library(scatterplot3d)
x1 <- c(1.9,0.8,1.1,0.1,-0.1,4.4,4.6,1.6,5.5,3.4)
x2 <- c(66, 62, 64, 61, 63, 70, 68, 62, 68, 66)
y <- c(0.7,-1.0,-0.2,-1.2,-0.1,3.4,0.0,0.8,3.7,2.0)
dataset = cbind.data.frame(x1,x2,y)
# Regresión:
my.lm<- lm(y ~ x1 + x2,data=dataset)
scatterplot3d(x1,x2,y)
ls <- function(dataset, par)
{with(dataset, sum((y-par[1]-par[2]*x1-par[3]*x2)^2))}
result <- optim(par=c(0,0,0),ls,data=dataset)
# starting values for the parameters to be optimised (0,0,0)
coef <- result$par
coef
## [1] -11.4591902 0.4500494 0.1726229
y_hat=my.lm$fitted.values
plot3d <- scatterplot3d(x1,x2,y,
angle=55, scale.y=0.7, pch=16, color ="red", main ="Regression Plane")
plot3d$plane3d(my.lm, lty.box = "solid")
plot3d$points3d(x1,x2,y_hat,col="blue", type="h", pch=16)
stargazer(my.lm,type="html")
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Dependent variable:
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y
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x1
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0.450
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(0.414)
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x2
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0.173
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(0.271)
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Constant
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-11.453
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(16.795)
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Observations
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10
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R2
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0.663
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Adjusted R2
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0.567
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Residual Std. Error
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1.125 (df = 7)
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F Statistic
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6.887** (df = 2; 7)
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Note:
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p<0.1; p<0.05; p<0.01
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