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 Pearsons 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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