On 02/07/2013 08:22 PM, Antonio Silva wrote:
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)$df[2]) # isn't it?
The dt() function is giving you the value from the density function, not
the probability. Use pt() to obtain probabilities.
But this value is different from summary(fit1)$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
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