For example, we may assume there is some true regression line in the population, \(\beta\), and we get some estimate of it, \(\hat{\beta}\). Mixed-effects models might include factors that are not necessarily multilevel or hierarchical, for example crossed factors. A further mixed-effects model is applied to the three WER components SUB, DEL and INS to evaluate how they affect the two systems. The effects are conditional on other predictors and group membership, which … Now consider a standard regression model, i.e. 0000002962 00000 n Let’s understand how the patients’ response can be estimated using both fixed effects model, and, mixed model which combines both fixed and the random effects. With linear mixed effects models, we wish to model a linear relationship for data points with inputs of varying type, categorized into subgroups, and associated to a real-valued output. <<050702A324ECEC43A1F0A889E3B500B8>]>> Therefore, using a mixed model allows you to systematically account for item-level variability (within subjects) and subject-level variability (within groups). Below are references for additional information # References Checking assumptions More theory here, here, and here. For example, for unbalanced design with blocking, probably these methods … - The slopes and intercepts of pizza consumption and time will be correlated (shared variance) Fixed effects: - Expecting there to be an overall main effect of pizza consumption over time. These are a few hypothetical random effects structures: The lmer package can be used for modeling, and the general syntax is as follows: ``` modelname <- lmer (dv ~ 1 + IV +(randomeffects), data = data.name, REML = FALSE). β is a p -by-1 fixed-effects vector. Download Rmd. 0000002334 00000 n Generic functions such as print, plot and summary have methods to show the results of the fit. A mixed-effects model consists of fixed-effects and random-effects terms. Mixed-effects models, however, recognize correlations within sample subgroups. Now consider a standard regression model, i.e. an object of class nlme representing the nonlinear mixed-effects model fit. The presence of … 0 To cover some frequently asked questions by users, we’ll fit a mixed model, inlcuding an interaction term and a quadratic resp. In a completely crossed design, all subjects provide responses for all conditions/time-points. My analysis used a Bayesian nonlinear mixed effects beta regression model. Some terms might be more historical, others are more often seen in a specific discipline, others might refer to a certain data structure, and still others are special cases. b is a q -by-1 random-effects vector. 0000002885 00000 n The data set denotes: 1. students as s 2. instructors as d 3. departments as dept 4. service as service 49 15 no clustering. Do they interact? Results show significant effects of both pizza consumption and time on mood! Some specific linear mixed effects models are. In this way, they provide a compromise between ignoring data groups entirely and fitting each group with a separate model. After installation, load the lme4 package into R with For example, we could say that \(\beta\) is … Check correlation between intercept and slope (i.e. Model 1 - Pizza consumption predict mood (main effect): This model appears to show pizza consumption as a positive predictor of mood, as indicated by a posi. Random-effects terms are associated with individual experimental units drawn at random from a population, and account for variations between groups that might affect the response. Mixed models are especially useful when working with a within-subjects design because it works around the ANOVA assumption that data points are independent of one another. They are also common in scientific experiments where a given effect is assumed to be present among all study individuals which needs to be teased out from a … timepoint, condition, etc.). 