Re: [R] Weird SEs with effect()

From: Gustaf Granath <Gustaf.Granath_at_ebc.uu.se>
Date: Mon, 18 Feb 2008 09:57:32 +0100

Dear John & Brian,

Ok. Now I start to understand. So basically I cannot use the given SEs for my purposes. They only make sense on the scale of log-counts. However in a paper, the log-count scale is not very informative (the reader want to see the effect on the scale you measure). If I understand you right, the confidence intervals are fine though and maybe I will use them to illustrate the reliability of the estimate. The problem is that showing SEs of the adjusted means has become sort of the standard way to illustrate things in my field (too many SAS users ?) and I might run into trouble with the reviewers. I have several data sets with similar data and my plan was to use effect(). That is why I want to figure this out. I hope I haven't been too annoying ;).

Finally, is there a way to get correct SEs on the count scale (with adjusted means)??
I guess not, judging by your answers.

Thanks again for your help,

Gustaf

John Fox wrote:
> Dear Gustaf,
>
>
>> -----Original Message-----
>> From: Gustaf Granath [mailto:gustaf.granath_at_ebc.uu.se]
>> Sent: February-17-08 4:18 PM
>> To: John Fox
>> Cc: 'Prof Brian Ripley'; r-help_at_r-project.org
>> Subject: RE: [R] Weird SEs with effect()
>>
>> Dear John and Brian,
>> Thank you for your help. I get the feeling that it is something
>> fundamental that I do not understand here. Furthermore, a day of
>> reading did not really help so maybe we have reached a dead end here.
>> Nevertheless, here comes one last try.
>>
>> I thought that the values produced by effect() were logs (e.g. in
>> $fit). And then they were transformed (antilogged) with summary(). Was
>> I wrong?
>>
>
> I'm sorry that you're continuing to have problems with this.
>
> Yes, there is a point that you don't understand: The SEs are on the scale of
> the log-counts, but you can't get correct SEs on the scale of the counts by
> exponentiating the SEs on the scale of the log-counts. What summary(), etc.,
> do (and you can do) to produce confidence intervals on the count scale is
> first to compute the intervals on the log-count scale and then to transform
> the end-points.
>
> I'm afraid that I can't make the point more clearly than that.
>
> I hope this helps,
> John
>
>
>> What I want:
>> I am trying to make a barplot with adjusted means with SEs (error
>> bars), with the y axis labeled on the response scale.
>>
>> #One of my GLM models (inf.level & def.level=factors, initial.size =
>> covariate) #used as an example.
>> #I was not able to make a reproducible example though. Sorry.
>>
>> model <-
>> glm(tot.fruit~initial.size+inf.level+def.level,family=quasipoisson)
>> summary(model)
>> Coefficients:
>> Estimate Std. Error t value Pr(>|t|)
>> (Intercept) 1.9368528 0.1057948 18.308 < 2e-16 ***
>> initial.size 0.0015245 0.0001134 13.443 < 2e-16 ***
>> inf.level50 -0.3142688 0.0908063 -3.461 0.000612 ***
>> def.level12.5 -0.2329221 0.1236992 -1.883 0.060620 .
>> def.level25 -0.1722354 0.1181993 -1.457 0.146062
>> def.level50 -0.3543826 0.1212906 -2.922 0.003731 **
>>
>> (Dispersion parameter for quasipoisson family taken to be 6.431139)
>> Null deviance: 2951.5 on 322 degrees of freedom
>> Residual deviance: 1917.2 on 317 degrees of freedom
