Description Usage Arguments Details Value Author(s) References See Also Examples

Function fits big ridge regression with special computational advantage for the cases when number of shrinkage parameters exceeds number of observations. The shrinkage parameter, lambda, can be pre-specified or estimated along with the model. Any subset of model parameter can be shrunk.

1 2 3 |

`y` |
the vector of response variable. |

`X` |
the design matrix related to the parameters not to be shrunk (i.e. fixed effects in the mixed model framework). |

`Z.name` |
file name to be combined with |

`Z.index` |
file index/indices to be combined with |

`family` |
the distribution family of |

`weight` |
a vector of prior weights for each of the shrinkage parameters. |

`lambda` |
the shrinkage parameter determines the amount of shrinkage. Default is |

`only.estimates` |
logical; |

`tol.err` |
internal tolerance level for extremely small values; default value is 1e-6. |

`tol.conv` |
tolerance level in convergence; default value is 1e-8. |

`save.cache` |
logical; specify whether internal cache files should be saved for fast future repeating analyses. If |

`...` |
unused arguments. |

The function does the same job as the `bigRR`

function, but allows huge size of data (the `Z`

matrix) that cannot be loaded into computer memory as a whole.
Instead of specifying the entire design matrix for random effects (`Z`

in `bigRR`

), the `Z`

can be split as `Z = cbind(Z1, Z2, ..., Zk)`

, and each piece of `Z`

is stored in `DatABEL`

format with file names specified by the arguments `Z.name`

and `Z.index`

.
For example (see also **Examples**), if the genotype data for each chromosome is stored in `DatABEL`

format with file names `chr1.fvd`

& `chr1.fvi`

, ..., `chr22.fvd`

& `chr22.fvi`

, the input argument should be specified as `Z.name = 'chr'`

and `Z.index = 1:22`

.

Returns a list of object class `bigRR`

containing the following values: (see **Examples** for how to use the estimated parameters for a prediction purpose.)

`phi` |
estimated residual variance (Non-genetic variance component). |

`lambda` |
estimated random effect variance (Genetic variance component). which is proportional to the usual |

`beta` |
fixed effects estimates - subset of model parameters which is/are not shrunk, i.e. those associated with the |

`u` |
random effects estimates (genetic effects of each marker) - subset of model parameters which are shrunk, i.e. those associated with the |

`leverage` |
hat values for the random effects. |

`hglm` |
the internal fitted |

`Call` |
how the bigRR was called. |

Xia Shen

Shen X, Alam M, Fikse F and Ronnegard L (2013). **A novel generalized ridge regression method for quantitative genetics**. *Genetics*, **193**, 1255-1268.

Ronnegard L, Shen X and Alam M (2010): **hglm: A Package for Fitting Hierarchical Generalized Linear Models**. *The R Journal*, **2**(2), 20-28.

`lm.ridge`

in MASS library.

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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ```
# --------------------------------------------- #
# Arabidopsis example #
# --------------------------------------------- #
require(bigRR)
data(Arabidopsis)
X <- matrix(1, length(y), 1)
## Not run:
# splitting the genotype data into two pieces and re-saving in DatABEL format
#
dimnames(Z) <- list(NULL, NULL)
Z <- scale(Z)
matrix2databel(Z[,1:100000], 'part1')
matrix2databel(Z[,100001:ncol(Z)], 'part2')
# fitting SNP-BLUP, i.e. a ridge regression on all the markers across the genome
#
SNP.BLUP.result <- hugeRR(y = y, X = X, Z.name = 'part', Z.index = 1:2,
family = binomial(link = 'logit'), save.cache = TRUE)
# re-run SNP-BLUP - a lot faster since cache data are stored
SNP.BLUP.result <- hugeRR(y = y, X = X, Z.name = 'part', Z.index = 1:2,
family = binomial(link = 'logit'))
# fitting HEM, i.e. a generalized ridge regression with marker-specific shrinkage
#
HEM.result <- hugeRR_update(SNP.BLUP.result, Z.name = 'part', Z.index = 1:2,
family = binomial(link = 'logit'))
# plot and compare the estimated effects from both methods
#
split.screen(c(1, 2))
split.screen(c(2, 1), screen = 1)
screen(3); plot(abs(SNP.BLUP.result$u), cex = .6, col = 'slateblue')
screen(4); plot(abs(HEM.result$u), cex = .6, col = 'olivedrab')
screen(2); plot(abs(SNP.BLUP.result$u), abs(HEM.result$u), cex = .6, pch = 19,
col = 'darkmagenta')
# create a random new genotypes for 10 individuals with the same number of markers
# and predict the outcome using the fitted HEM
#
Z.new <- matrix(sample(c(-1, 1), 10*ncol(Z), TRUE), 10)
y.predict <- as.numeric(HEM.result$beta + Z.new %*% HEM.result$u)
#
# NOTE: The above prediction may not be good due to the scaling in the HEM
# fitting above, and alternatively, one can either remove the scaling
# above or scale Z.new by row-binding it with the original Z matrix.
## End(Not run)
``` |

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