forked from GabrielHoffman/variancePartition
/
logLik.R
127 lines (92 loc) · 2.3 KB
/
logLik.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
#' Log-likelihood from model fit
#'
#' Log-likelihood from model fit
#'
#' @param object result of \code{lmFit()} or \code{dream()}
#' @param vobj \code{EList} used to fit model
#' @param ... See \code{?stats::logLik}
#'
#' @rawNamespace S3method("logLik", MArrayLM2)
#' @importFrom stats logLik
#' @export
logLik.MArrayLM = function(object, vobj, ...){
if( !is.null(object$logLik) ){
return( object$logLik )
}
if( is.null(object$residuals) ){
object$residuals = residuals.MArrayLM(object, vobj)
}
values = sapply(seq(nrow(object)), function(i){
obj = list(residuals = object$residuals[i,,drop=TRUE],
rank = object$rank,
weights = vobj$weights[i,,drop=TRUE])
class(obj) = "lm"
logLik( obj )
})
names(values) = rownames(object)
values
}
#' Log-likelihood from model fit
#'
#' Log-likelihood from model fit
#'
#' @param object result of \code{lmFit()} or \code{dream()}
#' @param ... See \code{?stats::logLik}
#'
#' @rawNamespace S3method("logLik", MArrayLM)
#' @export
logLik.MArrayLM2 = function(object, ...){
object$logLik
}
#' BIC from model fit
#'
#' BIC from model fit
#'
#' @param object result of \code{lmFit()} or \code{dream()}
#' @param vobj \code{EList} used to fit model
#' @param ... See \code{?stats::BIC}
#'
#' @rawNamespace S3method("BIC", MArrayLM2)
#' @importFrom stats BIC
#' @export
BIC.MArrayLM = function(object, vobj, ...){
if( !is.null(object$BIC) ){
return( object$BIC )
}
if( is.null(object$residuals) ){
object$residuals = residuals.MArrayLM(object, vobj)
}
# n = nrow(object$design)
# df = object$rank + 1
values = sapply(seq(nrow(object)), function(i){
obj = list(residuals = object$residuals[i,,drop=TRUE],
rank = object$rank,
weights = vobj$weights[i,,drop=TRUE])
class(obj) = "lm"
BIC(obj)
# ll = logLik( obj )
# browser()
# -2*ll + df * log(n)
})
names(values) = rownames(object)
values
}
#' BIC from model fit
#'
#' BIC from model fit using edf
#'
#' @param object result of \code{dream()}
#' @param vobj \code{EList} used to fit model
#' @param ... See \code{?stats::BIC}
#'
#' @rawNamespace S3method("BIC", MArrayLM)
#' @export
BIC.MArrayLM2 = function(object, vobj, ...){
if( !is.null(object$BIC) ){
return( object$BIC )
}
df = object$edf
n = ncol(object$residuals)
ll = object$logLik
-2*ll + df * log(n)
}