Reporting binomial logistic regression from R results

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Reporting binomial logistic regression from R results

Frodo Jedi-2
Dear list members,
I need some help in understanding whether I am doing correctly a binomial
logistic regression and whether I am interpreting the results in the
correct way. Also I would need an advice regarding the reporting of the
results from the R functions.

I want to report the results of a binomial logistic regression where I want
to assess difference between the 3 levels of a factor (called System) on
the dependent variable (called Response) taking two values, 0 and 1. My
goal is to understand if the effect of the 3 systems (A,B,C) in System
affect differently Response in a significant way. I am basing my analysis
on this URL: https://stats.idre.ucla.edu/r/dae/logit-regression/

This is the result of my analysis:

> fit <- glm(Response ~ System, data = scrd, family = "binomial")
> summary(fit)

Call:
glm(formula = Response ~ System, family = "binomial", data = scrd)

Deviance Residuals:
    Min       1Q   Median       3Q      Max
-2.8840   0.1775   0.2712   0.2712   0.5008

Coefficients:
             Estimate Std. Error z value Pr(>|z|)
(Intercept)    3.2844     0.2825  11.626  < 2e-16 ***
SystemB  -1.2715     0.3379  -3.763 0.000168 ***
SystemC    0.8588     0.4990   1.721 0.085266 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 411.26  on 1023  degrees of freedom
Residual deviance: 376.76  on 1021  degrees of freedom
AIC: 382.76

Number of Fisher Scoring iterations: 6
Following this analysis I perform the wald test in order to understand
whether there is an overall effect of System:

library(aod)

> wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3)
Wald test:
----------

Chi-squared test:
X2 = 354.6, df = 3, P(> X2) = 0.0
The chi-squared test statistic of 354.6, with 3 degrees of freedom is
associated with a p-value < 0.001 indicating that the overall effect of
System is statistically significant.

Now I check whether there are differences between the coefficients using
again the wald test:

# Here difference between system B and C:

> l <- cbind(0, 1, -1)
> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
Wald test:
----------

Chi-squared test:
X2 = 22.3, df = 1, P(> X2) = 2.3e-06



# Here difference between system A and C:

> l <- cbind(1, 0, -1)
> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
Wald test:
----------

Chi-squared test:
X2 = 12.0, df = 1, P(> X2) = 0.00052



# Here difference between system A and B:

> l <- cbind(1, -1, 0)
> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
Wald test:
----------

Chi-squared test:
X2 = 58.7, df = 1, P(> X2) = 1.8e-14

My understanding is that from this analysis I can state that the three
systems lead to a significantly different Response. Am I right? If so, how
should I report the results of this analysis? What is the correct way?


Thanks in advance

Best wishes

FJ

        [[alternative HTML version deleted]]

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Re: Reporting binomial logistic regression from R results

PIKAL Petr
Dear Frodo (or Jedi)

The results seems to confirm your assumption that 3 systems are different. How you should present results probably depends on how it is usual to report such results in your environment.

BTW. It seems to me like homework and this list has no homework policy (Sorry, if I am mistaken).

Cheers
Petr

> -----Original Message-----
> From: R-help <[hidden email]> On Behalf Of Frodo Jedi
> Sent: Monday, November 12, 2018 2:08 AM
> To: [hidden email]
> Subject: [R] Reporting binomial logistic regression from R results
>
> Dear list members,
> I need some help in understanding whether I am doing correctly a binomial
> logistic regression and whether I am interpreting the results in the correct way.
> Also I would need an advice regarding the reporting of the results from the R
> functions.
>
> I want to report the results of a binomial logistic regression where I want to
> assess difference between the 3 levels of a factor (called System) on the
> dependent variable (called Response) taking two values, 0 and 1. My goal is to
> understand if the effect of the 3 systems (A,B,C) in System affect differently
> Response in a significant way. I am basing my analysis on this URL:
> https://stats.idre.ucla.edu/r/dae/logit-regression/
>
> This is the result of my analysis:
>
> > fit <- glm(Response ~ System, data = scrd, family = "binomial")
> > summary(fit)
>
> Call:
> glm(formula = Response ~ System, family = "binomial", data = scrd)
>
> Deviance Residuals:
>     Min       1Q   Median       3Q      Max
> -2.8840   0.1775   0.2712   0.2712   0.5008
>
> Coefficients:
>              Estimate Std. Error z value Pr(>|z|)
> (Intercept)    3.2844     0.2825  11.626  < 2e-16 ***
> SystemB  -1.2715     0.3379  -3.763 0.000168 ***
> SystemC    0.8588     0.4990   1.721 0.085266 .
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> (Dispersion parameter for binomial family taken to be 1)
>
>     Null deviance: 411.26  on 1023  degrees of freedom Residual deviance:
> 376.76  on 1021  degrees of freedom
> AIC: 382.76
>
> Number of Fisher Scoring iterations: 6
> Following this analysis I perform the wald test in order to understand whether
> there is an overall effect of System:
>
> library(aod)
>
> > wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 354.6, df = 3, P(> X2) = 0.0
> The chi-squared test statistic of 354.6, with 3 degrees of freedom is associated
> with a p-value < 0.001 indicating that the overall effect of System is statistically
> significant.
>
> Now I check whether there are differences between the coefficients using again
> the wald test:
>
> # Here difference between system B and C:
>
> > l <- cbind(0, 1, -1)
> > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 22.3, df = 1, P(> X2) = 2.3e-06
>
>
>
> # Here difference between system A and C:
>
> > l <- cbind(1, 0, -1)
> > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 12.0, df = 1, P(> X2) = 0.00052
>
>
>
> # Here difference between system A and B:
>
> > l <- cbind(1, -1, 0)
> > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 58.7, df = 1, P(> X2) = 1.8e-14
>
> My understanding is that from this analysis I can state that the three systems
> lead to a significantly different Response. Am I right? If so, how should I report
> the results of this analysis? What is the correct way?
>
>
> Thanks in advance
>
> Best wishes
>
> FJ
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
Osobní údaje: Informace o zpracování a ochraně osobních údajů obchodních partnerů PRECHEZA a.s. jsou zveřejněny na: https://www.precheza.cz/zasady-ochrany-osobnich-udaju/ | Information about processing and protection of business partner’s personal data are available on website: https://www.precheza.cz/en/personal-data-protection-principles/
Důvěrnost: Tento e-mail a jakékoliv k němu připojené dokumenty jsou důvěrné a podléhají tomuto právně závaznému prohláąení o vyloučení odpovědnosti: https://www.precheza.cz/01-dovetek/ | This email and any documents attached to it may be confidential and are subject to the legally binding disclaimer: https://www.precheza.cz/en/01-disclaimer/

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[hidden email] mailing list -- To UNSUBSCRIBE and more, see
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Re: Reporting binomial logistic regression from R results

PIKAL Petr
Hi Frodo

I do not consider myself as an arbiter in statistical results and their presentation. Again your text seems to as good as any other.

You should keep responses to mailing list as others could have another opinion.

