Torvon,
There is some confusion in your postings, as in your first posting the models
were GLM's but with the default gaussian family (not binomial) since the
'family' argument was not present in the glm() call and in the second post you
have references to clm() which is for ordinal response cumulative link models
in the 'ordinal' CRAN package.
If you want binomial logistic regression models, you need to use:
m1 <- glm(sym_bin ~ phq_index, data = data2, family = binomial)
As an example, using the ?infert dataset with a single IV:
MOD <- glm(case ~ education, data = infert, family = binomial)
> summary(MOD)
Call:
glm(formula = case ~ education, family = binomial, data = infert)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.9053 -0.9053 -0.9005 1.4765 1.4823
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -6.931e-01 6.124e-01 -1.132 0.258
education6-11yrs 4.477e-15 6.423e-01 0.000 1.000
education12+ yrs 1.290e-02 6.431e-01 0.020 0.984
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 316.17 on 247 degrees of freedom
Residual deviance: 316.17 on 245 degrees of freedom
AIC: 322.17
Number of Fisher Scoring iterations: 4
> anova(MOD, test = "Chisq")
Analysis of Deviance Table
Model: binomial, link: logit
Response: case
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 247 316.17
education 2 0.0022894 245 316.17 0.9989
Regards,
Marc Schwartz
On Sep 11, 2013, at 11:17 AM, Torvon <tor...@gmail.com> wrote:
> José,
>
> I get the following error message:
>
>> m1<-clm(sym_bin ~ phq_index, data=data2)
>> m2<-clm(sym_bin ~ 1, data=data2)
>> anova(m1,m2,test="Chisq")
>
>> Error in anova.clm(m1, m2, test = "Chisq") :
>> only 'clm' and 'clmm' objects are allowed
>
> My dependent variable is binary, so I don't know what the problem could be.
> See below the model summaries. Thank you! Eiko
>
>> summary(m1)
> formula: sym_bin ~ phq_index
> data: data2
>
> link threshold nobs logLik AIC niter max.grad cond.H
> logit flexible 12348 -4846.49 9710.97 7(0) 2.53e-08 1.4e+02
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> phq_index2 -0.29705 0.11954 -2.485 0.013 *
> phq_index3 0.63382 0.10262 6.176 6.56e-10 ***
> phq_index4 1.53022 0.09664 15.834 < 2e-16 ***
> phq_index5 0.90720 0.09996 9.075 < 2e-16 ***
> phq_index6 -0.03855 0.11337 -0.340 0.734
> phq_index7 -0.06488 0.11394 -0.569 0.569
> phq_index8 -1.15618 0.15156 -7.628 2.38e-14 ***
> phq_index9 -2.50064 0.25670 -9.741 < 2e-16 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Threshold coefficients:
> Estimate Std. Error z value
> 0|1 1.87770 0.07959 23.59
>
>
>> summary(m2)
> formula: sym_bin ~ 1
> data: data2
>
> link threshold nobs logLik AIC niter max.grad
> logit flexible 12348 -5472.48 10946.96 5(0) 1.01e-11
>
> Threshold coefficients:
> 0|1
> 1.642
>
>
>
>
>
>
>
> On 11 September 2013 18:03, Jose Iparraguirre <
> jose.iparragui...@ageuk.org.uk> wrote:
>
>> Hi Eiko,
>>
>> How about this?
>>
>>> anova (m1, m2, test="Chisq")
>>
>> See: ?anova.glm
>>
>> Regards,
>> José
>>
>>
>> Prof. José Iparraguirre
>> Chief Economist
>> Age UK
>>
>>
>>
>> -----Original Message-----
>> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
>> On Behalf Of Torvon
>> Sent: 11 September 2013 16:48
>> To: r-help@r-project.org
>> Subject: [R] Chi-square values in GLM model comparison
>>
>> Hello --
>> I am comparing two
>> GLMs (binomial dependent variable)
>> , the results are the following:
>>> m1<-glm(symptoms ~ phq_index, data=data2) m2<-glm(symptoms ~ 1,
>>> data=data2)
>>
>> Trying to compare these models using
>>> anova (m1, m2)
>> I do not obtain chi-square values or a chi-square difference test;
>> instead, I get loglikelihood ratios:
>>
>>> Likelihood ratio tests of cumulative link models:
>>> formula: link: threshold:
>>> m2 sym_bin ~ 1 logit flexible
>>> m1 sym_bin ~ phq_index logit flexible
>>> no.par AIC logLik LR.stat df Pr(>Chisq)
>>> m2 1 10947 -5472.5
>>> m1 9 9711 -4846.5 1252 8 < 2.2e-16 ***
>>
>> Since reviewers would like me to report chi-square values: how to I obtain
>> them when comparing GLMs? I'm looking for an output similar to the output
>> of the GLMER function in LME4, e.g.:
>>
>>> anova(m3,m4)
>> ...
>>> Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
>>> m3 13 11288 11393 -5630.9
>>> m4 21 11212 11382 -5584.9 92.02 8 < 2.2e-16 ***
>>
>> Thank you!
>> Eiko
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