---
title: "Quick start for the sommer package"
author: "Giovanny Covarrubias-Pazaran"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Quick start for the sommer package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

The sommer package was developed to provide R users a powerful and reliable multivariate mixed model solver. The package is focused in problems of the type p > n (more effects to estimate than observations) and its core algorithm is coded in C++ using the Armadillo library. This package allows the user to fit mixed models with the advantage of specifying the variance-covariance structure for the random effects, and specify heterogeneous variances, and obtain other parameters such as BLUPs, BLUEs, residuals, fitted values, variances for fixed and random effects, etc. 

The purpose of this quick start guide is to show the flexibility of the package under certain common scenarios:  


B1) Background on mixed models

B2) Background on covariance structures

1) Univariate homogeneous variance models
2) Univariate heterogeneous variance models
3) Univariate unstructured variance models
4) Multivariate homogeneous variance models
5) Multivariate heterogeneous variance models
6) Multivariate unstructured variance models
7) Details on special functions for variance models
  + the major vs() function for special variance models and its auxiliars:
    + at() specific levels heterogeneous variance structure
    + ds() diagonal covariance structure
    + us() unstructured covariance
    + cs() customized covariance structure
8) The specification of constraints in the variance components (Gtc argument)
    + unsm() unstructured constraint
    + uncm() unconstrained 
    + fixm() fixed constraint
    + fcm() constraints on fixed effects
9) Special functions for special models
    + Random regression models
    + Overlayed models
    + Spatial models
    + GWAS models
    + Customized random effects
10) Genomic selection (predicting mendelian sampling)
    + GBLUP
    + rrBLUP
11) Final remarks

## B1) Background on mixed models

The core of the package is the `mmer` function which solve the mixed model equations. The functions are an interface to call the `NR` Direct-Inversion Newton-Raphson or Average Information algorithms (Tunnicliffe 1989; Gilmour et al. 1995; Lee et al. 2016). From version 2.0, sommer can handle multivariate models. Following Maier et al. (2015), the multivariate (and by extension the univariate) mixed model implemented has the form:

<br>

$y_1 = X_1\beta_1 + Z_1u_1 + \epsilon_1$ 

$y_2 = X_2\beta_2 + Z_2u_2 + \epsilon_2$ 

...

$y_i = X_i\beta_i + Z_iu_i + \epsilon_i$ 

<br>

where $y_i$ is a vector of trait phenotypes, $\beta_i$ is a vector of fixed effects, $u_i$ is a vector of random effects for individuals and $e_i$ are residuals for trait ‘i’ (i = 1, …, t). The random effects ($u_1$ ... $u_i$ and $e_i$) are assumed to be normally distributed with mean zero. X and Z are incidence matrices for fixed and random effects respectively. The distribution of the multivariate response and the phenotypic variance covariance (V) are:

<br> 

$Y = X\beta + ZU + \epsilon_i$

<br>

Y ~ MVN($X\beta$, V)

<br>

$$\mathbf{Y} = \left[\begin{array}
{r}
y_1 \\
y_2 \\
... \\
y_t \\
\end{array}\right]
$$

<br>

$$\mathbf{X} = \left[\begin{array}
{rrr}
X_1 & 0 & 0 \\
\vdots & \ddots & \vdots\\
0 & 0 & X_t \\
\end{array}\right]
$$
    
<br>

$$\mathbf{V} = \left[\begin{array}
{rrr}
Z_1 K{\sigma^2_{g_{1}}} Z_1' + H{\sigma^2_{\epsilon_{1}}} & ... & Z_1 K{\sigma_{g_{1,t}}} Z_t' + H{\sigma_{\epsilon_{1,t}}} \\
\vdots & \ddots & \vdots\\
Z_1 K{\sigma_{g_{1,t}}} Z_t' + H{\sigma_{\epsilon_{1,t}}} & ... & Z_t K{\sigma^2_{g_{t}}} Z_t' + H{\sigma^2_{\epsilon_{t}}} \\
\end{array}\right]
$$


