Grokbase Groups R r-help June 2016
FAQ
Hi all


I am using bam to analyse the data from my experiment.
It's a learning experiment, "acc" denotes accuracy and "cnd" denotes a
within-subjects variable (with two levels, "label" and "ideo")."Ctrial" is
centered trial (ranging from 1 to 288).


The model is:
bam(acc~ 1 + cnd + s(ctrial) + s(ctrial, sbj, bs = "fs", m = 1), dataÚta,
family=binomial)


The model doesn't include two different smooths (one for each condition)
since including two smooths does not result to a more parsimonious model,
according to following model comparison:
compareML(m0.2, m1.2)
m0.2: acc ~ 1 + cnd + s(ctrial) + s(ctrial, sbj, bs = "fs", m = 1)


m1.2: acc ~ 1 + cnd + s(ctrial, by = cnd) + s(ctrial, sbj, bs = "fs",
     m = 1)


Chi-square test of fREML scores
-----
   Model Score Edf Chisq Df p.value Sig.
1 m0.2 10183.31 6
2 m1.2 10173.33 8 9.975 2.000 4.654e-05 ***


AIC difference: -2.16, model m0.2 has lower AIC.




So, I'm trying to assess if there's a difference in accuracy between the
two conditions.


When using the plot_smooth function, the model predictions are the ones
shown in Fig.1.
The code used is:
plot_smooth(fm, view="ctrial",
cond=list(cnd="pseudo"),main="Model",xaxt="n",
xlab="Trial",ylab="Proportion Correct", lwd=2, las=2, rm.ranef=TRUE,
rugúLSE, shade=T, col="red" )
plot_smooth(fm, view="ctrial", cond=list(cnd="ideo"), xaxt="n",
rm.ranef=TRUE, rugúLSE, shade=T, col="blue", add=T , lty=2, lwd=2)
legend(x=0.8, y=1.5,legend=c('Label', 'Ideogram'),col=c('red', 'blue'),
lty=c(1,2), bty="n", lwd=2)


Since the 95% confidence intervals overlap, I would assume that there is no
difference in accuracy between the two conditions.


I am also using plot_diff to directly plot the difference:
plot_diff(fm, view="ctrial",comp=list(cnd=c("pseudo", "ideo")),
transform.view=dnrmlz,rm.ranef=T)
(dnrmlz is a simple function to de-normalize trial)


The output of the function is:
Summary:
* ctrial : numeric predictor; with 100 values ranging from -1.725936 to
1.725936.
* sbj : factor; set to the value(s): aggmpo96. (Might be canceled as random
effect, check below.)
* NOTE : The following random effects columns are canceled: s(ctrial,sbj)


* Note: x-values are transformed.
             Significant
1 0.759461 - 288.240539


So, it seems that accuracy in the label condition is higher compared to the
ideo condition throughout the experiment.
This result seems to contradict the previous one.


I am obviously misinterpreting something.
Any ideas on what am I doing wrong?


Thank you in advance for your time,
Fotis














--
PhD Candidate
Department of Philosophy and History of Science
University of Athens, Greece.
http://users.uoa.gr/~aprotopapas/LLL/en/members.html#fotisfotiadis


Notice: Please do not use this account for social networks invitations, for
sending chain-mails to me, or as it were a facebook account. Thank you for
respecting my privacy.


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  • Bert Gunter at Jun 12, 2016 at 2:50 pm
    To be clear, I know nothing about bam; I just wanted to correct a
    statistical error:


    "Since the 95% confidence intervals overlap, I would assume that there is no
    difference in accuracy between the two conditions."


    That is false. You need to look at a CI for the difference.


    As you appear to be confused about the statistical issues, I suggest
    you post on a statistical site like stats.stackexchange.com or consult
    a local statistician.


    Cheers,
    Bert




    Bert Gunter


    "The trouble with having an open mind is that people keep coming along
    and sticking things into it."
    -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )



    On Sun, Jun 12, 2016 at 7:03 AM, Fotis Fotiadis wrote:
    Hi all

    I am using bam to analyse the data from my experiment.
    It's a learning experiment, "acc" denotes accuracy and "cnd" denotes a
    within-subjects variable (with two levels, "label" and "ideo")."Ctrial" is
    centered trial (ranging from 1 to 288).

