[R] interpret a p-value result as a significance of a linear regression in terms of sigmas
jean-philippe
je@nphilippe@font@ine @ending from g@@i@infn@it
Wed Jun 20 12:53:00 CEST 2018
dear R community,
I am running a linear regression for my dataset between 2 variables
(disk mass and velocities).
This is the result returned by the summary function onto the lm object
for one of my dataset.
Call:
lm(formula = df$md1 ~ df$logV, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.64856 -0.16492 0.04127 0.18027 0.45727
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.2582 0.2682 23.333 < 2e-16 ***
df$logV 1.2926 0.2253 5.738 6.5e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3067 on 24 degrees of freedom
Multiple R-squared: 0.5784, Adjusted R-squared: 0.5609
F-statistic: 32.93 on 1 and 24 DF, p-value: 6.504e-06
I am interested to give the significance in terms of sigmas (as
generally done in particle physics, see for instance the 7 \sigma
discovery of the Higgs particle)
of my regression.
For this, if I understood well, I should look at the p-value for the
F-statistic which is in this univariate linear regression the same as
the one for logV.
My question is, am I right if I state that the significance in terms of
sigmas (sign) is given by: p = 2*(1-pnorm(sign)) since I guess the
p-value returned by R is for a two sided test (and assuming Gaussianity
for my dataset)?
Otherwise is there any way to get the significance of this linear
regression in terms of sigmas?
I would have a similar question also, as extension, for a multivariate
linear regression for which the p-value associated to F statistics is
not the same as the p-value for each variable of the regression.
Thanks in advance,
Best Regards
Jean-Philippe Fontaine
--
Jean-Philippe Fontaine
PhD Student in Astroparticle Physics,
Gran Sasso Science Institute (GSSI),
Viale Francesco Crispi 7,
67100 L'Aquila, Italy
Mobile: +393487128593, +33615653774
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