F test

summary(my_lm) will give you t-values, anova(my_lm) will give you
(equivalent) F-values. summary() might be preferred because it also
provides the estimates & SE.

a=data.frame(dv=rnorm(10),iv1=rnorm(10),iv2=rnorm(10))
my_lm=lm(dv~iv1*iv2,a)
summary(my_lm)

Call:
lm(formula = dv ~ iv1 * iv2, data = a)

Residuals:
    Min      1Q  Median      3Q     Max
-1.8484 -0.2059  0.1627  0.4623  1.0401

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  -0.4864     0.4007  -1.214    0.270
iv1           0.8233     0.5538   1.487    0.188
iv2           0.2314     0.3863   0.599    0.571
iv1:iv2      -0.4110     0.5713  -0.719    0.499

Residual standard error: 1.017 on 6 degrees of freedom
Multiple R-squared: 0.3161,	Adjusted R-squared: -0.02592
F-statistic: 0.9242 on 3 and 6 DF,  p-value: 0.4842

anova(my_lm)

Analysis of Variance Table

Response: dv
          Df Sum Sq Mean Sq F value Pr(>F)
iv1        1 1.9149  1.9149  1.8530 0.2223
iv2        1 0.4156  0.4156  0.4021 0.5494
iv1:iv2    1 0.5348  0.5348  0.5175 0.4990
Residuals  6 6.2004  1.0334

Hi,


How can I find the p-value for the F test for the interaction terms in a
regression linear model lm ?

I appreciate your help


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