In Chapter 3 we illustrated how *intraindividual covariation* is examined within the multilevel modeling framework. We now build on that foundation in various ways. In particular, this tutorial demonstrates how the *generalized multilevel model* is used when the outcome variable is binary (or Poisson). Using link functions, the generalized model provides opportunity to articulate and test hypotheses about another set of *dynamic characteristics* using experience sampling data.

Much of the push into “ecological momentary assessment”, as one type of experience sampling study design, was done in health area. Often, the variables of interest are behaviors or states that are categorical - often binary (e.g., sick vs. not-sick, adherence vs. non-adherence) or ordinal counts (e.g., number of drinks, number of cigarettes). When these variables are the outcome variables, we need to use a *generalized linear multilevel model*.

After some explanantion of the modeling framework, we will work through a few examples.

The first example follows directly from Bolger & Laurenceau (2013) Chapter 6. The second example is drawn from the AMIB data.

In this session we cover …

A. Introduction to the Generalized Multilevel Model

B. Modeling Binary Outcome

Example 1: Categorical Outcomes Dataset from Bolger & Laurenceau (2013) Chapter 6

C. Modeling Count Outcome

Example 2: Poisson Outcome from AMIB Dataset

*Note that we switch from the `nlme`

package to the `lme4`

package. The functions work similarly, but the coding is slightly different.

```
library(psych)
library(ggplot2)
library(lme4)
library(gtools)
library(plyr)
library(nlme)
library(MASS)
library(glmmTMB)
```

The basic linear multilevel model is written as

\[y_{it} = \beta_{0i} + \beta_{1i}x_{it} + e_{it}\] where \(\beta_{1i}\) is individual *i*’s level of * … -ity*, and where

\[\beta_{0i} = \gamma_{00} + \gamma_{01}z_{i} + u_{0i}\] \[\beta_{1i} = \gamma_{10} + \gamma_{11}z_{i} + u_{1i}\]

where \[e_{it} \tilde{} N(0,\mathbf{R})\], and \[\mathbf{U}_{i} \tilde{} N(0,\mathbf{G})\]

\[\mathbf{R} = \mathbf{I}\sigma^{2}_{e}\], where where \(I\) is the identity matrix (diagonal matrix of 1s) and \(\sigma^{2}_{e}\) is the residual (within-person) variance.

\[\mathbf{G} = \left[\begin{array} {rr} \sigma^{2}_{u0} & \sigma_{u0u1} \\ \sigma_{u1u0} & \sigma^{2}_{u1} \end{array}\right]\]

The *generalized linear multilevel model* is an extension of linear multilevel models that allows that response variables from different distributions besides Gaussian (see also http://www.ats.ucla.edu/stat/mult_pkg/glmm.htm). To do this, we introduce a *link* function.

Let the linear predictor (the right hand side of the equation) be called \(\mathbf{\eta}\). \(\mathbf{\eta}\) is the combination of the fixed and random effects *excluding the residuals*. Writing the above model in a general way common in statistics \[\mathbf{\eta = X\beta + Z\gamma}\]

We introduce a generic link function, \(g(\cdot)\), that relates the outcome \(\mathbf{y}\) to the linear predictor \(\mathbf{\eta}\).

\(g(\cdot)\) = link function

\(h(\cdot)=g^{−1}(\cdot)\) = inverse link function

We use these link functions to formalize that the conditional expectation of \(\mathbf{y}\) (conditional because it is the expected value depending on the level of the predictors) is

\[g(E(\mathbf{y}))=\mathbf{\eta}\] So, basically the link function “transforms” the outcome variable \(\mathbf{y}\) into a normal outcome.

We could also model the expectation of \(\mathbf{y}\) as \[E(\mathbf{y})=h(\mathbf{\eta})=\mathbf{\mu}\]

With \(\mathbf{y}\) itself equal to

\[\mathbf{y}=h(\mathbf{\eta}) + \mathbf{e}\]

There are many different link functions. We only focus on a few here (for binary and count outcomes).