<br>

where K is the relationship or covariance matrix for the kth random effect (u=1,…,k), and H=I is an identity matrix or a partial identity matrix for the residual term. The terms $\sigma^2_{g_{i}}$ and $\sigma^2_{\epsilon_{i}}$ denote the genetic (or any of the kth random terms) and residual variance of trait ‘i’, respectively and $\sigma_{g_{_{ij}}}$ and $\sigma_{\epsilon_{_{ij}}}$ the genetic (or any of the kth random terms) and residual covariance between traits ‘i’ and ‘j’ (i=1,…,t, and j=1,…,t). The algorithm implemented optimizes the log likelihood:


<br>

$logL = 1/2 * ln(|V|) + ln(X'|V|X) + Y'PY$

<br>


where || is the determinant of a matrix. And the REML estimates are updated using a Newton optimization algorithm of the form:


<br>

$\theta^{k+1} = \theta^{k} + (H^{k})^{-1}*\frac{dL}{d\sigma^2_i}|\theta^k$

<br>


Where, $\theta$ is the vector of variance components for random effects and covariance components among traits, $H^{-1}$ is the inverse of the Hessian matrix of second derivatives for the kth cycle, $\frac{dL}{d\sigma^2_i}$ is the vector of first derivatives of the likelihood with respect to the variance-covariance components. The Eigen decomposition of the relationship matrix proposed by Lee and Van Der Werf (2016) was included in the Newton-Raphson algorithm to improve time efficiency. Additionally, the popular pin function to estimate standard errors for linear combinations of variance components (i.e. heritabilities and genetic correlations) was added to the package as well.

Please refer to the canonical papers listed in the Literature section to check how the algorithms work. We have tested widely the methods to make sure they provide the same solution when the likelihood behaves well but for complex problems they might lead to slightly different answers. If you have any concern please contact me at cova_ruber@live.com.mx.

In the following section we will go in detail over several examples on how to use mixed models in univariate and multivariate case and their use in quantitative genetics.

<br>

## B2) Background on covariance structures

One of the major strenghts of linear mixed models is the flexibility to specify variance-covariance structures at all levels. In general, variance structures of mixed models can be seen as tensor (kronecker) products of multiple variance-covariance stuctures. For example, a multi-response model (i.e. 2 traits) where "g" individuals (i.e. 100 individuals) are tested in "e" treatments (i.e. 3 environments), the variance-covariance for the random effect "individuals" can be seen as the following multiplicative model:

T $\otimes$ G $\otimes$ A

where:

$$\mathbf{T} = \left[\begin{array}
{rr}
{\sigma^2_{g_{_{t1,t1}}}} & {\sigma_{g_{_{t1,t2}}}} \\
{\sigma_{g_{_{t2,t1}}}} & {\sigma^2_{g_{_{t2,t2}}}} \\
\end{array}\right]
$$

is the covariance structure for individuals among traits.

$$\mathbf{G} = \left[\begin{array}
{rrr}
{\sigma^2_{g_{_{e1,e1}}}} & {\sigma_{g_{_{e1,e2}}}} & {\sigma_{g_{_{e1,e3}}}} \\
{\sigma_{g_{_{e2,e1}}}} & {\sigma^2_{g_{_{e2,e2}}}} & {\sigma_{g_{_{e2,e3}}}} \\
{\sigma_{g_{_{e3,e1}}}} & {\sigma_{g_{_{e3,e2}}}} & {\sigma^2_{g_{_{e3,e3}}}} \\
\end{array}\right]
$$

is the covariance structure for individuals among environments.

and $A$ is a square matrix representing the covariance among the levels of the individuals (any known relationship matrix).

The T and G covariance structures shown above are unknown matrices to be estimated whereas A is known. The T and G matrices shown above are called as unstructured (US) covariance matrices, although this type is just one example from several covariance structures that the linear mixed models enable. For example, other popular covariance structures are:

Diagonal (DIAG) covariance structures

$$\mathbf{\Sigma} = \left[\begin{array}
{rrr}
{\sigma^2_{g_{_{e1,e1}}}} & 0 & 0 \\
\vdots & \ddots & \vdots \\
0 & 0 & {\sigma^2_{g_{_{ei,ei}}}} \\
\end{array}\right]
$$
Compound simmetry (CS) covariance structures

$$\mathbf{\Sigma} = \left[\begin{array}
{rrrr}
{\sigma^2_{g}} + {\sigma^2_{ge}} & {\sigma^2_{g}} & {\sigma^2_{g}} & {\sigma^2_{g}} \\
{\sigma^2_{g}} & {\sigma^2_{g}} + {\sigma^2_{ge}} & {\sigma^2_{g}} & {\sigma^2_{g}}\\
\vdots & \vdots & \ddots & \vdots\\
{\sigma^2_{g}} & {\sigma^2_{g}} & {\sigma^2_{g}} & {\sigma^2_{g}} + {\sigma^2_{ge}}\\
\end{array}\right]
$$

First order autoregressive (AR1) covariance structures

$$\mathbf{\Sigma} = \sigma^2 \left[\begin{array}
{rrrr}
1 & {\rho} & {\rho^2} & {\rho^3} \\
{\rho} & 1 & {\rho} & {\rho^2}\\
{\rho^2} & {\rho} & 1 & {\rho} \\
{\rho^3} & {\rho^2} & {\rho}  & 1  \\
\end{array}\right]
$$

or the already mentioned Unstructured (US) covariance structures

$$\mathbf{\Sigma} = \left[\begin{array}
{rrr}
{\sigma^2_{g_{_{e1,e1}}}} & {\sigma_{g_{_{e1,e2}}}} & {\sigma_{g_{_{e1,e3}}}} \\
\vdots & \ddots & \vdots \\
{\sigma_{g_{_{e3,e1}}}} & {\sigma_{g_{_{e3,e2}}}} & {\sigma^2_{g_{_{e3,e3}}}} \\
\end{array}\right]
$$

among others. Sommer has the capabilities to fit some of these covariance structures in the mixed model machinery. 

## 1) Univariate homogeneous variance models

This type of models refer to single response models where a variable of interest (i.e. genotypes) needs to be analized as interacting with a 2nd random effect (i.e. environments), but you assume that across environments the genotypes have the same variance component. This is the so-called compound simmetry (CS) model.

```{r}
library(sommer)
data(DT_example)
DT <- DT_example
head(DT)

ans1 <- mmer(Yield~Env,
              random= ~ Name + Env:Name,
              rcov= ~ units,
              data=DT)
summary(ans1)

```

## 2) Univariate heterogeneous variance models

Very often in multi-environment trials, the assumption that the genetic variance or the residual variance is the same across locations may be too naive. Because of that, specifying a general genetic component and a location specific genetic variance is the way to go. This requires a CS+DIAG model (also called heterogeneous CS model).

```{r}

data(DT_example)
DT <- DT_example
head(DT)
ans2 <- mmer(Yield~Env,
              random= ~Name + vs(ds(Env),Name),
              rcov= ~ vs(ds(Env),units),
              data=DT)
summary(ans2)

```

As you can see the special function `at` or `diag` can be used to indicate that there's a different variance for the genotypes in each environment. Same was done for the residual. The difference between `at` and `diag` is that the `at` function can be used to specify the levels or specific environments where the variance is different.

## 3) Unstructured variance models

A more relaxed asumption than the CS+DIAG model is the unstructured model (US) which assumes that among the levels of certain factor (i.e. Environments) there's a covariance struture of a second random effect (i.e. Genotypes). This can be done in sommer using the `us(.)` function: 

```{r}

data(DT_example)
DT <- DT_example
head(DT)
ans3 <- mmer(Yield~Env,
             random=~ vs(us(Env),Name),
             rcov=~vs(us(Env),units), 
             data=DT)
summary(ans3)

```

As can be seen the `us(Env)` indicates that the genotypes (Name) can have a covariance structure among environments (Env).

## 4) Multivariate homogeneous variance models

Currently there's a great push for multi-response models. This is motivated by the correlation that certain variables hide and that could benefit in the prediction perspective. In sommer to specify multivariate models the response requires the use of the `cbind()` function in the response, and the `us(trait)`, `diag(trait)`, or `at(trait)` functions in the random part of the model. 

```{r}

data(DT_example)
DT <- DT_example
head(DT)
DT$EnvName <- paste(DT$Env,DT$Name)
ans4 <- mmer(cbind(Yield, Weight) ~ Env,
              random= ~ vs(Name, Gtc=unsm(2)) + vs(EnvName, Gtc=unsm(2)),
              rcov= ~ vs(units, Gtc=unsm(2)),
              data=DT)
summary(ans4)

```

You may notice that we have added the `us(trait)` behind the random effects. This is to indicate the structure that should be assume in the multivariate model. The `diag(trait)` used behind a random effect (i.e. Name) indicates that for the traits modeled (Yield and Weight) there's no a covariance component and should not be estimated, whereas `us(trait)` assumes that for such random effect, there's a covariance component to be estimated (i.e. covariance between Yield and Weight for the random effect Name). Same applies for the residual part (rcov).

## 5) Multivariate heterogeneous variance models 

This is just an extension of the univariate heterogeneous variance models but at the multivariate level. This would be a CS+DIAG multivariate model:

```{r}

data(DT_example)
DT <- DT_example
head(DT)
DT$EnvName <- paste(DT$Env,DT$Name)
ans5 <- mmer(cbind(Yield, Weight) ~ Env,
              random= ~ vs(Name, Gtc=unsm(2)) + vs(ds(Env),Name, Gtc=unsm(2)),
              rcov= ~ vs(ds(Env),units, Gtc=unsm(2)),
              data=DT)
summary(ans5)

```

## 6) Multivariate unstructured variance models 

This is just an extension of the univariate unstructured variance models but at the multivariate level. This would be a US multivariate model:

```{r}

data(DT_example)
DT <- DT_example
head(DT)
DT$EnvName <- paste(DT$Env,DT$Name)
ans6 <- mmer(cbind(Yield, Weight) ~ Env,
              random= ~ vs(us(Env),Name, Gtc=unsm(2)),
              rcov= ~ vs(ds(Env),units, Gtc=unsm(2)),
              data=DT)
summary(ans6)

```

Any number of random effects can be specified with different structures.

## 7) Details on special functions for variance models

# the major vs() function for special variance models and its auxiliars

The sommer function `vs()` allows to construct complex variance models that are passed to the `mmer()` function it constitutes one of the most important features of the sommer package. Its specification of the `vs()` function has the form:

`random=~vs(..., Gu, Gt, Gtc)`

The idea is that the `vs()` function reflects  the special variance structure that each random effect could have in the matrix notation:

$var(u) = T \bigotimes E \bigotimes ... \bigotimes A$

where the ... argument in the `vs()` function is used to specify the kronecker products from all matrices that form the variance for the random effect , where the auxiliar function ds(), us(), cs(), at(), can be used to define such structure variance structure. The idea is that a variance model for a random effect x (i.e. individuals) might require a more flexible model than just:

`random=~x`

For example, if individuals are tested in different time-points and environment, we can assume a different variance and covariance components among the individuals in the different environment-timepoint combinations. An example of variance structure of the type:

$var(u) = T \bigotimes E \bigotimes S \bigotimes A$

would be specified in the `vs()` function as:

`random=~vs(us(e),us(s),x, Gu=A, Gtc=T)`

where the `e` would be a column vector in a data frame for the environments, `s` a column vector in the dataframe for the time points, `x` is the vector in the datrame for the identifier of individuals, `A` is a known square variance covariance matrix among individuals (usually an identity matrix; default if not specified), and T is a square matrices with as many rows and columns as the number of traits that specifyies the trait covariance structure. 

The auxiliar function to build the variance models for the random effect are:
  + ds() diagonal covariance structure
  + us() unstructured covariance
  + at() specific levels heterogeneous variance structure
  + cs() customized covariance structure


# ds() to specify a diagonal (DIAG) covariance structures

A diagonal covariance structure looks like this:

$$\mathbf{\Sigma} = \left[\begin{array}
{rrr}
{\sigma^2_{g_{_{e1,e1}}}} & 0 & 0 \\
\vdots & \ddots & \vdots \\
0 & 0 & {\sigma^2_{g_{_{ei,ei}}}} \\
\end{array}\right]
$$
Considering an example for one random effect (g; indicating i.e. individuals) evaluated in different treatment levels (e; indicating i.e. the different treatments) the model would look like:

`random=~vs(ds(e),g)`

# us() to specify an unstructured (US) covariance

A unstructured covariance looks like this:

$$\mathbf{G} = \left[\begin{array}
{rrr}
{\sigma^2_{g_{_{e1,e1}}}} & {\sigma_{g_{_{e1,e2}}}} & {\sigma_{g_{_{e1,e3}}}} \\
{\sigma_{g_{_{e2,e1}}}} & {\sigma^2_{g_{_{e2,e2}}}} & {\sigma_{g_{_{e2,e3}}}} \\
{\sigma_{g_{_{e3,e1}}}} & {\sigma_{g_{_{e3,e2}}}} & {\sigma^2_{g_{_{e3,e3}}}} \\
\end{array}\right]
$$

Considering same example for one random effect (g; indicating i.e. individuals) evaluated in different treatment levels (e; indicating i.e. the different treatments) the model would look like:

`random=~vs(us(e),g)`

# at() to specify a level-specific heterogeneous variance

A diagonal covariance structure for specific levels of the second random effect looks like this:

$$\mathbf{\Sigma} = \left[\begin{array}
{rrr}
{\sigma^2_{g_{_{e1,e1}}}} & 0 & 0 \\
\vdots & \ddots & \vdots \\
0 & 0 & {\sigma^2_{g_{_{ei,ei}}}} \\
\end{array}\right]
$$

Considering same example for one random effect (g; indicating i.e. individuals) evaluated in different treatment levels (e; indicating i.e. the different treatments A,B,C) the model would look like:

`random=~vs(at(e,c("A","B")),g)`

where the variance component for g is only fitted at levels A and B.

# cs() to specify a level-specific variance-covariance structure

A customized covariance structure for specific levels of the second random effect (variance and covariances) looks i.e. like this:

$$\mathbf{\Sigma} = \left[\begin{array}
{rrr}
{\sigma^2_{g_{_{e1,e1}}}} & {\sigma_{g_{_{e1,e2}}}} & 0 \\
\vdots & \ddots & \vdots \\
0 & 0 & {\sigma^2_{g_{_{ei,ei}}}} \\
\end{array}\right]
$$

Considering same example for one random effect (g; indicating i.e. individuals) evaluated in different treatment levels (e; indicating i.e. the different treatments A,B,C) the model would look like:

`random=~vs(cs(e,mm),g)`

where mm indicates which variance and covariance components are estimated for g.

## 8) The specification of constraints in the variance components (Gtc argument)

One of the major strengths of sommer is its extreme flexibility to specify variance-covariance structures in the multi-trait framework. Since sommer 3.7 this is easily achieved by the use of the `vs()` function and it's argument `Gtc`. The `Gtc` argument expects a matrix of constraints for the variance-covariance components for the random effect filled with numbers according to the following rules:

0: parameter not to be estimated
1: estimated and constrained to be positive
2: estimated and unconstrained
3: not to be estimated but fixed value provided in Gt

Some useful function to specify quickly the contraint matrices are `unsm()` for unstructured, `uncm` for unconstrained, `fixm()` for fixed constraint, and `fcm()` for fixed effect constrains. 

Consider a multi-trait model with 4 traits (y1,...,y4) and 1 random effects (u) and 1 fixed effect (x)

`fixed=cbind(y1,y2,y3,y4)~x`

`random= ~vs(u, Gtc=?)`

The constraint for the 4 x 4 matrix of variance covariance components to be estimated can be an: 

a) unstructured (variance components have to be positive and covariances either positive or negative)
`random= ~vs(u, Gtc=unsm(4))`

```{r}
unsm(4)
```
b) unconstrained (any component variance or covariance can be positive or negative)
`random= ~vs(u, Gtc=uncm(4))`

```{r} 
uncm(4)
```
c) fixed (variance or covariance components indicated with a 3 are considered fixed and values are provided in the Gt argument)
`random= ~vs(u, Gtc=fixm(4), Gt=mm)`

```{r} 
fixm(4)
```
where mm is a 4 x 4 matrix with initial values for the variance components.

d) constraints for fixed effects 
`fixed= cbind(y1,y2,y3,y4)~vs(x, Gtc=fcm(c(1,0,1,0)))`

```{r}
fcm(c(1,0,1,0))
```

where 1's and 0's indicate the traits where the fixed effect will be estimated (1's) and where it won't (0's).


## 9) Special functions for special models

# Random regression models

In order to fit random regression models the user can use the `leg()` function to fit Legendre polynomials. This can be combined with other special covariance structures such as `ds()`, `us()`, etc.

```{r}
library(orthopolynom)
data(DT_legendre)
DT <- DT_legendre
head(DT)
mRR2<-mmer(Y~ 1 + Xf
           , random=~ vs(us(leg(X,1)),SUBJECT)
           , rcov=~vs(units)
           , data=DT)
summary(mRR2)$varcomp

```

Here, a numeric covariate X is used to explain the trajectory of the SUBJECT's and combined with an unstructured covariance matrix. The details can be found in the theory.

