means , CIs from lmer, glmer

Thanks
Am also replying to list, so excuse duplication
used install packages followed by library
though that was happening on 1st attempt - but sadly no


On 27 Feb 2018, at 12:53, Rune Haubo <rune.haubo at gmail.com> wrote:

Well, you could also just do a linear mixed model on the
logit-transformed proportions, which would make the Satterthwaite
F-tests via lmerTest available to you. This can be OK if all the
proportions have (approximately) the same denominator but



usually

*ALWAYS*

is better to fit the 'correct' binomial mixed model with glmer - even
when all the proportions have the same denominator. Using glmer leaves
you with likelihood ratio tests which are matched against the
chi-square distribution - use anova(model2, model1) to obtain these
likelihood ratio tests.

It would be helpful if glmer did not [rpobably incorrectly] label
chi-square tests as ?F?

But this is a generalised linear model and was hoping get a mean and sd
for the random subject (Participant) effect

Also really do want an F comparing the predictor effects with appropriate
error effects

It seems the in spite of R supposedly being more sophisticated than
standard packages like SPSS, it has fewer options on the crucial issue of
specifying error variance



And by the way: you probably want to install the emmeans package via
'install.packages("emmeans")' in R - not via gitHub using devtools.
See https://github.com/runehaubo/lmerTest under 'Installation' for the
advice we give on installing lmerTest. Short version is to install
using install.packages() before thinking about installing from github.

good advice

results form SPSS and R are very different
R glmer   MIXED
Rab Between emmean SE df asymp.LCL asymp.UCL Rab Between Mean SE lcl ucl
1 1 -.316 .170 Inf -.650 .018 1 1 -.267 .148 -.562 .027
2 1 -.820 .172 Inf -1.158 -.482 2 1 -.688 .169 -1.023 -.352
3 1 -1.838 .183 Inf -2.196 -1.480 3 1 -1.550 .188 -1.924 -1.176
4 1 -2.558 .198 Inf -2.946 -2.170 4 1 -2.168 .230 -2.624 -1.712
1 2 .357 .165 Inf .034 .681 1 2 .297 .144 .011 .583
2 2 -.895 .167 Inf -1.223 -.567 2 2 -.745 .165 -1.073 -.417
3 2 -2.607 .192 Inf -2.984 -2.230 3 2 -2.238 .235 -2.705 -1.772
4 2 -2.891 .201 Inf -3.285 -2.497 4 2 -2.498 .255 -3.005 -1.992
  Df SS MS F Source F df1 df2 Sig.
Rab 3 870.17 290.06 290.06 Rab 66.76 3 94 .000000
Between 1 .12 .12 .12 Between .38 1 93 .539643
Rab:Between 3 63.20 21.07 21.07 Rab*Between 5.31 3 94 .001998
Corrected 30.64 7 101 .000000


best
Diana


Best regards,

Rune Haubo B. Christensen, PhD, MSc.
Director, Owner

Christensen Statistics
Bringetoften 7
<https://maps.google.com/?q=Bringetoften+7&entry=gmail&source=g>
3500 V?rl?se - Denmark
+45 3026 4554 <+45%2030%2026%2045%2054>
Rune at ChristensenStatistics.dk
www.ChristensenStatistics.dk


On 27 February 2018 at 12:32, Kornbrot, Diana <d.e.kornbrot at herts.ac.uk>
wrote:

thanks

do you have any suggestions for glmer?
SPSS seems happy to do Sattherwaite, which seems to be  alogical approach
for any model that is effectively doing a multivariate ANOVA on the logit
transformed proportion
best
Diana

On 27 Feb 2018, at 10:22, Rune Haubo <rune.haubo at gmail.com> wrote:

Just a small note that lmerTest (and the Satterthwaite method for
degrees of freedom) is only meaningful for _linear_ mixed models - not
for the generalized variants such as the one considered here for
proportions.

Best,
Rune

On 27 February 2018 at 02:02, Ben Bolker <bbolker at gmail.com> wrote:

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