**Binary Outcomes** For a binary outcome, we use a *logistic* link function and the probability density function (PDF) associated with the logistic. These are:

\[g(\cdot) = log_{e}\left(\frac{p}{1−p}\right)\] \[h(\cdot)=\frac{e^{(\cdot)}}{1+e^{(\cdot)}}\] \[PDF = \frac{e^{-\left(\frac{x-\mu}{s}\right)}}{s\left(1+e^{-\left( \frac{x-\mu}{s}\right)}\right)^{2}}\] where \(s=1\) which is the most common default (scale fixed at 1), so that

\[PDF=\frac{e^{−(x−\mu)}}{\left(1+e^{−(x−\mu)}\right)^{2}}\] \[E(X)=\mu\] \[Var(X)=\frac{\pi^{2}}{3}\]

**Count Outcomes** For a count outcome, we use a *log* link function and the probability mass function (PMF) for the Poisson distribution. These are:

\[g(\cdot) = log_{e}(\cdot)\] \[h(\cdot)=e^{(\cdot)}\] \[PMF = Pr(X = k) = \frac{\lambda^ke^-\lambda}{k!}\] \[E(X)=\lambda\] \[Var(X)=\lambda\]

**Continuous Outcomes** For a continuous outcome where we assume a normal distribution, the most common link function is simply the *identity*. As with other identity functions (e.g., 1, *I*), the identity has special properties that make things simple.

\[g(\cdot) = \cdot\] \[h(\cdot) = \cdot\] \[g(\cdot)=h(\cdot)\] \[g\left(E(X)\right)=E(X)=\mu\] \[g\left(Var(X)\right)=Var(X)=\sigma^{2}\] \[PDF(X)=\left(\frac{1}{\sigma\sqrt{2\pi}}\right)e^{\frac{−(x−\mu)^2}{2\sigma^2}}\]

When we use an “identity” link function, we are in our usual specification of means and variances for the normal (Gaussian) distribution. *“Generalized”* is good becasue it covers the usual case and expands to other situations (binary, count, etc.). However, the estimation algorithms are different when working on the more general problem, and so the usual (normal continuous) case can be slow when using the software for generalized linear modeling. Stick with the non-generalized software for the normal case, and use the specialized programs when needed.

**Interpretation** After inclusion of the link function - the modeling proceeds as usual. Interpretation of results is similar. On the linearized metric (after using the link function) interpretation is done in a standard way - interpreting significance and sign of parameters. However, for substantive interpretation, it is often easier to back transform the results to the original metric. For example, in a random effects logistic model, one might want to talk about the probability of an event given some specific values of the predictors. Likewise in a poisson (count) model, one might want to talk about the expected *count* rather than the expected *log count*. These transformations complicate matters because they are nonlinear and so even random intercepts no longer play a strictly additive role and instead can have a multiplicative effect. Good explanation can be found, among other places, at http://www.ats.ucla.edu/stat/mult_pkg/glmm.htm, and a rich resource of practical questions is here http://glmm.wikidot.com/faq.

For the first example, we use data from the book. As explained in section 6.1 (p. 106-107), the dataset is from a daily diary study of couples during what was for them a typical 4-week period. Each day both partners in each couple provided reports in the morning and in the evening. For this illustration we use a categorical outcome. In particular, following the example in the book, we examine evening reports of conflict from the view of the male partner, testing whether the anger/irritability of the female partner at the beginning of a given day increased the risk of conflicts later in the day.

The data can be downloaded from the book’s website … http://www.intensivelongitudinal.com/ch6/ch6R.zip

and then needs to be unzipped.

We then read in the data from the Downloads folder …

```
#Setting the working directory
#setwd("~/Downloads/Ch6R") #Person 1 Computer
#setwd("~/Desktop/Fall_2017") #Person 2 Computer
#set filepath for data file
#filepath <- "https://quantdev.ssri.psu.edu/sites/qdev/files/AMIBbrief_raw_daily1.csv"
#read in the .csv file using the url() function
daily <- read.csv(file="categorical.csv",header=TRUE)
```

