With my data (2 variables, Xt and Yt), I performed a Linear model in R Commander, which is named as LinearModel.1
Then, I wanted to predict the values that Yt would acquire when using different values of Xt, as in their 95% of confidence limits.
After the linear model was calculated, I used these commands:
> valoracalcular0=data.frame(Xt=0)
> valoracalcular10=data.frame(Xt=10)
> valoracalcular20=data.frame(Xt=20)
> valoracalcular30=data.frame(Xt=30)
> valoracalcular40=data.frame(Xt=40)
> valoracalcular50=data.frame(Xt=50)
> valoracalcular60=data.frame(Xt=60)
> valoracalcular70=data.frame(Xt=70)
> valoracalcular80=data.frame(Xt=80)
> valoracalcular90=data.frame(Xt=90)
> valoracalcular100=data.frame(Xt=100)
> predict(LinearModel.1,valoracalcular0,interval="confidence")
fit lwr upr
1 89.20547 86.9144 91.49653
> predict(LinearModel.1,valoracalcular10,interval="confidence")
fit lwr upr
1 93.83208 92.25312 95.41103
> predict(LinearModel.1,valoracalcular20,interval="confidence")
fit lwr upr
1 95.46144 94.32788 96.595
> predict(LinearModel.1,valoracalcular30,interval="confidence")
fit lwr upr
1 94.09356 93.12245 95.06466
> predict(LinearModel.1,valoracalcular40,interval="confidence")
fit lwr upr
1 89.72843 88.7478 90.70905
> predict(LinearModel.1,valoracalcular50,interval="confidence")
fit lwr upr
1 82.36605 81.36062 83.37148
> predict(LinearModel.1,valoracalcular60,interval="confidence")
fit lwr upr
1 72.00643 71.03851 72.97435
> predict(LinearModel.1,valoracalcular70,interval="confidence")
fit lwr upr
1 58.64956 57.77695 59.52217
> predict(LinearModel.1,valoracalcular80,interval="confidence")
fit lwr upr
1 42.29544 41.46939 43.12149
> predict(LinearModel.1,valoracalcular90,interval="confidence")
fit lwr upr
1 22.94408 21.90812 23.98004
> predict(LinearModel.1,valoracalcular100,interval="confidence")
fit lwr upr
1 0.5954731 -0.9804254 2.171372
My question is, how can I obtain a unique table with three columns (fit, lwr, upr) and all their predictions, instead of 10 different and independent predictions?