>>
>> library(effects)
>> def <- effect("def.level",model,se=TRUE)
>> summary(def)
>> $effect
>> def.level
>> 0 12.5 25 50
>> 11.145382 8.829541 9.381970 7.819672
>> $lower
>> def.level
>> 0 12.5 25 50
>> 9.495220 7.334297 7.867209 6.467627
>> $upper
>> def.level
>> 0 12.5 25 50
>> 13.08232 10.62962 11.18838 9.45436
>> #Confidence intervals makes sense and are in line with the glm model
>> result. Now #lets look at the standard errors. Btw, why aren't they
>> given with summary?
>> def$se
>> 324 325 326 327
>> 0.08144281 0.09430438 0.08949864 0.09648573
>> # As you can see, the SEs are very very very small.
>> #In a graph it would look weird in combination with the glm result.
>> #I thought that these values were logs. Thats why I used exp() which
>> seems to be wrong.
>>
>> Regards,
>>
>> Gustaf
>>
>>
>>
>>> Quoting John Fox <jfox_at_mcmaster.ca>:
>>> Dear Brian and Gustaf,
>>>
>>> I too have a bit of trouble following what Gustaf is doing, but I
>>>
>> think that
>>
>>> Brian's interpretation -- that Gustaf is trying to transform the
>>>
>> standard
>>
>>> errors via the inverse link rather than transforming the ends of the
>>> confidence intervals -- is probably correct. If this is the case,
>>>
>> then what
>>
>>> Gustaf has done doesn't make sense.
>>>
>>> It is possible to get standard errors on the scale of the response
>>>
>> (using,
>>
>>> e.g., the delta method), but it's probably better to work on the
>>>
>> scale of
>>
>>> the linear predictor anyway. This is what the summary, print, and
>>>
>> plot
>>
>>> methods in the effects package do (as is documented in the help files
>>>
>> for
>>
>>> the package -- see the transformation argument under ?effect and the
>>>
>> type
>>
>>> argument under ?summary.eff).
>>>
>>> Regards,
>>> John
>>>
>>> --------------------------------
>>> John Fox, Professor
>>> Department of Sociology
>>> McMaster University
>>> Hamilton, Ontario, Canada L8S 4M4
>>> 905-525-9140x23604
>>> http://socserv.mcmaster.ca/jfox
>>>
>>>
>>>
>>>> -----Original Message-----
>>>> From: Prof Brian Ripley [mailto:ripley_at_stats.ox.ac.uk]
>>>> Sent: February-17-08 6:42 AM
>>>> To: Gustaf Granath
>>>> Cc: John Fox; r-help_at_r-project.org
>>>> Subject: Re: [R] Weird SEs with effect()
>>>>
>>>> On Sun, 17 Feb 2008, Gustaf Granath wrote:
>>>>
>>>>
>>>>> Hi John,
>>>>>
>>>>> In fact I am still a little bit confused because I had read the
>>>>> ?effect help and the archives.
>>>>>
>>>>> ?effect says that the confidence intervals are on the linear
>>>>>
>>>> predictor
>>>>
>>>>> scale as well. Using exp() on the untransformed confidence
>>>>>
>> intervals
>>
>>>>> gives me the same values as summary(eff). My confidence intervals
>>>>> seems to be correct and reflects the results from my glm models.
>>>>>
>>>>> But when I use exp() to get the correct SEs on the response scale
>>>>>
>> I
>>
>>>>> get SEs that sometimes do not make sense at all. Interestingly I
>>>>>
>> have
>>
>>>> What exactly are you doing here? I suspect you are not using the
>>>> correct
>>>> formula to transform the SEs (you do not just exponeniate them), but
>>>> without the reproducible example asked for we cannot tell.
>>>>
>>>>
>>>>> found a trend. For my model with adjusted means ~ 0.5-1.5 I get
>>>>>
>> huge
>>
>>>>> SEs (SEs > 1, but my glm model shows significant differences
