# Semiparametric model

    library(serosv)
    library(dplyr)

## Penalized splines

Proposed model

`Penalized splines`

A general model relating the prevalence to age can be written as a GLM

\[ g(P(Y_i = 1| a _i)) = g(\pi(a_i)) = \eta(a_i) \]

- Where \(g\) is the link function and \(\eta\) is the linear predictor

The linear predictor can be estimated semi-parametrically using penalized spline with truncated power basis functions of degree \(p\) and fixed knots \(\kappa_1,..., \kappa_k\) as followed

\[ \eta(a_i) = \beta_0 + \beta_1a_i + ... + \beta_p a_i^p + \Sigma_{k=1}^ku_k(a_i - \kappa_k)^p_+ \]

- Where

  \[ (a_i - \kappa_k)^p_+ = \begin{cases} 0, & a_i \le \kappa_k \\ (a_i - \kappa_k)^p, & a_i > \kappa_k \end{cases} \]

In matrix notation, the mean structure model for \(\eta(a_i)\) becomes

\[ \eta = X\beta + Zu \]

Where \(\eta = [\eta(a_i) ... \eta(a_N) ]^T\), \(\beta = [\beta_0 \beta_1 .... \beta_p]^T\), and \(u = [u_1 u_2 ... u_k]^T\) are the regression with corresponding design matrices

\[ X = \begin{bmatrix} 1 & a_1 & a_1^2 & ... & a_1^p \\ 1 & a_2 & a_2^2 & ... & a_2^p \\ \vdots & \vdots & \vdots & \dots & \vdots \\ 1 & a_N & a_N^2 & ... & a_N^p \end{bmatrix}, Z = \begin{bmatrix} (a_1 - \kappa_1 )_+^p & (a_1 - \kappa_2 )_+^p & \dots & (a_1 - \kappa_k)_+^p \\ (a_2 - \kappa_1 )_+^p & (a_2 - \kappa_2 )_+^p & \dots & (a_2 - \kappa_k)_+^p \\ \vdots & \vdots & \dots & \vdots \\ (a_N - \kappa_1 )_+^p & (a_N - \kappa_2 )_+^p & \dots & (a_N - \kappa_k)_+^p \end{bmatrix} \]

FOI can then be derived as

\[ \hat{\lambda}(a_i) = [\hat{\beta_1} , 2\hat{\beta_2}a_i, ..., p \hat{\beta} a_i ^{p-1} + \Sigma^k_{k=1} p \hat{u}_k(a_i - \kappa_k)^{p-1}_+] \delta(\hat{\eta}(a_i)) \]

- Where \(\delta(.)\) is determined by the link function use in the model

### Penalized likelihood framework

Refer to Chapter `8.2.1`

Proposed approach

A first approach to fit the model is by maximizing the following penalized likelihood

\[\begin{equation} \phi^{-1}[y^T(X\beta + Zu ) - 1^Tc(X\beta + Zu )] - \frac{1}{2}\lambda^2 \begin{bmatrix} \beta \\u \end{bmatrix}^T D\begin{bmatrix} \beta \\u \end{bmatrix} \tag{1} \end{equation}\]

Where:

- \(X\beta + Zu\) is the linear predictor

- \(D\) is a known semi-definite penalty matrix (Wahba 1978), (Green and Silverman 1993)

- \(y\) is the response vector

- 1 the unit vector, \(c(.)\) is determined by the link function used

- \(\lambda\) is the smoothing parameter (larger values –> smoother curves)

- \(\phi\) is the overdispersion parameter and equals 1 if there is no overdispersion

Fitting data

To fit the data using the penalized likelihood framework, specify `framework = "pl"`

Basis function can be defined via the `s` parameter, some values for s includes:

- `"tp"` thin plate regression splines

- `"cr"` cubic regression splines

- `"ps"` P-splines proposed by (Eilers and Marx 1996)

- `"ad"` for Adaptive smoothers

For more options, refer to the mgcv documentation (Wood 2017)

    pl <- parvob19_be_2001_2003 %>%
      rename(status = seropositive) %>%
      penalized_spline_model(s = "tp", framework = "pl")
    pl$info
    #>
    #> Family: binomial
    #> Link function: logit
    #>
    #> Formula:
    #> spos ~ s(age, bs = s, sp = sp)
    #>
    #> Estimated degrees of freedom:
    #> 6.16  total = 7.16
    #>
    #> UBRE score: 0.1206458

    plot(pl)

### Generalized Linear Mixed Model framework

Refer to Chapter `8.2.2`

Proposed approach

Looking back at (1), a constraint for \(u\) would be \(\Sigma_ku_k^2 < C\) for some positive value \(C\)

This is equivalent to choosing \((\beta, u)\) to maximise (1) with \(D = diag(0, 1)\) where \(0\) denotes zero vector length \(p+1\) and 1 denotes the unit vector of length \(K\)

For a fixed value for \(\lambda\) this is equivalent to fitting the following generalized linear mixed model Ngo and Wand (2004)

\[ f(y|u) = exp\{ \phi^{-1} [y^T(X\beta + Zu) - c(X\beta + Zu)] + 1^Tc(y)\},\\ u \sim N(0, G) \]

- With similar notations as before and \(G = \sigma^2_uI_{K \times K}\)

Thus \(Z\) is penalized by assuming the corresponding coefficients \(u\) are random effect with \(u \sim N(0, \sigma^2_uI)\).

Fitting data

To fit the data using the penalized likelihood framework, specify `framework = "glmm"`

    glmm <- parvob19_be_2001_2003 %>%
      rename(status = seropositive) %>%
      penalized_spline_model(s = "tp", framework = "glmm")
    #>
    #>  Maximum number of PQL iterations:  20
    #> iteration 1
    #> iteration 2
    #> iteration 3
    #> iteration 4
    glmm$info$gam
    #>
    #> Family: binomial
    #> Link function: logit
    #>
    #> Formula:
    #> spos ~ s(age, bs = s, sp = sp)
    #>
    #> Estimated degrees of freedom:
    #> 6.45  total = 7.45

    plot(glmm)

Eilers, Paul H. C., and Brian D. Marx. 1996. “Flexible Smoothing with b-Splines and Penalties.” Statistical Science 11 (2). https://doi.org/10.1214/ss/1038425655.
Green, P. J., and Bernard. W. Silverman. 1993. Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. Chapman; Hall/CRC. https://doi.org/10.1201/b15710.
Ngo, Long, and Matthew P. Wand. 2004. “Smoothing with Mixed Model Software.” Journal of Statistical Software 9 (1). https://doi.org/10.18637/jss.v009.i01.
Ruppert, David, M. P. Wand, and R. J. Carroll. 2003. Semiparametric Regression. Cambridge University Press. https://doi.org/10.1017/cbo9780511755453.
Wahba, Grace. 1978. “Improper Priors, Spline Smoothing and the Problem of Guarding Against Model Errors in Regression.” Journal of the Royal Statistical Society Series B: Statistical Methodology 40 (3): 364–72. https://doi.org/10.1111/j.2517-6161.1978.tb01050.x.
Wand, M. P. 2003. “Smoothing and Mixed Models.” Computational Statistics 18 (2): 223–49. https://doi.org/10.1007/s001800300142.
Wood, Simon N. 2017. Generalized Additive Models: An Introduction with r. Chapman; Hall/CRC. https://doi.org/10.1201/9781315370279.