The latest version of the Dow-Eff functions (Manual: pdf; html) can perform analyses on four different ethnological datasets:

abbreviation |
dataset |
codebook |
---|---|---|

WNAI | Western North American Indians | codebook |

SCCS | Standard Cross-Cultural Sample | codebook |

EA | Ethnographic Atlas | codebook |

LRB | Louis R. Binford's forager data | codebook |

The code below outlines the workflow for working with the LRB.

You will need a number of R packages to run the Dow-Eff functions. These are loaded using the “library” command. If a package is “not found”, it should be first installed. The following command will initiate the installation of a package named “mice”, for example:

```
install.packages("mice")
```

```
# --set working directory and load needed libraries--
setwd("/home/yagmur/Dropbox/functions")
```

```
## Error: cannot change working directory
```

```
library(mice)
library(foreign)
library(stringr)
library(psych)
library(AER)
library(spdep)
library(geosphere)
library(relaimpo)
```

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/DEf01.Rdata"), .GlobalEnv)
ls() #-can see the objects contained in DEf01.Rdata
```

```
## [1] "addesc" "chK" "chkpmc" "CSVwrite" "doMI"
## [6] "doOLS" "EA" "EAcov" "EAfact" "EAkey"
## [11] "gSimpStat" "kln" "llm" "LRB" "LRBcov"
## [16] "LRBfact" "LRBkey" "mkdummy" "SCCS" "SCCScov"
## [21] "SCCSfact" "SCCSkey" "setDS" "WNAI" "WNAIcov"
## [26] "WNAIfact" "WNAIkey"
```

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

object name |
description |
---|---|

cov |
Names of covariates to use during imputation step |

dx |
The selected ethnological dataset is now called dx |

dxf |
The factor version of dx |

key |
A metadata file for dx |

wdd |
A geographic proximity weight matrix for the societies in dx |

wee |
An ecological similarity weight matrix for the societies in dx |

wll |
A linguistic proximity weight matrix for the societies in dx |

```
setDS("LRB")
```

The next step in the workflow is to create any new variables and add them to the dataset *dx*. New variables can be created directly, as in the following example. When created in this way, one should also record a description of the new variable, using the command **addesc()**. The syntax takes first the name of the new variable, and then the description.

```
dx$lnarea <- log(dx$area)
addesc("lnarea", "log of total land area occupied by the group")
```

Dummy variables (variables taking on the values zero or one) should be added using the command **mkdummy()**. This command will, in most cases, automatically record a variable description. Dummy variables are appropriate for categorical variables. The syntax of **mkdummy()** takes first the categorical variable name, and then the category number (these can be found in the codebook for each ethnological dataset).
Note that the resulting dummy variable will be called *variable name*+“.d”+*category number*.

```
mkdummy("systate3", 1)
```

```
## [1] "This dummy variable is named systate3.d1"
```

```
mkdummy("systate3", 2)
```

```
## [1] "This dummy variable is named systate3.d2"
```

After making any new variables, list the variables you intend to use in your analysis in the following form.