Cheers
Petr


From: Frodo Jedi <[hidden email]>
Sent: Monday, November 12, 2018 1:48 PM
To: PIKAL Petr <[hidden email]>
Subject: Re: [R] Reporting binomial logistic regression from R results

Dear Petr,
many thanks for your reply. I was wondering whether in your opinion it is correct to report in a journal the following text:


“A logistic regression was performed to ascertain the effects of the system type on the likelihood that participants report correct identifications. The logistic regression model was statistically significant, χ2(3) = 354.6, p < 0.001, indicating an overall effect of the system type on participants' identification performances. The Wald test was used to compare the model coefficients related to the three systems. Results showed that participants’ accuracy was significantly lower for the system B compared to both the system C (χ2(1) = 22.3, p < 0.001) and the system A (χ2(1) = 58.7, p < 0.001), as well as that the system C led to significantly higher identification accuracies than the system A (χ2(1) = 12, p < 0.001).”


Best wishes

FJ





On Mon, Nov 12, 2018 at 10:05 AM PIKAL Petr <[hidden email]<mailto:[hidden email]>> wrote:
Dear Frodo (or Jedi)

The results seems to confirm your assumption that 3 systems are different. How you should present results probably depends on how it is usual to report such results in your environment.

BTW. It seems to me like homework and this list has no homework policy (Sorry, if I am mistaken).

Cheers
Petr

> -----Original Message-----
> From: R-help <[hidden email]<mailto:[hidden email]>> On Behalf Of Frodo Jedi
> Sent: Monday, November 12, 2018 2:08 AM
> To: [hidden email]<mailto:[hidden email]>
> Subject: [R] Reporting binomial logistic regression from R results
>
> Dear list members,
> I need some help in understanding whether I am doing correctly a binomial
> logistic regression and whether I am interpreting the results in the correct way.
> Also I would need an advice regarding the reporting of the results from the R
> functions.
>
> I want to report the results of a binomial logistic regression where I want to
> assess difference between the 3 levels of a factor (called System) on the
> dependent variable (called Response) taking two values, 0 and 1. My goal is to
> understand if the effect of the 3 systems (A,B,C) in System affect differently
> Response in a significant way. I am basing my analysis on this URL:
> https://stats.idre.ucla.edu/r/dae/logit-regression/
>
> This is the result of my analysis:
>
> > fit <- glm(Response ~ System, data = scrd, family = "binomial")
> > summary(fit)
>
> Call:
> glm(formula = Response ~ System, family = "binomial", data = scrd)
>
> Deviance Residuals:
>     Min       1Q   Median       3Q      Max
> -2.8840   0.1775   0.2712   0.2712   0.5008
>
> Coefficients:
>              Estimate Std. Error z value Pr(>|z|)
> (Intercept)    3.2844     0.2825  11.626  < 2e-16 ***
> SystemB  -1.2715     0.3379  -3.763 0.000168 ***
> SystemC    0.8588     0.4990   1.721 0.085266 .
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> (Dispersion parameter for binomial family taken to be 1)
>
>     Null deviance: 411.26  on 1023  degrees of freedom Residual deviance:
> 376.76  on 1021  degrees of freedom
> AIC: 382.76
>
> Number of Fisher Scoring iterations: 6
> Following this analysis I perform the wald test in order to understand whether
> there is an overall effect of System:
>
> library(aod)
>
> > wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 354.6, df = 3, P(> X2) = 0.0
> The chi-squared test statistic of 354.6, with 3 degrees of freedom is associated
> with a p-value < 0.001 indicating that the overall effect of System is statistically
> significant.
>
> Now I check whether there are differences between the coefficients using again
> the wald test:
>
> # Here difference between system B and C:
>
> > l <- cbind(0, 1, -1)
> > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 22.3, df = 1, P(> X2) = 2.3e-06
>
>
>
> # Here difference between system A and C:
>
> > l <- cbind(1, 0, -1)
> > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 12.0, df = 1, P(> X2) = 0.00052
>
>
>
> # Here difference between system A and B:
>
> > l <- cbind(1, -1, 0)
> > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 58.7, df = 1, P(> X2) = 1.8e-14
>
> My understanding is that from this analysis I can state that the three systems
> lead to a significantly different Response. Am I right? If so, how should I report
> the results of this analysis? What is the correct way?
>
>
> Thanks in advance
>
> Best wishes
>
> FJ
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email]<mailto:[hidden email]> mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
Osobní údaje: Informace o zpracování a ochraně osobních údajů obchodních partnerů PRECHEZA a.s. jsou zveřejněny na: https://www.precheza.cz/zasady-ochrany-osobnich-udaju/ | Information about processing and protection of business partner’s personal data are available on website: https://www.precheza.cz/en/personal-data-protection-principles/
Důvěrnost: Tento e-mail a jakékoliv k němu připojené dokumenty jsou důvěrné a podléhají tomuto právně závaznému prohláąení o vyloučení odpovědnosti: https://www.precheza.cz/01-dovetek/ | This email and any documents attached to it may be confidential and are subject to the legally binding disclaimer: https://www.precheza.cz/en/01-disclaimer/

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: Reporting binomial logistic regression from R results

Frodo Jedi-2
Dear Petr,
thank you very much for your feedback.

Can anyone in the list advise me if the way I report the results is correct?

Kind regards

FJ


On Mon, Nov 12, 2018 at 1:02 PM PIKAL Petr <[hidden email]> wrote:

