Description Usage Arguments Value Examples

Generalized least squares using precomputed eigen decomposition of covariance matrix

1 2 3 |

`formula` |
a formula expression as for regression models, of the form response ~ predictors. See the documentation of formula for other details. |

`data` |
an optional data frame in which to interpret the variables occurring in formula. |

`U` |
eigen vectors of covariance matrix |

`lambda` |
eigen values of covariance matrix. If not specified, set all values to 1 |

`subset` |
expression saying which subset of the rows of the data should be used in the fit. All observations are included by default. |

`na.action` |
a function to filter missing data. |

`rank` |
number of eigen vectors of U to use |

`quantileTransform` |
Use a non-parametric test that only uses the ranks of the transformed test statistics. |

`method` |
method to be used by lm.fit. |

`model` |
should the model frame be returned? |

`x` |
should the design matrix be returned? |

`y` |
should the response be returned? |

`contrasts` |
a list of contrasts to be used for some or all of |

`...` |
additional arguments to lm.fit. |

lm.gls object for use with summary()

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 | ```
library(mvtnorm)
library(clusterGeneration)
# number of samples
N = 100
# Generate positive definite covariance matrix
C = genPositiveDefMat(N)$Sigma
# simulate response from covariance matrix C
y = t(rmvnorm(1, rep(0, N), C))
# eigen decomposition of matrix C
dcmp = eigen(C)
U = dcmp$vectors
lambda = dcmp$values
# simulate random covariate
X_cov = rnorm(N)
# fit GLS from eigen decomposition
fit = glslr( y~ X_cov, U=U, lambda=lambda)
# get results
summary(fit)
``` |

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