0000048443 00000 n Code. Mixed-effects models is a more general term than the latter two. We can now conclude that after controlling for random effects, more pizza consumption does lead to improvements in mood over time, but there is no interaction with time. A mixed model is similar in many ways to a linear model. trailer You should expect to see differences in the slopes of your random factors. Some technical detail: We can actually get the correct p-value for the mixed effects model from the above fixed effects model output. The Mixed Modeling framework can specify a variety of model types including random coefficients models, hierarchical linear models, variance components models, nested models, and split-plot designs. The following example will illustrate the logic behind mixed effects models. Results show that while pizza consumption and time are still significant main predictors, their interaction term did not reach significance. Random intercepts: Variability in baseline measurements, Fixed intercepts: Baseline variance is not affected. See nlmeObject for the components of the fit. Intercepts: The baseline relationship between IV & DV. Another common set of experiments where linear mixed-effects models are used is repeated measures where time provide an additional source of correlation between measures. Random Effects. Each data point consists of inputs of varying type—categorized into groups—and a real-valued output. Practical example: Logistic Mixed Effects Model with Interaction Term Daniel Lüdecke 2020-12-14. Hypotheses For Study Random effects: - “Subjects” will have their own intercepts. However, this time the data were collected in many different farms. However, in mixed effects logistic models, the random effects also bear on the results. Linear Mixed Effects Models. m2 <-glmer (outcome ~ var_binom * var_cont + (1 | group), data = dat, family = binomial (link = "logit")) To compute or plot marginal effects of interaction terms, simply specify these terms, i.e. If some models are livestock and some are pets, this model is my dearest pet. Sometimes mixed-effects models are expressed as multilevel regression models (first level and grouping level models) that are fit simultaneously. Mixed Effects Model can be used to model both linear and nonlinear relationships between dependent and independent variables. The ANOVA function allows you to compute Chi-squares between each model to see the improvement in model fit. w�00�ng ���� ��A� �� p1 Check correlation of fixed effects – if too high, this may imply. Linear Mixed-Effects Models. In a within subjects design, one participant provides multiple data points and those data will correlate with one … For example, in the above example we would most likely treat the mean income in a given ZIP as a sample from a normal distribution, with unknown mean and sigma to be estimated by the mixed … Lastly, the course goes over repeated-measures analysis as a special case of mixed-effect modeling. Random effects are random variables in the population Typically assume that random effects are zero-mean Gaussian Typically want to estimate the variance parameter(s) Models with fixed and random effects are calledmixed-effects models. By the end of this lesson you will: Have learned the math of an LMEM. Data. A revolution is taking place in the statistical analysis of psychological studies. Pizza study: Controlling for random effects of subject, pizza consumption, and effect of time on subject, all of which vary across participants. Random effects are best defined as noise in your data. This can be accounted for in random structures as well. For the models in general, I prefer the terms ‘mixed models’ or ‘random effects … Repeated measures and split-plot models are special cases … Chapter 17: Mixed Effects Modeling. The random effects have prior distributions, whereas the fixed … 0000005014 00000 n An interactive version with Jupyter notebook is available here. Specific predictors can now be introduced into our model by specifying the DV followed by the predictor, random effects, and the dataframe. In contrast,random effects are parameters that are themselves randomvariables. However, the researcher wants to be able to model how the alfalfas will grow in fields that are not in the experiment. 3.3 Types of mixed-effects models. First, however, we need to specify the random effects term that best fits the data. This concludes the tutorial on mixed effects models. Subject level variability is often a random effect. We use the InstEval data set from the popular lme4 R package (Bates, Mächler, Bolker, & Walker, 2015). As you can see by the p-values, while there is an improvement in fit from model 1 to model 2, model 3 did not explain more variance. We can calculate the … endstream endobj 50 0 obj <> endobj 51 0 obj <>/Font<>/ProcSet[/PDF/Text]/ExtGState<>>>/Type/Page>> endobj 52 0 obj <> endobj 53 0 obj <> endobj 54 0 obj <> endobj 55 0 obj [/ICCBased 60 0 R] endobj 56 0 obj <> endobj 57 0 obj <> endobj 58 0 obj <> endobj 59 0 obj <>stream We are going to work in lme4, so load