> Hi Frodo
>
>
>
> I do not consider myself as an arbiter in statistical results and their
> presentation. Again your text seems to as good as any other.
>
>
>
> You should keep responses to mailing list as others could have another
> opinion.
>
>
>
> Cheers
>
> Petr
>
>
>
>
>
> *From:* Frodo Jedi <[hidden email]>
> *Sent:* Monday, November 12, 2018 1:48 PM
> *To:* PIKAL Petr <[hidden email]>
> *Subject:* Re: [R] Reporting binomial logistic regression from R results
>
>
>
> Dear Petr,
>
> many thanks for your reply. I was wondering whether in your opinion it is
> correct to report in a journal the following text:
>
>
>
>
>
> “A logistic regression was performed to ascertain the effects of the
> system type on the likelihood that participants report correct
> identifications. The logistic regression model was statistically
> significant, χ2(3) = 354.6, p < 0.001, indicating an overall effect of the
> system type on participants' identification performances. The Wald test was
> used to compare the model coefficients related to the three systems.
> Results showed that participants’ accuracy was significantly lower for the
> system B compared to both the system C (χ2(1) = 22.3, p < 0.001) and the
> system A (χ2(1) = 58.7, p < 0.001), as well as that the system C led to
> significantly higher identification accuracies than the system A (χ2(1) =
> 12, p < 0.001).”
>
>
>
>
>
> Best wishes
>
>
>
> FJ
>
>
>
>
>
>
>
>
>
>
>
> On Mon, Nov 12, 2018 at 10:05 AM PIKAL Petr <[hidden email]>
> wrote:
>
> Dear Frodo (or Jedi)
>
> The results seems to confirm your assumption that 3 systems are different.
> How you should present results probably depends on how it is usual to
> report such results in your environment.
>
> BTW. It seems to me like homework and this list has no homework policy
> (Sorry, if I am mistaken).
>
> Cheers
> Petr
> > -----Original Message-----
> > From: R-help <[hidden email]> On Behalf Of Frodo Jedi
> > Sent: Monday, November 12, 2018 2:08 AM
> > To: [hidden email]
> > Subject: [R] Reporting binomial logistic regression from R results
> >
> > Dear list members,
> > I need some help in understanding whether I am doing correctly a binomial
> > logistic regression and whether I am interpreting the results in the
> correct way.
> > Also I would need an advice regarding the reporting of the results from
> the R
> > functions.
> >
> > I want to report the results of a binomial logistic regression where I
> want to
> > assess difference between the 3 levels of a factor (called System) on the
> > dependent variable (called Response) taking two values, 0 and 1. My goal
> is to
> > understand if the effect of the 3 systems (A,B,C) in System affect
> differently
> > Response in a significant way. I am basing my analysis on this URL:
> > https://stats.idre.ucla.edu/r/dae/logit-regression/
> >
> > This is the result of my analysis:
> >
> > > fit <- glm(Response ~ System, data = scrd, family = "binomial")
> > > summary(fit)
> >
> > Call:
> > glm(formula = Response ~ System, family = "binomial", data = scrd)
> >
> > Deviance Residuals:
> >     Min       1Q   Median       3Q      Max
> > -2.8840   0.1775   0.2712   0.2712   0.5008
> >
> > Coefficients:
> >              Estimate Std. Error z value Pr(>|z|)
> > (Intercept)    3.2844     0.2825  11.626  < 2e-16 ***
> > SystemB  -1.2715     0.3379  -3.763 0.000168 ***
> > SystemC    0.8588     0.4990   1.721 0.085266 .
> > ---
> > Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >
> > (Dispersion parameter for binomial family taken to be 1)
> >
> >     Null deviance: 411.26  on 1023  degrees of freedom Residual deviance:
> > 376.76  on 1021  degrees of freedom
> > AIC: 382.76
> >
> > Number of Fisher Scoring iterations: 6
> > Following this analysis I perform the wald test in order to understand
> whether
> > there is an overall effect of System:
> >
> > library(aod)
> >
> > > wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3)
> > Wald test:
> > ----------
> >
> > Chi-squared test:
> > X2 = 354.6, df = 3, P(> X2) = 0.0
> > The chi-squared test statistic of 354.6, with 3 degrees of freedom is
> associated
> > with a p-value < 0.001 indicating that the overall effect of System is
> statistically
> > significant.
> >
> > Now I check whether there are differences between the coefficients using
> again
> > the wald test:
> >
> > # Here difference between system B and C:
> >
> > > l <- cbind(0, 1, -1)
> > > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> > Wald test:
> > ----------
> >
> > Chi-squared test:
> > X2 = 22.3, df = 1, P(> X2) = 2.3e-06
> >
> >
> >
> > # Here difference between system A and C:
> >
> > > l <- cbind(1, 0, -1)
> > > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> > Wald test:
> > ----------
> >
> > Chi-squared test:
> > X2 = 12.0, df = 1, P(> X2) = 0.00052
> >
> >
> >
> > # Here difference between system A and B:
> >
> > > l <- cbind(1, -1, 0)
> > > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> > Wald test:
> > ----------
> >
> > Chi-squared test:
> > X2 = 58.7, df = 1, P(> X2) = 1.8e-14
> >
> > My understanding is that from this analysis I can state that the three
> systems
> > lead to a significantly different Response. Am I right? If so, how
> should I report
> > the results of this analysis? What is the correct way?
> >
> >
> > Thanks in advance
> >
> > Best wishes
> >
> > FJ
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> Osobní údaje: Informace o zpracování a ochraně osobních údajů obchodních
> partnerů PRECHEZA a.s. jsou zveřejněny na:
> https://www.precheza.cz/zasady-ochrany-osobnich-udaju/ | Information
> about processing and protection of business partner’s personal data are
> available on website:
> https://www.precheza.cz/en/personal-data-protection-principles/
> Důvěrnost: Tento e-mail a jakékoliv k němu připojené dokumenty jsou
> důvěrné a podléhají tomuto právně závaznému prohláąení o vyloučení
> odpovědnosti: https://www.precheza.cz/01-dovetek/ | This email and any
> documents attached to it may be confidential and are subject to the legally
> binding disclaimer: https://www.precheza.cz/en/01-disclaimer/
>
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
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Re: Reporting binomial logistic regression from R results

Eik Vettorazzi-2
In reply to this post by Frodo Jedi-2
Dear Jedi,
please use the source carefully. A and C are not statistically different
at the 5% level, which can be inferred from glm output. Your last two
wald.tests don't test what you want to, since your model contains an
intercept term. You specified contrasts which tests A vs B-A, ie A-
(B-A)==0 <-> 2*A-B==0 which is not intended I think. Have a look at
?contr.treatment and re-read your source doc to get an idea what dummy
coding and indicatr variables are about.

Cheers


Am 12.11.2018 um 02:07 schrieb Frodo Jedi:

> Dear list members,
> I need some help in understanding whether I am doing correctly a binomial
> logistic regression and whether I am interpreting the results in the
> correct way. Also I would need an advice regarding the reporting of the
> results from the R functions.
>
> I want to report the results of a binomial logistic regression where I want
> to assess difference between the 3 levels of a factor (called System) on
> the dependent variable (called Response) taking two values, 0 and 1. My
> goal is to understand if the effect of the 3 systems (A,B,C) in System
> affect differently Response in a significant way. I am basing my analysis
> on this URL: https://stats.idre.ucla.edu/r/dae/logit-regression/
>
> This is the result of my analysis:
>
>> fit <- glm(Response ~ System, data = scrd, family = "binomial")
>> summary(fit)
>
> Call:
> glm(formula = Response ~ System, family = "binomial", data = scrd)
>
> Deviance Residuals:
>      Min       1Q   Median       3Q      Max
> -2.8840   0.1775   0.2712   0.2712   0.5008
>
> Coefficients:
>               Estimate Std. Error z value Pr(>|z|)
> (Intercept)    3.2844     0.2825  11.626  < 2e-16 ***
> SystemB  -1.2715     0.3379  -3.763 0.000168 ***
> SystemC    0.8588     0.4990   1.721 0.085266 .
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> (Dispersion parameter for binomial family taken to be 1)
>
>      Null deviance: 411.26  on 1023  degrees of freedom
> Residual deviance: 376.76  on 1021  degrees of freedom
> AIC: 382.76
>
> Number of Fisher Scoring iterations: 6
> Following this analysis I perform the wald test in order to understand
> whether there is an overall effect of System:
>
> library(aod)
>
>> wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 354.6, df = 3, P(> X2) = 0.0
> The chi-squared test statistic of 354.6, with 3 degrees of freedom is
> associated with a p-value < 0.001 indicating that the overall effect of
> System is statistically significant.
>
> Now I check whether there are differences between the coefficients using
> again the wald test:
>
> # Here difference between system B and C:
>
>> l <- cbind(0, 1, -1)
>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 22.3, df = 1, P(> X2) = 2.3e-06
>
>
>
> # Here difference between system A and C:
>
>> l <- cbind(1, 0, -1)
>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 12.0, df = 1, P(> X2) = 0.00052
>
>
>
> # Here difference between system A and B:
>
>> l <- cbind(1, -1, 0)
>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 58.7, df = 1, P(> X2) = 1.8e-14
>
> My understanding is that from this analysis I can state that the three
> systems lead to a significantly different Response. Am I right? If so, how
> should I report the results of this analysis? What is the correct way?
>
>
> Thanks in advance
>
> Best wishes
>
> FJ
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

