Complex R - function/loop -


here challenging yet quiet interesting conflict have... wish this, hope me out here

this "r"

here code running r-squared x1 training

model=randomforest(x1~.,data=training,importance=true,keep.forest=true) predicted=predict(model,newdata=testing[,-1]) actual=testing$x1 rsq=1-sum((actual-predicted)^2)/sum((actual-mean(actual))^2) print(rsq)

here head of training let know how looks like

head(training)         x1        x2        x3         x4        x5         x6 68   -3.556526  4.588409 -2.756521  -2.742035 11.542023 -18.405807 23   -1.915947 -0.179710 -0.240580  -0.278259 -0.284058   0.553627 129 -24.252174 -4.869564  4.800001 -14.608688  5.255074 -20.228981 5    -1.637680 -1.147827 -2.005795  -1.121750  0.101440  -1.608688 147 -68.289856 -0.626083 19.933334  -6.637680 15.379715 -11.515945

there x77 1 of these number of rows 73.

my objective make loop of

model=randomforest(x1~.,data=training,importance=true,keep.forest=true) predicted=predict(model,newdata=testing[,-1]) actual=testing$x1 rsq=1-sum((actual-predicted)^2)/sum((actual-mean(actual))^2) print(rsq)

up x77

so to

model=randomforest(x77~.,data=training,importance=true,keep.forest=true) predicted=predict(model,newdata=testing[,-77]) actual=testing$x77 rsq=1-sum((actual-predicted)^2)/sum((actual-mean(actual))^2) print(rsq)

so achieve 77 of r-sqared

my final objective take mean of 77 r-squared

to maxim.k , others

rsq=function(i){ model=randomforest(testing[,1]~.,data=training,importance=true,keep.forest=true) predicted=predict(model,newdata=testing[,-i]) actual=testing[,i] 1-sum((actual-predicted)^2)/sum((actual-mean(actual))^2) }  rsq=function(i){ model=randomforest(xi~.,data=training,importance=true,keep.forest=true) predicted=predict(model,newdata=testing[,-i]) actual=testing[,i] 1-sum((actual-predicted)^2)/sum((actual-mean(actual))^2) }

i know second 1 logically makes no sense, need. testing$x1 testing[,1], won't work putting testing[,1] have put in form of "x1"

how that...?

i think maxim.k has alluded already, work

rsq = function(i) {   n = colnames(testing)[i]   model=randomforest(as.formula(paste(n,"~.")),data=training,importance=true,keep.forest=true)   predicted=predict(model,newdata=testing[,-i])   actual=testing[[n]]   1-sum((actual-predicted)^2)/sum((actual-mean(actual))^2) } sapply(1:77, rsq)

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