Dear List,



I am comparing the squared R values of linear models and its spatial
autoregressive counterparts. (SARerror)

(1. lm (Y~X1)

2. lm (Y~ X1+X2)

3. lm(Y~X1+X2+X3))



The squared R values of linear models are generated by command summary (lm).


Similarly, I tried to produce those of spatial autoregressive models based
on

the squared Pearson’s correlation of explanatory and response variables. It
failed



The code is as followed.

Please kindly modify the code and thank you.



1. single predictor

sar.x1 <-errorsarlm(Y~X1,data=datam.std,listw=nb8.w, na.action=na.omit,
method="Matrix", zero.policy=TRUE)

summary(sar.x1)

cor(sar.x1$X1, sar.x1$Y, method = "pearson")



error message

error in cor(sar.x1$ X1, sar.x1$Y, method = "pearson") :

  supply both 'x' and 'y' or a matrix-like 'x'



2. multiple predictors

sar.all <-errorsarlm(Y~X1+X2+X3,data=datam.std,listw=nb8.w,
na.action=na.omit, method="Matrix", zero.policy=TRUE)

summary(sar.all)

cor(sar.all$X1+ sar.all$X2+ sar.all$X3, sar.x1$Y, method = "pearson")



error message

error in cor(sar.all$X1+ sar.all$X2+ sar.all$X3, sar.x1$Y, method =
"pearson") :

  supply both 'x' and 'y' or a matrix-like 'x'



Elaine

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