108
108
setClass("nlmer",
109
109
representation(## original data
110
env = "environment", # evaluation environment for model
111
model = "call", # model function as a function call
110
112
frame = "data.frame", # model frame or empty frame
111
113
pnames = "character", # parameter names for nonlinear model
112
114
call = "call", # matched call to model-fitting function
113
terms = "terms", # terms for fixed-effects
114
115
flist = "list", # list of grouping factors
115
116
Xt = "dgCMatrix", # sparse form of X'
116
117
Zt = "dgCMatrix", # sparse form of Z'
119
120
cnames = "list", # column names of model matrices
120
121
Gp = "integer", # pointers to groups of columns in Z
121
122
dims = "integer", # dimensions and indicators
122
## quantities that vary with Z, X, y, weights or offset
123
123
## slots that vary during the optimization
124
124
ST = "list", # list of TSST' rep of rel. var. mats
125
L = "CHMfactor", # sparse Cholesky factor of A*
125
Vt = "dgCMatrix", # sparse form of V'=(ZTS)'
126
L = "CHMfactor", # sparse Cholesky factor of V'V + I
127
mu = "numeric", # fitted values at current values of beta and b
128
Mt = "dgCMatrix", # transpose of gradient matrix d mu/d u
126
129
deviance = "numeric", # ML and REML deviance and components
130
fixef = "numeric", # the fixed effects, beta
131
ranef = "numeric", # the random effects, b
132
uvec = "numeric" # orthogonal random effects, u, s.t. b=TSu
130
134
validity = function(object) .Call(nlmer_validate, object)
added
removed
R/AllClass.R