```
evm <- c("group2", "hunting", "gatherin", "fishing", "war1", "reven", "nomov",
"dismov", "store", "subdiv2", "systate3.d2", "systate3.d1", "lnarea", "nagp")
```

Missing values of these variables are then imputed, using the command **doMI()**. Below, the number of imputed datasets is 2, and 3 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 = 2, maxit = 3)
```

```
## [1] "group2"
## [1] "nomov"
## [1] "dismov"
## [1] "store"
## [1] "systate3.d2"
## [1] "systate3.d1"
## Time difference of 2.65 secs
```

```
dim(smi) # dimensions of new dataframe smi
```

```
## [1] 678 85
```

```
smi[1:2, ] # first two rows of new dataframe smi
```

```
## .imp .id group2 nomov dismov store systate3.d2 systate3.d1 hunting
## 1 1 Punan 30 45 240 1 0 0 30
## 2 1 Batek 58 6 50 2 1 0 30
## gatherin fishing war1 reven subdiv2 lnarea nagp mht.name.d2 mht.name.d8
## 1 65 5 1 1.26 69.86 3.388 4738 0 0
## 2 65 5 1 2.10 69.86 2.282 3852 0 0
## mht.name.d11 koeppengei.d4 koeppengei.d13 koeppengei.d18 continent.d3
## 1 0 0 0 0 0
## 2 0 1 0 0 0
## continent.d4 region.d2 region.d7 bio.1 bio.2 bio.3 bio.4 bio.5 bio.6
## 1 0 0 0 1.240 -1.339 2.971 -1.571 0.2588 1.566
## 2 0 0 0 1.258 -1.327 2.079 -1.489 0.4143 1.538
## bio.8 bio.9 bio.10 bio.11 bio.12 bio.13 bio.14 bio.15 bio.16 bio.17
## 1 1.125 0.9913 0.8121 1.403 4.036 1.9917 7.7985 -1.5882 2.163 7.68694
## 2 1.097 0.9936 0.9058 1.399 1.322 0.9502 -0.2036 0.1907 1.126 -0.05763
## bio.18 bio.19 meanalt mnnpp sdalt x y x2 y2 xy
## 1 3.6274 2.635 -0.4907 3.250 0.2440 1.500 -0.7185 2.251 0.5163 -1.078
## 2 0.2692 1.765 -0.3770 0.378 0.7853 1.549 -0.5004 2.398 0.2504 -0.775
## Australian NaDene UtoAztecan nomovSq storeSq huntingSq gatherinSq
## 1 0 0 0 2025 1 900 4225
## 2 0 0 0 36 4 900 4225
## fishingSq war1Sq revenSq subdiv2Sq lnareaSq bio.1Sq bio.2Sq bio.3Sq
## 1 25 1 1.588 4880 11.477 1.538 1.792 8.828
## 2 25 1 4.410 4880 5.209 1.581 1.762 4.324
## bio.4Sq bio.5Sq bio.6Sq bio.8Sq bio.9Sq bio.10Sq bio.11Sq bio.12Sq
## 1 2.469 0.06697 2.451 1.266 0.9826 0.6596 1.968 16.291
## 2 2.217 0.17168 2.364 1.202 0.9872 0.8205 1.958 1.747
## bio.13Sq bio.14Sq bio.15Sq bio.16Sq bio.17Sq bio.18Sq bio.19Sq
## 1 3.967 60.81624 2.52237 4.680 59.089006 13.15782 6.941
## 2 0.903 0.04144 0.03638 1.269 0.003321 0.07247 3.115
## meanaltSq mnnppSq sdaltSq
## 1 0.2408 10.5599 0.05954
## 2 0.1421 0.1429 0.61662
```

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 <- "group2"
# --independent variables in UNrestricted model--
UiV <- c("hunting", "gatherin", "fishing", "war1", "reven", "nomov", "dismov",
"store", "subdiv2", "systate3.d2", "systate3.d1", "lnarea")
# --additional exogenous variables (use in Hausman tests)--
oxog <- c("nagp")
# --independent variables in restricted model (all must be in UiV above)--
RiV <- c("hunting", "gatherin", "fishing", "war1", "reven")
```

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 R^{2;} 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.

```
h <- doOLS(smi, depvar = dpV, indpv = UiV, rindpv = RiV, othexog = oxog, dw = TRUE,
lw = TRUE, ew = FALSE, stepW = TRUE, relimp = TRUE, slmtests = FALSE)
```

```
## [1] "--finding optimal weight matrix------"
## [1] "--looping through the imputed datasets--"
## [1] 1
## [1] 2
## Time difference of 23 secs
```

```
names(h)
```

```
## [1] "DependVarb" "URmodel" "Rmodel"
## [4] "EndogeneityTests" "Diagnostics" "OtherStats"
## [7] "DescripStats" "totry" "didwell"
## [10] "dfbetas" "data"
```

The output from doOLS, here called *h*, is a list containing 11 items.

name |
description |
---|---|

DependVarb | Description of dependent variable |

URmodel | Coefficient estimates from the unrestricted model (includes standardized coefficients and VIFs). Two pvalues are given for H0: \( \beta \)=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. |

Rmodel | Coefficient estimates from the restricted model. If relimp=TRUE, the R^{2} assigned to each independent variable is shown here. |

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). |

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 lag term not needed) are reported here. |

OtherStats | Other statistics: Composite weight matrix weights (see details); R^{2} for restricted model and unrestricted model; number of imputations; number of observations; Fstat for weak instruments for Wy. |

DescripStats | Descriptive statistics for variables in unrestricted model. |

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. |

didwell | Character string of variables that were most significant in the unrestricted model. |

dfbetas | Influential observations for dfbetas (see details) |

data | Data as used in the estimations. Observations with missing values of the dependent variable have been dropped. |

The last two items in the list are large, but the first nine provide a nice overview.

