# DEv67.d3

Currently you can map and apply convex hulls to any variable in the unrestricted model (dpV,Wy,UiV). But you can only plot the dfbetas for RiV.
I could put all of the evm variables in, if you want, but thought that might be a little big. Let me know... Anthon

## 1

Douglas R. White (talk) 13:15, 11 September 2013 (PDT)

## 2

The R script in section 3 is a simplified version of DEf01b_SCCS which is Anthon Eff's version DEf01b of DEf, the Dow-Eff functions. This version of the complete script is **in side this subfile**, followed by functions for finding and computing elements of composite **scales** and **making maps** that show convex hulls for autocorrelation clusters.

This portion of DEf01b does make maps for variables (e.g., dpV=dependent and RiV=restricted independent) used in the model but does not make scales.

Section 4 is Eff's description of the output but the output of the program is at DEf01b_SCCS that result from the h function. with values from 1-12.

Experiments in color maps for use http://SocSciCompute.ss.uci.edu through the **Visual_Manual** instructions and for intersciwiki maps are at Draw.Rworldmap.

## Script

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(plyr) library(aod) library(reshape) library(RColorBrewer) library(XML) library(tm) library(mlogit) library(mapproj) #trying URL 'http://cran.rstudio.com/bin/macosx/leopard/contrib/2.15/mapproj_1.2-1.tgz' #library(map) used by Eff

The Dow-Eff functions, as well as the four ethnological datasets, are contained in an R-workspace, located in the cloud.

load(url("http://dl.dropbox.com/u/9256203/DEf01b.Rdata"), .GlobalEnv) ls() #-can see the objects contained in DEf01b.Rdata

The setDS( xx ) command sets one of the four ethnological datasets as the source for the subsequent analysis. The options for xx are: “WNAI”, “LRB”, “EA”, “SCCS”. The setDS() command creates objects:

setDS("SCCS")

dx$rectang <- (dx$v65 >= 8 & dx$v65 <= 9) * 1 addesc("rectang", "Dwelling is rectangular")

mkdummy("v279", 1) ## [1] "This dummy variable is named v279.d1" ## [1] "The variable description is: 'Inheritance of Movable Property: Rule or Practice for Inheritance == Absence of individual property rights or rules'" mkdummy("v213", 3) ## [1] "This dummy variable is named v213.d3" ## [1] "The variable description is: 'Marital Residence with Kin: First Years (Atlas 10 Combined) == Uxorilocal: with wifes parents'" mkdummy("v279", 5) ## [1] "This dummy variable is named v279.d5" ## [1] "The variable description is: 'Inheritance of Movable Property: Rule or Practice for Inheritance == Children, equally for both sexes'" mkdummy("v1127", 2) ## [1] "This dummy variable is named v1127.d2" ## [1] "The variable description is: 'Crop Type Plow-positive or -negative == Plow-positive (Buckwheat, Wheat, Barley, Wet Rice, Rye,'" mkdummy("v2002", 2) ## [1] "This dummy variable is named v2002.d2" ## [1] "The variable description is: 'World Religions (1807) == Deep Islamization'" mkdummy("v67", 3) ## [1] "This dummy variable is named v67.d3" ## [1] "The variable description is: 'Household Form == Single family dwellings'" femecon <- fv4scale(lookword = c("market", "exchange", "wage", "trade", "subsistence", "goods", "product", "labor"), keepword = c("female", "women", "woman"), coreword = c("subsistence"), nmin = 60, chklevels = TRUE, verbose = FALSE) ##RETURNS## c("v889", "v890", "v887", "v826", "v886", "v885", "v585", "v888", "v593", "v658", "v660", "v594") #After making any new variables, list the variables you intend to use in your analysis in the following form.

evm <- c("v67.d3", "v2002.d2", "v1845", "v1649", "v1127.d2", "v2137", "v279.d5","v213.d3", "v1265", "v1", "v234", "rectang", femecon, "v1260","v2002.d2") #"v2002.d2" from ToTry) #Missing values of these variables are then imputed, using the command doMI(). Below, the number of imputed datasets is 5, and 7 iterations are used to estimate each imputed value (these values are too low: nimp=10 and maxit=7 are the defaults and are reasonable for most purposes). The stacked imputed datasets are collected into a single dataframe which here is called smi.

