[R-sig-ME] R-sig-mixed-models Digest, Vol 134, Issue 39

Tue Feb 27 11:06:55 CET 2018

thanks to ben bolker

"Look into the (very well-documented) emmeans package:
https://CRAN.R-project.org/package=emmeans”

well documented? you cannot be serious - this is R

the install instructions are as follows

* To install the latest development version from Github, install the newest version of the **devtools** package; then run
```
devtools::install_github("rvlenth/emmeans", dependencies = TRUE,
                        build_vignettes = TRUE)
```

of course this requires a whole to of other stuff like rmarkdown with no idea as to where they may be found
yet to find ANY R documentation that simply lists ALL required packages and library items

for any help to be useful one needs to know
1. ALL the necessary  packages
2. all the necessary commands. ? libraries
meanwhile here is  super friendly SPSS output
SPSS

Model Summary

Target

Events

FreqPos

Trials

Nmax

Probability Distribution

Binomial

Link Function

Logit

Information Criterion

Akaike Corrected

1189.377

Bayesian

1227.957

Information criteria are based on the -2 log likelihood (1168.768) and are used to compare models. Models with smaller information criterion values fit better.

Fixed Effectsa

Source

F

df1

df2

Sig.

Corrected Model

30.641

7

101

.000

Rab

66.755

3

94

.000

Between

.379

1

93

.540

Rab * Between

5.312

3

94

.002

Probability distribution: Binomial
Link function: Logit

a. Target: FreqPos/Nmax

Fixed Coefficientsa

Model Term

Coefficient

Std. Error

t

Sig.

95% Confidence Interval

Exp(Coefficient)

95% Confidence Interval for Exp(Coefficient)

Lower

Upper

Lower

Upper

Intercept

-.267

.1484

-1.801

.075

-.562

.027

.765

.570

1.028

Rab=4

-1.901

.2332

-8.149

.000

-2.364

-1.438

.149

.094

.237

Rab=3

-1.282

.2151

-5.963

.000

-1.710

-.855

.277

.181

.425

Rab=2

-.420

.1477

-2.847

.005

-.714

-.127

.657

.490

.881

Rab=1

0b

.

.

.

.

.

.

.

.

Between=2

.564

.2069

2.727

.008

.153

.975

1.758

1.166

2.651

Between=1

0b

.

.

.

.

.

.

.

.

[Rab=4]*[Between=2]

-.895

.3438

-2.602

.011

-1.577

-.212

.409

.207

.809

[Rab=4]*[Between=1]

0b

.

.

.

.

.

.

.

.

[Rab=3]*[Between=2]

-1.253

.3296

-3.800

.000

-1.907

-.598

.286

.149

.550

[Rab=3]*[Between=1]

0b

.

.

.

.

.

.

.

.

[Rab=2]*[Between=2]

-.621

.2064

-3.010

.003

-1.031

-.211

.537

.357

.810

[Rab=2]*[Between=1]

0b

.

.

.

.

.

.

.

.

[Rab=1]*[Between=2]

0b

.

.

.

.

.

.

.

.

[Rab=1]*[Between=1]

0b

.

.

.

.

.

.

.

.

Probability distribution: Binomial
Link function: Logit

a. Target: FreqPos/Nmax

b. This coefficient is set to zero because it is redundant.

R output
Analysis of Variance Table
                     Df  Sum Sq Mean Sq     F value
Rab                  3 870.17   290.058     290.058
Between          1   0.12          0.122         0.122
Rab:Between  3  63.20        21.067      21.067
Notice that F = Mean Sq, which is a SURPRISE usually F = MSeffect/MSerror, so where is the MS error?

fixef(Logit1)
  (Intercept)          Rab2          Rab3                 Rab4           Between2     Rab2:Between2 Rab3:Between2 Rab4:Between2
   -0.3162873    -0.5033874    -1.5218751    -2.2416235     0.6735183    -0.7491242      -1.4426385       -1.0069334