```
h[1:9]
```

```
## $DependVarb
## [1] "Dependent variable='group2': the mean size of the consumer group that regularly camps together during the most aggregated phase of the yearly economic cycles; (Table: 5.01 & 8.01); (Binford 2001:117)"
##
## $URmodel
## coef stdcoef VIF stepkept hcpval pval star
## (Intercept) 113.69410 NaN NaN 1 0.90675 0.92474
## dismov -0.00539 -0.00922 4.172 0 0.89684 0.91644
## fishing -0.64579 -0.20906 8245.517 0 0.94682 0.95708
## gatherin -0.57577 -0.17291 7103.915 0 0.95258 0.96175
## hunting -0.48762 -0.11715 4564.148 0 0.96009 0.96767
## lnarea -4.56222 -0.09918 1.958 1 0.02751 0.09841 *
## nomov -0.33721 -0.03723 1.980 0 0.42697 0.53756
## reven 5.25852 0.04181 1.230 0 0.16742 0.37843
## store -4.00381 -0.04312 1.995 0 0.40697 0.47714
## subdiv2 -0.99775 -0.12553 1.224 1 0.00621 0.00800 ***
## systate3.d1 39.25214 0.13450 1.736 1 0.02809 0.01713 **
## systate3.d2 -5.33676 -0.01531 1.073 0 0.46762 0.72970
## war1 16.39673 0.19027 1.871 1 0.00091 0.00115 ***
## Wy 1.07421 0.50513 2.836 1 0.00001 0.00000 ***
## desc
## (Intercept) <NA>
## dismov Total distance residence moved in a year (sum of all moves); (Table: 5.01 & 8.04); (Binford 2001:117)
## fishing Percent dependence on aquatic organisms ; (Table: 5.01); (Binford 2001:117)
## gatherin Percent dependence on terrestrial plants; (Table: 5.01); (Binford 2001:117)
## hunting Percent dependence on terrestrial animals; (Table: 5.01); (Binford 2001:117)
## lnarea log of total land area occupied by the group
## nomov Total number of annual moves in residence of a household unit; (Table: 5.01 & 8.04); (Binford 2001:117)
## reven Unevenness in rainfall across seasons; (Equation: 4.04); (Binford 2001:70)
## store Dependence upon storage; (Binford 2001:388)
## subdiv2 Subsistence diversity; (Equation: 100-stddev("hunting","gatherin","fishing") ); (Binford 2001:403,fn2)
## systate3.d1 Classification of foragers: system's state; (Table: 9.01); (Binford 2001:375) == mounted hunters
## systate3.d2 Classification of foragers: system's state; (Table: 9.01); (Binford 2001:375) == horticulturally augmented cases
## war1 Scale of intensity of warfare. How frequent and how widespread it may be regionally.
## Wy Network lag term
##
## $Rmodel
## coef stdcoef VIF relimp hcpval pval star
## (Intercept) -306.511 NaN NaN NaN 0.76198 0.80269
## fishing 2.328 0.75350 8157.171 0.01229 0.81854 0.84950
## gatherin 2.622 0.78745 7025.986 0.02739 0.79589 0.83080
## hunting 2.372 0.56988 4509.124 0.00830 0.81685 0.84694
## reven 7.396 0.05881 1.075 0.00234 0.03407 0.19693
## war1 14.872 0.17258 1.758 0.11945 0.00175 0.00308 ***
## Wy 1.267 0.59563 2.173 0.27697 0.00000 0.00000 ***
## desc
## (Intercept) <NA>
## fishing Percent dependence on aquatic organisms ; (Table: 5.01); (Binford 2001:117)
## gatherin Percent dependence on terrestrial plants; (Table: 5.01); (Binford 2001:117)
## hunting Percent dependence on terrestrial animals; (Table: 5.01); (Binford 2001:117)
## reven Unevenness in rainfall across seasons; (Equation: 4.04); (Binford 2001:70)
## war1 Scale of intensity of warfare. How frequent and how widespread it may be regionally.