#This new dataframe smi will contain not only the variables in evm, but also a set of normalized (mean=0, sd=1) variables related to climate, location, and ecology (these are used in the OLS analysis to address problems of endogeneity). In addition, squared values are calculated automatically for variables with at least three discrete values and maximum absolute values no more than 300. These squared variables are given names in the format variable name+“Sq”.

#Finally, smi contains a variable called “.imp”, which identifies the imputed dataset, and a variable called “.id” which gives the society name.

smi <- doMI(evm, nimp = 5, maxit = 7)

#New Sept 13 Anthon Eff oo<-h12 k<-match(rownames(oo),SCCS$society) oo$numid<-SCCS[k,"sccsid"] oo<-oo[order(oo$numid),]

bb<-aggregate(smi[,sapply(smi,function(x) is.numeric(x))],list(smi$.id),mean) #IMPUTED AVERAGESUsed in [[1]]#bb$v1 ##The two scripts for maps are: #1) Thomas Uram, Doug White, and Tolga Oztan's [[2]] in color (not good for publication because of cost) #2) Anthon Eff's Black and White maps with local autocorrelation

dim(smi) # dimensions of new dataframe smi smi[1:2, ] # first two rows of new multiiple imputation dataframe smi #The variables for a scale can be combined using the function mkscale. The function can calculate three different kinds of scales: 1) based on linear programming as described in Eff (2010); 2) the mean of the standardized values; 3) the first principal component of the standardized values. Below the variables contained in femecon are combined into a scale based on linear programming.

fec <- mkscale(compvarbs = "femecon", udnavn = paste("femecon", ".lp", sep = ""), impdata = smi, type = "LP", add.descrip = "female economic contribution (LP scale)") names(fec) fec$stats fec$corrs

smi[, names(fec$scales)] <- fec$scales

All of the variables selected to play a role in the model must be found in the new dataframe smi. Below, the variables are organized according to the role they will play.

# --dependent variable-- dpV <- "v67.d3" # --independent variables in UNrestricted model-- UiV <- c("v2002.d2", "v1845", "v1649", "v1127.d2", "v2137", "v279.d5", "v213.d3", "v1265", "v1", "v234", "femecon.lp", "rectang","v2002.d2") #"v2002.d2" from ToTry # --additional exogenous variables (use in Hausman tests)-- oxog <- c("v1260") # --independent variables in restricted model (all must be in UiV above)-- RiV <- c("v1649", "v1127.d2", "v2137", "v279.d5", "v1265","v2002.d2") #"v2002.d2" from ToTry, "v1" doesnt work also "v234", "v213.d3", Eff's variables #The command doOLS() estimates the model on each of the imputed datasets, collecting output from each estimation and processing them to obtain final results. To control for Galton's Problem, a network lag model is used, with the user able to choose a combination of geographic proximity (dw), linguistic proximity (lw), and ecological similarity (ew) weight matrices. In most cases, the user should choose the default of dw=TRUE, lw=TRUE, ew=FALSE.

#There are several options that increase the time doOLS() takes to run: stepW runs a background stepwise regression to find which variables perform best over the set of estimations; relimp calculates the relative importance of each variable in the restricted model, using a technique to partition R2; slmtests calculates LaGrange multiplier tests for spatial dependence using the three weight matrices. All of these should be set to FALSE if one wishes to speed up estimation times.

# Do not forget to rename or erase prior copy of olsresults.csv because new copy will not overwrite. #Eff: Unlike distance or language, ecology is not a transmission channel (does not represent horizontal or vertical cultural transmission). It is something to which societies adapt. To combine it with distance and language creates a network lag term that is hard to interpret. My view is that it is better to have ecological variables in the model as distinct independent variables (for example, variables measuring annual precipitation or number of frost months), so that the network lag term is a clear measure of cultural transmission. Nevertheless, sometimes, when playing with a model, one might want to take a look at a network lag term consisting wholly or in part of ecological similarities. So it's in there as an option. #activate ecological autocorrelation with ew = TRUE 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, "DEv67.d3.eW.olsresults", FALSE) #deactivate ecological autocorrelation with ew = 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, "DEv67.d3.No.eW.olsresults", FALSE) getwd() #to see what your working directory is. Then look in that folder.