# GWAS models

Although genome wide association studies can be conducted through a variety of approaches, the use of mixed models to find association between markers and phenotypes still one of the most popular approaches. Two of the most classical and popular approaches is to test marker by marker trough mixed modeling (1 model by marker) to obtain the marker effect and an statistic reflecting the level of association usually provided as the -log10 p-value. The second most popular approach is to assume that the genetic variance component is similar for all markers and therefore the variance components are only estimated once (1 model for all markers) and use the inverse of the phenotypic variance matrix (V.inverse) to test all markers in the generalized linear model b=(XV-X)-XV-y. This makes the GWAS much faster and efficient without major loses. Given the straight forward extension, sommer provides the `GWAS` function which can fit both type of approaches (be aware that these are 2 among many existant in the literature) in univariate and multivariate models, that way genetically correlated traits can be tested together to increase the power of detection. In summary the GWAS model implemented in sommer to obtain marker effect is a generalized linear model of the form:

b = (X'V-X)X'V-y

with X = ZMi

where:
b is the marker effect (dimensions 1 x mt)
y is the response variable (univariate or multivariate) (dimensions 1 x nt)
V- is the inverse of the phenotypic variance matrix (dimensions nt x nt)
Z is the incidence matrix for the random effect selected (gTerm argument) to perform the GWAS (dimensions nt x ut)
Mi is the ith column of the marker matrix (M argument) (dimensions u x m)

for t traits, n observations, m markers and u levels of the random effect. Depending if P3D is TRUE or FALSE the V- matrix will be calculated once and used for all marker tests (P3D=TRUE) or estimated through REML for each marker (P3D=FALSE).

Here we show a simple GWAS model for an univariate example. 

```{r}

data(DT_cpdata)
DT <- DT_cpdata
GT <- GT_cpdata
MP <- MP_cpdata
#### create the variance-covariance matrix
A <- A.mat(GT) # additive relationship matrix
#### look at the data and fit the model
head(DT,3)
head(MP,3)
GT[1:3,1:4]
mix1 <- GWAS(color~1,
             random=~vs(id,Gu=A)
             + Rowf + Colf,
             rcov=~units,
             data=DT,
             M=GT, gTerm = "u:id")

ms <- as.data.frame(t(mix1$scores))
ms$Locus <- rownames(ms)
MP2 <- merge(MP,ms,by="Locus",all.x = TRUE);
manhattan(MP2, pch=20,cex=.5, PVCN = "color score")

```

Be aware that the marker matrix M has to be imputed (no missing data allowed) and make sure that the number of rows in the M matrix is equivalent to the levels of the gTerm specified (i.e. if the gTerm is "id" and has 300 levels or in other words 300 individuals, then M has dimensions 300 x m, being m the number of markers).

# Overlayed models [the overlay() function]

Another very useful function is the `overlay` function, which allows to overlay matrices of different random effects and estimate a single variance component for the overlayed terms.

```{r}

data("DT_halfdiallel")
DT <- DT_halfdiallel
head(DT)
DT$femalef <- as.factor(DT$female)
DT$malef <- as.factor(DT$male)
DT$genof <- as.factor(DT$geno)
#### model using overlay
modh <- mmer(sugar~1, 
             random=~vs(overlay(femalef,malef)) 
             + genof,
             data=DT)

```

here the femalef and malef random effects are overlayed becoming a single random effect that has the same variance component.

# Spatial models (using the 2-dimensional spline)

We will use the CPdata to show the use of 2-dimensional splines for accomodating spatial effects in field experiments. In early generation variety trials the availability of seed is low, which makes the use of unreplicated design a neccesity more than anything else. Experimental designs such as augmented designs and partially-replicated (p-rep) designs become every day more common this days. 

In order to do a good job modeling the spatial trends happening in the field special covariance structures have been proposed to accomodate such spatial trends (i.e. autoregressive residuals; ar1). Unfortunately, some of these covariance structures make the modeling rather unstable. More recently other research groups have proposed the use of 2-dimensional splines to overcome such issues and have a more robust modeling of the spatial terms (Lee et al. 2013; Rodríguez-Álvarez et al. 2018).

