Description Usage Arguments Details Value Examples

Estimation of fixed and random effects in linear mixed models

1 |

`data` |
Input matrix of dimension n * p; each row is an observation vector. The intercept should be included in the first column as (1,...,1). If not, it is added. |

`Y` |
Response variable of length n. |

`z` |
Random effects matrix. Of size n*q. |

`grp` |
Grouping variable of length n. |

`D` |
Logical value. If TRUE, the random effects are considered to be independent, i.e. |

`step` |
The algorithm performs at most |

`showit` |
Logical value. If TRUE, shows the convergence process of the algorithm. Default is FALSE. |

`lmme`

performs an ML-estimation of fixed and random effects in linear mixed models when no selection is involved.
Two algorithms are available: one when the random effects are assumed to be independent (D=TRUE) and one when they are not (D=FALSE).

`data` |
List of the user-data: the scaled matrix used in the algorithm, the first column being (1,...,1); Y; z and grp. |

`beta` |
Estimation of the selected fixed effects. |

`Psi` |
Variance of the random effects. Matrix of dimension q*q. |

`sigma_e` |
Variance of the noise. |

`fitted.values` |
Fitted values calculated with the fixed effects and the random effects. |

`it` |
Number of iterations of the algorithm. |

`converge` |
Did the algorithm converge? |

`u` |
Vector of the concatenation of the estimated random effects (u_1',...,u_q')'. |

`call` |
The call that produced this object. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
## Not run:
N <- 20 # number of groups
p <- 80 # number of covariates (including intercept)
q <- 2 # number of random effect covariates
ni <- rep(6,N) # observations per group
n <- sum(ni) # total number of observations
grp <- factor(rep(1:N,ni)) # grouping variable
grp=rbind(grp,grp)
beta <- c(1,2,4,3,rep(0,p-3)) # fixed-effects coefficients
x <- cbind(1,matrix(rnorm(n*p),nrow=n)) # design matrix
u1=rnorm(N,0,sd=sqrt(2))
u2=rnorm(N,0,sd=sqrt(2))
bi1 <- rep(u1,ni)
bi2 <- rep(u2,ni)
bi <- rbind(bi1,bi2)
z=x[,1:2,drop=FALSE]
epsilon=rnorm(120)
y <- numeric(n)
for (k in 1:n) y[k] <- x[k,]%*%beta + t(z[k,])%*%bi[,k] + epsilon[k]
########
fit=lmme(x,y,z,grp)
## End(Not run)
``` |

MMS documentation built on May 17, 2018, 9:05 a.m.

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