## Results

## [1] "--finding optimal weight matrix------" ## [1] "Exogenous variables used to instrument Wy: xWv1845, xWv1649, xWv1127.d2, xWv2137, xWv279.d5, xWv1265, xWv1, xWv234, xWrectang, xWv234Sq, xWv213.d3" ## [1] "--looping through the imputed datasets--" ## [1] 1 ## [1] 2 ## [1] 3 ## [1] 4 ## [1] 5 ## Time difference of 24.22 secs

names(h) ## [1] "DependVarb" "URmodel" ## [3] "Rmodel" "EndogeneityTests" ## [5] "Diagnostics" "OtherStats" ## [7] "DescripStatsImputedData" "DescripStatsOriginalData" ## [9] "totry" "didwell" ## [11] "dfbetas" "data"

The output from doOLS, here called h, is a list containing 12 items.

name description #1 DependVarb Description of dependent variable #2 URmodel Coefficient estimates from the unrestricted model (includes standardized coefficients and VIFs). Two pvalues are given for H0: ÃŽÂ² =0. One is the usual pvalue, the other (hcpval) is heteroskedasticity consistent. If stepkept=TRUE, the table will also include the proportion of times a variable is retained in the model using stepwise regression. #3 Rmodel Coefficient estimates from the restricted model. If relimp=TRUE, the R2 assigned to each independent variable is shown here. #4 EndogeneityTests Hausman tests (H0: variable is exogneous), with F-statistic for weak instruments (a rule of thumb is that the instrument is weak if the F-stat is below 10), and Sargan test (H0: instrument is uncorrelated with second-stage 2SLS residuals). #5 Diagnostics Regression diagnostics for the restricted model: RESET test (H0: model has correct functional form); Wald test (H0: appropriate variables dropped); Breusch-Pagan test (H0: residuals homoskedastic; Shapiro-Wilkes test (H0: residuals normal); Hausman test (H0: Wy is exogenous); Sargan test (H0: residuals uncorrelated with instruments for Wy). If slmtests=TRUE, the LaGrange multiplier tests (H0: spatial error model not appropriate) are reported here. #6 OtherStats Other statistics: Composite weight matrix weights (see details); R2 for restricted model and unrestricted model; number of imputations; number of observations; Fstat for weak instruments for Wy. #7 DescripStatsImputedData Descriptive statistics for variables in unrestricted model. #8 DescripStatsOriginalData Descriptive statistics for variables in unrestricted model. #9 totry Character string of variables that were most significant in the unrestricted model as well as additional variables that proved significant using the add1 function on the restricted model. #10 didwell Character string of variables that were most significant in the unrestricted model. #11 dfbetas Influential observations for dfbetas (see details) #12 data Data as used in the estimations. Observations with missing values of the dependent variable have been dropped. If mean.data=TRUE, will output format that can be used to make maps.

The last two items in the list can be fairly large, but the first ten provide a nice overview.

