OK, I found the answer:

2*pt(t,summary(fit1)$df[2])

Thanks anyway ...


2013/2/7 Antonio Silva <aolinto.lst@gmail.com>

> Dear list members
>
> I have a doubt on how p-values for t-statistics are calculated in the
> summary of Linear Models.
>
> Here goes an example:
>
> x <- rnorm(100,50,10)
> y <- rnorm(100,0,5)
> fit1<-lm(y~x)
> summary(fit1)
> summary(fit1)$coef[2] # b
> summary(fit1)$coef[4] # Std. Error
> summary(fit1)$coef[6] # t-statistic
> summary(fit1)$coef[8] # Pr(>|t|
> summary(fit1)$df [2] # degrees of freedom
>
> # t-statistic can be calculated as:
> t<-(summary(fit1)$coef[2])/summary(fit1)$coef[4]
> t # t-statistic
>
> # the critical value for t0.05(2)df can be obtained in a t distribuition
> table
> # http://www.math.unb.ca/~knight/utility/t-table.htm or with
> qt(0.975,summary(fit1)$df[2])
>
> # Two-sided p-value should be estimated with
> dt(t,summary(fit1)$df[2]) # isn't it?
>
> But this value is different from summary(fit1)$coef[8]
>
> My question is: how to reach to the same p-value indicated in Pr(>|t|) or
> summary(fit1)$coef[8]?
>
> Thanks in advance,
>
> Antonio Olinto
>



-- 
Antônio Olinto Ávila da Silva
Biólogo / Oceanógrafo
Instituto de Pesca (Fisheries Institute)
São Paulo, Brasil

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