Hello,
Apologies if this is the wrong list, I am a first-time poster here. I
have an experiment in which an output is measured in response to 42
different categories.
I am only interested which of the categories is significantly different
from a reference category.
Here is the summary of the results:
summary(simple.fit)
Call:
lm(formula = as.numeric(as.vector(TNFa)) ~ Mutant.ID, data =
imputed.data)
Residuals:
Min 1Q Median 3Q Max
-238.459 -25.261 -0.868 25.660 309.496
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.0479 10.5971 4.628 5.08e-06 ***
Mutant.IDB 149.8070 23.1632 6.467 3.09e-10 ***
Mutant.IDC 98.7443 23.1632 4.263 2.55e-05 ***
Mutant.IDD 97.2203 23.1632 4.197 3.37e-05 ***
Mutant.IDE 118.9820 23.1632 5.137 4.49e-07 ***
Mutant.IDF 241.8537 23.1632 10.441 < 2e-16 ***
Mutant.IDG 107.4883 23.1632 4.640 4.80e-06 ***
Mutant.IDH 105.7664 23.1632 4.566 6.74e-06 ***
Mutant.IDI 517.4650 23.1632 22.340 < 2e-16 ***
Mutant.IDJ 19.7777 23.1632 0.854 0.393735
Mutant.IDK 47.4240 23.1632 2.047 0.041313 *
Mutant.IDL 3.2542 23.1632 0.140 0.888347
Mutant.IDM 180.9638 23.1632 7.813 5.63e-14 ***
Mutant.IDN 19.0582 23.1632 0.823 0.411155
Mutant.IDO 61.8684 23.1632 2.671 0.007891 **
Mutant.IDP -0.5306 23.1632 -0.023 0.981738
Mutant.IDQ -10.6972 23.1632 -0.462 0.644478
Mutant.IDR 1.5377 23.1632 0.066 0.947107
Mutant.IDS 14.6333 23.1632 0.632 0.527934
Mutant.IDT 48.8900 23.1632 2.111 0.035458 *
Mutant.IDU 58.9597 23.1632 2.545 0.011313 *
Mutant.IDV 81.7657 23.1632 3.530 0.000467 ***
Mutant.IDW 82.9576 23.1632 3.581 0.000386 ***
Mutant.IDY 49.1926 23.1632 2.124 0.034343 *
Mutant.IDZ 51.0381 23.1632 2.203 0.028170 *
Mutant.IDZA 116.0487 23.1632 5.010 8.38e-07 ***
Mutant.IDZB 56.4402 23.1632 2.437 0.015287 *
Mutant.IDZC -14.5305 23.1632 -0.627 0.530838
Mutant.IDZD -5.0069 23.1632 -0.216 0.828983
Mutant.IDZE 9.1176 23.1632 0.394 0.694080
Mutant.IDZF 232.2879 23.1632 10.028 < 2e-16 ***
Mutant.IDZG -27.1671 23.1632 -1.173 0.241595
Mutant.IDZH 0.8757 23.1632 0.038 0.969862
Mutant.IDZI 4.7952 23.1632 0.207 0.836108
Mutant.IDZJ -5.5859 23.1632 -0.241 0.809568
Mutant.IDZK -12.9263 23.1632 -0.558 0.577138
Mutant.IDZL 38.8621 23.1632 1.678 0.094224 .
Mutant.IDZM 39.2643 23.1632 1.695 0.090880 .
Mutant.IDZN 73.8419 23.1632 3.188 0.001553 **
Mutant.IDZO 147.7804 23.1632 6.380 5.20e-10 ***
Mutant.IDZP 0.5654 23.1632 0.024 0.980540
Mutant.IDZQ 50.5117 23.1632 2.181 0.029824 *
Mutant.IDZR 217.6824 23.1632 9.398 < 2e-16 ***
Mutant.IDZS 237.3227 23.1632 10.246 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 61.79 on 377 degrees of freedom
Multiple R-Squared: 0.7351, Adjusted R-squared: 0.7049
F-statistic: 24.33 on 43 and 377 DF, p-value: < 2.2e-16
>
My question relates to the meaning of the p-values. Do the p-values
relate to
a) the confidence in the estimate
or
b)the confidence that the non-intercept categories are different to the
intercept
Somebody mentioned to me that the p-value for the intercept is the
confidence in the estimate of the intercept, whereas the remaining
entries are the confidence in each strain being different from the
reference / intercept
Note the contrasts setting is contr.treatment.
Any help would be appreciated
Andrew McDonagh,
PhD Candidate,
Department of Infectious Diseases,
Commonwealth Building,
Hammersmith Hospital,
Du Cane Road,
London W12 ONN
[EMAIL PROTECTED]
______________________________________________
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