h[1:10] ## $DependVarb ## [1] "Dependent variable='v67.d3': Household Form == Single family dwellings" ## ## $URmodel ## coef stdcoef VIF stepkept pval hcpval bootpval star ## (Intercept) 0.56936 NaN NaN 1 0.07821 0.06646 0.07688 * ## femecon.lp 0.02209 0.07177 1.101 1 0.35853 0.35050 0.34491 ## rectang 0.00715 0.00560 1.289 0 0.94231 0.94266 0.94127 ## v1 -0.03829 -0.09646 1.229 1 0.20922 0.18920 0.20516 ## v1127.d2 0.30583 0.23690 1.527 1 0.00490 0.00445 0.00407 *** ## v1265 -0.06027 -0.12881 1.124 1 0.08825 0.08585 0.08006 * ## v1649 -0.01990 -0.25963 1.085 1 0.00031 0.00016 0.00011 *** ## v1845 -0.01301 -0.02906 1.103 0 0.70071 0.70756 0.70460 ## v2002.d2 0.17249 0.10470 1.248 1 0.16824 0.16698 0.14189 ## v213.d3 0.10764 0.08142 1.150 1 0.27027 0.27385 0.27530 ## v2137 -0.23618 -0.20116 1.816 1 0.02772 0.01935 0.02601 ** ## v234 -0.01241 -0.05981 1.881 0 0.52170 0.51398 0.50356 ## v279.d5 0.18898 0.13961 1.126 1 0.07309 0.06527 0.06628 * ## Wy 0.83896 0.16875 1.378 1 0.04191 0.01010 0.03228 ** ## desc ## (Intercept) <NA> ## femecon.lp female economic contribution (LP scale) ## rectang Dwelling is rectangular ## v1 Intercommunity Trade as Food Source ## v1127.d2 Crop Type Plow-positive or -negative == Plow-positive (Buckwheat, Wheat, Barley, Wet Rice, Rye, ## v1265 Occurrence of Famine ## v1649 Frequency of Internal Warfare (Resolved Rating) ## v1845 Modernization: Sum of Technological Changes ## v2002.d2 World Religions (1807) == Deep Islamization ## v213.d3 Marital Residence with Kin: First Years (Atlas 10 Combined) == Uxorilocal: with wifes parents ## v2137 Food Production: Planting (task present==1, absent==0) ## v234 Settlement Patterns ## v279.d5 Inheritance of Movable Property: Rule or Practice for Inheritance == Children, equally for both sexes ## Wy Network lag term ## ## $Rmodel ## coef stdcoef VIF relimp pval hcpval bootpval star ## (Intercept) 0.60247 NaN NaN NaN 0.01714 0.00643 0.01384 ** ## v1127.d2 0.30383 0.23535 1.225 0.04059 0.00159 0.00186 0.00195 *** ## v1265 -0.06801 -0.14534 1.030 0.02733 0.04071 0.04090 0.03888 ** ## v1649 -0.01825 -0.23820 1.055 0.05583 0.00076 0.00044 0.00029 *** ## v213.d3 0.11246 0.08506 1.095 0.00856 0.23195 0.24697 0.22988 ## v2137 -0.22205 -0.18912 1.767 0.03650 0.03505 0.02459 0.03157 ** ## v234 -0.01362 -0.06561 1.727 0.01520 0.45943 0.44812 0.45550 ## v279.d5 0.16850 0.12453 1.103 0.02694 0.09902 0.09514 0.09201 * ## Wy 0.77311 0.15559 1.281 0.06117 0.05118 0.01229 0.03928 * ## desc ## (Intercept) <NA> ## v1127.d2 Crop Type Plow-positive or -negative == Plow-positive (Buckwheat, Wheat, Barley, Wet Rice, Rye, ## v1265 Occurrence of Famine ## v1649 Frequency of Internal Warfare (Resolved Rating) ## v213.d3 Marital Residence with Kin: First Years (Atlas 10 Combined) == Uxorilocal: with wifes parents ## v2137 Food Production: Planting (task present==1, absent==0) ## v234 Settlement Patterns ## v279.d5 Inheritance of Movable Property: Rule or Practice for Inheritance == Children, equally for both sexes ## Wy Network lag term ## ## $EndogeneityTests ## weakidF p.Sargan n.IV Fstat df pvalue star ## v213.d3 2.855 0.53 2 0 4497 0.993 ## ## $Diagnostics ## Fstat df ## RESET test. H0: model has correct functional form 0.0000 3.855e+11 ## Wald test. H0: appropriate variables dropped 0.9474 2.542e+04 ## Breusch-Pagan test. H0: residuals homoskedastic 1.0066 1.254e+03 ## Shapiro-Wilkes test. H0: residuals normal 8.1091 2.300e+03 ## Hausman test. H0: Wy is exogenous 7.5717 9.200e+01 ## Sargan test. H0: residuals uncorrelated with instruments 0.0482 1.149e+05 ## pvalue star ## RESET test. H0: model has correct functional form 0.9988 ## Wald test. H0: appropriate variables dropped 0.3304 ## Breusch-Pagan test. H0: residuals homoskedastic 0.3159 ## Shapiro-Wilkes test. H0: residuals normal 0.0044 *** ## Hausman test. H0: Wy is exogenous 0.0071 *** ## Sargan test. H0: residuals uncorrelated with instruments 0.8262 ## ## $OtherStats ## d l e Weak.Identification.Fstat R2.final.model R2.UR.model nimp ## 1 0.52 0.48 0 14.65 0.201 0.219 5 ## nobs BClambda ## 1 186 none ## ## $DescripStatsImputedData ## desc nobs mean sd min max ## femecon.lp female economic contribution (LP scale) 930 5.78 1.686 1 9 ## ## $DescripStatsOriginalData ## desc ## v67.d3 Household Form == Single family dwellings ## v2002.d2 World Religions (1807) == Deep Islamization ## v1845 Modernization: Sum of Technological Changes ## v1649 Frequency of Internal Warfare (Resolved Rating) ## v1127.d2 Crop Type Plow-positive or -negative == Plow-positive (Buckwheat, Wheat, Barley, Wet Rice, Rye, ## v2137 Food Production: Planting (task present==1, absent==0) ## v279.d5 Inheritance of Movable Property: Rule or Practice for Inheritance == Children, equally for both sexes ## v213.d3 Marital Residence with Kin: First Years (Atlas 10 Combined) == Uxorilocal: with wifes parents ## v1265 Occurrence of Famine ## v1 Intercommunity Trade as Food Source ## v234 Settlement Patterns ## rectang Dwelling is rectangular ## nobs mean sd min max ## v67.d3 186 0.468 0.500 0 1 ## v2002.d2 186 0.102 0.304 0 1 ## v1845 135 1.637 1.156 0 6 ## v1649 152 7.250 6.483 1 17 ## v1127.d2 184 0.185 0.389 0 1 ## v2137 185 0.762 0.427 0 1 ## v279.d5 152 0.145 0.353 0 1 ## v213.d3 185 0.173 0.379 0 1 ## v1265 170 3.318 1.057 1 4 ## v1 183 3.546 1.261 1 7 ## v234 186 4.925 2.411 1 8 ## rectang 186 0.188 0.392 0 1 ## ## $totry ## [1] "v2137:v234" "femecon.lp" "v1" "v2002.d2" ## ## $didwell ## [1] "v1127.d2" "v1265" "v1649" "v213.d3" "v2137" "v279.d5"

