 7- Money  0.0600266746
v51sq                                                                 FaHelperInfant  0.0218561781
v625        High Value Placed on Males Being Aggressive, Strong, And Sexually Potent  0.1836843281
v64                                                               Population Density -0.0891842484
v676          Creation Stories (composite of 675 And 656, plus Additional Societies) -0.1068036940
v991         Importance of Fathers for Both Boys And Girls, without Regard to Gender  0.0368537129
            stdcoef pvalue star   VIF
(Intercept)      NA  0.000  ***    NA
Wy           -0.020  0.763      1.227
bio.11        0.202  0.007  *** 1.499
v1122        -0.212  0.045   ** 2.120
v149         -0.219  0.018   ** 2.057
v155          0.197  0.025   ** 2.214
v51sq         0.257  0.000  *** 1.234
v625          0.315  0.001  *** 1.408
v64          -0.393  0.000  *** 2.613
v676         -0.176  0.015   ** 1.241
v991          0.173  0.153      1.167
  h[6]
$OtherStats
         d          l r e R2.IV.composite. R2.final.model R2.UR.model nimp nobs
1 0.9393006 0.06069942 0 0        0.9086675      0.5668844   0.7153162   10   93
The optimal weight matrix is W=.94*Wd+.06*Wl.
h[6]
$OtherStats
   d   l e Weak.Identification.Fstat R2.final.model R2.UR.model nimp nobs
1 0.2 0.8 0                  189.0876       0.466893   0.4674711    2 1253
The optimal weight matrix is W=.20*Wd+.80*Wl.
 h[7]
$DescripStats
                                                                                   desc   n    mean
femaleEquality                                           Beliefs in Women's Inferiority  93   1.710
 bio.1                                                    BIO1 = Annual Mean Temperature 186 184.165
bio.11                                      BIO11 = Mean Temperature of Coldest Quarter 186 131.879
bio.6                                           BIO6 = Min Temperature of Coldest Month 186  71.047
bio.9                                         BIO9 = Mean Temperature of Driest Quarter 186 169.755
v1122                                                         Log10 of Total Population 185   4.027
v149                                                       Scale 1- Writing And Records 186   2.349
v155                                                                     Scale 7- Money 186   2.511
v51sq                                                                    FaHelperInfant 162   7.049
v54                                                     Role of Father, Early Childhood 150   3.433
v625           High Value Placed on Males Being Aggressive, Strong, And Sexually Potent  81   1.951
v621                       an Explicit View That Men Should And Do Dominate Their Wives  63   1.365
v64                                                                  Population Density 184   3.761
v676             Creation Stories (composite of 675 And 656, plus Additional Societies) 112   2.321
v991            Importance of Fathers for Both Boys And Girls, without Regard to Gender  67   4.522
v854            Niche Temperature (approximate) Adapted from William Goode, World Atlas 186   2.054
v204                                                              Dependence on Hunting 186   1.554
NoPlowCplx                                                                  NoPlowCmplx 186  20.263
v5                                        Animal Husbandry- Contribution to Food Supply 186   3.441
v158.1                                                       Sum of Complexity measures 186  27.823
v154                                                            Scale 6- Land Transport 186   1.790
v52                                         Non-maternal Relationships, Early Childhood 136   3.044
v53                                                             Role of Father, Infancy 154   3.045
v369                                              Sex of Parental Caretakers: Early Boy 173   4.387
v371                                               Sex of Parental Caretakers: Late Boy 132   4.174
                    sd      min     max
femaleEquality   0.456    1.000   2.000
bio.1           97.998 -128.325 291.688
bio.11         142.867 -365.917 277.000
bio.6          147.237 -417.889 252.000
bio.9          124.096 -297.875 335.000
