lrt {edge} | R Documentation |
lrt
performs a generalized likelihood ratio test using the full and
null models.
lrt(object, de.fit, nullDistn = c("normal", "bootstrap"), weights = NULL, bs.its = 100, seed = NULL, verbose = TRUE, mod.F = FALSE, ...) ## S4 method for signature 'deSet,missing' lrt(object, de.fit, nullDistn = c("normal", "bootstrap"), weights = NULL, bs.its = 100, seed = NULL, verbose = TRUE, mod.F = FALSE, ...) ## S4 method for signature 'deSet,deFit' lrt(object, de.fit, nullDistn = c("normal", "bootstrap"), weights = NULL, bs.its = 100, seed = NULL, verbose = TRUE, mod.F = FALSE, ...)
object |
|
de.fit |
|
nullDistn |
|
weights |
|
bs.its |
|
seed |
|
verbose |
|
mod.F |
|
... |
Additional arguments for |
lrt
fits the full and null models to each gene using the
function fit_models
and then performs a likelihood ratio test.
The user has the option to calculate p-values a Normal distribution
assumption or through a bootstrap algorithm. If nullDistn
is
"bootstrap" then empirical p-values will be determined from the
qvalue
package (see empPvals
).
deSet
object
John Storey, Andrew Bass
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.
http://en.wikipedia.org/wiki/Likelihood-ratio_test
# 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) # lrt method de_lrt <- lrt(de_obj, nullDistn = "normal") # to generate p-values from bootstrap de_lrt <- lrt(de_obj, nullDistn = "bootstrap", bs.its = 30) # input a deFit object directly de_fit <- fit_models(de_obj, stat.type = "lrt") de_lrt <- lrt(de_obj, de.fit = de_fit) # summarize object summary(de_lrt)