[R] Reporting binomial logistic regression from R results
Bert Gunter
bgunter@4567 @ending from gm@il@com
Mon Nov 12 20:48:01 CET 2018
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 <frodojedi.mailinglist using gmail.com>
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 <pdalgd using gmail.com> 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 <E.Vettorazzi using uke.de> 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]]
> > >> ______________________________________________
> > >> R-help using r-project.org 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
> > > ______________________________________________
> > > R-help using r-project.org 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: pd.mes using cbs.dk Priv: PDalgd using gmail.com
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help using r-project.org 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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