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).
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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.
fit_models does not have to be called by the user to use
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.
Storey JD. (2007) The optimal discovery procedure: A new approach to simultaneous significance testing. Journal of the Royal Statistical Society, Series B, 69: 347-368.
Storey JD, Dai JY, and Leek JT. (2007) The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics, 8: 414-432.
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: 12837-12842.
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# 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)