the package … This framework is widely applicable across numerous fields within the … Sometimes mixed-effects models are expressed as multilevel regression models (first level and grouping level models) that are fit simultaneously. Psychology Definition of MIXED-EFFECTS MODEL: is used in the evaluation of variance where an experimenter assumes one or more variables as fixed and any further variables as random. This function can work with unbalanced designs: This function can work with unbalanced designs: lme1 = lme(yield ~ nf + bv * topo, random= ~1|rep, data=dat) Here, a double-blind, placebo-controlled clinical trial was conducted to determine whether an estrogen treatment reduces post-natal depression. In contrast, random effects are parameters that are themselves random variables. When building your models, you can treat your predictor as a fixed & random factor. In this example given below, the patients’ response to the vaccine is modelled as the probability of the vaccinated person falling sick due to Covid-19. Logistic Mixed Effects Model with Interaction Term. 49 0 obj <> endobj Mixed-effects models for binary outcomes have been used, for example, to analyze the effectiveness of toenail infection treatments (Lesaffre and Spiessens2001) and to model union membership of young males (Vella and Verbeek1998). The list of random effects implemented in INLA is quite rich. 14 answers. Mixed Effects Model can be used to model both linear and nonlinear relationships between dependent and independent variables. With mixed models we’ve been thinking of coefficients as coming from a distribution (normal). 0000001225 00000 n Linear Mixed Effects Models¶ Linear Mixed Effects models are used for regression analyses involving dependent data. pf (20.58, df1 = 2, df2 = 10, lower.tail = FALSE) ## [1] 0.0002853299. Mixed-effects models might include factors that are not necessarily multilevel or hierarchical, for example crossed factors. For example, a … 0000000016 00000 n Note: If 2 variables share a lot of variance, the random intercepts and slopes may be correlated with one another. 0000002185 00000 n The null model will be fit to the maximal likelihood estimate. You can name each model whatever you want, but note that the name of the dataframe containing your data is specified in each model. A random-intercepts model would adequately capture the two sources of variability mentioned above: the inter-subject variability in overall mean RT in the parameter \({\tau_{00}}^2\), and the trial-by-trial variability in the parameter \(\sigma^2\). It estimates the effects of one or more explanatory variables on a response variable. This generic function fits a nonlinear mixed-effects model in theformulation described in Lindstrom and Bates (1990) but allowing for nestedrandom effects. Generalized linear mixed-effects models allow you to model more kinds of data, including binary responses and count data. Mixed Effects Logistic Regression Example. Throughout the course you'll work with real data to answer … startxref A mixed model is similar in many ways to a linear model. The procedure uses the standard mixed model calculation engine to … Model 3 – Including an interaction term between pizza consumption and time (pizza consumption varies over time). When to choose mixed-effects models, how to determine fixed effects vs. random effects, and nested vs. crossed sampling designs. The mixed-effects model that we would fit to these data, with random intercepts but no random slopes, is known as a random intercepts model. %PDF-1.4 %���� If we divide the machine mean square by the mean square of the interaction effect we get 20.58. Such models are often called multilevel models. When to choose mixed-effects models, how to determine fixed effects vs. random effects, and nested vs. crossed sampling designs. 0000000596 00000 n Such data arise when working with longitudinal and other study designs in which multiple observations are made on each subject. Keep REML = FALSE. The subjects are sampled from … Because there was an improvement in between model 1 and model 2, but NO improvement between model 2 and model 3, we can proceed using the best fit model, nullmodel2, as our random effects structure for the rest of the analyses. You can also visualize your data to see what fits. For example, a … if intercept increases, slope increases). The effects package should also include p-values in the output. The functions resid, coef, fitted, fixed.effects, and random.effects can be used to extract some of its components. As with all regression models, their purpose is to describe a response variable as a function of the predictor variables. H��W˒�6��W�H�$����m���b;e+���DB+�\@E������[�d]�Ճ����4_�^����J�L�#����G����z�����y���?eF��*-d���-!