[R] conf. int. for lm() and Up-arrow
Snall, Tord
tord.snall at ebc.uu.se
Mon Aug 14 20:20:53 CEST 2000
Dear all,
Is there any function for calculating confidence limits
for coefficients in an lm() object? I know of the
confint() function in the MASS library working very
well on my binomial GLMs and I have tried it (using glm
() , family=gaussian) but it gives NAs according to
below. Does the confint() function not accept gaussian
GLMs? Could there be convergence problems in the GLM?
Note the very low R2-value. Could the Hauck & Donner
phenomenon discussed in V & R (1999) occur in a
gaussian GLM? I guess not and I have tried with
different epsilon but it does not change anything.
I use R 1.1.0 on Windows 98.
Sorry to bother you about this but earlier some R users
had problem using the Up arrow key to get the earlier
written text rows. I deleted those mails because I
usually use Windows NT. Now however I’m away from my
office using R on Windows 98 (something I didn\'t plan
to do). Could someone who have had this problem please
tell me how to do to be able to use the Up arrow key
again.
Thanks for all hints!
Sincerely,
Tord Snäll
> glm.spe.var<- glm(OSPEABUN~ V1+V2+V3+V4+V5+V6+V7+V8,
family=gaussian, data=R)
> lm.spe.var<- lm(OSPEABUN~V1+V2+V3+V4+V5+V6+V7+V8,
data=R)
> summary(lm.spe.var)
Residuals:
Min 1Q Median 3Q Max
-0.854243 -0.353655 -0.212623 -0.004446 2.729957
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.274985 0.028895 9.517 < 2e-16 ***
V1 0.145041 0.041001 3.537 0.000445 ***
V2 -0.002472 0.047220 -0.052 0.958276
V3 -0.016563 0.046899 -0.353 0.724120
V4 -0.065702 0.029530 -2.225 0.026573 *
V5 0.023614 0.031004 0.762 0.446665
V6 0.130698 0.031565 4.141 4.12e-05 ***
V7 -0.009698 0.042213 -0.230 0.818398
V8 -0.041318 0.031670 -1.305 0.192672
---
Signif. codes: 0 `***\\\' 0.001 `**\\\' 0.01 `*\\\'
0.05
`.\\\' 0.1 ` \\\' 1
Residual standard error: 0.6257 on 460 degrees of
freedom
Multiple R-Squared: 0.07881, Adjusted R-squared:
0.06279
F-statistic: 4.92 on 8 and 460 degrees of freedom,
p-value: 7.505e-006
> summary(glm.spe.var)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.854243 -0.353655 -0.212623 -0.004446 2.729957
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.274985 0.028895 9.517 < 2e-16 ***
V1 0.145041 0.041001 3.537 0.000445 ***
V2 -0.002472 0.047220 -0.052 0.958276
V3 -0.016563 0.046899 -0.353 0.724120
V4 -0.065702 0.029530 -2.225 0.026573 *
V5 0.023614 0.031004 0.762 0.446665
V6 0.130698 0.031565 4.141 4.12e-05 ***
V7 -0.009698 0.042213 -0.230 0.818398
V8 -0.041318 0.031670 -1.305 0.192672
---
Signif. codes: 0 `***\\\' 0.001 `**\\\' 0.01 `*\\\'
0.05
`.\\\' 0.1 ` \\\' 1
(Dispersion parameter for gaussian family taken to be
0.3915403)
Null deviance: 195.52 on 468 degrees of freedom
Residual deviance: 180.11 on 460 degrees of freedom
AIC: 902.11
Number of Fisher Scoring iterations: 2
> confint(glm.spe.var, level=0.95)
Waiting for profiling to be done...
2.5 % 97.5 %
(Intercept) NA NA
V1 NA NA
V2 NA NA
V3 NA NA
V4 NA NA
V5 NA NA
V6 NA NA
V7 NA NA
V8 NA NA
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