Dear all,
Even after a couple of hours looking at old messages I still haven't found a
solution for my problem.
I'm trying to fit an additive linear regression model with 2 effects, both
fixed, to some dataset. The function contrasts(effectA) <- contr.sum can
gaurantee that the coefficients per parameter sum to one, and the function
dummy.coef provices the estimates of all coefficientss. But I would also like to
be able to obtain the corresponding t-values for ALL parameters (not just the
number of effects minus 1, provided by summary()). Does anyone know how to get
(all of) them?
Here comes what I've already tried:
## Try data:
> Data <- rbinom(1000,50,.9);
> Dates <- Sys.Date()-(1000:1)
> facweek <- factor(weekdays(Dates,abbreviate=TRUE))
> facmonth <- factor(months(Dates,abbreviate=TRUE))
> contrasts(facweek) <- contr.sum ;
> contrasts(facmonth) <- contr.sum ;
> fit <- lm(formula = Data ~ facweek + facmonth)
> summary(fit)
Call:
lm(formula = Data ~ facweek + facmonth)
Residuals:
Min 1Q Median 3Q Max
-8.7498 -1.3774 0.1778 1.5108 5.1643
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.01452 0.06976 645.252 <2e-16 ***
facweek1 -0.37114 0.16836 -2.204 0.0277 *
facweek2 0.40360 0.16836 2.397 0.0167 *
facweek3 -0.19918 0.16885 -1.180 0.2384
facweek4 -0.13689 0.16834 -0.813 0.4163
facweek5 -0.07049 0.16835 -0.419 0.6755
facweek6 0.40974 0.16836 2.434 0.0151 *
facmonth1 -0.12046 0.22053 -0.546 0.5850
facmonth2 -0.10832 0.26155 -0.414 0.6789
facmonth3 0.25281 0.21731 1.163 0.2450
facmonth4 0.40161 0.22627 1.775 0.0762 .
facmonth5 -0.37409 0.21731 -1.721 0.0855 .
facmonth6 0.06645 0.26155 0.254 0.7995
facmonth7 0.13627 0.22509 0.605 0.5451
facmonth8 -0.04789 0.21731 -0.220 0.8256
facmonth9 -0.22910 0.21731 -1.054 0.2920
facmonth10 0.11752 0.22053 0.533 0.5942
facmonth11 -0.27233 0.21731 -1.253 0.2104
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 2.174 on 982 degrees of freedom
Multiple R-squared: 0.02673, Adjusted R-squared: 0.009879
F-statistic: 1.586 on 17 and 982 DF, p-value: 0.06097
> print(dummy.coef(fit),digits=3)
Full coefficients are
(Intercept): 45
facweek: Fri Mon Sat Sun Thu Tue
-0.3711 0.4036 -0.1992 -0.1369 -0.0705 0.4097
facmonth: Apr Aug Dec Feb Jan Jul
-0.1205 -0.1083 0.2528 0.4016 -0.3741 0.0664
(Intercept):
facweek: Wed
-0.0357
facmonth: Jun Mar May Nov Oct Sep
0.1363 -0.0479 -0.2291 0.1175 -0.2723 0.1775
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