> d <- data.frame(Factor1 = c(rep("A", 10), rep("B", 10), rep("C", 10)),
+
+               Factor2 = c(rep("D", 8), rep("E", 6), rep("F", 16)),
+
+               Factor3 = c(rep("1", 15), rep("2", 15)),
+
+               Response = c(1:30))
>
> summary.lm(lm(Response ~ Factor1 + Factor2 + Factor3,
+
+             data = d,
+
+             contrasts = list(Factor1 = contr.poly)))

Call:
lm(formula = Response ~ Factor1 + Factor2 + Factor3, data = d,
    contrasts = list(Factor1 = contr.poly))

Residuals:
   Min     1Q Median     3Q    Max
  -4.5   -1.5    0.0    1.5    4.5

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)    9.000      1.694   5.314 1.88e-05 ***
Factor1.L      7.425      1.750   4.243 0.000285 ***
Factor1.Q      1.837      1.010   1.818 0.081526 .
Factor2E       5.000      1.909   2.619 0.015056 *
Factor2F       7.500      3.307   2.268 0.032627 *
Factor32       3.000      2.646   1.134 0.268039
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.415 on 24 degrees of freedom
Multiple R-squared: 0.9377, Adjusted R-squared: 0.9247
F-statistic: 72.26 on 5 and 24 DF,  p-value: 1.133e-13

>
> Anova(lm(Response ~ Factor1 + Factor2 + Factor3,
+
+        data = d,
+
+        contrasts = list(Factor1 = contr.poly)),
+
+     type="II")
Anova Table (Type II tests)

Response: Response
          Sum Sq Df F value    Pr(>F)
Factor1    199.5  2 17.1000 2.418e-05 ***
Factor2     45.0  2  3.8571   0.03528 *
Factor3      7.5  1  1.2857   0.26804
Residuals  140.0 24
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1