HSI
From: Bayesian Models for Astrophysical Data, Cambridge Univ. Press
(c) 2017, Joseph M. Hilbe, Rafael S. de Souza and Emille E. O. Ishida
you are kindly asked to include the complete citation if you used this material in a publication
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Code 2.1 Example of linear regression in R.
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# Data
y <- c(13,15,9,17,8,5,19,23,10,7,10,6) # continuous response variable
x1 <- c(1,1,1,1,1,1,0,0,0,0,0,0) # binary predictor
x2 <- c( 1,1,1,1,2,2,2,2,3,3,3,3) # categorical predictor
# Fit
mymodel <- lm(y ~ x1 + x2) # linear regression of y on x1 and x2
# Output
summary(mymodel) # summary display
par(mfrow=c(2, 2)) # create a 2 by 2 window
plot(mymodel) # display of fitted vs. residuals plot, normal QQ plot
# scale-location plot and residuals vs. leverage plot
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Output on screen:
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Call:
lm(formula = y ~ x1 + x2)
Residuals:
Min 1Q Median 3Q Max
-5.4583 -1.6458 0.4792 1.2292 3.9167
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 38.833 5.090 7.630 3.23e-05 ***
x1 -14.500 3.024 -4.795 0.000980 ***
x2 -9.875 1.852 -5.333 0.000473 ***
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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Residual standard error: 3.024 on 9 degrees of freedom
Multiple R-squared: 0.7633, Adjusted R-squared: 0.7107
F-statistic: 14.51 on 2 and 9 DF, p-value: 0.001527