[R] F test

[Mike Lawrence]

> Ahem. "Equivalent", my tired foot...

My bad, I wasn't paying attention.

> May I suggest consulting a textbook *before* flunking ANOVA 101 ?

Harsh but warranted given my carelessness.


On Thu, Apr 16, 2009 at 3:47 PM, Emmanuel Charpentier
<charpent at bacbuc.dyndns.org> wrote:
> Le jeudi 16 avril 2009 à 14:08 -0300, Mike Lawrence a écrit :
>> 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.
>> >
>> > ______________________________________________
>> > R-help at r-project.org mailing list
>> > https://stat.ethz.ch/mailman/listinfo/r-help
>> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> > and provide commented, minimal, self-contained, reproducible code.
>> >
>>
>>
>>
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>



-- 
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University

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~ Certainty is folly... I think. ~