�I��g��o��O�_@F�{��$O�9Y�� ��AT�E�2�V$���rE�y��ȒGA>X{��H�|�?XM�n���� k�(��X�K8�"g�.��H��Y�ey��M��#�gi9�;�5���eT&��|Ƴ��������-��a�l����Gbj�еx#E�D�~&y�C��P:�T�������P���j�q"l��H*�Y�z/�V�}�q)Ώ�L��nW�7�ӧ-|)���E�yxX�����g��y�kiC~�����ضes�[R��1r��NGq����c{钳�\�Xq��y�iq/�i`V�! ")����46�[l6�����t cj��"�ݑ�,�-�{9Z���NB��A���}[1���0��W�qG�x��+Ƴq9Q���Jx�J� ��7 #�ֱ)�S���Z ��h�H^F��e��lN��PK��"��ʓʎ�{���qC=��TgGEM*ٶ�1��Q��D�乕�үiGS��qe>™���WwL�K&���ʀ4��J6 3M�`��Y���p?�h^���8�G��0�m��yF�P�0�c�F����G�/�$TZn,]0E�/�EfRL�. Sushmita Shrikanth. Mathematically, mixed-effects models can be seen as a hierarchical system of regression equations where L1 parameters are function of the L2 equations. 0000001774 00000 n Or maybe multiple fields each contain … The purpose of this workshop is to show the use of the mixed command in SPSS. While being connected to the internet, open R and type in: install.packages(“lme4”) Select a server close to you. For these models we do not need to worry about the assumptions from previous models, since these are very robust against all of them. For example, assume we have a dataset where again we are trying to model yield as a function of nitrogen level. Pizza study: The fixed effects are PIZZA consumption and TIME, because we’re interested in the effect of pizza consumption on MOOD, and if this effect varies over TIME. Let’s say that we are interested in examining the effect of pizza consumption on people’s moods. Thus, we have a crossed design. The researcher uses a mixed effects model to evaluate fixed and random effects together. Nathaniel E. Helwig (U of Minnesota) Linear Mixed-Effects Regression Updated 04-Jan-2017 : Slide 9 If an effect is associated with a This vignette demonstrate how to use ggeffects to compute and plot marginal effects of a logistic regression model. 0000000884 00000 n Nonlinear mixed-effects models are applied in many fields including medicine, public health, pharmacology, and ecology. Slope: The strength of the relationship between IV & DV (controlling for randomness), which represent random effects. We demonstrate with an example in Edward. Mixed-effect models are common i n political polling analysis where national-level characteristics are assumed to occur at a state-level while state-level sample sizes may be too small to drive those characteristics on their own. In a mixed-effects model, random effects contribute only to the covariance structure of the data. The core of mixed models is that they incorporatefixed and random effects. Here is some hypothetical data (code used to generate data can be found here): NOTE - This is a within-subjects study. Ordered outcomes have been studied by, for Because the purpose of this workshop is to show the use of the mixed command, rather than to teach about multilevel models in general, many topics important to multilevel modeling will be mentioned but not discussed in … Mixed Models – Repeated Measures Introduction This specialized Mixed Models procedure analyzes results from repeated measures designs in which the outcome (response) is continuous and measured at fixed time points. With mixed models we’ve been thinking of coefficients as coming from a distribution (normal). Some doctors’ patients may have a greater probability of recovery, and others may have a lower probability, even after we have accounted for the doctors’ experience and other meas… That is why mixed-effects is the terminology preferred here. A O indicates the variable has a fixed intercept and not a random one. Mixed effects, or simply mixed, models generally refer to a mixture of fixed and random effects. The output of a mixed model will give you a list of explanatory values, estimates and confidence intervals of their effect sizes, p-values for each effect, and at least one measure of how well the model fits. ### Insert ggplot2 reference. Mixed effects models—whether linear or generalized linear—are different in that there is more than one source of random variability in the data. Linear Mixed-Effects Models y is the n -by-1 response vector, and n is the number of observations. Such models are also called variance component models. Below are some important terms to know for understanding the statistical concepts used in