F test
Le jeudi 16 avril 2009 ? 14:08 -0300, Mike Lawrence a ?crit :
summary(my_lm) will give you t-values, anova(my_lm) will give you (equivalent) F-values.
Ahem. "Equivalent", my tired foot... In simple terms (the "real" real story may be more intricate....) : The "F values" stated by anova are something entierely different of t values in summary. The latter allow you to assess properties of *one* coefficient in your model (namely, do I have enough suport to state that it is nonzero ?). The former allows you to assess whether you have support for stating that *ALL* the coefficient related to the same factor cannot be *SIMULTANEOUSLY* null. Which is a horse of quite another color... By the way : if your "summary" indeed does give you the mean^K^K an unbiased estimate of your coefficient and an (hopefully) unbiased estimate of its standard error, the "F" ration is the ratio of estimates of "remaining" variabilities with and without the H0 assumption it tests, that is that *ALL* coefficients of your factor of interest are *SIMULTANEOUSLY* null. F and t "numbers" will be "equivalent" if and only if your "factor of interest" needs only one coefficient to get expressed, i. e. is a continuous covariable or a two-class discrete variable (such as boolean). In this case, you can test your factor either by the t value which, under H0, fluctuates as a Student's t with n_res dof (n_res being the "residual degrees of freedom" of the model) or by the F value, which will fluctuate as a Fisher F statistic with 1 and n_res dof, which happens (but that's not happenstance...) to be the *square* of a t with n_dof. May I suggest consulting a textbook *before* flunking ANOVA 101 ? Emmanuel Charpentier
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
On Thu, Apr 16, 2009 at 10:35 AM, kayj <kjaja27 at yahoo.com> wrote:
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 -- View this message in context: http://www.nabble.com/F-test-tp23078122p23078122.html Sent from the R help mailing list archive at Nabble.com.
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