Lets have a quick look at the data file and the descriptives.

```
#data structure
head(daily,10)
```

```
## id time time7c pconf lpconf lpconfc amang amangc
## 1 1 2 -1.8756988 0 0 -0.1568773 0.4166667 -0.0697026
## 2 1 3 -1.7275506 0 0 -0.1568773 0.0000000 -0.4863693
## 3 1 4 -1.5794025 0 0 -0.1568773 0.0000000 -0.4863693
## 4 1 5 -1.4312543 0 0 -0.1568773 0.0000000 -0.4863693
## 5 1 6 -1.2831062 1 0 -0.1568773 0.0000000 -0.4863693
## 6 1 7 -1.1349580 0 1 0.8431227 0.0000000 -0.4863693
## 7 1 8 -0.9868099 0 0 -0.1568773 0.0000000 -0.4863693
## 8 1 9 -0.8386617 0 0 -0.1568773 0.0000000 -0.4863693
## 9 1 10 -0.6905136 0 0 -0.1568773 0.0000000 -0.4863693
## 10 1 11 -0.5423654 0 0 -0.1568773 0.0000000 -0.4863693
## amangcb amangcw
## 1 -0.4709372 0.4012346
## 2 -0.4709372 -0.0154321
## 3 -0.4709372 -0.0154321
## 4 -0.4709372 -0.0154321
## 5 -0.4709372 -0.0154321
## 6 -0.4709372 -0.0154321
## 7 -0.4709372 -0.0154321
## 8 -0.4709372 -0.0154321
## 9 -0.4709372 -0.0154321
## 10 -0.4709372 -0.0154321
```

```
#unique ids
unique(daily$id)
```

```
## [1] 1 3 5 6 9 10 11 12 13 14 15 16 17 18 19 20 21
## [18] 22 23 24 26 27 30 31 32 33 34 36 37 38 42 43 44 45
## [35] 46 48 55 57 59 60 65 69 70 71 72 74 75 76 78 79 80
## [52] 81 83 87 89 91 92 93 95 100 102
```

```
#unique occasions
unique(daily$time)
```

```
## [1] 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## [24] 25 26 27 28
```

```
#descriptives
describe(daily)
```

```
## vars n mean sd median trimmed mad min max range
## id 1 1345 45.61 30.19 38.00 44.44 37.06 1.00 102.00 101.00
## time 2 1345 14.66 7.76 14.00 14.60 10.38 2.00 28.00 26.00
## time7c 3 1345 0.00 1.15 -0.10 -0.01 1.54 -1.88 1.98 3.85
## pconf 4 1345 0.14 0.35 0.00 0.06 0.00 0.00 1.00 1.00
## lpconf 5 1345 0.16 0.36 0.00 0.07 0.00 0.00 1.00 1.00
## lpconfc 6 1345 0.00 0.36 -0.16 -0.09 0.00 -0.16 0.84 1.00
## amang 7 1345 0.49 1.11 0.00 0.22 0.00 0.00 10.00 10.00
## amangc 8 1345 0.00 1.11 -0.49 -0.27 0.00 -0.49 9.51 10.00
## amangcb 9 1345 0.00 0.49 -0.18 -0.10 0.27 -0.47 1.61 2.08
## amangcw 10 1345 0.00 1.00 -0.17 -0.15 0.27 -2.10 9.40 11.50
## skew kurtosis se
## id 0.28 -1.29 0.82
## time 0.06 -1.18 0.21
## time7c 0.06 -1.18 0.03
## pconf 2.02 2.09 0.01
## lpconf 1.88 1.55 0.01
## lpconfc 1.88 1.55 0.01
## amang 3.96 21.05 0.03
## amangc 3.96 21.05 0.03
## amangcb 1.72 2.32 0.01
## amangcw 3.78 22.51 0.03
```

The variables of interest are:

*id* = couple ID number

*time* = diary day

*pconf* = male partner’s evening report of the occurrence of conflict that day

*amang* = female partner’s morning report of anger/irritability

Lets look at the data for each couple (a version of Figure 6.2)

```
#faceted plot
ggplot(data=daily, aes(x=amang,y=pconf)) +
geom_point() + #(position=position_jitter(h=.025)) +
xlab("Female Partner Morning Anger") +
ylab("Male Partner Evening Report of Conflict") +
scale_x_continuous(limits=c(0,10),breaks=c(0,5,10)) +
scale_y_continuous(limits=c(0,1), breaks=c(0,1)) +
facet_wrap( ~ id)
```

OK - Lets briefly consider the substantive framework of our inquiry. We *define* a person-level *dynamic characteristic*, **anger sensitivity** as the extent to which anger influences risk of conflict. *Anger sensitivity* is the *intraindividual covariation* of `pconf`

and `amang`

.