mixed models: Crossed designs refer to the within-subject variables (i.e. This kind of data appears when subjects are followed over time and measurements are collected at intervals. It is a data set of instructor evaluation ratings, where the inputs (covariates) include categories such as students and departments, and our response variable of interest is the instructor evaluation rating. In summary, we have seen how two schools of thought treat fixed and random effects, discussed when to use fixed effects and when to use random effects in both frameworks, discussed the assumptions behind the models, and seen how to implement a mixed effect model in R. Fixed and random effect models still remain a bit mysterious, but I hope that this discussion cleared … This class of models are used to account for more than one source of random variation. Create a basic mixed-effects model: I’m not going to walk through the steps to building models (at least not yet), but rather just show an example of a model with coral cover as the response variable (elkhorn_LAI), herbivore populations & depth as fixed effects (c.urchinden, c.fishmass, c.maxD), and survey site as a random effect (site).. Value. Random effects have a a very special meaning and allow us to use linear mixed in general as linear mixed models. If an effect, such as a medical treatment, affects the population mean, it is fixed. While we have what we are calling ‘fixed’ effects, the distinguishing feature of the mixed model is the addition of this random component. Mixed effects models are useful when we have data with more than one source of random variability. The Mixed Modeling framework can specify a variety of model types including random coefficients models, hierarchical linear models, variance components models, nested models, and split-plot designs. The general syntax is as follows: When there is a 1 before the line, you are accounting for random intercepts (varying baseline levels) in your variable. A fixed effect is a parameterthat does not vary. SD reflects the amount of variation. Summary. This is Part 1 of a two part lesson. Mixed-effects models is a more general term than the latter two. The core of mixed models is that they incorporate fixed and random effects. 63 0 obj <>stream 0000002636 00000 n In addition to patients, there may also be random variability across the doctors of those patients. Whereas before, analyses were limited to designs with a single random variable (either participants in so-called F1 analyses, or stimuli in so-called F2 analyses), mixed effects models currently allow researchers to take into account both participants and stimuli as random variables (Baayen, Davidson, & Bates, 2008; … NOTE - Predictor variables can be both fixed (i.e. Is a mixed model right for your needs? A random effect model is a model all of whose factors represent random effects. Z is an n -by- q random-effects design matrix. If you are willing to assume that all the children have the same slope and intercept relating age to height then you can fit a regular linear … Hence, the p-value of machine is given by. Such data arise when working with longitudinal and other study designs in which multiple observations are made on each subject. Because subjects start at. Random effects are factors whose levels were sampled randomly from a larger population about which we wish to generalize, but whose specific level values we actually don't care about. A mixed effects model has both random and fixed effects while a standard linear regression model has only fixed effects. Since we have … To fit a mixed-effects model we are going to use the function lme from the package nlme. the names of the variables, as character vector in the terms-argument. Next, we fit a model with an interaction between the binomial and continuous variable. A model that contains both fixed and random effects is called a mixed model. The random effects structure reflects YOUR understanding of where to expect variance, and how nested data will interact with that variance. When we do that we have to account for both within-person and across-person variability. Mixed-effects models account for both fixed and random effects. Effects coding Simulating data, ---
title: "Chapter 17: Mixed Effects Modeling"
author: "Sushmita Shrikanth"
output:
  html_document:
    theme: cerulean
    highlight: textmate
    fontsize: 8pt
    toc: true
    number_sections: true
    code_download: true
    toc_float:
      collapsed: false