Really do appreciate help
best
Diana

On 27 Feb 2018, at 06:22, r-sig-mixed-models-request at r-project.org<mailto:r-sig-mixed-models-request at r-project.org> wrote:

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Today's Topics:

  1. Re: zero-inflated-count-data? (Highland Statistics Ltd)
  2. Re: means , CIs from lmer, glmer (Ben Bolker)
  3. Re: zero-inflated-count-data? (Ben Bolker)
  4. Longitudinal logistic regression with continuous-time
     first-order autocorrelation structure (Dennis Ruenger)

----------------------------------------------------------------------

Message: 1
Date: Mon, 26 Feb 2018 17:26:02 +0000
From: Highland Statistics Ltd <highstat at highstat.com>
To: "r-sig-mixed-models at r-project.org"
<r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] zero-inflated-count-data?
Message-ID: <d1db8134-8ae0-ff56-4622-02a909a2ff1b at highstat.com>
Content-Type: text/plain; charset="utf-8"; Format="flowed"

----------------------------------------------------------------------

Message: 1
Date: Mon, 26 Feb 2018 14:57:00 +0100
From: "C. AMAL D. GLELE" <altessedac2 at gmail.com>
To: Jonathan Judge <bachlaw01 at outlook.com>
Cc: Ben Bolker <bbolker at gmail.com>,  R SIG Mixed Models
<r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] zero-inflated-count-data?
Message-ID:
<CANrzCv0SZxAXjoftdkN7v5M4g6wrd3GM7qx23dFB=fi7JHisCg at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Hi, dear all.
Many thanks to you all for your very helpful answers.
Jonathan,
I've started fitting a model using zeroinfl function from pscl package, but
I'm having the following

difficulty according to one of my regressors, let be H_var (categorical
with 8 levels):
as regressors, I have 7 categorical variables (with a total of 26 levels)
and two numerical

variables;
1) when I fit the model like follows,
model1<-zeroinfl(countdata~var1+H_var+var3+var4+var5+var6+var7+var8num

+var9num,dist="negbin",data=mydata)
, I receive the error message below:
"Error in solve.default(as.matrix(fit$hessian)) :
  system is computationally singular: reciprocal condition number =
7.05621e-21
In addition: Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
"
2)
but, if I remove H_var from the count component and fits model2 loke
follows,
model2<-zeroinfl(countdata~var1+var3+var4+var5+var6+var7+var8num+
var9num|H_var,dist="negbin",data=mydata)
 the model fits well and I do not receive error message anymore.
3)
If use H_var in both component of the model, like follows,
model3<-zeroinfl(countdata~var1+var3+var4+var5+var6+var7+var8num+
var9num+H_var|H_var,dist="negbin",data=mydata)
I receive the following error message:
"Error in solve.default(as.matrix(fit$hessian)) :
  system is computationally singular: reciprocal condition number =
4.2618e-20
"
Question:
 Does someone have any idea about probables causes of the problems posed
at points 1) and 3) ?

Without seeing the data......simplify your model? Collinearity? Start simple and build up the complexity of the model.
Maybe start with a Poisson GLM and figure out whether you really need a ZIP/ZINB? Why are you actually do a ZINB?

can you, please, provide me details (some ways to do it) and/or lead about
simulating data from a fitted model?

See step 10 in:

A protocol for conducting and presenting results of regression-type analyses (2016).
Zuur & Ieno.

http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12577/abstract

and see Figure 8 from that paper for an example. R code is somewhere online as well.