    The model is:
    bam(acc~ 1 + cnd + s(ctrial) + s(ctrial, sbj, bs = "fs", m = 1), dataÚta,
    family=binomial)

    The model doesn't include two different smooths (one for each condition)
    since including two smooths does not result to a more parsimonious model,
    according to following model comparison:
    compareML(m0.2, m1.2)
    m0.2: acc ~ 1 + cnd + s(ctrial) + s(ctrial, sbj, bs = "fs", m = 1)

    m1.2: acc ~ 1 + cnd + s(ctrial, by = cnd) + s(ctrial, sbj, bs = "fs",
    m = 1)

    Chi-square test of fREML scores
    -----
    Model Score Edf Chisq Df p.value Sig.
    1 m0.2 10183.31 6
    2 m1.2 10173.33 8 9.975 2.000 4.654e-05 ***

    AIC difference: -2.16, model m0.2 has lower AIC.


    So, I'm trying to assess if there's a difference in accuracy between the
    two conditions.

    When using the plot_smooth function, the model predictions are the ones
    shown in Fig.1.
    The code used is:
    plot_smooth(fm, view="ctrial",
    cond=list(cnd="pseudo"),main="Model",xaxt="n",
    xlab="Trial",ylab="Proportion Correct", lwd=2, las=2, rm.ranef=TRUE,
    rugúLSE, shade=T, col="red" )
    plot_smooth(fm, view="ctrial", cond=list(cnd="ideo"), xaxt="n",
    rm.ranef=TRUE, rugúLSE, shade=T, col="blue", add=T , lty=2, lwd=2)
    legend(x=0.8, y=1.5,legend=c('Label', 'Ideogram'),col=c('red', 'blue'),
    lty=c(1,2), bty="n", lwd=2)

    Since the 95% confidence intervals overlap, I would assume that there is no
    difference in accuracy between the two conditions.

    I am also using plot_diff to directly plot the difference:
    plot_diff(fm, view="ctrial",comp=list(cnd=c("pseudo", "ideo")),
    transform.view=dnrmlz,rm.ranef=T)
    (dnrmlz is a simple function to de-normalize trial)

    The output of the function is:
    Summary:
    * ctrial : numeric predictor; with 100 values ranging from -1.725936 to
    1.725936.
    * sbj : factor; set to the value(s): aggmpo96. (Might be canceled as random
    effect, check below.)
    * NOTE : The following random effects columns are canceled: s(ctrial,sbj)

    * Note: x-values are transformed.
    Significant
    1 0.759461 - 288.240539

    So, it seems that accuracy in the label condition is higher compared to the
    ideo condition throughout the experiment.
    This result seems to contradict the previous one.

    I am obviously misinterpreting something.
    Any ideas on what am I doing wrong?

    Thank you in advance for your time,
    Fotis







    --
    PhD Candidate
    Department of Philosophy and History of Science
    University of Athens, Greece.
    http://users.uoa.gr/~aprotopapas/LLL/en/members.html#fotisfotiadis

    Notice: Please do not use this account for social networks invitations, for
    sending chain-mails to me, or as it were a facebook account. Thank you for
    respecting my privacy.

    <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail>
    Virus-free.
    www.avast.com
    <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail>
    <#DDB4FAA8-2DD7-40BB-A1B8-4E2AA1F9FDF2>

    ______________________________________________
    R-help at 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.
  • Fotis Fotiadis at Jun 13, 2016 at 1:59 pm
    Dear Bert,
    Thank you for your response


    Best,
    Fotis


    On Sun, Jun 12, 2016 at 5:50 PM, Bert Gunter wrote:

    To be clear, I know nothing about bam; I just wanted to correct a
    statistical error:

    "Since the 95% confidence intervals overlap, I would assume that there is
    no
    difference in accuracy between the two conditions."

    That is false. You need to look at a CI for the difference.

    As you appear to be confused about the statistical issues, I suggest
    you post on a statistical site like stats.stackexchange.com or consult
    a local statistician.