---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

# Background Information
Mixed models are especially useful when working with a within-subjects design because it works around the ANOVA assumption that data points are independent of  one another. In a within subjects design, one participant provides multiple data points and those data will correlate with one another because they come from the same participant. Therefore, using a mixed model allows you to systematically account for item-level variability (within subjects) and subject-level variability (within groups).

**When to Use?** -- Studies that obtain multiple measurements over time (longitudinal, time-series) or multiple trials per participant (within subjects) lend themselves well to mixed model analyses.

The following example will illustrate the logic behind mixed effects models.

## Example: National Pizza Study
Let's say that we are interested in examining the effect of pizza consumption on people's moods. Each participant provided an average number of pizzas consumed, and measurements are collected at 15 timepoints 

- Hypothetical sample size, **n = 30**
- **DV**: Mood rating (scale)
- **IV1**: Pizza consumption 
- **IV2**: Time points (Weeks, 1-10)

Here is some hypothetical data (code used to generate data can be found [here](https://github.com/RInterested/SIMULATIONS_and_PROOFS/blob/master/Athletes%20mixed%20effects)): 

```{r include = FALSE}

rm(list = ls())
set.seed(0)
library(lme4)
library(mvtnorm)

subjects = 30
time = 10
 
i = 0.2 
s = 0.5 
r = 0.5
cov.matrix1<-  matrix(c(i^2, r * i * s, r * i * s, s^2), nrow = 2, byrow = T)

require(mvtnorm)
random.effects_subjects <-  rmvnorm(subjects, mean = c(0, 0), sigma = cov.matrix1)
subjects.df = data.frame(subject  = c(1:subjects)) 
subjects.df$alpha_subjects = 1 + random.effects_subjects[, 1]
subjects.df$beta_subjects =  2 + random.effects_subjects[, 2]

i =   0.8   
s =   0.2 
r = -0.01   
(cov.matrix2 <-  matrix(c(i^2, r * i * s, r * i * s, s^2), nrow = 2, byrow = T))

random.effects_time <-  rmvnorm(time, mean = c(0, 0), sigma = cov.matrix2)

time.df = data.frame(time  = c(1:time)) 
time.df$alpha_time   =    -1 + random.effects_time[, 1]
time.df$beta_time    =     1 + random.effects_time[, 2]
summary(time.df$beta_time) 
sd(time.df$beta_time)     
summary(time.df$alpha_time)
sd(time.df$alpha_time)
cor(time.df$alpha_time, time.df$beta_time) 

observations <- subjects * time
observations.df <-  data.frame(
  subject = sort(rep(c(1:subjects), time)),
  time = rep(c(1:time), subjects), 
  pizza = rep(rnorm(subjects * time, 30, 5)))
dat1   <-  merge(subjects.df, observations.df)
dat2   <-  merge(dat1, time.df)
dat3   <-  dat2[with(dat2, order(subject,time)), ]
rownames(dat3)   <-  1:nrow(dat3)


df <-  within(dat3, 
              mood <-  alpha_subjects + pizza * beta_subjects +
                alpha_time    + pizza * beta_time    +
                0.75 * rnorm(n = observations)) 

head(df)
pizzadata <- df[,-c(3,4,6,7)]
```


```{r echo = FALSE}
head(pizzadata)

```

**NOTE** - This is a within-subjects study. All participants are providing multiple measurements. 

## Important Terminology 
Below are some important terms to know for understanding the statistical concepts used in mixed models:

###Crossed & Nested Designs
**Crossed designs** refer to the *within-subject* variables (i.e. timepoint, condition, etc.). Crossed designs occur when multiple measurements are associated with multiple grouping variables. In a completely crossed design, all subjects provide responses for all conditions/time-points.

  - Pizza study: We have subjects providing responses at 10 time points. Thus, we have a crossed design. 
  
**Nested designs** refer to the *between-subject* variable. Generally this is a higher-level variable that subjects or items are grouped under.
  
  - Pizza study: Not nested.

###Fixed v. Random Effects
**Fixed effects** are, essentially, your predictor variables. This is the effect you are interested in after accounting for random variability (hence, fixed). 
 
  - Pizza study: The fixed effects are PIZZA consumption and TIME, because we're interested in the effect of pizza consumption on MOOD, and if this effect varies over TIME. 
  
**Random effects** are best defined as noise in your data. These are effects that arise from uncontrollable variability within the sample. *Subject* level variability is often a random effect.
 
  - Pizza study: Controlling for random effects of subject, pizza consumption, and effect of time on subject, all of which vary across participants. 

**NOTE** - Predictor variables can be both fixed (i.e. causing a main effect/interaction) and random (i.e. causing variance/variability in responses). When building your models, you can treat your predictor as a fixed & random factor. 

