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Setup
Loading some packages for demonstrations and analysis:
library(tidyverse) # Data wrangling and plotting library(ggdist) # Easy and aesthetic plotting of distributions library(ggExtra) # Adding marginal distributions for 2d plots library(tidybayes) # Tidy processing of Bayesian models and extraction of MCMC chains library(bayestestR) # Nice plots for brms models library(brms) # Bayesian modeling and posterior estimation with MCMC using Stan library(lme4) # fitting frequentist hierarchical linear models library(lmerTest) # fitting frequentist hierarchical linear models library(parameters) # clean extraction of model parameters library(distributional) # Plotting of theoretical distributions (for likelihood functions plots)
Introduction
This is the second part of “Bayesian modeling for Psychologists”, see part 1 here. In this post I will show how to model some more complicated regression models, more similar to the ones you will fit in you research.
< section id="gaussian-regression" class="level1 page-columns page-full">Gaussian regression
< section id="ols-linear-model" class="level2">OLS Linear model
In the first part we looked at a simple linear regression model with a categorical predictor (a t-test). Let’s look at linear regression with one continuous predictor:
iris <- iris head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa
Probably some relationship between Petal width and length:
ols_model <- lm(Petal.Length ~ Petal.Width, data = iris)
model_parameters(ols_model) |> insight::print_html()
| Parameter | Coefficient | SE | 95% CI | t(148) | p |
|---|---|---|---|---|---|
| (Intercept) | 1.08 | 0.07 | (0.94, 1.23) | 14.85 | < .001 |
| Petal Width | 2.23 | 0.05 | (2.13, 2.33) | 43.39 | < .001 |
Maybe some interaction with species?
ols_model_int <- lm(Petal.Length ~ Petal.Width * Species, data = iris)
model_parameters(ols_model_int) |> insight::print_html()
| Parameter | Coefficient | SE | 95% CI | t(144) | p |
|---|---|---|---|---|---|
| (Intercept) | 1.33 | 0.13 | (1.07, 1.59) | 10.14 | < .001 |
| Petal Width | 0.55 | 0.49 | (-0.42, 1.52) | 1.12 | 0.267 |
| Species (versicolor) | 0.45 | 0.37 | (-0.28, 1.19) | 1.21 | 0.227 |
| Species (virginica) | 2.91 | 0.41 | (2.11, 3.72) | 7.17 | < .001 |
| Petal Width × Species (versicolor) | 1.32 | 0.56 | (0.23, 2.42) | 2.38 | 0.019 |
| Petal Width × Species (virginica) | 0.10 | 0.52 | (-0.94, 1.14) | 0.19 | 0.848 |
We get the following model:
In order for the intercept to have practical meaning we need to center the predictor Petal.Width:
iris$Petal.Width_c <- scale(iris$Petal.Width, scale = FALSE)[,1]
Fitting the model again:
ols_model_centered <- lm(Petal.Length ~ Petal.Width_c * Species, data = iris)
Now the intercept will be the predicted Petal.Length for Setosa flowers with mean Petal.Width:
model_parameters(ols_model_centered) |> insight::print_html()
| Parameter | Coefficient | SE | 95% CI | t(144) | p |
|---|---|---|---|---|---|
| (Intercept) | 1.98 | 0.47 | (1.05, 2.91) | 4.22 | < .001 |
| Petal Width c | 0.55 | 0.49 | (-0.42, 1.52) | 1.12 | 0.267 |
| Species (versicolor) | 2.04 | 0.47 | (1.10, 2.98) | 4.31 | < .001 |
| Species (virginica) | 3.03 | 0.50 | (2.05, 4.02) | 6.10 | < .001 |
| Petal Width c × Species (versicolor) | 1.32 | 0.56 | (0.23, 2.42) | 2.38 | 0.019 |
| Petal Width c × Species (virginica) | 0.10 | 0.52 | (-0.94, 1.14) | 0.19 | 0.848 |
The model is:
Visualization
f_plot <- iris |>
ggplot(aes(x = Petal.Width, y = Petal.Length, color = Species, fill = Species)) +
geom_point(show.legend = F) +
geom_smooth(method = "lm") +
scale_fill_manual(values = c("#edae49", "#d1495b", "#00798c")) +
scale_color_manual(values = c("#edae49", "#d1495b", "#00798c")) +
blog_theme
f_plot
Bayesian linear model
In order to fit the same model in a Bayesian way, we will first need to define the prior distribution. In this case there are a total of 7 parameters.
Let’s verify this with the get_prior() function:
get_prior(formula = Petal.Length ~ Petal.Width_c * Species,
data = iris,
family = gaussian())
prior class coef group resp
(flat) b
(flat) b Petal.Width_c
(flat) b Petal.Width_c:Speciesversicolor
(flat) b Petal.Width_c:Speciesvirginica
(flat) b Speciesversicolor
(flat) b Speciesvirginica
student_t(3, 4.3, 2.5) Intercept
student_t(3, 0, 2.5) sigma
dpar nlpar lb ub source
default
(vectorized)
(vectorized)
(vectorized)
(vectorized)
(vectorized)
default
0 default
I will model some positive relationship between length and width, a relatively weakly-informed prior on the intercept, and a very wide prior on the not-interesting sigma parameter. On the other parameters I will have an “agnostic” zero-centered Normal distribution:
student-t prior
The student-t distribution is symmetrical like the Gaussian distribution, but it has thicker “tails”. This allows the model to explore wider area of the possible parameter value space.
prior_iris <- set_prior("normal(1, 3)", coef = "Petal.Width_c") +
set_prior("normal(0, 3)", coef = "Speciesversicolor") +
set_prior("normal(0, 3)", coef = "Speciesvirginica") +
set_prior("normal(0, 3)", coef = "Petal.Width_c:Speciesversicolor") +
set_prior("normal(0, 3)", coef = "Petal.Width_c:Speciesvirginica") +
set_prior("normal(0, 3)", class = "Intercept") +
set_prior("exponential(0.001)", class = "sigma")
Exponential prior
Because variances are strictly positive, the exponential distribution is a common choice for it (and other variance parameters):
Fitting a model:
bayes_model_iris <- brm(Petal.Length ~ Petal.Width_c * Species,
data = iris,
family = gaussian(),
prior = prior_iris,
cores = 4,
chains = 4,
iter = 4000,
backend = "cmdstanr")
model_parameters(bayes_model_iris, centrality = "all") |> insight::print_html()
| Model Summary | |||||||
| Parameter | Median | Mean | MAP | 95% CI | pd | Rhat | ESS |
|---|---|---|---|---|---|---|---|
| Fixed Effects | |||||||
| (Intercept) | 2.14 | 2.14 | 2.15 | (1.24, 3.04) | 100% | 1.002 | 1473.00 |
| Petal.Width_c | 0.71 | 0.71 | 0.71 | (-0.22, 1.65) | 93.39% | 1.002 | 1466.00 |
| Speciesversicolor | 1.89 | 1.88 | 1.90 | (0.98, 2.79) | 100% | 1.002 | 1507.00 |
| Speciesvirginica | 2.87 | 2.87 | 2.85 | (1.92, 3.81) | 100% | 1.002 | 1676.00 |
| Petal.Width_c:Speciesversicolor | 1.16 | 1.16 | 1.16 | (0.11, 2.22) | 98.38% | 1.002 | 1712.00 |
| Petal.Width_c:Speciesvirginica | -0.05 | -0.05 | -0.04 | (-1.07, 0.97) | 54.23% | 1.002 | 1618.00 |
| Sigma | |||||||
| sigma | 0.36 | 0.36 | 0.36 | (0.32, 0.41) | 100% | 1.001 | 3753.00 |
model_parameters(ols_model_centered)
Parameter | Coefficient | SE | 95% CI | t(144) | p ------------------------------------------------------------------------------------------- (Intercept) | 1.98 | 0.47 | [ 1.05, 2.91] | 4.22 | < .001 Petal Width c | 0.55 | 0.49 | [-0.42, 1.52] | 1.12 | 0.267 Species [versicolor] | 2.04 | 0.47 | [ 1.10, 2.98] | 4.31 | < .001 Species [virginica] | 3.03 | 0.50 | [ 2.05, 4.02] | 6.10 | < .001 Petal Width c × Species [versicolor] | 1.32 | 0.56 | [ 0.23, 2.42] | 2.38 | 0.019 Petal Width c × Species [virginica] | 0.10 | 0.52 | [-0.94, 1.14] | 0.19 | 0.848
Convergence metrics looking good, we can proceed to interpret the posterior.
< section id="visualization-1" class="level3 page-columns page-full">Visualization
Visualizing a Bayesian regression model can be done in several ways:
< section id="visualizing-regression-parameters-themselves" class="level4">Visualizing regression parameters themselves
Like we saw in Part 1, we can directly inspect the marginal posteriors of the coefficients themselves. First we will extract them into a data.frame (and rename interaction terms for convenience):
posterior_iris <- spread_draws(bayes_model_iris, b_Intercept, b_Petal.Width_c, b_Speciesversicolor, b_Speciesvirginica, !!sym("b_Petal.Width_c:Speciesversicolor"), !!sym("b_Petal.Width_c:Speciesvirginica"), sigma) |>
rename(b_Petal.WidthXSpeciesversicolor = !!sym("b_Petal.Width_c:Speciesversicolor"),
b_Petal.WidthXSpeciesvirginica = !!sym("b_Petal.Width_c:Speciesvirginica"))
head(posterior_iris)
# A tibble: 6 × 10 .chain .iteration .draw b_Intercept b_Petal.Width_c b_Speciesversicolor <int> <int> <int> <dbl> <dbl> <dbl> 1 1 1 1 1.82 0.393 2.18 2 1 2 2 1.56 0.160 2.39 3 1 3 3 1.61 0.102 2.34 4 1 4 4 1.69 0.231 2.30 5 1 5 5 1.67 0.243 2.29 6 1 6 6 1.71 0.250 2.26 # ℹ 4 more variables: b_Speciesvirginica <dbl>, # b_Petal.WidthXSpeciesversicolor <dbl>, # b_Petal.WidthXSpeciesvirginica <dbl>, sigma <dbl>
In the output of spread_draws we get 10 columns:
* .chain = the index of the MCMC chain (4 total in our case).
* .iteration = the index of the draw within it’s chain (each chain is 500 samples long).
* .draw = the overall index of the draw (2000 samples in the posterior overall).
* parameter_name – draw’s value for each parameter.
Plotting the marginal posteriors:
posterior_iris |>
pivot_longer(cols = c(b_Intercept, b_Petal.Width_c, b_Speciesversicolor, b_Speciesvirginica, b_Petal.WidthXSpeciesversicolor, b_Petal.WidthXSpeciesvirginica),
names_to = "variable") |>
ggplot(aes(x = value, y = variable, fill = after_stat(x > 0))) +
stat_halfeye() +
geom_vline(xintercept = 0, linetype = "dashed") +
scale_fill_manual(values = c("#d1495b", "#00798c"), labels = c("Negative", "Positive")) +
scale_x_continuous(breaks = seq(-4, 4, 0.5), labels = seq(-4, 4, 0.5)) +
labs(fill = "Direction of Effect") +
blog_theme
Visualizing the regression line(s)
Marginal posteriors are nice, but are not a visualization of the model itself. Like in the frequentist model, we would like to see the regression line itself. How to do it?
The regression line is a line of conditional means:
But, we have 4000 samples from the posterior containing possible values for each parameter
Uncertainty
Why not summarizing the MCMC chains when inferring or visualizing a Bayesian model?
A key aspect of statistical modeling and visualization is the measurement of uncertainty in the data. In the frequentist world, this is represented with standard errors and confidence intervals.
In the Bayesian world uncertainty is represented by the MCMC samples themselves, creating the posterior distribution. For that reason we will make calculations and visualizations with the MCMC samples and not with their summaries.
Creating a data.frame of independent variables (a design matrix) for creating predictions from the posterior:
new_data <- expand_grid(Petal.Width_c = seq(min(iris$Petal.Width_c), max(iris$Petal.Width_c), length=200),
Species = unique(iris$Species))
head(new_data)
# A tibble: 6 × 2
Petal.Width_c Species
<dbl> <fct>
1 -1.10 setosa
2 -1.10 versicolor
3 -1.10 virginica
4 -1.09 setosa
5 -1.09 versicolor
6 -1.09 virginica
We actually need to remove some rows from this data frame. This is because not all values of Petal.Width appear in all species. The range of observed widths is different between different species.
range_setosa <- c(min(iris$Petal.Width_c[iris$Species == "setosa"]), max(iris$Petal.Width_c[iris$Species == "setosa"]))
range_versicolor <- c(min(iris$Petal.Width_c[iris$Species == "versicolor"]), max(iris$Petal.Width_c[iris$Species == "versicolor"]))
range_virginica <- c(min(iris$Petal.Width_c[iris$Species == "virginica"]), max(iris$Petal.Width_c[iris$Species == "virginica"]))
new_data <- new_data |>
mutate(extrapolation = case_when((Species == "setosa" & Petal.Width_c < range_setosa[1]) | (Species == "setosa" & Petal.Width_c > range_setosa[2]) ~ TRUE,
(Species == "versicolor" & Petal.Width_c < range_versicolor[1]) | (Species == "versicolor" & Petal.Width_c > range_versicolor[2]) ~ TRUE,
(Species == "virginica" & Petal.Width_c < range_virginica[1]) | (Species == "virginica" & Petal.Width_c > range_virginica[2]) ~ TRUE,
.default = FALSE)) |>
filter(!extrapolation)
Calculating the distribution of conditional means for the second row:
posterior_iris |> mutate(conditional_mean = b_Intercept + b_Petal.Width_c * -1.099333 + b_Speciesversicolor + b_Petal.WidthXSpeciesversicolor * -1.099333) |> head()
# A tibble: 6 × 11 .chain .iteration .draw b_Intercept b_Petal.Width_c b_Speciesversicolor <int> <int> <int> <dbl> <dbl> <dbl> 1 1 1 1 1.82 0.393 2.18 2 1 2 2 1.56 0.160 2.39 3 1 3 3 1.61 0.102 2.34 4 1 4 4 1.69 0.231 2.30 5 1 5 5 1.67 0.243 2.29 6 1 6 6 1.71 0.250 2.26 # ℹ 5 more variables: b_Speciesvirginica <dbl>, # b_Petal.WidthXSpeciesversicolor <dbl>, # b_Petal.WidthXSpeciesvirginica <dbl>, sigma <dbl>, conditional_mean <dbl>
posterior_iris |>
mutate(conditional_mean = b_Intercept + b_Petal.Width_c * -1.099333 + b_Speciesversicolor + b_Petal.WidthXSpeciesversicolor * -1.099333) |>
ggplot(aes(y = conditional_mean)) +
stat_slab(fill = "#d1495b", color = "gray34") +
labs(y = expression(mu)) +
blog_theme +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
Calculating this for several rows gives us several more distributions of conditional means. I here use values realistic for each species to avoid extrapolation.
width_values_setosa <- c(-1, -0.9, -0.7)
width_values_versicolor <- c(0, 0.1, 0.3)
width_values_virginica <- c(0.2, 0.8, 1.1)
posterior_iris |>
mutate(conditionalmean_1_setosa = b_Intercept + b_Petal.Width_c * width_values_setosa[1],
conditionalmean_2_setosa = b_Intercept + b_Petal.Width_c * width_values_setosa[2],
conditionalmean_3_setosa = b_Intercept + b_Petal.Width_c * width_values_setosa[3],
conditionalmean_1_versicolor = b_Intercept + b_Petal.Width_c * width_values_versicolor[1] + b_Speciesversicolor + b_Petal.WidthXSpeciesversicolor * width_values_versicolor[1],
conditionalmean_2_versicolor = b_Intercept + b_Petal.Width_c * width_values_versicolor[2] + b_Speciesversicolor + b_Petal.WidthXSpeciesversicolor * width_values_versicolor[2],
conditionalmean_3_versicolor = b_Intercept + b_Petal.Width_c * width_values_versicolor[3] + b_Speciesversicolor + b_Petal.WidthXSpeciesversicolor * width_values_versicolor[3],
conditionalmean_1_virginica = b_Intercept + b_Petal.Width_c * width_values_virginica[1] + b_Speciesvirginica + b_Petal.WidthXSpeciesvirginica * width_values_virginica[1],
conditionalmean_2_virginica = b_Intercept + b_Petal.Width_c * width_values_virginica[2] + b_Speciesvirginica + b_Petal.WidthXSpeciesvirginica * width_values_virginica[2],
conditionalmean_3_virginica = b_Intercept + b_Petal.Width_c * width_values_virginica[3] + b_Speciesvirginica + b_Petal.WidthXSpeciesvirginica * width_values_virginica[3]) |>
pivot_longer(cols = c(conditionalmean_1_setosa, conditionalmean_2_setosa, conditionalmean_3_setosa,
conditionalmean_1_versicolor, conditionalmean_2_versicolor, conditionalmean_3_versicolor,
conditionalmean_1_virginica, conditionalmean_2_virginica, conditionalmean_3_virginica),
names_to = c("junk", "index", "Species"),
names_sep = "_") |>
mutate(index = case_when(index == 1 & Species == "setosa" ~ width_values_setosa[1],
index == 2 & Species == "setosa" ~ width_values_setosa[2],
index == 3 & Species == "setosa" ~ width_values_setosa[3],
index == 1 & Species == "versicolor" ~ width_values_versicolor[1],
index == 2 & Species == "versicolor" ~ width_values_versicolor[2],
index == 3 & Species == "versicolor" ~ width_values_versicolor[3],
index == 1 & Species == "virginica" ~ width_values_virginica[1],
index == 2 & Species == "virginica" ~ width_values_virginica[2],
index == 3 & Species == "virginica" ~ width_values_virginica[3])) |>
ggplot(aes(x = index, y = value, fill = Species)) +
stat_slab(color = "gray33", linewidth = 0.5) +
scale_fill_manual(values = c("#edae49", "#d1495b", "#00798c")) +
labs(x = "Petal.Width", y = expression(mu)) +
blog_theme
We can use the add_linpred_draws() function from the awesome tidybayes package (Kay 2020) in order to do this thing for all values of X, and finally plot the regression line with the credible interval around it:
new_data |>
add_linpred_draws(bayes_model_iris) |>
ggplot(aes(x = Petal.Width_c, y = .linpred, fill = Species, color = Species)) +
geom_point(data = iris, aes(y = Petal.Length, color = Species), show.legend = F) +
stat_lineribbon(.width = c(0.80, 0.85, 0.97), alpha = 0.7) +
scale_fill_manual(values = c("#edae49", "#d1495b", "#00798c")) +
scale_color_manual(values = c("#edae49", "#d1495b", "#00798c")) +
labs(y = expression(mu), fill = "Species", title = "80%, 85% and 97% Credible Intervals") +
guides(color = "none") +
blog_theme
This is similar to the frequentist confidence interval, just remember that these interval actually represents 80%, 85%, and 97% of regression lines.
I think that a better way of visualizing uncertainty is by plotting the individual line themselves. Each draw represents a different regression line, plotting them all will give a nice uncertainty visualization:
new_data <- new_data |> select(-extrapolation) |> add_linpred_draws(bayes_model_iris, ndraws = 44, seed = 14)
We can look at individual draws:
new_data |>
filter(.draw == 14) |>
ggplot(aes(x = Petal.Width_c, y = .linpred, color = Species)) +
geom_line(linewidth = 1) +
geom_point(data = iris, aes(x = Petal.Width_c, y = Petal.Length, color = Species), inherit.aes = F, show.legend = F) +
scale_color_manual(values = c("#edae49", "#d1495b", "#00798c")) +
labs(y = "Petal.Length", title = "Draw number 14") +
blog_theme
Or we can group by draw index and look at all the lines:
new_data |>
ggplot(aes(x = Petal.Width_c, y = .linpred, group = interaction(.draw, Species), color = Species)) +
geom_point(data = iris, aes(x = Petal.Width_c, y = Petal.Length, color = Species), inherit.aes = F, show.legend = F) +
geom_line(alpha = 0.4) +
scale_fill_manual(values = c("#edae49", "#d1495b", "#00798c")) +
scale_color_manual(values = c("#edae49", "#d1495b", "#00798c")) +
labs(y = "Petal.Length") +
blog_theme
Hierarchical Linear Models (Mixed Effects Linear Models)
Hierarchical linear regression models, or Mixed effects linear models are very popular in Psychology due to the fact that data in the field tends to be hierarchical (e.g. repeated measures of participants, participants nested in some larger groups, etc…). Because this post is already quite long, I will assume you have some knowledge about hierarchical structure, fixed and random effects and model estimation.
Luckily for us, brms is more then capable at estimating mixed effects models. It even has the lme4/nlme syntax in the logo!
Let’s look at the classic sleepstudy dataset containing “The average reaction time per day (in milliseconds) for subjects in a sleep deprivation study”.
sleep <- sleepstudy glimpse(sleep)
Rows: 180 Columns: 3 $ Reaction <dbl> 249.5600, 258.7047, 250.8006, 321.4398, 356.8519, 414.6901, 3… $ Days <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0… $ Subject <fct> 308, 308, 308, 308, 308, 308, 308, 308, 308, 308, 309, 309, 3…
sleep |> ggplot(aes(x = Days, y = Reaction)) + geom_point() + geom_smooth(method = "lm", fill = "#d1495b", color = "#5D001E") + scale_x_continuous(breaks = seq(0, 9, 1), labels = seq(0, 9, 1)) + facet_wrap(~Subject) + blog_theme
Full model for predicting reaction time can be:
And:
Level 1: Reaction time of subject
Level 2 (random offset of coefficients for subject):/
Where:
And
Maximum Likelihood Estimation
Fitting this model with the frequentist ML method:
sleep_model_ml <- lmer(Reaction ~ 1 + Days + (1 + Days | Subject),
data = sleep)
Using the sjPlot::tab_model() function to produce a nice summarized statistics of mixed models:
sjPlot::tab_model(sleep_model_ml)
| Reaction | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 251.41 | 237.94 – 264.87 | <0.001 |
| Days | 10.47 | 7.42 – 13.52 | <0.001 |
| Random Effects | |||
| σ2 | 654.94 | ||
| τ00 Subject | 612.10 | ||
| τ11 Subject.Days | 35.07 | ||
| ρ01 Subject | 0.07 | ||
| ICC | 0.72 | ||
| N Subject | 18 | ||
| Observations | 180 | ||
| Marginal R2 / Conditional R2 | 0.279 / 0.799 | ||
Bayesian Estimation
How many parameters? we have 2 fixed effects (intercept and slope), 3 random effects (random intercept, random slope and their correlation), and sigma – A total of 6 parameters.
get_prior(formula = Reaction ~ 1 + Days + (1 + Days | Subject),
data = sleep,
family = gaussian())
prior class coef group resp dpar nlpar lb ub
(flat) b
(flat) b Days
lkj(1) cor
lkj(1) cor Subject
student_t(3, 288.7, 59.3) Intercept
student_t(3, 0, 59.3) sd 0
student_t(3, 0, 59.3) sd Subject 0
student_t(3, 0, 59.3) sd Days Subject 0
student_t(3, 0, 59.3) sd Intercept Subject 0
student_t(3, 0, 59.3) sigma 0
source
default
(vectorized)
default
(vectorized)
default
default
(vectorized)
(vectorized)
(vectorized)
default
Prior elicitation
prior_sleep <- set_prior("normal(10, 4)", coef = "Days") + # RT should increase with continued sleep deprivation
set_prior("exponential(1)", class = "sd") + # setting a prior on all random variances at once
set_prior("exponential(0.01)", class = "sigma")
Model estimation
bayes_model_sleep <- brm(formula = Reaction ~ 1 + Days + (1 + Days | Subject),
data = sleep,
family = gaussian(),
prior = prior_sleep,
chains = 4,
cores = 4,
iter = 2000,
backend = "cmdstanr")
As our models get more complicated, the posterior distribution gets more hard for the MCMC sampler to sample from. That can result in low ECC and/or high Rhat. One solution can be simply increasing the length of each MCMC chain! this change to model definition doesn’t require compiling the Stan code again, use the update() function instead:
bayes_model_sleep <- update(bayes_model_sleep, iter = 9000)
model_parameters(bayes_model_sleep, centrality = "all", effects = "all") |> insight::print_html()
| Model Summary | |||||||
| Parameter | Median | Mean | MAP | 95% CI | pd | Rhat | ESS |
|---|---|---|---|---|---|---|---|
| Fixed Effects | |||||||
| (Intercept) | 251.44 | 251.44 | 251.58 | (243.45, 259.61) | 100% | 1.000 | 18190.00 |
| Days | 10.38 | 10.37 | 10.47 | (7.53, 13.22) | 100% | 1.000 | 12965.00 |
| Sigma | |||||||
| sigma | 28.66 | 28.72 | 28.59 | (25.51, 32.32) | 100% | 1.000 | 14323.00 |
| Random Effects Variances | |||||||
| SD (Intercept: Subject) | 2.58 | 3.57 | 0.16 | (0.06, 11.71) | 100% | 1.000 | 4650.00 |
| SD (Days: Subject) | 5.75 | 5.82 | 5.64 | (3.94, 8.08) | 100% | 1.000 | 6902.00 |
| Cor (Intercept~Days: Subject) | 0.59 | 0.46 | 0.96 | (-0.74, 0.98) | 84.40% | 1.001 | 1453.00 |
Extracting MCMC draws:
posterior_sleep <- spread_draws(bayes_model_sleep, b_Intercept, b_Days, sd_Subject__Intercept, sd_Subject__Days, cor_Subject__Intercept__Days)
The parameters themselves
posterior_sleep |>
ggplot(aes(x = b_Intercept, fill = "#d1495b")) +
stat_slab(color = "gray34") +
guides(color = "none", fill = "none") +
labs(title = "Intercept", y = NULL, x = NULL) +
scale_x_continuous(breaks = seq(230, 270, 5), labels = seq(230, 270, 5)) +
blog_theme +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank())
posterior_sleep |>
ggplot(aes(x = b_Days, fill = "#d1495b")) +
stat_slab(color = "gray34") +
guides(color = "none", fill = "none") +
labs(title = "b_Days", y = NULL, x = NULL) +
scale_x_continuous(breaks = seq(5, 17, 1), labels = seq(5, 17, 1)) +
blog_theme +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank())
posterior_sleep |>
select(.draw, sd_Subject__Intercept, sd_Subject__Days) |>
pivot_longer(cols = c(sd_Subject__Intercept, sd_Subject__Days),
names_to = "variable") |>
ggplot(aes(x = value, y = variable)) +
stat_slab(fill = "#d1495b", color = "gray34") +
scale_x_continuous(breaks = seq(0, 24, 2), labels = seq(0, 24, 2)) +
labs(title = "SD of random effects") +
blog_theme
These are actually the error terms of the random effects. We saw that they should be correlated:
posterior_sleep |>
ggplot(aes(x = cor_Subject__Intercept__Days, fill = after_stat(x < 0))) +
stat_halfeye() +
geom_vline(xintercept = 0, linetype = "dashed") +
scale_fill_manual(values = c("#d1495b", "#00798c"), labels = c("Negative", "Positive")) +
scale_x_continuous(breaks = seq(-1, 1, 0.25), labels = seq(-1, 1, 0.25)) +
labs(title = "Correlation between random effects", fill = "Direction of Correlation", y = NULL, x = expression(r)) +
blog_theme
p_direction(bayes_model_sleep, effects = "random", parameters = "cor*")
Probability of Direction SD/Cor: Subject Parameter | pd ------------------------- Intercept ~ Days | 84.40%
The line
Generating an empty design matrix:
new_data_sleep <- expand_grid(Subject = factor(unique(sleep$Subject)),
Days = c(0:9)) |>
add_linpred_draws(bayes_model_sleep, ndraws = 45, seed = 14)
new_data_sleep |>
ggplot(aes(x = Days, y = .linpred)) +
stat_lineribbon(.width = c(0.80, 0.85, 0.97), alpha = 0.7) +
geom_point(data = sleep, aes(y = Reaction), color = "#5D001E") +
scale_fill_manual(values = c("#edae49", "#d1495b", "#00798c")) +
scale_color_manual(values = c("#edae49", "#d1495b", "#00798c")) +
scale_x_continuous(breaks = c(0:9), labels = c(0:9)) +
scale_y_continuous(breaks = seq(200, 600, 50), labels = seq(200, 600, 50)) +
labs(y = expression(mu), fill = "% Credible Interval", title = "80%, 85% and 97% Credible Intervals") +
guides(color = "none") +
blog_theme
Individual lines:
new_data_sleep |> ggplot(aes(x = Days, y = .linpred, group = interaction(.draw, Subject))) + geom_line(color = "#d1495b") + geom_point(data = sleep, aes(x = Days, y = Reaction, group = Subject), inherit.aes = F, color = "#5D001E") + scale_x_continuous(breaks = c(0:9), labels = c(0:9)) + scale_y_continuous(breaks = seq(200, 600, 50), labels = seq(200, 600, 50)) + labs(y = "Reaction Time (ms)") + guides(color = "none") + blog_theme
Faceting by Subject
new_data_sleep |>
ggplot(aes(x = Days, y = .linpred)) +
stat_lineribbon(.width = c(0.80, 0.85, 0.99), alpha = 0.7) +
geom_point(data = sleep, aes(y = Reaction)) +
facet_wrap(~Subject) +
scale_fill_manual(values = c("#edae49", "#d1495b", "#00798c")) +
scale_color_manual(values = c("#edae49", "#d1495b", "#00798c")) +
scale_x_continuous(breaks = c(0:9), labels = c(0:9)) +
scale_y_continuous(breaks = seq(200, 600, 100), labels = seq(200, 600, 100)) +
labs(y = expression(mu), fill = "% Credible Interval", title = "80%, 85% and 99% Credible Intervals") +
guides(color = "none") +
blog_theme
Individual lines:
new_data_sleep |> ggplot(aes(x = Days, y = .linpred, group = .draw)) + geom_line(color = "#d1495b") + geom_point(data = sleep, aes(x = Days, y = Reaction), color = "#5D001E", inherit.aes = F) + scale_x_continuous(breaks = c(0:9), labels = c(0:9)) + scale_y_continuous(breaks = seq(200, 600, 100), labels = seq(200, 600, 100)) + labs(y = "Reation Time (ms)") + facet_wrap(~Subject) + blog_theme
Another way of visualizing the uncertainty in Mixed effects models is:
new_data_sleep |> ggplot(aes(x = Days, y = .linpred, group = interaction(.draw, Subject))) + geom_line(aes(color = Subject)) + geom_point(data = sleep, aes(x = Days, y = Reaction, group = Subject), inherit.aes = F, color = "#5D001E") + scale_x_continuous(breaks = c(0:9), labels = c(0:9)) + scale_y_continuous(breaks = seq(200, 600, 100), labels = seq(200, 600, 100)) + scale_color_brewer(type = "div", palette = 9) + labs(y = "Reaction Time (ms)", title = "Subjects represented with colors") + guides(color = "none") + blog_theme
In this kind of plot we can also observe the random effects: low variability in intercepts + moderate variability in slopes.
< section id="generalized-linear-models-glms" class="level1 page-columns page-full">Generalized Linear Models (GLMs)
< section id="not-so-professional-intro-to-glms" class="level2">Not-so-professional intro to GLMs
< section id="likelihood-function" class="level3">Likelihood Function
Often, our interesting research questions demand yet more complex models. Recall that ordinary regression model assumes that the dependent variable
In part 1 we called this the assumption of a Data Generating Process (DGP).
< section id="link-function" class="level3">Link Function
Sometimes modeling other parameters creates other complexities: for example: the parameter
For Bernoulli models this link function if often the logit function:
The term
Logistic Regression
We will use the Smarket dataset from the package ISLR containing information about daily percentage returns for the S&P500 index between 2001-2005.
data <- ISLR::Smarket head(data)
Year Lag1 Lag2 Lag3 Lag4 Lag5 Volume Today Direction 1 2001 0.381 -0.192 -2.624 -1.055 5.010 1.1913 0.959 Up 2 2001 0.959 0.381 -0.192 -2.624 -1.055 1.2965 1.032 Up 3 2001 1.032 0.959 0.381 -0.192 -2.624 1.4112 -0.623 Down 4 2001 -0.623 1.032 0.959 0.381 -0.192 1.2760 0.614 Up 5 2001 0.614 -0.623 1.032 0.959 0.381 1.2057 0.213 Up 6 2001 0.213 0.614 -0.623 1.032 0.959 1.3491 1.392 Up
Maximum Likelihood Estimation
Predicting the direction of change Direction from returns the previous day Lag1:
sp500 <- data |>
mutate(Direction = factor(Direction, levels = c("Down", "Up")),
Lag1 = scale(Lag1, scale = F)[,1])
logistic_model <- glm(Direction ~ Lag1,
data = sp500,
family = binomial(link = "logit")) # 'binomial' is the DGP/likelihood function, 'link' is the link function
model_parameters(logistic_model) |> insight::print_html()
| Parameter | Coefficient | SE | 95% CI | z | p |
|---|---|---|---|---|---|
| (Intercept) | 0.07 | 0.06 | (-0.04, 0.18) | 1.30 | 0.193 |
| Lag1 | -0.07 | 0.05 | (-0.17, 0.03) | -1.40 | 0.160 |
Back-transforming the parameters gives:
model_parameters(logistic_model, exponentiate = T) |> insight::print_html()
| Parameter | Coefficient | SE | 95% CI | z | p |
|---|---|---|---|---|---|
| (Intercept) | 1.08 | 0.06 | (0.96, 1.20) | 1.30 | 0.193 |
| Lag1 | 0.93 | 0.05 | (0.84, 1.03) | -1.40 | 0.160 |
And this is the logit function fitted to the data, the negative relationship between Lag1 and market direction today can be seen:
new_data_sp500 <- expand_grid(Lag1 = seq(-100, 100, 0.1))
new_data_sp500$prob <- predict(logistic_model, new_data_sp500, type = "response")
new_data_sp500 |>
ggplot(aes(x = Lag1, y = prob)) +
geom_smooth(method = "glm", method.args = list(family = "binomial"),
color = "#5D001E") +
scale_x_continuous(breaks = seq(-100, 100, 10), labels = seq(-100, 100, 10)) +
labs(x = "Market return yesterday (%)", y = "Predicted Probability", title = "Predicted probability of market going Up today") +
blog_theme
When the market doesn’t change –
And, for an increase in one unit of Lag1, the odds ratio
Bayesian Estimation
< section id="prior-elicitation-1" class="level4">Prior elicitation
Thinking about the priors in a model with a link function can be tricky. So we will define them on the response level (in this case: odds ratio), and transform them to the link level (logit).
get_prior(Direction ~ Lag1,
data = sp500,
family = bernoulli(link = "logit"))
prior class coef group resp dpar nlpar lb ub source
(flat) b default
(flat) b Lag1 (vectorized)
student_t(3, 0, 2.5) Intercept default
Let’s guess that a positive return yesterday predicts a positive return today. Meaning a...
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