Alain

 In advance, thanks for your answers.
Best,

2018-02-25 23:55 GMT+01:00 Jonathan Judge <bachlaw01 at outlook.com>:
--

Dr. Alain F. Zuur
Highland Statistics Ltd.
9 St Clair Wynd
AB41 6DZ Newburgh, UK
Email: highstat at highstat.com
URL:   www.highstat.com

And:
NIOZ Royal Netherlands Institute for Sea Research,
Department of Coastal Systems, and Utrecht University,
P.O. Box 59, 1790 AB Den Burg,
Texel, The Netherlands

Author of:
1. Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. (2017).
2. Beginner's Guide to Zero-Inflated Models with R (2016).
3. Beginner's Guide to Data Exploration and Visualisation with R (2015).
4. Beginner's Guide to GAMM with R (2014).
5. Beginner's Guide to GLM and GLMM with R (2013).
6. Beginner's Guide to GAM with R (2012).
7. Zero Inflated Models and GLMM with R (2012).
8. A Beginner's Guide to R (2009).
9. Mixed effects models and extensions in ecology with R (2009).
10. Analysing Ecological Data (2007).

------------------------------

Message: 2
Date: Mon, 26 Feb 2018 20:02:57 -0500
From: Ben Bolker <bbolker at gmail.com>
To: "Kornbrot, Diana" <d.e.kornbrot at herts.ac.uk>
Cc: "r-sig-mixed-models at r-project.org"
<r-sig-mixed-models at r-project.org>, "Paice, Andrew"
<a.paice at herts.ac.uk>,  "Georgiou, George" <g.j.georgiou at herts.ac.uk>,
"Sullivan, Keith" <k.sullivan3 at herts.ac.uk>
Subject: Re: [R-sig-ME] means , CIs from lmer, glmer
Message-ID:
<CABghstTnvHcvQab_dH9j_BcfxvnbEREuHOkZaen9m_TmRLSovw at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

 Hi Diana,

A reproducible example is always helpful/increases your chances of
getting a useful answer ...
It might help if you included the SPSS output (or posted it somewhere
-- note that this list doesn't take HTML-formatted messages nor most
attachments), as many of us don't have access to it.

Look into the (very well-documented) emmeans package:
https://CRAN.R-project.org/package=emmeans
and the lmerTest package (for Satterthwaite df approximations)

On Mon, Feb 26, 2018 at 12:11 PM, Kornbrot, Diana
<d.e.kornbrot at herts.ac.uk> wrote:
I am keen to promote the use of generalised mixed models for the analysis of proportions to psychologists
Have straight fowl code in SPSS [costly] and would like to supply equivalent R Code without ‘tears’
Design is a follows raw frequencies are: FreqPos for ‘success’ and FreqNeg for ‘failure’
Predictors are Rab with 4 levels, repeated over participants and Between with 2 separate groups of participants
Model is binomial with logit link

Require following output to correspond to SPSS output from code below
Descriptive: Means, se and 95% CIs  by Rab, by Between and by Rab*Between
Inferential: fo  Rab, Between and  Rab*Between: F value, MSE, numerator df, denominator df [this enables p-values]

Have tried

logit1 <- glmer(cbind(FreqPos,FreqNeg) ~ Rab + Between + Rab*Between + (1| Participant), family=binomial(link="logit"))
gives F and MSE no denominator df or MSE. Different results to SPSS
nb F=MSE - that can’t be right F is supposed to be ratio of chi-squares

summary (logit1)
gives coefficients  and SEs. Different results to SPSS
also tried predicted and fitted but still no means

have spent days searching internet for examples - but none of them seem to show how to get the output I need