The 12th item in list h is a dataframe containing mean values of variables across imputations. This can be used to make maps, employing the function mkmapppng.

#Map the dependent variable dpV and Others mkmappng(h12, "v67.d3", "v67.d3SingleFamilyDwelling", show = "ydata", numnb.lg = 3, numnb.lm = 20, numch = 5, pvlm = 0.05, dfbeta.show = TRUE) mkmappng(h12, "v1649", "v1649FrequencyInternalWarfare", show = "ydata", numnb.lg = 3, numnb.lm = 20, numch = 5, pvlm = 0.05, dfbeta.show = TRUE) mkmappng(h12, "v1", "v1Dependence_on_Trade", show = "ydata", numnb.lg = 3, numnb.lm = 20, numch = 5, pvlm = 0.05, dfbeta.show = TRUE) #aa=h$data #=# not alphabetized #aa=h12 #=# not alphabetized aa$v1 is incorrect as listed in h12 below BECAUSE THESE CASES ARE ALPHABETIZED !!! v1=1.0 is O, 5.0 is S=Ajie, 4.0 is U=Ainu ## Loading required package: mapproj ## pdf ## 2 ###h[[12]] ### v67.d3 Wy v2002.d2 v1845 v1649 v1127.d2 v2137 v279.d5 v213.d3 v1265 v1 v234 femecon.lp rectang dfb.Wy dfb.v1649 dfb.v1127.d2 dfb.v2137 dfb.v279.d5 ###Abipon 1 0.5844529 0 1.0 13.0 0 0 0.4 1.0 2.0 1.0 1 5.2 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 ###Abkhaz 0 0.4675963 0 1.0 17.0 0 1 0.0 0.0 4.0 1.0 5 6.2 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 ###Ainu 0 0.4490349 0 4.0 5.0 0 1 0.0 0.0 4.0 4.0 3 7.4 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 ###Ajie 0 0.4321597 0 0.6 15.8 0 1 0.0 0.0 4.0 5.0 7 6.0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000

You can't click here to see Eff's png but it is saved to your directory from R gui

One can also write the list h to a csv format file that can be opened as a spreadsheet. The following command writes h to a file in the working directory called “olsresults.csv”.

CSVwrite(h, "olsresults", FALSE)

Models with binary dependent variables are usually estimated with logit or probit ML methods. However, it is a good idea to first estimate the model with OLS, as we did above, to find a good model, and then estimate it with logit, as we do below, using the function doLogit.