Formally,

\[log\left(\frac{p(y_{it})}{1-p(y_{it})}\right) = \beta_{0i} + \beta_{1i}x_{it}\]

where the outcome (left side of equation) has been “transformed” using the logistic link function, \(g(\cdot) = log_{e}\left(\frac{p}{1−p}\right)\), and where \(\beta_{1i}\) is individual *i*’s level of *anger sensitivity*.

As usual we split the predictor into “trait” (between-person differences) and “state” (within-person deviations) components. In these data, this has already been done. Specifically, the variable `amang`

has been split into two varaibles: `amangcb`

is the sample-mean centered between-person component, and `amangcw`

is the person-centered within-person component. Covariates include `lpconfc`

which is a centered variable that indicates yesterday’s conflict report, and `time7c`

which is a centered variable that indicates passage of time in weeks.

The `lme4`

package contains tools for fitting generalized linear mixed effects models (GLLM), in particular the `glmer()`

function.

Note that `lme()`

(from the `nlme`

package) and `glmer()`

have different coding setups.

Usage:

glmer(formula, data = NULL, family = gaussian, control = glmerControl(), start = NULL, verbose = 0L, nAGQ = 1L, subset, weights, na.action, offset, contrasts = NULL, mustart, etastart, devFunOnly = FALSE, …)

formula = a two-sided linear formula object describing both the fixed-effects and fixed-effects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right. Random-effects terms are distinguished by vertical bars (“|”) separating expressions for design matrices from grouping factors.

For illustration of the code, lets fit the usual unconditional means model …

```
um.fit <- glmer(formula = pconf ~ 1 + (1|id),
data=daily,
family="binomial",
na.action=na.exclude)
summary(um.fit)
```

```
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pconf ~ 1 + (1 | id)
## Data: daily
##
## AIC BIC logLik deviance df.resid
## 1098.7 1109.1 -547.3 1094.7 1343
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6792 -0.4056 -0.3746 -0.3175 3.1767
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.3132 0.5596
## Number of obs: 1345, groups: id, 61
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8625 0.1117 -16.67 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```

The output is similar to what we are used to - although, note that there is no residual term (as per the equation above).

OK - lets add the predictor, `amangcw`

to examine *anger sensitivity*.

```
#simple model
model1.fit <- glmer(formula = pconf ~ 1 + amangcw +
(1|id),
data=daily,
family="binomial",
na.action=na.exclude)
summary(model1.fit)
```

```
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pconf ~ 1 + amangcw + (1 | id)
## Data: daily
##
## AIC BIC logLik deviance df.resid
## 1090.6 1106.2 -542.3 1084.6 1342
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8490 -0.4081 -0.3614 -0.3040 4.0245
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.3244 0.5695
## Number of obs: 1345, groups: id, 61
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.88160 0.11345 -16.586 < 2e-16 ***
## amangcw 0.21986 0.06605 3.329 0.000872 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## amangcw -0.099
```

```
#other useful outputs:
#confint(fit) # 95% CI for the coefficients
#exp(coef(fit)) # exponentiated coefficients
#exp(confint(fit, method="Wald")) # 95% CI for exponentiated coefficients
#predict(fit, type="response") # predicted values
#residuals(fit, type="deviance") # residuals
```

Interpretation occurs in the usual way for logistic models.

```
# Odds Ratio Estimates
exp(fixef(model1.fit))
```

```
## (Intercept) amangcw
## 0.1523457 1.2459069
```

```
#with confidence intervals
exp(cbind(OR = fixef(model1.fit), confint(model1.fit, method="Wald")[-1,]))
```

```
## OR 2.5 % 97.5 %
## (Intercept) 0.1523457 0.1219735 0.1902808
## amangcw 1.2459069 1.0946212 1.4181015
```

Lets look at predicted scores. This can be done in two ways.

1. We can predict in the `link`

scale (for us that is log odds), or

2. We can predict in the `response`

scale (for us that is probability).

```
daily$pred.m1logit <- predict(model1.fit, type="link")
daily$pred.m1prob <- predict(model1.fit, type="response")
```

Plotting predicted scores.

```
#Logit predictions
ggplot(data=daily, aes(x=amangcw,y=pred.m1logit, group=id)) +
geom_line() +
xlab("Female Morning Anger (centered)") +
ylab("Predicted Log Odds")
```