>>>>>
>> between
>>
>>>>> level 1 = 0.55 and level 2 = 1.15). Models with means around 10-20
>>>>>
>> my
>>
>>>>> SEs are fine with exp(). Models with means around 75-125 my SEs
>>>>>
>> get
>>
>>>>> way too small with exp().
>>>>>
>>>>> Something is not right here (or maybe they are but I don not
>>>>> understand it) so I think my best option will be to use the
>>>>>
>>>> confidence
>>>>
>>>>> intervals instead of SEs in my plot.
>>>>>
>>>> If you want confidence intervals, you are better off computing those
>>>>
>> on
>>
>>>> a
>>>> reasonable scale and transforming then. Or using a profile
>>>>
>> likelihood
>>
>>>> to
>>>> compute them (which will be equivariant under monotone scale
>>>> transformations).
>>>>
>>>>
>>>>> Regards,
>>>>>
>>>>> Gustaf
>>>>>
>>>>>
>>>>>
>>>>>> Quoting John Fox <jfox_at_mcmaster.ca>:
>>>>>>
>>>>>> Dear Gustaf,
>>>>>>
>>>>>> From ?effect, "se: a vector of standard errors for the effect, on
>>>>>>
>>>> the scale
>>>>
>>>>>> of the linear predictor." Does that help?
>>>>>>
>>>>>> Regards,
>>>>>> John
>>>>>>
>>>>>> --------------------------------
>>>>>> John Fox, Professor
>>>>>> Department of Sociology
>>>>>> McMaster University
>>>>>> Hamilton, Ontario, Canada L8S 4M4
>>>>>> 905-525-9140x23604
>>>>>> http://socserv.mcmaster.ca/jfox
>>>>>>
>>>>>>
>>>>>>
>>>>>>> -----Original Message-----
>>>>>>> From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-
>>>>>>> project.org] On Behalf Of Gustaf Granath
>>>>>>> Sent: February-16-08 11:43 AM
>>>>>>> To: r-help_at_r-project.org
>>>>>>> Subject: [R] Weird SEs with effect()
>>>>>>>
>>>>>>> Hi all,
>>>>>>>
>>>>>>> Im a little bit confused concerning the effect() command,
>>>>>>>
>> effects
>>
>>>>>>> package.
>>>>>>> I have done several glm models with family=quasipoisson:
>>>>>>>
>>>>>>> model <-glm(Y~X+Q+Z,family=quasipoisson)
>>>>>>>
>>>>>>> and then used
>>>>>>>
>>>>>>> results.effects <-effect("X",model,se=TRUE)
>>>>>>>
>>>>>>> to get the "adjusted means". I am aware about the debate
>>>>>>>
>> concerning
>>
>>>>>>> adjusted means, but you guys just have to trust me - it makes
>>>>>>>
>> sense
>>
>>>>>>> for me.
>>>>>>> Now I want standard error for these means.
>>>>>>>
>>>>>>> results.effects$se
>>>>>>>
>>>>>>> gives me standard error, but it is now it starts to get
>>>>>>>
>> confusing.
>>
>>>> The
>>>>
>>>>>>> given standard errors are very very very small - not realistic.
>>>>>>>
>> I
>>
>>>>>>> thought that maybe these standard errors are not back
>>>>>>>
>> transformed
>>
>>>> so I
>>>>
>>>>>>> used exp() and then the standard errors became realistic.
>>>>>>>
>> However,
>>
>>>> for
>>>>
>>>>>>> one of my glm models with quasipoisson the standard errors make
>>>>>>>
>>>> kind
>>>>
>>>>>>> of sense without using exp() and gets way to big if I use exp().
>>>>>>>
>> To
>>
>>>> be
>>>>
>>>>>>> honest, I get the feeling that Im on the wrong track here.
>>>>>>>
>>>>>>> Basically, I want to know how SE is calculated in effect() (all
>>>>>>>
>> I
>>
>>>> know
>>>>
>>>>>>> is that the reported standard errors are for the fitted values)
>>>>>>>
>> and
>>
>>>> if
>>>>
>>>>>>> anyone knows what is going on here.
>>>>>>>
>>>>>>> Regards,
>>>>>>>
>>>>>>> Gustaf Granath
>>>>>>>
>>>>>>> ______________________________________________
>>>>>>>
>

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