--
Eik Vettorazzi

Department of Medical Biometry and Epidemiology
University Medical Center Hamburg-Eppendorf

Martinistrasse 52
building W 34
20246 Hamburg

Phone: +49 (0) 40 7410 - 58243
Fax:   +49 (0) 40 7410 - 57790
Web: www.uke.de/imbe
--

_____________________________________________________________________

Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen Rechts; Gerichtsstand: Hamburg | www.uke.de
Vorstandsmitglieder: Prof. Dr. Burkhard Göke (Vorsitzender), Prof. Dr. Dr. Uwe Koch-Gromus, Joachim Prölß, Marya Verdel
_____________________________________________________________________

SAVE PAPER - THINK BEFORE PRINTING
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Re: Reporting binomial logistic regression from R results

Peter Dalgaard-2
Yes, only one of the pairwise comparisons (B vs. C) is right. Also, the overall test has 3 degrees of freedom whereas a comparison of 3 groups should have 2. You (meaning Frodo) are testing that _all 3_ regression coefficients are zero, intercept included. That would imply that all three systems have response probablilities og 0.5, which is not likely what you want.

This all suggests that you are struggling with the interpretation of the regression coefficients and their role in the linear predictor. This should be covered by any good book on logistic regression.

-pd  

> On 12 Nov 2018, at 14:15 , Eik Vettorazzi <[hidden email]> wrote:
>
> Dear Jedi,
> please use the source carefully. A and C are not statistically different at the 5% level, which can be inferred from glm output. Your last two wald.tests don't test what you want to, since your model contains an intercept term. You specified contrasts which tests A vs B-A, ie A- (B-A)==0 <-> 2*A-B==0 which is not intended I think. Have a look at ?contr.treatment and re-read your source doc to get an idea what dummy coding and indicatr variables are about.
>
> Cheers
>
>
> Am 12.11.2018 um 02:07 schrieb Frodo Jedi:
>> Dear list members,
>> I need some help in understanding whether I am doing correctly a binomial
>> logistic regression and whether I am interpreting the results in the
>> correct way. Also I would need an advice regarding the reporting of the
>> results from the R functions.
>> I want to report the results of a binomial logistic regression where I want
>> to assess difference between the 3 levels of a factor (called System) on
>> the dependent variable (called Response) taking two values, 0 and 1. My
>> goal is to understand if the effect of the 3 systems (A,B,C) in System
>> affect differently Response in a significant way. I am basing my analysis
>> on this URL: https://stats.idre.ucla.edu/r/dae/logit-regression/
>> This is the result of my analysis:
>>> fit <- glm(Response ~ System, data = scrd, family = "binomial")
>>> summary(fit)
>> Call:
>> glm(formula = Response ~ System, family = "binomial", data = scrd)
>> Deviance Residuals:
>>     Min       1Q   Median       3Q      Max
>> -2.8840   0.1775   0.2712   0.2712   0.5008
>> Coefficients:
>>              Estimate Std. Error z value Pr(>|z|)
>> (Intercept)    3.2844     0.2825  11.626  < 2e-16 ***
>> SystemB  -1.2715     0.3379  -3.763 0.000168 ***
>> SystemC    0.8588     0.4990   1.721 0.085266 .
>> ---
>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>> (Dispersion parameter for binomial family taken to be 1)
>>     Null deviance: 411.26  on 1023  degrees of freedom
>> Residual deviance: 376.76  on 1021  degrees of freedom
>> AIC: 382.76
>> Number of Fisher Scoring iterations: 6
>> Following this analysis I perform the wald test in order to understand
>> whether there is an overall effect of System:
>> library(aod)
>>> wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3)
>> Wald test:
>> ----------
>> Chi-squared test:
>> X2 = 354.6, df = 3, P(> X2) = 0.0
>> The chi-squared test statistic of 354.6, with 3 degrees of freedom is
>> associated with a p-value < 0.001 indicating that the overall effect of
>> System is statistically significant.
>> Now I check whether there are differences between the coefficients using
>> again the wald test:
>> # Here difference between system B and C:
>>> l <- cbind(0, 1, -1)
>>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
>> Wald test:
>> ----------
>> Chi-squared test:
>> X2 = 22.3, df = 1, P(> X2) = 2.3e-06
>> # Here difference between system A and C:
>>> l <- cbind(1, 0, -1)
>>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
>> Wald test:
>> ----------
>> Chi-squared test:
>> X2 = 12.0, df = 1, P(> X2) = 0.00052
>> # Here difference between system A and B:
>>> l <- cbind(1, -1, 0)
>>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
>> Wald test:
>> ----------
>> Chi-squared test:
>> X2 = 58.7, df = 1, P(> X2) = 1.8e-14
>> My understanding is that from this analysis I can state that the three
>> systems lead to a significantly different Response. Am I right? If so, how
>> should I report the results of this analysis? What is the correct way?
>> Thanks in advance
>> Best wishes
>> FJ
>> [[alternative HTML version deleted]]
>> ______________________________________________
>> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> --
> Eik Vettorazzi
>
> Department of Medical Biometry and Epidemiology
> University Medical Center Hamburg-Eppendorf
>
> Martinistrasse 52
> building W 34
> 20246 Hamburg
>
> Phone: +49 (0) 40 7410 - 58243
> Fax:   +49 (0) 40 7410 - 57790
> Web: www.uke.de/imbe
> --
>
> _____________________________________________________________________
>
> Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen Rechts; Gerichtsstand: Hamburg | www.uke.de
> Vorstandsmitglieder: Prof. Dr. Burkhard Göke (Vorsitzender), Prof. Dr. Dr. Uwe Koch-Gromus, Joachim Prölß, Marya Verdel
> _____________________________________________________________________
>
> SAVE PAPER - THINK BEFORE PRINTING
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: [hidden email]  Priv: [hidden email]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Re: Reporting binomial logistic regression from R results

Frodo Jedi-2
Dear Peter and Eik,
I am very grateful to you for your replies.
My current understanding is that from the GLM analysis I can indeed
conclude that the response predicted by System A is significantly different
from that of System B, while the pairwise comparison A vs C leads to non
significance. Now the Wald test seems to be correct only for Systems B vs
C, indicating that the pairwise System B vs System C is significant. Am I
correct?