dpV <- "v67.d3" UiV <- c("v2002.d2", "v1845", "v1649", "v1127.d2", "v2137", "v279.d5", "v213.d3", "v1265", "v1", "v234", "femecon.lp", "rectang") RiV <- c("v1649", "v1127.d2", "v2137", "v1265") q <- doLogit(smi, depvar = dpV, indpv = UiV, rindpv = RiV, dw = TRUE, lw = TRUE, ew = FALSE, doboot = 1000, mean.data = TRUE, getismat = FALSE, othexog = NULL) ## [1] "--finding optimal weight matrix------" ## [1] "Exogenous variables used to instrument Wy: xWv2002.d2, xWv1845, xWv1649, xWv1127.d2, xWv2137, xWv279.d5, xWv1265, xWv1, xWv234, xWrectang, xWv1845Sq, xWv234Sq" ## [1] "--looping through the imputed datasets--" ## [1] 1 ## [1] 2 ## [1] 3 ## [1] 4 ## [1] 5 ## Time difference of 1.103 mins names(q) ## [1] "DependVarb" "URmodel" "Rmodel" "Diagnostics1" ## [5] "Diagnostics2" "OtherStats" "data" q[1:6] ## $DependVarb ## [1] "Dependent variable='v67.d3': Household Form == Single family dwellings" ## ## $URmodel ## coef fst df pval star ## (Intercept) 0.54656 0.08 4 0.7871 ## Wy 4.45190 4.27 5 0.0936 * ## v2002.d2 1.02552 1.59 4 0.2755 ## v1845 -0.07276 0.12 5 0.7383 ## v1649 -0.11460 10.53 4 0.0315 ** ## v1127.d2 1.64360 7.20 4 0.0551 * ## v2137 -1.18435 4.16 4 0.1110 ## v279.d5 0.99704 2.15 5 0.2025 ## v213.d3 0.53154 0.80 4 0.4227 ## v1265 -0.34476 2.86 5 0.1518 ## v1 -0.24574 1.61 4 0.2728 ## v234 -0.07469 0.47 4 0.5307 ## femecon.lp 0.12949 0.86 5 0.3958 ## rectang 0.01716 0.00 4 0.9768 ## desc ## (Intercept) <NA> ## Wy Network lag term ## v2002.d2 World Religions (1807) == Deep Islamization ## v1845 Modernization: Sum of Technological Changes ## v1649 Frequency of Internal Warfare (Resolved Rating) ## v1127.d2 Crop Type Plow-positive or -negative == Plow-positive (Buckwheat, Wheat, Barley, Wet Rice, Rye, ## v2137 Food Production: Planting (task present==1, absent==0) ## v279.d5 Inheritance of Movable Property: Rule or Practice for Inheritance == Children, equally for both sexes ## v213.d3 Marital Residence with Kin: First Years (Atlas 10 Combined) == Uxorilocal: with wifes parents ## v1265 Occurrence of Famine ## v1 Intercommunity Trade as Food Source ## v234 Settlement Patterns ## femecon.lp female economic contribution (LP scale) ## rectang Dwelling is rectangular ## ## $Rmodel ## coef fst df pval star ## (Intercept) 0.20696 0.03 5 0.8642 ## Wy 4.82723 7.26 5 0.0431 ** ## v1649 -0.09365 11.42 4 0.0278 ** ## v1127.d2 1.37058 9.02 4 0.0398 ** ## v2137 -1.26049 9.09 4 0.0394 ** ## v1265 -0.37668 5.26 5 0.0703 * ## desc ## (Intercept) <NA> ## Wy Network lag term ## v1649 Frequency of Internal Warfare (Resolved Rating) ## v1127.d2 Crop Type Plow-positive or -negative == Plow-positive (Buckwheat, Wheat, Barley, Wet Rice, Rye, ## v2137 Food Production: Planting (task present==1, absent==0) ## v1265 Occurrence of Famine ## ## $Diagnostics1 ## fst df pval star ## LRtestNull-R 36.6886 2788 0.0000 *** ## LRtestNull-UR 32.5188 13606 0.0000 *** ## LRtestR-R 2.2828 2069 0.1310 ## waldtestR-R 0.3387 15111317 0.5606 ## desc ## LRtestNull-R H0:All coefficients in restricted model equal zero ## LRtestNull-UR H0:All coefficients in UNrestricted model equal zero ## LRtestR-R H0:Variables dropped from unrestricted model have coefficients equal zero (likelihood ratio test) ## waldtestR-R H0:Variables dropped from unrestricted model have coefficients equal zero (Wald test) ## ## $Diagnostics2 ## R.model UR.model desc ## pLargest 0.5323 0.5323 max(Prob(y==1),Prob(y==0)) [best guess] ## pRight 0.6882 0.7129 Prob(y==yhat) [prop. correct] ## NetpRight 0.1559 0.1806 prop. correct net of best guess ## McIntosh.Dorfman 1.3743 1.4230 prop. correct 0s + prop. correct 1s ## McFadden.R2 0.2007 0.2502 McFadden pseudo R2 ## Nagelkerke.R2 0.2422 0.2923 Nagelkerke psuedo R2 ## ## $OtherStats ## d l e nimp nobs ## 1 0.6 0.4 0 5 186