    Cheers,
    Bert


    Bert Gunter

    "The trouble with having an open mind is that people keep coming along
    and sticking things into it."
    -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

    On Sun, Jun 12, 2016 at 7:03 AM, Fotis Fotiadis wrote:
    Hi all

    I am using bam to analyse the data from my experiment.
    It's a learning experiment, "acc" denotes accuracy and "cnd" denotes a
    within-subjects variable (with two levels, "label" and "ideo")."Ctrial" is
    centered trial (ranging from 1 to 288).

    The model is:
    bam(acc~ 1 + cnd + s(ctrial) + s(ctrial, sbj, bs = "fs", m = 1), dataÚta,
    family=binomial)

    The model doesn't include two different smooths (one for each condition)
    since including two smooths does not result to a more parsimonious model,
    according to following model comparison:
    compareML(m0.2, m1.2)
    m0.2: acc ~ 1 + cnd + s(ctrial) + s(ctrial, sbj, bs = "fs", m = 1)

    m1.2: acc ~ 1 + cnd + s(ctrial, by = cnd) + s(ctrial, sbj, bs = "fs",
    m = 1)

    Chi-square test of fREML scores
    -----
    Model Score Edf Chisq Df p.value Sig.
    1 m0.2 10183.31 6
    2 m1.2 10173.33 8 9.975 2.000 4.654e-05 ***

    AIC difference: -2.16, model m0.2 has lower AIC.


    So, I'm trying to assess if there's a difference in accuracy between the
    two conditions.

    When using the plot_smooth function, the model predictions are the ones
    shown in Fig.1.
    The code used is:
    plot_smooth(fm, view="ctrial",
    cond=list(cnd="pseudo"),main="Model",xaxt="n",
    xlab="Trial",ylab="Proportion Correct", lwd=2, las=2, rm.ranef=TRUE,
    rugúLSE, shade=T, col="red" )
    plot_smooth(fm, view="ctrial", cond=list(cnd="ideo"), xaxt="n",
    rm.ranef=TRUE, rugúLSE, shade=T, col="blue", add=T , lty=2, lwd=2)
    legend(x=0.8, y=1.5,legend=c('Label', 'Ideogram'),col=c('red', 'blue'),
    lty=c(1,2), bty="n", lwd=2)

    Since the 95% confidence intervals overlap, I would assume that there is no
    difference in accuracy between the two conditions.

    I am also using plot_diff to directly plot the difference:
    plot_diff(fm, view="ctrial",comp=list(cnd=c("pseudo", "ideo")),
    transform.view=dnrmlz,rm.ranef=T)
    (dnrmlz is a simple function to de-normalize trial)

    The output of the function is:
    Summary:
    * ctrial : numeric predictor; with 100 values ranging from -1.725936 to
    1.725936.
    * sbj : factor; set to the value(s): aggmpo96. (Might be canceled as random
    effect, check below.)
    * NOTE : The following random effects columns are canceled: s(ctrial,sbj)

    * Note: x-values are transformed.
    Significant
    1 0.759461 - 288.240539

    So, it seems that accuracy in the label condition is higher compared to the
    ideo condition throughout the experiment.
    This result seems to contradict the previous one.

    I am obviously misinterpreting something.
    Any ideas on what am I doing wrong?

    Thank you in advance for your time,
    Fotis







    --
    PhD Candidate
    Department of Philosophy and History of Science
    University of Athens, Greece.
    http://users.uoa.gr/~aprotopapas/LLL/en/members.html#fotisfotiadis

    Notice: Please do not use this account for social networks invitations, for
    sending chain-mails to me, or as it were a facebook account. Thank you for
    respecting my privacy.

    <
    https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail
    Virus-free.
    www.avast.com
    <
    https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail
    <#DDB4FAA8-2DD7-40BB-A1B8-4E2AA1F9FDF2>

    ______________________________________________
    R-help at 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.





    --
    PhD Candidate
    Department of Philosophy and History of Science
    University of Athens, Greece.
    http://users.uoa.gr/~aprotopapas/LLL/en/members.html#fotisfotiadis


    Notice: Please do not use this account for social networks invitations, for
    sending chain-mails to me, or as it were a facebook account. Thank you for
    respecting my privacy.


      [[alternative HTML version deleted]]

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