[R-sig-ME] Fwd: Advice on Mixed Models
Rose Rosei
roserosei2030 at gmail.com
Sat Mar 11 14:17:59 CET 2017
Many thanks, Professor Bolker- Very Much appriciated
When I use this model:
lmer(mark~ (1|A)+(1+D)+(1|S)+(1|Q)+ S1+ (1|S1/D/S/A), R) # session as a
fiexed effect
I got the following results, which are not consistent with the SPSS Output
( Variance Components)
Linear mixed model fit by REML ['lmerMod']
Formula: mark ~ (1 | A) + (1 + D) + (1 | S) + (1 | Q) + S1 + (1 | S1/D/S/A)
Data: R
REML criterion at convergence: 190.5212
Random effects:
Groups Name Std.Dev.
A:(S:(D:S1)) (Intercept) 0.9581
A (Intercept) 0.7198
Q (Intercept) 0.0000
S:(D:S1) (Intercept) 0.0000
D:S1 (Intercept) 0.0000
S1 (Intercept) 1.1411
S (Intercept) 0.0000
Residual 0.6722
Number of obs: 80, groups: A:(S:(D:S1)), 10; A, 10; Q, 8; S:(D:S1), 7;
D:S1, 4; S1, 2; S, 2
Fixed Effects:
(Intercept) D S1
2.31250 0.02679 -0.59821
convergence code 0; 2 optimizer warnings; 0 lme4 warnings
SPSS output
*Variance Estimates*
Component
Estimate
Var(A)
2.443
Var(D)
-.302a
Var(S1)
-.348a
Var(O)
.093
Var(A * D)
.000b
Var(A * S1)
.000b
Var(A * O)
.548
Var(A * S)
.000b
Var(D * S1)
.139
Var(D * O)
-.074a
Var(D * S)
.554
Var(S1 * O)
-.210a
Var(S1 * S)
.644
Var(O * S)
-.124a
Var(A * D * S1)
.000b
Var(A * D * O)
.000b
Var(A * D * S)
.000b
Var(A * S1 * O)
.000b
Var(A * S1 * S)
.000b
Var(A * O * S)
.000b
Var(D * S1 * O)
.090
Var(D * S1 * S)
-1.834a
Var(D * O * S)
.070
Var(S1 * O * S)
.338
Var(A * D * S1 * O)
.000b
Var(A * D * S1 * S)
.000b
Var(A * D * O * S)
.000b
Var(A * S1 * O * S)
.000b
Var(D * S1 * O * S)
-.276a
Var(A * D * S1 * O * S)
.000b
Var(Error)
.000b
Dependent Variable: mark
Method: Minimum Norm Quadratic Unbiased Estimation (Weight = 1 for Random
Effects and Residual)
a. For the ANOVA and MINQUE methods, negative variance component estimates
may occur. Some possible reasons for their occurrence are: (a) the
specified model is not the correct model, or (b) the true value of the
variance equals zero.
b. This estimate is set to zero because it is redundant.
When I use this model:
lmer(mark~ (1|A)+(1+D)+(1|S)+(1|Q)+ S1+ (1|S1:D/S/A), R)
I got the follwowig error
Error: couldn't evaluate grouping factor A:(S:(S1:`:`)) within model frame:
try adding grouping factor to data frame explicitly if possible
In addition: Warning message:
In S1:`:` : numerical expression has 80 elements: only the first used;
Would you advise me what went wrong with the model.
Many thanks,
Rose
On Fri, Mar 10, 2017 at 11:59 PM, Ben Bolker <bbolker at gmail.com> wrote:
>
>
> On 17-03-08 02:01 PM, Rose Rosei wrote:
> > Dear Advisors
> >
> > Would you please advise me. I would like to fit my model, but I struggled
> > to do it
> >
> >
> > A= Applicant = 10 persons
> > S= Stream ( four levels, 1, 2)
>
> Not sure what "four levels, 1, 2" means here. Do you mean "four
> levels, 1-4" ... ?
>
> > D= Day (1,2)
> > S1= Session ( 1,2)
> > Q = Qestion ( 1-to 8)
> > Applicants are crossed in Questions, but Applicants nested in Stream,
> > nested in Day, nested in session (S1). All variables are a a random
> factor
>
> You need to know that **with modern mixed-model machinery (e.g. nlme,
> lme4 as opposed to aov() in R) it is not in general practical to
> estimate random-effects terms for variables with fewer than 5 or 6
> levels**.
> See e.g. http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#singu
> lar-fit
>
> >
> > I want to calculate SD for A, S, D, S1 and Q, and their interaction
> > .score=dependent variable
> >
> > I have used the following codes, but it seems they are wrong.
> >
> > lmer(score~ (1|A)+(1|S)+(1+D)+(1|S1)+(1|Q)
> +(1|A/S)+1|S/D)+(1|D/S1)+(1|S1/Q),
> > R)
>
> (1|S1/D/S/A) gives "Applicants nested in Stream, nested in Day, nested
> in session (S1)". As I said above, it would be wiser if possible to use
> S1+ (1|S1:D/S/A) (i.e. treat session as a fixed effect).
> I would probably handle questions via (1|Q); if each applicant receives
> each question no more than once within a session/day/stream combination,
> then the S1:D:S:A:Q interaction will be handled by the residual variance
> term.
>
> In addition to the problems stated above, many of these terms are
> redundant. The nesting syntax (1|A/S) expands to (1|A) + (1|A:S) (i.e.
> variability among levels of A, and variability among the interacting
> levels of A and S). Fitting a crossed term as compactly as possible
> would use (1|A*S), but I think this doesn't actually work: (1|A) + (1|S)
> or (1|A:S) or (1|A/S)+(1|S) both describe crossed random effects of A
> and S. You may also have the nesting order backwards: (1|A/S) means
> "Stream nested within Applicants", not "Applicants nested within Stream".
>
>
> >
> > Linear mixed model fit by REML ['lmerMod']
> > Formula: score ~ (1 | A) + (1 | S) + (1 + D) + (1 | S1) + (1 | Q) + (1 |
> > A/S) + (1 | S/D) + (1 | D/S1) + (1 | S1/Q)
> > Data: R
> > REML criterion at convergence: 192.4591
> > Random effects:
> > Groups Name Std.Dev.
> > Q.S1 (Intercept) 0.000e+00
> > A (Intercept) 2.383e-01
> > S.A (Intercept) 7.692e-01
> > A.1 (Intercept) 8.399e-01
> > Q (Intercept) 0.000e+00
> > S1.D (Intercept) 1.386e-08
> > D.S (Intercept) 0.000e+00
> > S1 (Intercept) 0.000e+00
> > D (Intercept) 9.498e-01
> > S (Intercept) 0.000e+00
> > S1.1 (Intercept) 0.000e+00
> > S.1 (Intercept) 0.000e+00
> > Residual 6.722e-01
> > Number of obs: 80, groups:
> > Q:S1, 16; A, 10; S:A, 10; Q, 8; S1:D, 4; D:S, 4; S1, 2; D, 2; S, 2
> > Fixed Effects:
> > (Intercept) D
> > 1.61458 -0.07292
> > convergence code 0; 2 optimizer warnings; 0 lme4 warnings
> >
> > Very much appreciated for your help.
> > looking forward to hearing from you.
> > Rose
> >
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
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