All help greatly appreciated

____
Spss syntax

*Generalized Linear Mixed Models.
GENLINMIXED
 /DATA_STRUCTURE SUBJECTS=Participant REPEATED_MEASURES=Rab COVARIANCE_TYPE=UNSTRUCTURED
 /FIELDS TARGET=FreqPos TRIALS=FIELD(Nmax)  OFFSET=NONE
 /TARGET_OPTIONS DISTRIBUTION=BINOMIAL LINK=LOGIT
 /FIXED  EFFECTS=Rab Between Rab*Between USE_INTERCEPT=TRUE
 /BUILD_OPTIONS TARGET_CATEGORY_ORDER=DESCENDING INPUTS_CATEGORY_ORDER=DESCENDING MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=SATTERTHWAITE COVB=MODEL PCONVERGE=0.000001(ABSOLUTE) SCORING=0 SINGULAR=0.000000000001
 /EMMEANS TABLES=Rab COMPARE=Rab CONTRAST=DEVIATION
  /EMMEANS TABLES=Between CONTRAST=NONE
  /EMMEANS TABLES=Rab*Between CONTRAST=NONE
 /EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=LSD.

best
Diana

_____________________________________
Professor Diana Kornbrot
Mobile
+44 (0) 7403 18 16 12
Work
University of Hertfordshire
College Lane, Hatfield, Hertfordshire AL10 9AB, UK
+44 (0) 170 728 4626
d.e.kornbrot at herts.ac.uk<mailto:d.e.kornbrot at herts.ac.uk>
http://dianakornbrot.wordpress.com/
http://go.herts.ac.uk/Diana_Kornbrot
skype:  kornbrotme
Home
19 Elmhurst Avenue
London N2 0LT, UK
+44 (0) 208 444 2081
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------------------------------

Message: 3
Date: Mon, 26 Feb 2018 20:17:14 -0500
From: Ben Bolker <bbolker at gmail.com>
To: Highland Statistics Ltd <highstat at highstat.com>
Cc: "r-sig-mixed-models at r-project.org"
<r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] zero-inflated-count-data?
Message-ID:
<CABghstRR41zVjimCX=Q3mc+AckchFOTc_8kb8ASfVb6pUSokyQ at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

For some model types (unfortunately not pscl::zeroinlf(), it looks
like) you can just
use the simulate() method ...

By the way, Amal (hope that's a reasonable way to address you) - folks
are really helpful here (as
you will have noticed), but the list is primarily for questions about
*mixed* (hierarchical/multilevel/whatever) models.
At present your questions are more generic questions about
zero-inflation and generalized linear modeling.
I do recommend the books by Alain and his co-authors as a good way to
get started on the fairly
complex stuff you're attempting here.

On Mon, Feb 26, 2018 at 12:26 PM, Highland Statistics Ltd
<highstat at highstat.com> wrote:

----------------------------------------------------------------------

Message: 1
Date: Mon, 26 Feb 2018 14:57:00 +0100
From: "C. AMAL D. GLELE" <altessedac2 at gmail.com>
To: Jonathan Judge <bachlaw01 at outlook.com>
Cc: Ben Bolker <bbolker at gmail.com>,  R SIG Mixed Models
       <r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] zero-inflated-count-data?
Message-ID:
       <CANrzCv0SZxAXjoftdkN7v5M4g6wrd3GM7qx23dFB=fi7JHisCg at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Hi, dear all.
Many thanks to you all for your very helpful answers.
Jonathan,
I've started fitting a model using zeroinfl function from pscl package, but
I'm having the following

difficulty according to one of my regressors, let be H_var (categorical
with 8 levels):
as regressors, I have 7 categorical variables (with a total of 26 levels)
and two numerical

variables;
1) when I fit the model like follows,
model1<-zeroinfl(countdata~var1+H_var+var3+var4+var5+var6+var7+var8num

+var9num,dist="negbin",data=mydata)
, I receive the error message below:
"Error in solve.default(as.matrix(fit$hessian)) :
 system is computationally singular: reciprocal condition number =
7.05621e-21
In addition: Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
"
2)
but, if I remove H_var from the count component and fits model2 loke
follows,
model2<-zeroinfl(countdata~var1+var3+var4+var5+var6+var7+var8num+
var9num|H_var,dist="negbin",data=mydata)
the model fits well and I do not receive error message anymore.
3)
If use H_var in both component of the model, like follows,
model3<-zeroinfl(countdata~var1+var3+var4+var5+var6+var7+var8num+
var9num+H_var|H_var,dist="negbin",data=mydata)
I receive the following error message:
"Error in solve.default(as.matrix(fit$hessian)) :
 system is computationally singular: reciprocal condition number =
4.2618e-20
"
Question:
Does someone have any idea about probables causes of the problems posed
at points 1) and 3) ?

