View source: R/delta_deviance_lf.R

delta_deviance_lf | R Documentation |

This function fits, at each locus of a given genotype matrix, two logistic
models, and under the assumption that the models are nested, calculates the
delta deviance between the two.
This general function is intended for testing models in a broad setting; for
the specific problem of genetic association, the interface in
`gcat()`

and
`gcat.stat()`

are more user-friendly.

delta_deviance_lf(X, LF0, LF1)

`X` |
A matrix of SNP genotypes, i.e. an integer matrix of 0's,
1's, 2's and |

`LF0` |
Logistic factors for null model. |

`LF1` |
Logistic factors for alternative model. |

The vector of delta deviance values, one per locus of `X`

.

library(lfa) # make example data smaller so example is fast # goes from 1000 to 100 individuals indexes <- sample.int( ncol(sim_geno), 100 ) sim_geno <- sim_geno[ , indexes ] sim_trait <- sim_trait[ indexes ] # now run LFA and get delta deviances for trait assoc # (recapitulating `gcat.stat` in this case) LF <- lfa(sim_geno, 3) LF0 <- LF # structure is null LF1 <- cbind(LF, sim_trait) # trait is alt devdiff_assoc <- delta_deviance_lf(sim_geno, LF0, LF1) # can instead do delta deviances for structure only LF0 <- cbind(rep.int(1, ncol(sim_geno))) # intercept only is null LF1 <- LF # structure is alt, no trait devdiff_struc <- delta_deviance_lf(sim_geno, LF0, LF1)

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