However, my current understanding is also that I should use contrasts
instead of the wald test. So the default contrasts is with the System A,
now I should re-perform the GLM with another base. I tried to use the
option "contrasts" of the glm:

> fit1 <- glm(Response ~ System, data = scrd, family = "binomial",
contrasts = contr.treatment(3, base=1,contrasts=TRUE))
> summary(fit1)

> fit2 <- glm(Response ~ System, data = scrd, family = "binomial",
contrasts = contr.treatment(3, base=2,contrasts=TRUE))
> summary(fit2)

> fit3 <- glm(Response ~ System, data = scrd, family = "binomial",
contrasts = contr.treatment(3, base=3,contrasts=TRUE))
> summary(fit3)

However, the output of these three summary functions are identical. Why?
That option should have changed the base, but apparently this is not the
case.


Another analysis I found online (at this link
https://stats.stackexchange.com/questions/60352/comparing-levels-of-factors-after-a-glm-in-r
)
to understand the differences between the 3 levels is to use glth with
Tuckey. I performed the following:

> library(multcomp)
> summary(glht(fit, mcp(System="Tukey")))

Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: glm(formula = Response ~ System, family = "binomial", data = scrd)

Linear Hypotheses:
                      Estimate Std. Error z value Pr(>|z|)
B - A == 0  -1.2715     0.3379  -3.763 0.000445 ***
C - A == 0    0.8588     0.4990   1.721 0.192472
C - B == 0     2.1303     0.4512   4.722  < 1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)


Is this Tukey analysis correct?


I am a bit confused on what analysis I should do. I am doing my very best
to study all resources I can find, but I would really need some help from
experts, especially in using R.


Best wishes

FJ






On Mon, Nov 12, 2018 at 1:46 PM peter dalgaard <[hidden email]> wrote:

> Yes, only one of the pairwise comparisons (B vs. C) is right. Also, the
> overall test has 3 degrees of freedom whereas a comparison of 3 groups
> should have 2. You (meaning Frodo) are testing that _all 3_ regression
> coefficients are zero, intercept included. That would imply that all three
> systems have response probablilities og 0.5, which is not likely what you
> want.
>
> This all suggests that you are struggling with the interpretation of the
> regression coefficients and their role in the linear predictor. This should
> be covered by any good book on logistic regression.
>
> -pd
>
> > On 12 Nov 2018, at 14:15 , Eik Vettorazzi <[hidden email]> wrote:
> >
> > Dear Jedi,
> > please use the source carefully. A and C are not statistically different
> at the 5% level, which can be inferred from glm output. Your last two
> wald.tests don't test what you want to, since your model contains an
> intercept term. You specified contrasts which tests A vs B-A, ie A-
> (B-A)==0 <-> 2*A-B==0 which is not intended I think. Have a look at
> ?contr.treatment and re-read your source doc to get an idea what dummy
> coding and indicatr variables are about.
> >
> > Cheers
> >
> >
> > Am 12.11.2018 um 02:07 schrieb Frodo Jedi:
> >> Dear list members,
> >> I need some help in understanding whether I am doing correctly a
> binomial
> >> logistic regression and whether I am interpreting the results in the
> >> correct way. Also I would need an advice regarding the reporting of the
> >> results from the R functions.
> >> I want to report the results of a binomial logistic regression where I
> want
> >> to assess difference between the 3 levels of a factor (called System) on
> >> the dependent variable (called Response) taking two values, 0 and 1. My
> >> goal is to understand if the effect of the 3 systems (A,B,C) in System
> >> affect differently Response in a significant way. I am basing my
> analysis
> >> on this URL: https://stats.idre.ucla.edu/r/dae/logit-regression/
> >> This is the result of my analysis:
> >>> fit <- glm(Response ~ System, data = scrd, family = "binomial")
> >>> summary(fit)
> >> Call:
> >> glm(formula = Response ~ System, family = "binomial", data = scrd)
> >> Deviance Residuals:
> >>     Min       1Q   Median       3Q      Max
> >> -2.8840   0.1775   0.2712   0.2712   0.5008
> >> Coefficients:
> >>              Estimate Std. Error z value Pr(>|z|)
> >> (Intercept)    3.2844     0.2825  11.626  < 2e-16 ***
> >> SystemB  -1.2715     0.3379  -3.763 0.000168 ***
> >> SystemC    0.8588     0.4990   1.721 0.085266 .
> >> ---
> >> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >> (Dispersion parameter for binomial family taken to be 1)
> >>     Null deviance: 411.26  on 1023  degrees of freedom
> >> Residual deviance: 376.76  on 1021  degrees of freedom
> >> AIC: 382.76
> >> Number of Fisher Scoring iterations: 6
> >> Following this analysis I perform the wald test in order to understand
> >> whether there is an overall effect of System:
> >> library(aod)
> >>> wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3)
> >> Wald test:
> >> ----------
> >> Chi-squared test:
> >> X2 = 354.6, df = 3, P(> X2) = 0.0
> >> The chi-squared test statistic of 354.6, with 3 degrees of freedom is
> >> associated with a p-value < 0.001 indicating that the overall effect of
> >> System is statistically significant.
> >> Now I check whether there are differences between the coefficients using
> >> again the wald test:
> >> # Here difference between system B and C:
> >>> l <- cbind(0, 1, -1)
> >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> >> Wald test:
> >> ----------
> >> Chi-squared test:
> >> X2 = 22.3, df = 1, P(> X2) = 2.3e-06
> >> # Here difference between system A and C:
> >>> l <- cbind(1, 0, -1)
> >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> >> Wald test:
> >> ----------
> >> Chi-squared test:
> >> X2 = 12.0, df = 1, P(> X2) = 0.00052
> >> # Here difference between system A and B:
> >>> l <- cbind(1, -1, 0)
> >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> >> Wald test:
> >> ----------
> >> Chi-squared test:
> >> X2 = 58.7, df = 1, P(> X2) = 1.8e-14
> >> My understanding is that from this analysis I can state that the three
> >> systems lead to a significantly different Response. Am I right? If so,
> how
> >> should I report the results of this analysis? What is the correct way?
> >> Thanks in advance
> >> Best wishes
> >> FJ
> >>      [[alternative HTML version deleted]]
> >> ______________________________________________
> >> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> >> https://stat.ethz.ch/mailman/listinfo/r-help
> >> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> >> and provide commented, minimal, self-contained, reproducible code.
> >
> > --
> > Eik Vettorazzi
> >
> > Department of Medical Biometry and Epidemiology
> > University Medical Center Hamburg-Eppendorf
> >
> > Martinistrasse 52
> > building W 34
> > 20246 Hamburg
> >
> > Phone: +49 (0) 40 7410 - 58243
> > Fax:   +49 (0) 40 7410 - 57790
> > Web: www.uke.de/imbe
> > --
> >
> > _____________________________________________________________________
> >
> > Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen
> Rechts; Gerichtsstand: Hamburg | www.uke.de
> > Vorstandsmitglieder: Prof. Dr. Burkhard Göke (Vorsitzender), Prof. Dr.
> Dr. Uwe Koch-Gromus, Joachim Prölß, Marya Verdel
> > _____________________________________________________________________
> >
> > SAVE PAPER - THINK BEFORE PRINTING
> > ______________________________________________
> > [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
> --
> Peter Dalgaard, Professor,
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Office: A 4.23
> Email: [hidden email]  Priv: [hidden email]
>
>
>
>
>
>
>
>
>
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
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Re: Reporting binomial logistic regression from R results

Bert Gunter-2
Generally speaking, this list is about questions on R programming, not
statistical issues. However, I grant you that your queries are in something
of a gray area intersecting both.