v1122            1.457    1.000   8.000
v149             1.467    1.000   5.000
v155             1.479    1.000   5.000
v51sq            4.636    1.000  36.000
v54              0.878    1.000   5.000
v625             0.773    1.000   3.000
v621             0.548    1.000   3.000
v64              1.977    1.000   7.000
v676             0.762    1.000   3.000
v991             2.003    2.000   8.000
v854             1.905    1.000   8.000
v204             1.730    0.000   9.000
NoPlowCplx      11.853    0.000  50.000
v5               1.488    1.000   7.000
v158.1          10.312   10.000  50.000
v154             1.178    1.000   5.000
v52              0.778    2.000   5.000
v53              0.866    1.000   5.000
v369             0.866    1.000   5.000
v371             1.245    1.000   5.000
 h[8]
$dfbetas
     dfbeta variable     observation   dataValue depvarValue
2  -0.3610       Wy     Babylonians    1.155982           2
4  -0.3064       Wy  Kenuzi Nubians    1.124944           2
5  -0.2484       Wy       Saulteaux    1.879454           1
6  -0.2254       Wy         Shilluk    1.362477           2
3   0.2099       Wy        Comanche    1.952350           2
1   0.3264       Wy           Aztec    1.844054           2
7   0.3689       Wy   Yurak Samoyed    1.308073           1
61 -0.3156   bio.11        Yanomamo  259.750000           1
31 -0.2817   bio.11           Kaska -180.071429           2
11 -0.2288   bio.11           Aleut  -13.680000           2
41 -0.2108   bio.11      Montagnais -178.650000           2
71  0.2719   bio.11   Yurak Samoyed -156.700000           1
51  0.3744   bio.11       Saulteaux -184.500000           1
21  0.4548   bio.11        Chukchee -253.000000           1
72 -0.2859    v1122   Uttar Pradesh    8.000000           1
12 -0.2153    v1122     Babylonians    5.000000           2
22  0.2145    v1122          Mbundu    6.000000           2
42  0.2330    v1122          Tanala    5.000000           2
62  0.2897    v1122       Tupinamba    5.000000           2
32  0.3720    v1122 Pastoral Fulani    6.000000           2
52  0.4708    v1122            Toda    2.000000           1
13 -0.3584     v149          Aranda    3.000000           1
73 -0.2935     v149      Marquesans    3.000000           1
53 -0.2704     v149    Fur (Darfur)    5.000000           1
8  -0.2405     v149          Mbundu    1.000000           2
9   0.2132     v149       Saramacca    3.000000           2
33  0.2337     v149        Chukchee    1.000000           1
43  0.2520     v149        Comanche    3.000000           2
63  0.2543     v149          Kikuyu    1.000000           1
10  0.2636     v149   Yurak Samoyed    2.000000           1
23  0.3828     v149           Aztec    5.000000           2
44 -0.3295     v155   Yurak Samoyed    3.000000           1
24 -0.2440     v155           Creek    4.000000           1
14  0.2170     v155          Aranda    1.000000           1
34  0.3976     v155      Marquesans    1.000000           1
15 -0.2966    v51sq           Aztec    4.000000           2
45 -0.2110    v51sq     New Ireland   16.000000           2
35  0.2687    v51sq        Javanese    4.000000           1
25  0.4559    v51sq           Irish   25.000000           2
46 -0.2999      v64            Toda    5.000000           1
26 -0.2314      v64          Kikuyu    6.000000           1
16  0.3029      v64           Aztec    7.000000           2
36  0.3398      v64       Saulteaux    1.000000           1
27 -0.3263     v676            Toda    3.000000           1
37 -0.2537     v676        Yanomamo    3.000000           1
17 -0.2090     v676           Aztec    1.800000           2
47 -0.2188     v991 Pastoral Fulani    2.000000           2
18 -0.2174     v991        Javanese    7.000000           1
28 -0.2089     v991           Kaska    2.000000           2
54  0.2224     v991            Toda    2.000000           1
38  0.2326     v991      Marquesans    2.000000           1


A better version can made by the IBM-PC program TEXTPAD, add a space in row 1, Control-A copy and paste from the screen to textpad; there, Control-A to copy, then paste to the Wiki page. // The Macbook#Textwrangler_has_Shift_Right_like_the_IBMPC_Textpad but I haven't figured out how to parse equal spaces.