# ResultsEff0001

• Wald, Abraham. 1943. Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large. Transactions of the American Mathematical Society 54: 426-482.
• #Read in the dbf format weight matrices-the dbf file is 186x186 (no row names)
• #Comment the matrix you do NOT want to use
• #convert to matrix
• lds<-as.matrix(lds)
• #take a quick look at the upper left hand corner to see that it is OK
• lds[1:5,1:5]
```  X1 X2 X3 X4 X5
1  0  3  1  1  1
2  3  0  1  1  1
3  1  1  0  3  3
4  1  1  3  0  3
5  1  1  3  3  0
```
• #read in SCCS data. It is in STATA format, since this is numeric--there are problems with the SPSS version,
• #since R imports the value labels from SPSS and the variables become non-numeric
• length(gg[,1]) #the number of observations

[1] 186

• length(gg[1,]) #the number of variables

[1] 1849

• #create a data frame containing our variables, also give the variables names
• df<-data.frame(femsubs=gg\$v826,fishimp=gg\$v816,huntimp=gg\$v817,pathstress=gg\$v1260,rainfall=gg\$v855,polygamy=gg\$v79,eboysxp=gg\$v353,fixres=gg\$v150,landtrans=gg\$v154,polinteg=gg\$v157,socstrat=gg\$v158)
• #since the estimation doesn't work with missing values, here we identify all observations with non-missing values
• kk<-as.matrix(df)
• oo<-matrix(1,length(kk[1,]),1)
• tm<-kk%*%oo
• rr<-which(tm[,1]!="NA")
• #here we restrict the weight matrix and data to include only non-missing values
• wmat<-mat2listw(lds[rr,rr])
• ffd<-df[rr,]
• length(df[,1]) #number of observations before dropping those with missing values

[1] 186

• length(ffd[,1]) #number of observations after dropping those with missing values

[1] 180

• summary(ffd)
```   femsubs         fishimp         huntimp        pathstress
Min.   : 0.00   Min.   : 0.00   Min.   : 0.00   Min.   : 7.00
1st Qu.:21.00   1st Qu.: 5.00   1st Qu.: 5.00   1st Qu.: 9.00
Median :32.50   Median : 5.00   Median : 5.00   Median :12.00
Mean   :32.74   Mean   :15.89   Mean   :15.28   Mean   :12.55
3rd Qu.:42.25   3rd Qu.:25.00   3rd Qu.:25.00   3rd Qu.:15.25
Max.   :79.00   Max.   :90.00   Max.   :80.00   Max.   :21.00
rainfall        polygamy        eboysxp          fixres
Min.   :1.000   Min.   :1.000   Min.   :2.000   Min.   :1.000
1st Qu.:1.750   1st Qu.:3.000   1st Qu.:3.000   1st Qu.:2.000
Median :3.000   Median :3.000   Median :3.000   Median :5.000
Mean   :3.278   Mean   :3.128   Mean   :3.367   Mean   :3.722
3rd Qu.:5.000   3rd Qu.:4.000   3rd Qu.:3.250   3rd Qu.:5.000
Max.   :7.000   Max.   :4.000   Max.   :5.000   Max.   :5.000
landtrans        polinteg        socstrat
Min.   :1.000   Min.   :1.000   Min.   :1.000
1st Qu.:1.000   1st Qu.:2.000   1st Qu.:1.000
Median :1.000   Median :3.000   Median :2.000
Mean   :1.783   Mean   :2.944   Mean   :2.439
3rd Qu.:2.000   3rd Qu.:4.000   3rd Qu.:4.000
Max.   :5.000   Max.   :5.000   Max.   :5.000
```
• #We estimate a spatial lag model
• col.lm<-lagsarlm(femsubs~fishimp+huntimp+pathstress+rainfall+polygamy+eboysxp+fixres+landtrans+polinteg+socstrat,
• + data=ffd,wmat,quiet=FALSE)
```Spatial lag model
Jacobian calculated using neighbourhood matrix eigenvalues
Computing eigenvalues ...

(eigen) rho:     -0.2003254     function value:  -1196.641
(eigen) rho:     -0.1181220     function value:  -1063.774
(eigen) rho:     -0.06731753    function value:  -955.0824
(eigen) rho:     -0.03591862    function value:  -858.619
(eigen) rho:     -0.01651303    function value:  -780.7875
(eigen) rho:     -0.004519716   function value:  -736.6456
(eigen) rho:     0.002892561    function value:  -724.8947
(eigen) rho:     0.006528       function value:  -726.4253
(eigen) rho:     0.003551092    function value:  -724.7921
(eigen) rho:     0.00362369     function value:  -724.791
(eigen) rho:     0.003625334    function value:  -724.791
(eigen) rho:     0.003625231    function value:  -724.791
(eigen) rho:     0.003625226    function value:  -724.791
(eigen) rho:     0.003625236    function value:  -724.791
(eigen) rho:     0.003625231    function value:  -724.791
```
• #this next displays parameter estimates and diagnostics for the spatial lag model
• summary(col.lm)
```Call:lagsarlm(formula = femsubs ~ fishimp + huntimp + pathstress +
rainfall + polygamy + eboysxp + fixres + landtrans + polinteg +
socstrat, data = ffd, listw = wmat, quiet = FALSE)
```
```Residuals:
Min       1Q   Median       3Q      Max
-31.0141  -9.7284  -1.0099   9.3039  41.2642
```
```Type: lag
Coefficients: (asymptotic standard errors)
Estimate Std. Error z value  Pr(*|z|)
(Intercept) 42.629091   9.235650  4.6157 3.918e-06
fishimp     -0.178894   0.064200 -2.7865 0.0053277
huntimp     -0.390293   0.079525 -4.9078 9.209e-07
pathstress  -1.359711   0.382740 -3.5526 0.0003815
rainfall    -1.970160   0.640417 -3.0764 0.0020954
polygamy     2.557231   1.682321  1.5201 0.1284956
eboysxp      4.902086   1.706921  2.8719 0.0040803
fixres      -1.348417   0.876313 -1.5387 0.1238681
landtrans   -1.720732   1.153186 -1.4922 0.1356585
polinteg     2.203866   1.305567  1.6881 0.0914011
socstrat    -2.667423   1.103580 -2.4171 0.0156463

Rho: 0.0036252 LR test value: 4.9754 p-value: 0.02571
Asymptotic standard error: 0.0015828 z-value: 2.2904 p-value: 0.021995
Wald statistic: 5.2462 p-value: 0.021995
```
```Log likelihood: -724.791 for lag model
ML residual variance (sigma squared): 183.87, (sigma: 13.56)
Number of observations: 180
Number of parameters estimated: 13
AIC: 1475.6, (AIC for lm: 1478.6)
LM test for residual autocorrelation
test value: 0.14304 p-value: 0.70528
```

The model evaluated regression coefficients predicting female contribution to subsistence

• femsubs~fishimp+huntimp+pathstress+rainfall+polygamy+eboysxp+fixres+landtrans+polinteg+socstrat
variables having .15 > p > .10: polygamy fixres landtrans
variables having .10 > p > .05: polinteg (+)
variables having .05 > p > .01: socstrat (-)
variables having .01 > p >.001: rainfall eboysxp (+) fishimp (-)
variables having .001>p: hunting path(ogen)stress (-)

Residual autocorrelation is nonsignificant