Nevertheless, based on your admitted confusion, I would recommend that you
find a local statistical expert with whom you can consult 1-1 if at all
possible. As others have already noted, you statistical understanding is
muddy, and it can be quite difficult to resolve such confusion in online
forums like this that cannot provide the close back and forth that may be
required (as well as further appropriate study).

Best,
Bert

On Mon, Nov 12, 2018 at 11:09 AM Frodo Jedi <[hidden email]>
wrote:

> Dear Peter and Eik,
> I am very grateful to you for your replies.
> My current understanding is that from the GLM analysis I can indeed
> conclude that the response predicted by System A is significantly different
> from that of System B, while the pairwise comparison A vs C leads to non
> significance. Now the Wald test seems to be correct only for Systems B vs
> C, indicating that the pairwise System B vs System C is significant. Am I
> correct?
>
> However, my current understanding is also that I should use contrasts
> instead of the wald test. So the default contrasts is with the System A,
> now I should re-perform the GLM with another base. I tried to use the
> option "contrasts" of the glm:
>
> > fit1 <- glm(Response ~ System, data = scrd, family = "binomial",
> contrasts = contr.treatment(3, base=1,contrasts=TRUE))
> > summary(fit1)
>
> > fit2 <- glm(Response ~ System, data = scrd, family = "binomial",
> contrasts = contr.treatment(3, base=2,contrasts=TRUE))
> > summary(fit2)
>
> > fit3 <- glm(Response ~ System, data = scrd, family = "binomial",
> contrasts = contr.treatment(3, base=3,contrasts=TRUE))
> > summary(fit3)
>
> However, the output of these three summary functions are identical. Why?
> That option should have changed the base, but apparently this is not the
> case.
>
>
> Another analysis I found online (at this link
>
> https://stats.stackexchange.com/questions/60352/comparing-levels-of-factors-after-a-glm-in-r
> )
> to understand the differences between the 3 levels is to use glth with
> Tuckey. I performed the following:
>
> > library(multcomp)
> > summary(glht(fit, mcp(System="Tukey")))
>
> Simultaneous Tests for General Linear Hypotheses
>
> Multiple Comparisons of Means: Tukey Contrasts
>
>
> Fit: glm(formula = Response ~ System, family = "binomial", data = scrd)
>
> Linear Hypotheses:
>                       Estimate Std. Error z value Pr(>|z|)
> B - A == 0  -1.2715     0.3379  -3.763 0.000445 ***
> C - A == 0    0.8588     0.4990   1.721 0.192472
> C - B == 0     2.1303     0.4512   4.722  < 1e-04 ***
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> (Adjusted p values reported -- single-step method)
>
>
> Is this Tukey analysis correct?
>
>
> I am a bit confused on what analysis I should do. I am doing my very best
> to study all resources I can find, but I would really need some help from
> experts, especially in using R.
>
>
> Best wishes
>
> FJ
>
>
>
>
>
>
> On Mon, Nov 12, 2018 at 1:46 PM peter dalgaard <[hidden email]> wrote:
>
> > Yes, only one of the pairwise comparisons (B vs. C) is right. Also, the
> > overall test has 3 degrees of freedom whereas a comparison of 3 groups
> > should have 2. You (meaning Frodo) are testing that _all 3_ regression
> > coefficients are zero, intercept included. That would imply that all
> three
> > systems have response probablilities og 0.5, which is not likely what you
> > want.
> >
> > This all suggests that you are struggling with the interpretation of the
> > regression coefficients and their role in the linear predictor. This
> should
> > be covered by any good book on logistic regression.
> >
> > -pd
> >
> > > On 12 Nov 2018, at 14:15 , Eik Vettorazzi <[hidden email]> wrote:
> > >
> > > Dear Jedi,
> > > please use the source carefully. A and C are not statistically
> different
> > at the 5% level, which can be inferred from glm output. Your last two
> > wald.tests don't test what you want to, since your model contains an
> > intercept term. You specified contrasts which tests A vs B-A, ie A-
> > (B-A)==0 <-> 2*A-B==0 which is not intended I think. Have a look at
> > ?contr.treatment and re-read your source doc to get an idea what dummy
> > coding and indicatr variables are about.
> > >
> > > Cheers
> > >
> > >
> > > Am 12.11.2018 um 02:07 schrieb Frodo Jedi:
> > >> Dear list members,
> > >> I need some help in understanding whether I am doing correctly a
> > binomial
> > >> logistic regression and whether I am interpreting the results in the
> > >> correct way. Also I would need an advice regarding the reporting of
> the
> > >> results from the R functions.
> > >> I want to report the results of a binomial logistic regression where I
> > want
> > >> to assess difference between the 3 levels of a factor (called System)
> on
> > >> the dependent variable (called Response) taking two values, 0 and 1.
> My
> > >> goal is to understand if the effect of the 3 systems (A,B,C) in System
> > >> affect differently Response in a significant way. I am basing my
> > analysis
> > >> on this URL: https://stats.idre.ucla.edu/r/dae/logit-regression/
> > >> This is the result of my analysis:
> > >>> fit <- glm(Response ~ System, data = scrd, family = "binomial")
> > >>> summary(fit)
> > >> Call:
> > >> glm(formula = Response ~ System, family = "binomial", data = scrd)
> > >> Deviance Residuals:
> > >>     Min       1Q   Median       3Q      Max
> > >> -2.8840   0.1775   0.2712   0.2712   0.5008
> > >> Coefficients:
> > >>              Estimate Std. Error z value Pr(>|z|)
> > >> (Intercept)    3.2844     0.2825  11.626  < 2e-16 ***
> > >> SystemB  -1.2715     0.3379  -3.763 0.000168 ***
> > >> SystemC    0.8588     0.4990   1.721 0.085266 .
> > >> ---
> > >> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> > >> (Dispersion parameter for binomial family taken to be 1)
> > >>     Null deviance: 411.26  on 1023  degrees of freedom
> > >> Residual deviance: 376.76  on 1021  degrees of freedom
> > >> AIC: 382.76
> > >> Number of Fisher Scoring iterations: 6
> > >> Following this analysis I perform the wald test in order to understand
> > >> whether there is an overall effect of System:
> > >> library(aod)
> > >>> wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3)
> > >> Wald test:
> > >> ----------
> > >> Chi-squared test:
> > >> X2 = 354.6, df = 3, P(> X2) = 0.0
> > >> The chi-squared test statistic of 354.6, with 3 degrees of freedom is
> > >> associated with a p-value < 0.001 indicating that the overall effect
> of
> > >> System is statistically significant.
> > >> Now I check whether there are differences between the coefficients
> using
> > >> again the wald test:
> > >> # Here difference between system B and C:
> > >>> l <- cbind(0, 1, -1)
> > >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> > >> Wald test:
> > >> ----------
> > >> Chi-squared test:
> > >> X2 = 22.3, df = 1, P(> X2) = 2.3e-06
> > >> # Here difference between system A and C:
> > >>> l <- cbind(1, 0, -1)
> > >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> > >> Wald test:
> > >> ----------
> > >> Chi-squared test:
> > >> X2 = 12.0, df = 1, P(> X2) = 0.00052
> > >> # Here difference between system A and B:
> > >>> l <- cbind(1, -1, 0)
> > >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> > >> Wald test:
> > >> ----------
> > >> Chi-squared test:
> > >> X2 = 58.7, df = 1, P(> X2) = 1.8e-14
> > >> My understanding is that from this analysis I can state that the three
> > >> systems lead to a significantly different Response. Am I right? If so,
> > how
> > >> should I report the results of this analysis? What is the correct way?
> > >> Thanks in advance
> > >> Best wishes
> > >> FJ
> > >>      [[alternative HTML version deleted]]
> > >> ______________________________________________
> > >> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> > >> https://stat.ethz.ch/mailman/listinfo/r-help
> > >> PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > >> and provide commented, minimal, self-contained, reproducible code.
> > >
> > > --
> > > Eik Vettorazzi
> > >
> > > Department of Medical Biometry and Epidemiology
> > > University Medical Center Hamburg-Eppendorf
> > >
> > > Martinistrasse 52
> > > building W 34
> > > 20246 Hamburg
> > >
> > > Phone: +49 (0) 40 7410 - 58243
> > > Fax:   +49 (0) 40 7410 - 57790
> > > Web: www.uke.de/imbe
> > > --
> > >
> > > _____________________________________________________________________
> > >
> > > Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen
> > Rechts; Gerichtsstand: Hamburg | www.uke.de
> > > Vorstandsmitglieder: Prof. Dr. Burkhard Göke (Vorsitzender), Prof. Dr.
> > Dr. Uwe Koch-Gromus, Joachim Prölß, Marya Verdel
> > > _____________________________________________________________________
> > >
> > > SAVE PAPER - THINK BEFORE PRINTING
> > > ______________________________________________
> > > [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> > > https://stat.ethz.ch/mailman/listinfo/r-help
> > > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > > and provide commented, minimal, self-contained, reproducible code.
> >
> > --
> > Peter Dalgaard, Professor,
> > Center for Statistics, Copenhagen Business School
> > Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> > Phone: (+45)38153501
> > Office: A 4.23
> > Email: [hidden email]  Priv: [hidden email]
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