Without seeing the data......simplify your model? Collinearity? Start simple
and build up the complexity of the model.
Maybe start with a Poisson GLM and figure out whether you really need a
ZIP/ZINB? Why are you actually do a ZINB?

can you, please, provide me details (some ways to do it) and/or lead about
simulating data from a fitted model?

See step 10 in:

A protocol for conducting and presenting results of regression-type analyses
(2016).
Zuur & Ieno.

http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12577/abstract

and see Figure 8 from that paper for an example. R code is somewhere online
as well.

Alain

In advance, thanks for your answers.
Best,

2018-02-25 23:55 GMT+01:00 Jonathan Judge <bachlaw01 at outlook.com>:
--

Dr. Alain F. Zuur
Highland Statistics Ltd.
9 St Clair Wynd
AB41 6DZ Newburgh, UK
Email: highstat at highstat.com
URL:   www.highstat.com

And:
NIOZ Royal Netherlands Institute for Sea Research,
Department of Coastal Systems, and Utrecht University,
P.O. Box 59, 1790 AB Den Burg,
Texel, The Netherlands

Author of:
1. Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological
Data Analysis with R-INLA. (2017).
2. Beginner's Guide to Zero-Inflated Models with R (2016).
3. Beginner's Guide to Data Exploration and Visualisation with R (2015).
4. Beginner's Guide to GAMM with R (2014).
5. Beginner's Guide to GLM and GLMM with R (2013).
6. Beginner's Guide to GAM with R (2012).
7. Zero Inflated Models and GLMM with R (2012).
8. A Beginner's Guide to R (2009).
9. Mixed effects models and extensions in ecology with R (2009).
10. Analysing Ecological Data (2007).

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

------------------------------

Message: 4
Date: Mon, 26 Feb 2018 22:22:17 -0800
From: Dennis Ruenger <dennis.ruenger at gmail.com>
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Longitudinal logistic regression with
continuous-time first-order autocorrelation structure
Message-ID:
<CAFvg1=vdVbz28pw9B6GrOXNsnceXK3UgXksMDwJUOQ9PYoLK_g at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Dear All.

I need to analyze an intensive longitudinal data set with a binary outcome
variable. In the “Ecological Momentary Assessment” (EMA) study,
participants received five random prompts per day for six weeks, asking
them (among other things) whether they were craving a particular drug
(yes/no). At the most basic level, I want to know whether the likelihood of
craving the drug changed across time.

Given the variable time intervals of measurement and many missing data
points, a continuous-time first-order autocorrelation model seems
necessary.

I found tutorials on how to allow for continuous-time autocorrelation and
missing data in an LMM, using nlme::lme and corCAR1, but I am at a loss as
to what to do in a GLMM.

I would be thankful for any suggestions on how to analyze this kind of data
in R.

Dennis

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End of R-sig-mixed-models Digest, Vol 134, Issue 39
***************************************************

_____________________________________
Professor Diana Kornbrot
Mobile
+44 (0) 7403 18 16 12
Work
University of Hertfordshire
College Lane, Hatfield, Hertfordshire AL10 9AB, UK
+44 (0) 170 728 4626
d.e.kornbrot at herts.ac.uk<mailto:d.e.kornbrot at herts.ac.uk>
http://dianakornbrot.wordpress.com/
http://go.herts.ac.uk/Diana_Kornbrot
skype:  kornbrotme
Home
19 Elmhurst Avenue
London N2 0LT, UK
+44 (0) 208 444 2081
 ------------------------------------------------------------

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