[R] Linear Regression Question
(Ted Harding)
Ted.Harding at manchester.ac.uk
Wed Oct 14 11:53:31 CEST 2009
On 13-Oct-09 21:17:11, Alexandre Cohen wrote:
> Dear Sir or Madam,
> I am a student at MSc Probability and Finance at Paris 6 University/
> Ecole Polytechnique. I am using R and I can't find an answer to the
> following question. I will be very thankful if you can answer it.
>
> I have two vectors rendements_CAC40 and rendements_AlcatelLucent.
> I use the lm function as follows, and then the sumarry function:
>
> regression=lm(rendements_CAC40 ~ rendements_AlcatelLucent);
> sum=summarry(regression);
>
> I obtain:
>
> Call:
> lm(formula = rendements_CAC40 ~ rendements_AlcatelLucent)
>
> Residuals:
> Min 1Q Median 3Q Max
> -6.43940 -0.84170 -0.01124 0.76235 9.08087
>
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> (Intercept) -0.03579 0.07113 -0.503 0.615
> rendements_AlcatelLucent 0.33951 0.01732 19.608 <2e-16 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Residual standard error: 1.617 on 515 degrees of freedom
> Multiple R-squared: 0.4274, Adjusted R-squared: 0.4263
> F-statistic: 384.5 on 1 and 515 DF, p-value: < 2.2e-16
>
> I would like to access to the p-value field, but I can't find the name
> of it, as we can see it below:
>
> > names(sum)
> [1] "call" "terms" "residuals" "coefficients"
> "aliased" "sigma" "df" "r.squared"
> [9] "adj.r.squared" "fstatistic" "cov.unscaled"
>
> I thought that I could find it in the fstatistic field, but it is not:
>
> sum$fstatistic
> value numdf dendf
> 384.4675 1.0000 515.0000
>
> Thank in advance for your time,
> Kind regards,
> Alexandre Cohen
Assuming you gave executed your code with "summary" correctly spelled
(i.e. not "summarry" or "sumarry" as you have written above), then
the information you require can be found in
sum$coefficients
which you can as well write as sum$coef
You will find that sum$coef is an array with 4 columns ("Estimate",
"Std. Error", "t value" and "Pr(>|t|)"), so the P-values are in the
final column sum$coef[,4].
Emulating your calculation above with toy regression data:
X <- (0:10) ; Y <- 1.0 + 0.25*X + 2.5*rnorm(11)
regression <- lm(Y~X)
sum <- summary(regression)
sum
# Call:
# lm(formula = Y ~ X)
# Residuals:
# Min 1Q Median 3Q Max
# -5.7182 -1.5383 0.2989 1.9806 3.9364
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 2.10035 1.81418 1.158 0.277
# X -0.03147 0.30665 -0.103 0.921
#
# Residual standard error: 3.216 on 9 degrees of freedom
# Multiple R-squared: 0.001169, Adjusted R-squared: -0.1098
# F-statistic: 0.01053 on 1 and 9 DF, p-value: 0.9205
sum$coef
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 2.1003505 1.8141796 1.1577412 0.2767698
# X -0.0314672 0.3066523 -0.1026152 0.9205184
sum$coef[,4]
# (Intercept) X
# 0.2767698 0.9205184
[And, by the way, although it in fact works, it is not a good idea
to use a function name ("sum") as the name of a variable.]
Hoping this helps,
Ted.
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E-Mail: (Ted Harding) <Ted.Harding at manchester.ac.uk>
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Date: 14-Oct-09 Time: 10:53:28
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