### Slopes v. Intercepts: 
To better understand slopes and intercepts it maybe helpful to imagine plotting the relationship between the IVs and DV for each subject.

**Intercepts**: The baseline relationship between IV & DV. Fixed effects are plotted as intercepts to reflect the baseline level of your DV.
  
  -	Random intercepts: Variability in baseline measurements 
      
      * Pizza Study: Different baseline levels of pizza consumption across subjects
      
  - Fixed intercepts: Baseline variance is not affected
  
      * Pizza study: 

**Slope**: The strength of the relationship between IV & DV (controlling for randomness), which represent random effects. You should expect to see differences in the slopes of your random factors. 
  
  - Pizza study: The strength of the relationship between pizza consumption and mood will vary from person to person, resulting in random slopes per subject. Because subjects start at  

**Note**: If 2 variables share a lot of variance, the random intercepts and slopes may be correlated with one another. This can be accounted for in random structures as well. 

**Hypotheses For Study**
Random effects: 
- "Subjects" will have their own intercepts. 
- Subjects' slope will vary by pizza consumption intercepts, and by timepoint intercepts. 
- The slopes and intercepts of pizza consumption and time will be correlated (shared variance)
Fixed effects: 
- Expecting there to be an overall main effect of pizza consumption over time. 
- Expecting interaction such that more pizza over time predicts mood. 

# Setting up data in R 
- **Coding**: Recode your variable (mean-centered, effects) as best suited for your data. 
- **Long Format** : Refer to [TidyR chapter](http://ademos.people.uic.edu/Chapter9.html) 
- **Packages**: Make sure you have the following packages downloaded: 

``` {r, message=FALSE, echo=TRUE}

library (lmerTest) # Mixed model package by Douglas Bates, comes w/ pvalues! 
library (texreg) #Helps us make tables of the mixed models
library (afex) # Easy ANOVA package to compare model fits
library (plyr) # Data manipulator package
library (ggplot2) # GGplot package for visualizing data

```


#Modeling Procedure
Modeling conventions differ by field, but this example will begin by fitting the null model first, then building up hierarchically.
 

## Random effects structure
The *null model* will be fit to the [maximal likelihood estimate](http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf). The random effects structure reflects YOUR understanding of where to expect variance, and how nested data will interact with that variance. The general syntax is as follows:

``` 
(1 + IV | unit level)  
(1 + IV.1*IV.2 | unit level)

#or

(0 + IV | unit level)
(0 + IV.1*IV.2 | unit level)

```
When there is a 1 before the line, you are accounting for random intercepts (varying baseline levels) in your variable. A O indicates the variable has a fixed intercept and not a random one.  These are a few hypothetical random effects structures:

  - ```(1| subject)``` = Random intercepts and slopes for subjects (different baselines, different average effect per subject).
  - ```(1 + pizza |subject)``` = The effect of pizza will vary *between* subjects. Random intercepts for pizza consumption, random slopes
for subjects influenced by pizza consumption. 
  - ``` (1 + pizza | subject) + (0 + time| subject)``` = Subjects have random intercepts and slopes as influenced by pizza consumption. Time slopes can vary as function of the subject, but variance between pizza consumption and time as independent
  - ``` (1 + pizza + time | subject)``` = Same as above, but variance between pizza consumption and time are SHARED (pizza consumption has relationship with time that varies by subject). 
  - ``` (1 + pizza * time | subject)`` =  Each subject can have their intercept, random slopes influenced by pizza and time, and their interaction between pizza and time. IMPORTANTLY, all random slopes and intercepts can be *correlated*. 
  
### Fitting Best Random Effects Structure
The ```lmer``` package can be used for modeling, and the general syntax is as follows: 
 ```
 modelname <- lmer (dv ~ 1 + IV +(randomeffects), data = data.name, REML = FALSE)
 
 ```

You can name each model whatever you want, but note that the name of the dataframe containing your data is specified in each model. Keep ``` REML = FALSE ```. 

First, however, we need to specify the random effects term that best fits the data. Try out different structures, and use the ```anova``` function to find the best fitting random effects structure. This function compares the fit of the model to see how fit has improved with additional items. You can also **visualize your data** to see what fits. ### Insert ggplot2 reference.  

