# [R] zero random effect sizes with binomial lmer [sorry, ignore previous]

From: Daniel Ezra Johnson <johnson4_at_babel.ling.upenn.edu>
Date: Sun, 31 Dec 2006 04:28:40 -0500 (EST)

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1.

I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening here, and in comparing other subsets of the data.

In the data below, which has been adjusted to show this phenomenon clearly, the Subject random effect variance is comparable for A (1.63) and B (1.712), but the Item random effect variance comes out as 0.109 for B and essentially zero for A (5.00e-10).

Note that the only difference between data set A and data set B occurs on row 19, where a single instance of Response is changed.

```Item	avg. in A (%)	avg. in B (%)
1	9		9
2	9		9
3	9		9
4	17		17
5	4%		4
6	22	<->	26
7	17		17

```

Why does the Item random effect sometimes "crash" to zero, so sensitively? Surely there is some more reasonable estimate of the Item effect here than zero. The items still have clearly different Response behavior.

If one compares the random effect estimates, in fact, one sees that they are in the correct proportion, with the expected signs. They are just approximately eight orders of magnitude too small. Is this a bug?

More broadly, is it hopeless to analyze this data in this manner, or else, what should I try doing differently? It would be very useful to be able to have reliable estimates of random effect sizes, even when they are rather small.

I've included replicable code below, sorry that I did not know how to make it more compact!

```a1 <- c(0,0,0,0,0,0,0)
a2 <- c(0,0,0,0,0,0,0)
a3 <- c(0,0,0,0,0,0,0)
a4 <- c(0,0,0,0,0,0,0)
a5 <- c(0,0,0,0,0,0,0)
a6 <- c(0,0,0,0,0,0,0)
a7 <- c(0,0,0,0,0,0,0)
a8 <- c(0,0,0,0,0,0,0)
a9 <- c(0,0,0,0,0,0,0)
a10 <- c(0,0,0,0,0,0,0)
a11 <- c(0,0,0,0,0,0,0)
a12 <- c(0,0,0,0,0,0,0)
a13 <- c(0,0,0,0,0,0,1)
a14 <- c(0,0,0,0,0,0,1)
a15 <- c(0,0,0,0,0,1,0)
a16 <- c(0,0,0,0,1,0,0)
a17 <- c(0,0,0,1,0,0,0)
a18 <- c(0,0,1,0,0,0,0)
a19 <- c(0,1,0,0,0,0,0)
a20 <- c(0,1,0,0,0,1,0)
a21 <- c(0,0,0,1,0,1,1)
```

a22 <- c(1,0,0,1,0,1,1)
a23 <- c(1,0,1,1,0,1,0)
aa <- rbind
(a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13,a14,a15,a16,a17,a18,a19,a20, a21,a22,a23)
```b1 <- c(0,0,0,0,0,0,0)
b2 <- c(0,0,0,0,0,0,0)
b3 <- c(0,0,0,0,0,0,0)
b4 <- c(0,0,0,0,0,0,0)
b5 <- c(0,0,0,0,0,0,0)
b6 <- c(0,0,0,0,0,0,0)
b7 <- c(0,0,0,0,0,0,0)
b8 <- c(0,0,0,0,0,0,0)
b9 <- c(0,0,0,0,0,0,0)
b10 <- c(0,0,0,0,0,0,0)
b11 <- c(0,0,0,0,0,0,0)
b12 <- c(0,0,0,0,0,0,0)
b13 <- c(0,0,0,0,0,0,1)
b14 <- c(0,0,0,0,0,0,1)
b15 <- c(0,0,0,0,0,1,0)
b16 <- c(0,0,0,0,1,0,0)
b17 <- c(0,0,0,1,0,0,0)
b18 <- c(0,0,1,0,0,0,0)
b19 <- c(0,1,0,0,0,1,0)
b20 <- c(0,1,0,0,0,1,0)
b21 <- c(0,0,0,1,0,1,1)
```

b22 <- c(1,0,0,1,0,1,1)
b23 <- c(1,0,1,1,0,1,0)
bb <- rbind
(b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11,b12,b13,b14,b15,b16,b17,b18,b19,b20, b21,b22,b23)

a <- array(0, c(161,3), list(NULL,c("Subject","Item","Response")))

``` 	for (s in c(1:23))
for (i in c(1:7))
a[7*(s-1)+i,] <- c(s,i,aa[s,i])
```

A <- data.frame(a)

b <- array(0, c(161,3), list(NULL,c("Subject","Item","Response")))

``` 	for (s in c(1:23))
for (i in c(1:7))
b[7*(s-1)+i,] <- c(s,i,bb[s,i])
```

B <- data.frame(b)
```A.fit <- lmer(Response~(1|Subject)+(1|Item),A,binomial)
B.fit <- lmer(Response~(1|Subject)+(1|Item),B,binomial)
A.fit
B.fit
```

ranef(A.fit)\$Item
ranef(B.fit)\$Item

Generalized linear mixed model fit using Laplace Formula: Response ~ (1 | Subject) + (1 | Item)

Data: A
AIC BIC logLik deviance
120 129 -56.8 114
Random effects:
Groups Name Variance Std.Dev.
Subject (Intercept) 1.63e+00 1.28e+00
Item (Intercept) 5.00e-10 2.24e-05
number of obs: 161, groups: Subject, 23; Item, 7

Estimated scale (compare to 1 ) 0.83326

Fixed effects:

```              Estimate Std. Error z value Pr(>|z|)
(Intercept)   -2.517      0.395   -6.38  1.8e-10 ***

```

> B.fit

Generalized linear mixed model fit using Laplace Formula: Response ~ (1 | Subject) + (1 | Item)

Data: B
AIC BIC logLik deviance
123 133 -58.6 117
Random effects:
Groups Name Variance Std.Dev.
Subject (Intercept) 1.712 1.308
Item (Intercept) 0.109 0.330
number of obs: 161, groups: Subject, 23; Item, 7

Estimated scale (compare to 1 ) 0.81445

Fixed effects:

```              Estimate Std. Error z value Pr(>|z|)
(Intercept)   -2.498      0.415   -6.02  1.8e-09 ***

```

> ranef(A.fit)\$Item

(Intercept)

```1 -2.8011e-10
2 -2.8011e-10
3 -2.8011e-10
4  7.1989e-10
5 -7.8011e-10
```

6 1.2199e-09
7 7.1989e-10

> ranef(B.fit)\$Item

(Intercept)

```1   -0.056937
2   -0.056937
3   -0.056937
4    0.120293
5   -0.146925
```

6 0.293893
7 0.120293

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