        [[alternative HTML version deleted]]

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Re: Reporting binomial logistic regression from R results

Frodo Jedi-2
Dear Bert,
I understand and thanks for your recommendation. Unfortunately I do not
have any possibility to contact a statistical expert at the moment. So this
forum experts' recommendation would be crucial to me to understand how R
works in relation to my question.
I hope that someone could reply to my last questions.

Best regards

FJ

On Mon, Nov 12, 2018 at 7:48 PM Bert Gunter <[hidden email]> wrote:

> Generally speaking, this list is about questions on R programming, not
> statistical issues. However, I grant you that your queries are in something
> of a gray area intersecting both.
>
> Nevertheless, based on your admitted confusion, I would recommend that you
> find a local statistical expert with whom you can consult 1-1 if at all
> possible. As others have already noted, you statistical understanding is
> muddy, and it can be quite difficult to resolve such confusion in online
> forums like this that cannot provide the close back and forth that may be
> required (as well as further appropriate study).
>
> Best,
> Bert
>
> On Mon, Nov 12, 2018 at 11:09 AM Frodo Jedi <
> [hidden email]> wrote:
>
>> Dear Peter and Eik,
>> I am very grateful to you for your replies.
>> My current understanding is that from the GLM analysis I can indeed
>> conclude that the response predicted by System A is significantly
>> different
>> from that of System B, while the pairwise comparison A vs C leads to non
>> significance. Now the Wald test seems to be correct only for Systems B vs
>> C, indicating that the pairwise System B vs System C is significant. Am I
>> correct?
>>
>> However, my current understanding is also that I should use contrasts
>> instead of the wald test. So the default contrasts is with the System A,
>> now I should re-perform the GLM with another base. I tried to use the
>> option "contrasts" of the glm:
>>
>> > fit1 <- glm(Response ~ System, data = scrd, family = "binomial",
>> contrasts = contr.treatment(3, base=1,contrasts=TRUE))
>> > summary(fit1)
>>
>> > fit2 <- glm(Response ~ System, data = scrd, family = "binomial",
>> contrasts = contr.treatment(3, base=2,contrasts=TRUE))
>> > summary(fit2)
>>
>> > fit3 <- glm(Response ~ System, data = scrd, family = "binomial",
>> contrasts = contr.treatment(3, base=3,contrasts=TRUE))
>> > summary(fit3)
>>
>> However, the output of these three summary functions are identical. Why?
>> That option should have changed the base, but apparently this is not the
>> case.
>>
>>
>> Another analysis I found online (at this link
>>
>> https://stats.stackexchange.com/questions/60352/comparing-levels-of-factors-after-a-glm-in-r
>> )
>> to understand the differences between the 3 levels is to use glth with
>> Tuckey. I performed the following:
>>
>> > library(multcomp)
>> > summary(glht(fit, mcp(System="Tukey")))
>>
>> Simultaneous Tests for General Linear Hypotheses
>>
>> Multiple Comparisons of Means: Tukey Contrasts
>>
>>
>> Fit: glm(formula = Response ~ System, family = "binomial", data = scrd)
>>
>> Linear Hypotheses:
>>                       Estimate Std. Error z value Pr(>|z|)
>> B - A == 0  -1.2715     0.3379  -3.763 0.000445 ***
>> C - A == 0    0.8588     0.4990   1.721 0.192472
>> C - B == 0     2.1303     0.4512   4.722  < 1e-04 ***
>> ---
>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>> (Adjusted p values reported -- single-step method)
>>
>>
>> Is this Tukey analysis correct?
>>
>>
>> I am a bit confused on what analysis I should do. I am doing my very best
>> to study all resources I can find, but I would really need some help from
>> experts, especially in using R.
>>
>>
>> Best wishes
>>
>> FJ
>>
>>
>>
>>
>>
>>
>> On Mon, Nov 12, 2018 at 1:46 PM peter dalgaard <[hidden email]> wrote:
>>
>> > Yes, only one of the pairwise comparisons (B vs. C) is right. Also, the
>> > overall test has 3 degrees of freedom whereas a comparison of 3 groups
>> > should have 2. You (meaning Frodo) are testing that _all 3_ regression
>> > coefficients are zero, intercept included. That would imply that all
>> three
>> > systems have response probablilities og 0.5, which is not likely what
>> you
>> > want.
>> >
>> > This all suggests that you are struggling with the interpretation of the
>> > regression coefficients and their role in the linear predictor. This
>> should
>> > be covered by any good book on logistic regression.
>> >
>> > -pd
>> >
>> > > On 12 Nov 2018, at 14:15 , Eik Vettorazzi <[hidden email]>
>> wrote:
>> > >
>> > > Dear Jedi,
>> > > please use the source carefully. A and C are not statistically
>> different
>> > at the 5% level, which can be inferred from glm output. Your last two
>> > wald.tests don't test what you want to, since your model contains an
>> > intercept term. You specified contrasts which tests A vs B-A, ie A-
>> > (B-A)==0 <-> 2*A-B==0 which is not intended I think. Have a look at
>> > ?contr.treatment and re-read your source doc to get an idea what dummy
>> > coding and indicatr variables are about.
>> > >
>> > > Cheers
>> > >
>> > >
>> > > Am 12.11.2018 um 02:07 schrieb Frodo Jedi:
>> > >> Dear list members,
>> > >> I need some help in understanding whether I am doing correctly a
