fit_models
fits a model matrix to each gene by using the least
squares method. Model fits can be either statistic type "odp" (optimal
discovery procedure) or "lrt" (likelihood ratio test).
1 2 3 4 5  fit_models(object, stat.type = c("lrt", "odp"), weights = NULL)
## S4 method for signature 'deSet'
fit_models(object, stat.type = c("lrt", "odp"),
weights = NULL)

object 

stat.type 

weights 

If "odp" method is implemented then the null model is removed from the full model (see Storey 2007). Otherwise, the statistic type has no affect on the model fit.
deFit
object
fit_models
does not have to be called by the user to use
odp
, lrt
or kl_clust
as it is an
optional input and is implemented in the methods. The
deFit
object can be created by the user if a different
statistical implementation is required.
John Storey
Storey JD. (2007) The optimal discovery procedure: A new approach to simultaneous significance testing. Journal of the Royal Statistical Society, Series B, 69: 347368.
Storey JD, Dai JY, and Leek JT. (2007) The optimal discovery procedure for largescale significance testing, with applications to comparative microarray experiments. Biostatistics, 8: 414432.
Storey JD, Xiao W, Leek JT, Tompkins RG, and Davis RW. (2005) Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences, 102: 1283712842.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  # import data
library(splines)
data(kidney)
age < kidney$age
sex < kidney$sex
kidexpr < kidney$kidexpr
cov < data.frame(sex = sex, age = age)
# create models
null_model < ~sex
full_model < ~sex + ns(age, df = 4)
# create deSet object from data
de_obj < build_models(data = kidexpr, cov = cov, null.model = null_model,
full.model = full_model)
# retrieve statistics from linear regression for each gene
fit_lrt < fit_models(de_obj, stat.type = "lrt") # lrt method
fit_odp < fit_models(de_obj, stat.type = "odp") # odp method
# summarize object
summary(fit_odp)

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