SAS to R Notes

How I did part of a SAS assignment in R

The residual plot in SAS was corrupted, luckily I was able to recreate
it in R. (And actually it looks better in R too.)

# read in data from file 
gpa.data <- read.table("GPA.txt");
# create multiple linear regression model
gpa.fit <- lm(V4 ~ V1 + V2 + V3 + V5 + V6 + V7 + V8, data=gpa.data)
# save residuals to a variable
gpa.resid <- residuals(gpa.fit)
# save Predicted values to a variable
gpa.yhat <- fitted.values(gpa.fit)
# create png file, that plot will be saved to.
png("resid.png")
# create a plot of residuals vs predicted values
plot(gpa.yhat,gpa.resid, ylab="Residuals", xlab="Predicted Values of Cum GPA", main="Plot of Residuals*Predicted Values")
# create a line#
abline(0,0)
# write plot to file
dev.off()

resid.png

ANOVA table of the Multiple Regression Model


anova(gpa.fit)

Analysis of Variance Table
Response: V4
           Df  Sum Sq Mean Sq F value    Pr(>F)    
V1          1   0.567  0.5669  1.8709  0.172001    
V2          1  26.488 26.4877 87.4135 < 2.2e-16 ***
V3          1   9.096  9.0964 30.0194 6.839e-08 ***
V5          1   2.446  2.4459  8.0717  0.004683 ** 
V6          1   1.032  1.0324  3.4069  0.065524 .  
V7          1   0.020  0.0199  0.0655  0.798068    
V8          1   0.278  0.2784  0.9187  0.338288    
Residuals 492 149.084  0.3030                      
---
codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Summary of Linear Regression Model


summary(gpa.fit)

Call:
lm(formula = V4 ~ V1 + V2 + V3 + V5 + V6 + V7 + V8, data = gpa.data)
Residuals:
    Min      1Q  Median      3Q     Max 
-3.3345 -0.3387 -0.0059  0.3429  1.9661 
Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.7792451  0.5165056   3.445  0.00062 ***
V1          -0.0383924  0.0527532  -0.728  0.46710    
V2           0.2716557  0.0417213   6.511 1.84e-10 ***
V3           0.0010404  0.0003173   3.279  0.00111 ** 
V5           0.0010332  0.0003425   3.016  0.00269 ** 
V6          -0.0391177  0.0239319  -1.635  0.10279    
V7          -0.0006554  0.0028471  -0.230  0.81805    
V8          -0.0054118  0.0056462  -0.958  0.33829    
---
codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
Residual standard error: 0.5505 on 492 degrees of freedom
Multiple R-squared: 0.2112, Adjusted R-squared:   0.2 
F-statistic: 18.82 on 7 and 492 DF,  p-value: < 2.2e-16

Category: