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Adding a final note on LMMs.

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Marco Oesting 2023-10-11 09:39:29 +02:00
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material/3_wed/regression/MultipleRegressionBasics.qmd
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@ -44,7 +44,7 @@ $$y_i = \beta_0 + \beta_1 x_i + \varepsilon_i, \qquad i=1,...,n,$$
where $\varepsilon_i \sim \mathcal{N}(0,\sigma^2)$ are independent
normally distributed errors with unknown variance $\sigma^2$.
*Task:* Find the straight line that fits best, i.e., find the *optimal*
*Aim:* Find the straight line that fits best, i.e., find the *optimal*
estimators for $\beta_0$ and $\beta_1$.
*Typical choice*: Least squares estimator (= maximum likelihood
@ -191,17 +191,17 @@ regression model, but we provide explicit formulas now:
$$
::: {.callout-caution collapse="false"}
## Task 2
1. Implement functions that estimate the $\beta$-parameters,
the corresponding standard errors and the $t$-statistics.
2. Test your functions with the `tree' data set and try to reproduce the
output above.
1. Implement functions that estimate the $\beta$-parameters, the
corresponding standard errors and the $t$-statistics.
2. Test your functions with the \`tree' data set and try to reproduce
the output above.
:::
Which model is the best? For linear models, one often uses the $R^2$ characteristic.
Roughly speaking, it gives the percentage (between 0 and 1) of the variance that can be explained by the linear model.
Which model is the best? For linear models, one often uses the $R^2$
characteristic. Roughly speaking, it gives the percentage (between 0
and 1) of the variance that can be explained by the linear model.
``` julia
r2(linmod1)
@ -212,8 +212,11 @@ linmod3 = lm(@formula(Volume ~ Girth + Height + Girth*Height), trees)
r2(linmod3)
```
::: {.callout-note}
The more covariates you add the more variance can be explained by the linear model - $R^2$ increases. In order to balance goodness-of-fit of a model and its complexity, information criteria such as `aic` are considered.
::: callout-note
The more covariates you add the more variance can be explained by the
linear model - $R^2$ increases. In order to balance goodness-of-fit of a
model and its complexity, information criteria such as `aic` are
considered.
:::
## Generalized Linear Models
@ -256,22 +259,22 @@ $$
For the models above, these are:
+----------------+------------------+--------------------------+
+----------------+-----------------+-------------------------+
| Type of Data | Distribution | Link Function |
| | Family | |
+================+==================+==========================+
+================+=================+=========================+
| continuous | Normal | identity: |
| | | |
| | | $$ |
| | | g(x)=x |
| | | $$ |
+----------------+------------------+--------------------------+
+----------------+-----------------+-------------------------+
| count | Poisson | log: |
| | | |
| | | $$ |
| | | g(x) = \log(x) |
| | | $$ |
+----------------+------------------+--------------------------+
+----------------+-----------------+-------------------------+
| binary | Bernoulli | logit: |
| | | |
| | | $$ |
@ -279,7 +282,7 @@ For the models above, these are:
| | | \frac{x}{1-x} |
| | | \right) |
| | | $$ |
+----------------+------------------+--------------------------+
+----------------+-----------------+-------------------------+
In general, the parameter vector $\beta$ is estimated via maximizing the
likelihood, i.e.,
@ -311,10 +314,33 @@ model = glm(@formula(participation ~ age^2),
```
::: {.callout-caution collapse="false"}
## Task 3:
1. Reproduce the results of our data analysis of the `tree` data set
using a generalized linear model with normal distribution family.
2. Generate $n=20$ random covariates $\mathbf{x}$ and Poisson-distributed counting data with parameters $\beta_0 + \beta_1 x_i$. Re-estimate the parameters by a generalized linear model.
2. Generate $n=20$ random covariates $\mathbf{x}$ and
Poisson-distributed counting data with parameters
$\beta_0 + \beta_1 x_i$. Re-estimate the parameters by a generalized
linear model.
:::
## Outlook: Linear Mixed Models
In the linear regression models so far, we assumed that the response
variable $\mathbf{y}$ depends on the design matrix of covariates
$\mathbf{X}$ - which are assumed to be given/fixed - multiplied by the
so-called *fixed effects* coefficients $\mathbf{X}\beta$ and independent
errors $\varepsilon$. However, in many situations, there are also random
effects on several components of the response variable. These can be
included in the model by adding another design matrix $\mathbf{Z}$
multiplied by a random vector $u$, the so-called *random effects*
coefficients, that are assumed to be jointly normally distributed with
mean vector $0$ and variance-covariance matrix $\Sigma$ (typically *not*
a diagonal matrix). In matrix notation, we have the following form:
$$
\mathbf{y} = \mathbf{X} \beta + \mathbf{Z}u + \varepsilon
$$
Maximizing the likelihood, we can estimate $\beta$ and optimally
predict the random vector $u$.