``` {r echo = TRUE, message = FALSE}
nullmodel1 <- lmer( mood ~ 1 + (1|subject), data = pizzadata, REML=FALSE)
nullmodel2 <- lmer( mood ~ 1 + (1 + pizza |subject), data = pizzadata, REML=FALSE)
nullmodel3 <- lmer( mood ~ 1 + (1 + pizza * time |subject), data = pizzadata, REML=FALSE)

anova (nullmodel1, nullmodel2, nullmodel3)
```

Refer to the p-values in the output to see whether there was an improvement in fit. Because there was an improvement in between model 1 and model 2, but NO improvement between model 2 and model 3, we can proceed using the best fit model, `nullmodel2`, as our random effects structure for the rest of the analyses. 

## Fixed effects
Specific predictors can now be introduced into our model by specifying the DV followed by the predictor, random effects, and the dataframe. 

**Model 1** - Pizza consumption predict mood (main effect): 

```{r echo = TRUE, message = FALSE, error = FALSE}
m1=lmer(mood ~ pizza + (1 + pizza + time |subject), data=pizzadata, REML = FALSE)
summary(m1)

```

This model appears to show pizza consumption as a positive predictor of mood, as indicated by a posi

Random effects: 

  - SD reflects the amount of variation. Check correlation between intercept and slope (i.e. if intercept increases, slope increases). 
    
Fixed effects

  - Check estimates for beta value -- time has a significant effect, improvement in mood by about 1 point over time. 
  - Check correlation of fixed effects -- if too high, this may imply [multicollinearity](http://ademos.people.uic.edu/Chapter13.html)

**Model 2** -- Pizza consumption and timepoints included as predictors of mood. 
```{r echo = TRUE, message = FALSE, error = FALSE}
m2= lmer(mood ~ pizza + time + (1 + pizza + time |subject), data=pizzadata, REML = FALSE)
summary(m2)

```

```{r echo = TRUE, message = FALSE, error = FALSE}
m2= lmer(mood ~ pizza + time + (1 + pizza + time |subject), data=pizzadata, REML = FALSE)
summary(m2)

```

Results show significant effects of both pizza consumption and time on mood! Do they interact? 

**Model 3** -- Including an interaction term between pizza consumption and time (pizza consumption varies over time)

```{r echo = TRUE, message = FALSE, error = FALSE}
m3 = lmer(mood ~ pizza*time + (1 + pizza + time |subject), data=pizzadata, REML = FALSE)
summary(m3)

```

Results show that while pizza consumption and time are still significant main predictors, their interaction term did not reach significance. 

## Comparing Model Fit 
The ANOVA function allows you to compute Chi-squares between each model to see the improvement in model fit. The `effects` package should also include p-values in the output. 

```{r echo = TRUE}

anova (m1, m2, m3)

```

As you can see by the p-values, while there is an improvement in fit from model 1 to model 2, model 3 did not explain more variance. As such, model 2 appears to be the best fit.

We can now conclude that after controlling for random effects, more pizza consumption does lead to improvements in mood over time, but there is no interaction with time. 

This concludes the tutorial on mixed effects models. Below are references for additional information 
# References 
[Checking assumptions](http://ademos.people.uic.edu/Chapter18.html)
[More theory here](http://www.stat.cmu.edu/~hseltman/309/Book/chapter15.pdf), [here](http://jakewestfall.org/misc/BDB2008.pdf), and [here](http://www.bodowinter.com/tutorial/bw_LME_tutorial2.pdf).
[Effects coding](http://www.martijnwieling.nl/R/sheets.pdf)
[Simulating data](http://anythingbutrbitrary.blogspot.in/2012/10/hierarchical-linear-models-and-lmer.html)

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