>> > binomial
>> > >> logistic regression and whether I am interpreting the results in the
>> > >> correct way. Also I would need an advice regarding the reporting of
>> the
>> > >> results from the R functions.
>> > >> I want to report the results of a binomial logistic regression where
>> I
>> > want
>> > >> to assess difference between the 3 levels of a factor (called
>> System) on
>> > >> the dependent variable (called Response) taking two values, 0 and 1.
>> My
>> > >> goal is to understand if the effect of the 3 systems (A,B,C) in
>> System
>> > >> affect differently Response in a significant way. I am basing my
>> > analysis
>> > >> on this URL: https://stats.idre.ucla.edu/r/dae/logit-regression/
>> > >> This is the result of my analysis:
>> > >>> fit <- glm(Response ~ System, data = scrd, family = "binomial")
>> > >>> summary(fit)
>> > >> Call:
>> > >> glm(formula = Response ~ System, family = "binomial", data = scrd)
>> > >> Deviance Residuals:
>> > >>     Min       1Q   Median       3Q      Max
>> > >> -2.8840   0.1775   0.2712   0.2712   0.5008
>> > >> Coefficients:
>> > >>              Estimate Std. Error z value Pr(>|z|)
>> > >> (Intercept)    3.2844     0.2825  11.626  < 2e-16 ***
>> > >> SystemB  -1.2715     0.3379  -3.763 0.000168 ***
>> > >> SystemC    0.8588     0.4990   1.721 0.085266 .
>> > >> ---
>> > >> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>> > >> (Dispersion parameter for binomial family taken to be 1)
>> > >>     Null deviance: 411.26  on 1023  degrees of freedom
>> > >> Residual deviance: 376.76  on 1021  degrees of freedom
>> > >> AIC: 382.76
>> > >> Number of Fisher Scoring iterations: 6
>> > >> Following this analysis I perform the wald test in order to
>> understand
>> > >> whether there is an overall effect of System:
>> > >> library(aod)
>> > >>> wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3)
>> > >> Wald test:
>> > >> ----------
>> > >> Chi-squared test:
>> > >> X2 = 354.6, df = 3, P(> X2) = 0.0
>> > >> The chi-squared test statistic of 354.6, with 3 degrees of freedom is
>> > >> associated with a p-value < 0.001 indicating that the overall effect
>> of
>> > >> System is statistically significant.
>> > >> Now I check whether there are differences between the coefficients
>> using
>> > >> again the wald test:
>> > >> # Here difference between system B and C:
>> > >>> l <- cbind(0, 1, -1)
>> > >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
>> > >> Wald test:
>> > >> ----------
>> > >> Chi-squared test:
>> > >> X2 = 22.3, df = 1, P(> X2) = 2.3e-06
>> > >> # Here difference between system A and C:
>> > >>> l <- cbind(1, 0, -1)
>> > >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
>> > >> Wald test:
>> > >> ----------
>> > >> Chi-squared test:
>> > >> X2 = 12.0, df = 1, P(> X2) = 0.00052
>> > >> # Here difference between system A and B:
>> > >>> l <- cbind(1, -1, 0)
>> > >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
>> > >> Wald test:
>> > >> ----------
>> > >> Chi-squared test:
>> > >> X2 = 58.7, df = 1, P(> X2) = 1.8e-14
>> > >> My understanding is that from this analysis I can state that the
>> three
>> > >> systems lead to a significantly different Response. Am I right? If
>> so,
>> > how
>> > >> should I report the results of this analysis? What is the correct
>> way?
>> > >> Thanks in advance
>> > >> Best wishes
>> > >> FJ
>> > >>      [[alternative HTML version deleted]]
>> > >> ______________________________________________
>> > >> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
>> > >> https://stat.ethz.ch/mailman/listinfo/r-help
>> > >> PLEASE do read the posting guide
>> > http://www.R-project.org/posting-guide.html
>> > >> and provide commented, minimal, self-contained, reproducible code.
>> > >
>> > > --
>> > > Eik Vettorazzi
>> > >
>> > > Department of Medical Biometry and Epidemiology
>> > > University Medical Center Hamburg-Eppendorf
>> > >
>> > > Martinistrasse 52
>> > > building W 34
>> > > 20246 Hamburg
>> > >
>> > > Phone: +49 (0) 40 7410 - 58243
>> > > Fax:   +49 (0) 40 7410 - 57790
>> > > Web: www.uke.de/imbe
>> > > --
>> > >
>> > > _____________________________________________________________________
>> > >
>> > > Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen
>> > Rechts; Gerichtsstand: Hamburg | www.uke.de
>> > > Vorstandsmitglieder: Prof. Dr. Burkhard Göke (Vorsitzender), Prof. Dr.
>> > Dr. Uwe Koch-Gromus, Joachim Prölß, Marya Verdel
>> > > _____________________________________________________________________
>> > >
>> > > SAVE PAPER - THINK BEFORE PRINTING
>> > > ______________________________________________
>> > > [hidden email] mailing list -- To UNSUBSCRIBE and more, see
>> > > https://stat.ethz.ch/mailman/listinfo/r-help
>> > > PLEASE do read the posting guide
>> > http://www.R-project.org/posting-guide.html
>> > > and provide commented, minimal, self-contained, reproducible code.
>> >
>> > --
>> > Peter Dalgaard, Professor,
>> > Center for Statistics, Copenhagen Business School
>> > Solbjerg Plads 3, 2000 Frederiksberg, Denmark
>> > Phone: (+45)38153501
>> > Office: A 4.23
>> > Email: [hidden email]  Priv: [hidden email]
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>>
>>         [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.