In this example we assume an unreplicated population where row and range information is available which allows us to fit a 2 dimensional spline model.

```{r}
data(DT_cpdata)
DT <- DT_cpdata
GT <- GT_cpdata
MP <- MP_cpdata
### mimic two fields
A <- A.mat(GT)
mix <- mmer(Yield~1,
            random=~vs(id, Gu=A) +
              vs(Rowf) +
              vs(Colf) +
              vs(spl2D(Row,Col)),
            rcov=~vs(units),
            data=DT)
summary(mix)
```

Notice that the job is done by the `spl2D()` function that takes the Row and Col information to fit a spatial kernel. 

# Customized random effects

One of the most powerful features of sommer is the ability to provide any customized matrix and estimate any random effect. For example:

```{r}

data(DT_cpdata)
DT <- DT_cpdata
GT <- GT_cpdata
MP <- MP_cpdata
GT[1:4,1:4]
#### look at the data and fit the model
mix1 <- mmer(Yield~1,
              random=~vs(list(GT)),
              rcov=~units,
              data=DT)

```
the matrix GT is provided as a random effect by encapsulating the matrix in a list and provided in the `vs()` function.

## 10) Genomic selection

In this section I decided to show the way you can fit an rrBLUP and GBLUP model in sommer using some wheat example data from CIMMYT in the genomic selection framework. This is the case of prediction of specific individuals within a population. It basically uses a similar model of the form:

<br>

$y = X\beta + Zu + \epsilon$ 

<br>

and takes advantage of the variance covariance matrix for the genotype effect known as the additive relationship matrix (A) and calculated using the `A.mat` function to establish connections among all individuals and predict the BLUPs for individuals that were not measured. In case the interest is to get BLUPs for markers the random effect is the actual marker matrix and the relationship among markers can be specified as well but in this example is assume a diagonal.

```{r}
data(DT_wheat)
DT <- DT_wheat
GT <- GT_wheat
colnames(DT) <- paste0("X",1:ncol(DT))
DT <- as.data.frame(DT);DT$id <- as.factor(rownames(DT))
# select environment 1
rownames(GT) <- rownames(DT)
K <- A.mat(GT) # additive relationship matrix
colnames(K) <- rownames(K) <- rownames(DT)
# GBLUP pedigree-based approach
set.seed(12345)
y.trn <- DT
vv <- sample(rownames(DT),round(nrow(DT)/5))
y.trn[vv,"X1"] <- NA
head(y.trn)
## GBLUP
ans <- mmer(X1~1,
            random=~vs(id,Gu=K), 
            rcov=~units, 
            data=y.trn) # kinship based
ans$U$`u:id`$X1 <- as.data.frame(ans$U$`u:id`$X1)
rownames(ans$U$`u:id`$X1) <- gsub("id","",rownames(ans$U$`u:id`$X1))
cor(ans$U$`u:id`$X1[vv,],DT[vv,"X1"], use="complete")

## rrBLUP
ans2 <- mmer(X1~1,
             random=~vs(list(GT)), 
             rcov=~units,
             data=y.trn) # kinship based

u <- GT %*% as.matrix(ans2$U$`u:GT`$X1) # BLUPs for individuals
rownames(u) <- rownames(GT)
cor(u[vv,],DT[vv,"X1"]) # same correlation
# the same can be applied in multi-response models in GBLUP or rrBLUP
```

## 11) Final remarks

Keep in mind that sommer uses direct inversion (DI) algorithm which can be very slow for large datasets. The package is focused in problems of the type p > n (more random effect levels than observations) and models with dense covariance structures. For example, for experiment with dense covariance structures with low-replication (i.e. 2000 records from 1000 individuals replicated  twice with a covariance structure of 1000x1000) sommer will be faster than MME-based software. Also for genomic problems with large number of random effect levels, i.e. 300 individuals (n) with 100,000 genetic markers (p). For highly replicated trials with small covariance structures or n > p (i.e. 2000 records from 200 individuals replicated 10 times with covariance structure of 200x200) asreml or other MME-based algorithms will be much faster and we recommend you to opt for those software.

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