## Wy Network lag term
##
## $EndogeneityTests
## weakidF p.Sargan n.IV Fstat df pvalue star
## fishing 0.285 0.000 16 0.000 6.441e+06 1.000
## gatherin 0.301 0.440 16 0.092 1.075e+07 0.761
## hunting 0.000 0.440 16 0.008 7.526e+06 0.928
## reven 39.114 0.923 11 0.031 4.132e+06 0.861
## war1 8.893 0.000 5 1.000 3.100e+09 0.000 ***
##
## $Diagnostics
## Fstat df
## RESET test. H0: model has correct functional form 45.338 9.737e+02
## Wald test. H0: appropriate variables dropped 5.000 1.025e+04
## Breusch-Pagan test. H0: residuals homoskedastic 15.111 1.350e+07
## Shapiro-Wilkes test. H0: residuals normal 90.202 5.161e+09
## Hausman test. H0: Wy is exogenous 4.000 5.237e+05
## Sargan test. H0: residuals uncorrelated with instruments 0.692 6.244e+08
## pvalue star
## RESET test. H0: model has correct functional form 0.000 ***
## Wald test. H0: appropriate variables dropped 0.000 ***
## Breusch-Pagan test. H0: residuals homoskedastic 0.000 ***
## Shapiro-Wilkes test. H0: residuals normal 0.000 ***
## Hausman test. H0: Wy is exogenous 0.000 ***
## Sargan test. H0: residuals uncorrelated with instruments 0.405
##
## $OtherStats
## d l e Weak.Identification.Fstat R2.final.model R2.UR.model nimp nobs
## 1 1 0 0 16.05 0.4394 0.4821 2 297
##
## $DescripStats
## desc
## group2 the mean size of the consumer group that regularly camps together during the most aggregated phase of the yearly economic cycles; (Table: 5.01 & 8.01); (Binford 2001:117)
## hunting Percent dependence on terrestrial animals; (Table: 5.01); (Binford 2001:117)
## gatherin Percent dependence on terrestrial plants; (Table: 5.01); (Binford 2001:117)
## fishing Percent dependence on aquatic organisms ; (Table: 5.01); (Binford 2001:117)
## war1 Scale of intensity of warfare. How frequent and how widespread it may be regionally.
## reven Unevenness in rainfall across seasons; (Equation: 4.04); (Binford 2001:70)
## nomov Total number of annual moves in residence of a household unit; (Table: 5.01 & 8.04); (Binford 2001:117)
## dismov Total distance residence moved in a year (sum of all moves); (Table: 5.01 & 8.04); (Binford 2001:117)
## store Dependence upon storage; (Binford 2001:388)
## subdiv2 Subsistence diversity; (Equation: 100-stddev("hunting","gatherin","fishing") ); (Binford 2001:403,fn2)
## systate3.d2 Classification of foragers: system's state; (Table: 9.01); (Binford 2001:375) == horticulturally augmented cases
## systate3.d1 Classification of foragers: system's state; (Table: 9.01); (Binford 2001:375) == mounted hunters
## lnarea log of total land area occupied by the group
## nobs mean sd min max
## group2 297 74.908 85.420 19.500 650.000
## hunting 339 33.119 20.033 0.000 90.000
## gatherin 339 34.525 24.888 0.010 90.300
## fishing 339 32.391 27.316 0.000 95.000
## war1 339 1.808 0.980 1.000 5.000
## reven 339 2.250 0.672 1.190 5.260
## nomov 261 9.695 9.336 0.100 58.000
## dismov 236 171.661 143.703 4.000 570.000
## store 337 2.318 0.934 1.000 3.000
## subdiv2 339 72.343 10.780 46.550 94.230
## systate3.d2 338 0.056 0.231 0.000 1.000
## systate3.d1 338 0.083 0.276 0.000 1.000
## lnarea 339 4.559 1.802 -0.223 8.795
##
## $totry
## [1] "bio.15" "fishingSq" "gatherin:war1"
## [4] "gatherin:Wy" "hunting:fishing" "hunting:war1"
## [7] "hunting:Wy" "huntingSq" "subdiv2"
## [10] "subdiv2Sq" "systate3.d1" "war1:Wy"
## [13] "lnarea" "subdiv2" "systate3.d1"
##
## $didwell
## [1] "war1" "reven"
```

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)
```