# [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.
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
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
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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