[R] conf. int. for lm() and Up-arrow

[Snall, Tord]

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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