[R] retrieving p-values in lm
Dear list, I want to retrieve the p-value of a two-polynomial regression. For a one-polynomial lm I can easily do this with: summary(lm(b~a, data=c)[[4]][[8]]. But how do I find the final p-value in the two-polynomial regression? Under $coefficients I don't find it Any suggestions? Patrick alt -(2260,2183,2189,1930,2435, 2000,2100,2050,2020,2470, 1700,2310,2090,1560,2060, 1790,1940,2100,2250,2010) H - c(0.2034,0.1845,0.2053,0.1788,0.2196, 0.2037,0.1655,0.2176,0.1844,0.2033, 0.1393,0.2019,0.1975,0.1490,0.1917, 0.2180,0.2064,0.1943,0.2139,0.1320) X - data.frame(alt,H) lm.res - summary(lm(H~alt,data=X)) lm.res p1 - lm.res[[4]][[8]] p1 lm.res.2 - summary(lm(H~alt+I(alt^2),data=X)) lm.res.2 str(lm.res.2) # where is p p2 - lm.res.2[[???]][[]] -- Patrick Kuss PhD-student Institute of Botany University of Basel Schönbeinstr. 6 CH-4056 Basel +41 61 267 2976 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] retrieving p-values in lm
Hi Patrick, try: lm.res.2$coefficients which I found by looking at the content of the function 'summary.lm'. Christian __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] retrieving p-values in lm
On Fri, 2005-12-09 at 14:19 +0100, Patrick Kuss wrote: Dear list, I want to retrieve the p-value of a two-polynomial regression. For a one-polynomial lm I can easily do this with: summary(lm(b~a, data=c)[[4]][[8]]. But how do I find the final p-value in the two-polynomial regression? Under $coefficients I don't find it Any suggestions? Patrick alt -(2260,2183,2189,1930,2435, 2000,2100,2050,2020,2470, 1700,2310,2090,1560,2060, 1790,1940,2100,2250,2010) H - c(0.2034,0.1845,0.2053,0.1788,0.2196, 0.2037,0.1655,0.2176,0.1844,0.2033, 0.1393,0.2019,0.1975,0.1490,0.1917, 0.2180,0.2064,0.1943,0.2139,0.1320) X - data.frame(alt,H) lm.res - summary(lm(H~alt,data=X)) lm.res p1 - lm.res[[4]][[8]] p1 lm.res.2 - summary(lm(H~alt+I(alt^2),data=X)) lm.res.2 str(lm.res.2) # where is p p2 - lm.res.2[[???]][[]] First, you might want to review Chapter 11: Statistical Models in R in An Introduction to R, which is available with your R installation or from the main R web site under Documentation. Specifically, page 53 describes the extractor functions to be used for getting model information. In this case using coef() will extract the model coefficients in both cases: coef(lm.res) Estimate Std. Error t value Pr(|t|) (Intercept) 6.245371e-02 4.713400e-02 1.325024 0.20173833 alt 6.179038e-05 2.261665e-05 2.732074 0.01368545 coef(lm.res.2) Estimate Std. Errort value Pr(|t|) (Intercept) -9.433748e-02 3.133627e-01 -0.3010488 0.7670283 alt 2.178857e-04 3.091330e-04 0.7048283 0.4904618 I(alt^2)-3.838002e-08 7.579576e-08 -0.5063610 0.6191070 In both models, the coefficients are present if you review the structure as you have in your code above: names(lm.res) [1] call terms residuals coefficients [5] aliased sigma dfr.squared [9] adj.r.squared fstatisticcov.unscaled names(lm.res.2) [1] call terms residuals coefficients [5] aliased sigma dfr.squared [9] adj.r.squared fstatisticcov.unscaled So, you can get the term p values by using: coef(lm.res)[, 4] (Intercept) alt 0.20173833 0.01368545 coef(lm.res.2)[, 4] (Intercept) altI(alt^2) 0.7670283 0.4904618 0.6191070 In terms of the overall