residSVD | R Documentation |

This function uses the first `d`

components of the singular value
decomposition in order to approximate a vector of model residuals by a
sum of `d`

multiplicative terms, with the multiplicative
structure determined by two specified factors. It applies to models
of class `lm`

, `glm`

or `gnm`

.

residSVD(model, fac1, fac2, d = 1)

`model` |
an object of class |

`fac1` |
a factor |

`fac2` |
a factor |

`d` |
integer, the number of multiplicative terms to use in the approximation |

This function operates on the matrix of mean residuals, with rows
indexed by `fac1`

and columns indexed by `fac2`

. For
`glm`

and `glm`

models, the matrix entries are weighted
working residuals. The primary use of `residSVD`

is to
generate good starting values for the parameters in `Mult`

terms
in models to be fitted using `gnm`

.

If `d = 1`

, a numeric vector; otherwise a numeric
matrix with `d`

columns.

David Firth and Heather Turner

`gnm`

, `Mult`

set.seed(1) ## Goodman RC1 association model fits well (deviance 3.57, df 8) mentalHealth$MHS <- C(mentalHealth$MHS, treatment) mentalHealth$SES <- C(mentalHealth$SES, treatment) ## independence model indep <- gnm(count ~ SES + MHS, family = poisson, data = mentalHealth) mult1 <- residSVD(indep, SES, MHS) ## Now use mult1 as starting values for the RC1 association parameters RC1model <- update(indep, . ~ . + Mult(SES, MHS), start = c(coef(indep), mult1), trace = TRUE) ## Similarly for the RC2 model: mult2 <- residSVD(indep, SES, MHS, d = 2) RC2model <- update(indep, . ~ . + instances(Mult(SES, MHS), 2), start = c(coef(indep), mult2), trace = TRUE) ## ## See also example(House2001), where good starting values matter much more! ##

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