---
title: "Translating lme4 models to sommer"
author: "Giovanny Covarrubias-Pazaran"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{translate lme4 to sommer}
  %\VignetteEngine{knitr::knitr}
  %\VignetteEncoding{UTF-8}
---

The sommer package was developed to provide R users with a powerful and reliable multivariate mixed model solver for different genetic and non-genetic analyses in diploid and polyploid organisms. This package allows the user to estimate variance components for a mixed model with the advantages of specifying the variance-covariance structure of the random effects, specifying heterogeneous variances, and obtaining other parameters such as BLUPs, BLUEs, residuals, fitted values, variances for fixed and random effects, etc. The core algorithms of the package are coded in C++ using the Armadillo library to optimize dense matrix operations common in the derect-inversion algorithms. Although the vignette shows examples using the mmes function with the default direct inversion algorithm (henderson=FALSE) the Henderson's approach can be faster when the number of records surpasses the number of coefficients to estimate and setting the henderson argument to TRUE can bring significant speed ups.

The purpose of this vignette is to show how to translate the syntax formula from `lme4` models to `sommer` models. Feel free to remove the comment marks from the lme4 code so you can compare the results.

1) Random slopes with same intercept
2) Random slopes and random intercepts (without correlation)
3) Random slopes and random intercepts (with correlation)
4) Random slopes with a different intercept
5) Other models not available in lme4

## 1) Random slopes

This is the simplest model people use when a random effect is desired and the levels of the random effect are considered to have the same intercept.

```{r}
# install.packages("lme4")
# library(lme4)
library(sommer)
data(DT_sleepstudy, package="enhancer")
DT <- DT_sleepstudy
###########
## lme4
###########
# fm1 <- lmer(Reaction ~ Days + (1 | Subject), data=DT)
# summary(fm1) # or vc <- VarCorr(fm1); print(vc,comp=c("Variance"))
# Random effects:
#  Groups   Name        Variance Std.Dev.
#  Subject  (Intercept) 1378.2   37.12   
#  Residual              960.5   30.99   
# Number of obs: 180, groups:  Subject, 18
###########
## sommer
###########
fm2 <- mmes(Reaction ~ Days,
            random= ~ Subject, 
            data=DT, tolParInv = 1e-6, verbose = FALSE)
summary(fm2)$varcomp


```

## 2) Random slopes and random intercepts (without correlation)

This is the a model where you assume that the random effect has different intercepts based on the levels of another variable. In addition the `||` in `lme4` assumes that slopes and intercepts have no correlation.

```{r}
###########
## lme4
###########
# fm1 <- lmer(Reaction ~ Days + (Days || Subject), data=DT)
# summary(fm1) # or vc <- VarCorr(fm1); print(vc,comp=c("Variance"))
# Random effects:
#  Groups    Name        Variance Std.Dev.
#  Subject   (Intercept) 627.57   25.051  
#  Subject.1 Days         35.86    5.988  
#  Residual              653.58   25.565  
# Number of obs: 180, groups:  Subject, 18
###########
## sommer
###########
fm2 <- mmes(Reaction ~ Days,
            random= ~ Subject + vsm(ism(Days), ism(Subject)), 
            data=DT, tolParInv = 1e-6, verbose = FALSE)
summary(fm2)$varcomp

```
 
Notice that Days is a numerical (not factor) variable.

## 3) Random slopes and random intercepts (with correlation)

This is the a model where you assume that the random effect has different intercepts based on the levels of another variable. In addition a single `|` in `lme4` assumes that slopes and intercepts have a correlation to be estimated.

```{r}
###########
## lme4
###########
# fm1 <- lmer(Reaction ~ Days + (Days | Subject), data=DT)
# summary(fm1) # or # vc <- VarCorr(fm1); print(vc,comp=c("Variance"))
# Random effects:
#  Groups   Name        Variance Std.Dev. Corr
#  Subject  (Intercept) 612.10   24.741       
#           Days         35.07    5.922   0.07
#  Residual             654.94   25.592       
# Number of obs: 180, groups:  Subject, 18
###########
## sommer
###########
fm2 <- mmes(Reaction ~ Days, # henderson=TRUE,
            random= ~ covm( vsm(ism(Subject)) , vsm(ism(Days), ism(Subject)) ), 
            nIters = 200, data=DT, tolParInv = 1e-6, verbose = FALSE)
summary(fm2)$varcomp
cov2cor(fm2$theta[[1]])

```
Notice that this last model require a new function called covm() which creates the two random effects as before but now they have to be encapsulated in covm() instead of just added.  

## 4) Random slopes with a different intercept

This is the a model where you assume that the random effect has different intercepts based on the levels of another variable but there's not a main effect. The 0 in the intercept in lme4 assumes that random slopes interact with an intercept but without a main effect.

```{r}
###########
## lme4
###########
# fm1 <- lmer(Reaction ~ Days + (0 + Days | Subject), data=DT)
# summary(fm1) # or vc <- VarCorr(fm1); print(vc,comp=c("Variance"))
# Random effects:
#  Groups   Name Variance Std.Dev.
#  Subject  Days  52.71    7.26   
#  Residual      842.03   29.02   
# Number of obs: 180, groups:  Subject, 18
###########
## sommer
###########
fm2 <- mmes(Reaction ~ Days,
            random= ~ vsm(ism(Days), ism(Subject)), 
            data=DT, tolParInv = 1e-6, verbose = FALSE)
summary(fm2)$varcomp

```

## 4) Other models available in sommer but not in lme4

One of the strengths of sommer is the availability of other variance covariance structures. In this section we show 4 models available in sommer that are not available in lme4 and might be useful.

```{r}
library(orthopolynom)
## diagonal model
fm2 <- mmes(Reaction ~ Days,
            random= ~ vsm(dsm(Daysf), ism(Subject)), 
            data=DT, tolParInv = 1e-6, verbose = FALSE)
summary(fm2)$varcomp
## unstructured model
fm2 <- mmes(Reaction ~ Days,
            random= ~ vsm(usm(Daysf), ism(Subject)), 
            data=DT, tolParInv = 1e-6, verbose = FALSE)
summary(fm2)$varcomp
## random regression (legendre polynomials)
fm2 <- mmes(Reaction ~ Days,
            random= ~ vsm(dsm(leg(Days,1)), ism(Subject)), 
            data=DT, tolParInv = 1e-6, verbose = FALSE)
summary(fm2)$varcomp
## unstructured random regression (legendre)
fm2 <- mmes(Reaction ~ Days,
            random= ~ vsm(usm(leg(Days,1)), ism(Subject)), 
            data=DT, tolParInv = 1e-6, verbose = FALSE)
summary(fm2)$varcomp

```

## Literature

Covarrubias-Pazaran G. 2016. Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6):1-15.

Covarrubias-Pazaran G. 2018. Software update: Moving the R package sommer to multivariate mixed models for genome-assisted prediction. doi: https://doi.org/10.1101/354639

Bernardo Rex. 2010. Breeding for quantitative traits in plants. Second edition. Stemma Press. 390 pp.

Gilmour et al. 1995. Average Information REML: An efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51(4):1440-1450.

Henderson C.R. 1975. Best Linear Unbiased Estimation and Prediction under a Selection Model. Biometrics vol. 31(2):423-447.

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Lee et al. 2015. MTG2: An efficient algorithm for multivariate linear mixed model analysis based on genomic information. Cold Spring Harbor. doi: http://dx.doi.org/10.1101/027201.

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Rodriguez-Alvarez, Maria Xose, et al. Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spatial Statistics 23 (2018): 52-71.

Searle. 1993. Applying the EM algorithm to calculating ML and REML estimates of variance components. Paper invited for the 1993 American Statistical Association Meeting, San Francisco.

Yu et al. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Genetics 38:203-208.

Tunnicliffe W. 1989. On the use of marginal likelihood in time series model estimation. JRSS 51(1):15-27.
