fit.add.model: Fit the additive model to data
In Stickbreaker: Fits Stickbreaking, Multiplicative and Additive Models to Data

Description Usage Arguments Details Value

View source: R/model_fitting_functions.R

Fit the additive model to data

1	fit.add.model(geno.matrix, fit.matrix, wts = c(2, 1))

`geno.matrix`	Genotype matrix generated in `generate.geno.matrix` or read in
`fit.matrix`	Fitness matrix generated in `sim.mult.data` or read in
`wts`	Vector of weights to weight genotypes by. Used when `generate.geno.weight.matrix` is called (see that function). Default is `c(2,1)`.

wts: The coefficient estimates are obtained by weighted comparisons. The default is to give wild type to single mutation genotype comparisons twice the weight as all other comparisons based on the assumption that wild type is know with much lower error than the other genotypes (actually it is assumed to be known with no error). @examples n.muts <- length(Khan.data[1,])-1 geno.matrix <- Khan.data[,seq(1, n.muts)] fit.matrix <- as.matrix(Khan.data[,(n.muts+1)]) fit.add.model(geno.matrix, fit.matrix, c(2,1)) @seealso fit.stick.model.given.d

List:
[[1]] w.hats are the estimated additive effect coefficients;
[[2]] R2 is proportion of fitness variation explained by model. Does not include wild type in calculation.
[[3]] sig.hat is estimate of sigma
[[4]] logLike is log-likelihood of the data under the fitted model.
[[5]] regression.results List of results when regressing effects of mutations against the background fitness of mutations (see details). [[1]] p.vals gives p-value of each mutation, [[2]] lm.intercepts gives estimated intercept for mutation, [[3]] lm.slopes gives slope for each mutation, [[4]] P is the sum of the log of p-values. This is the summary statistic. [[5]] fitness.of.backs Matrix with fitness of backgrounds when each mutation (columns) is added to each genotype (rows). [[6]] effects.matrix Matrix with fitness effect when given mutation (column) is added to given create genotype (row).