model p value, this is actually calculated when you display (print) the model. It is not stored as part of the model object itself. If you review the code for print.summary.lm() using: stats:::print.summary.lm ... pf(x$fstatistic[1], x$fstatistic[2], x$fstatistic[3], lower.tail = FALSE) ... Where the first argument is the F statistic and the other two are the degrees of freedom: lm.res$fstatistic value numdf dendf 7.464231 1.00 18.00 lm.res.2$fstatistic value numdf dendf 3.706139 2.00 17.00 So, in the case of your two models: x - lm.res pf(x$fstatistic[1], x$fstatistic[2], x$fstatistic[3], lower.tail = FALSE) value 0.01368545 x - lm.res.2 pf(x$fstatistic[1], x$fstatistic[2], x$fstatistic[3], lower.tail = FALSE) value 0.0461472 HTH, Marc Schwartz __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] retrieving p-values in lm
On Fri, 2005-12-09 at 14:19 +0100, Patrick Kuss wrote:
Dear list,
I want to retrieve the p-value of a two-polynomial regression. For a
one-polynomial lm I can easily do this with:
summary(lm(b~a, data=c)[[4]][[8]].
But how do I find the final p-value in the two-polynomial regression? Under
$coefficients I don't find it
Any suggestions?
Judging from your code, you mean p-value of the F-statistic for the
whole model - this isn't stored anywhere, see:
getAnywhere(print.summary.lm)
In particular this section:
cat(\nResidual standard error:, format(signif(x$sigma,
digits)), on, rdf, degrees of freedom\n)
if (!is.null(x$fstatistic)) {
cat(Multiple R-Squared:, formatC(x$r.squared, digits = digits))
cat(,\tAdjusted R-squared:, formatC(x$adj.r.squared,
digits = digits), \nF-statistic:, formatC(x$fstatistic[1],
digits = digits), on, x$fstatistic[2], and, x$fstatistic[3],
DF, p-value:, format.pval(pf(x$fstatistic[1],
x$fstatistic[2], x$fstatistic[3], lower.tail = FALSE),
digits = digits), \n)
}
The relevant bit being:
format.pval(pf(x$fstatistic[1],
x$fstatistic[2], x$fstatistic[3], lower.tail = FALSE)
The reason this works for the first model is that with one covariate the
value in $coefficients is the overall model p-value, in that case. With
two covariates, the things in $coefficients relate to these, not to the
overall model - your assumption was wrong in the first usage, you just
lucked out that it gave the same result.
So,
p1 - pf(lm.res$fstatistic[1],
lm.res$fstatistic[2], lm.res$fstatistic[3],
lower.tail = FALSE)
p2 - pf(lm.res.2$fstatistic[1],
lm.res.2$fstatistic[2], lm.res.2$fstatistic[3],
lower.tail = FALSE)
Gives you the p-values:
p1
value
0.01368545
p2
value
0.0461472
HTH
G
Patrick
alt -(2260,2183,2189,1930,2435,
2000,2100,2050,2020,2470,
1700,2310,2090,1560,2060,
1790,1940,2100,2250,2010)
H - c(0.2034,0.1845,0.2053,0.1788,0.2196,
0.2037,0.1655,0.2176,0.1844,0.2033,
0.1393,0.2019,0.1975,0.1490,0.1917,
0.2180,0.2064,0.1943,0.2139,0.1320)
X - data.frame(alt,H)
lm.res - summary(lm(H~alt,data=X))
lm.res
p1 - lm.res[[4]][[8]]
p1
lm.res.2 - summary(lm(H~alt+I(alt^2),data=X))
lm.res.2
str(lm.res.2) # where is p
p2 - lm.res.2[[???]][[]]
--
Patrick Kuss
PhD-student
Institute of Botany
University of Basel
Schönbeinstr. 6
CH-4056 Basel
+41 61 267 2976
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Gavin Simpson [T] +44 (0)20 7679 5522
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