R/constants.R

### constants.R:  "constants" for the lcmix package

LC_LOG2 <- log(2)
LC_LOGPI <- log(pi)
LC_LOG2PI <- log(2*pi)
LC_LOGSQRT2PI <- log(sqrt(2*pi))
LC_LOGSQRT2 <- log(sqrt(2))
	# it's just useful to have these around

LC_EPS   <- sqrt(.Machine$double.eps)
LC_LEPS  <- log(LC_EPS)
LC_REPS  <- 1.0 - LC_EPS
LC_LREPS <- log(LC_REPS)
	# LC_EPS and LC_REPS ("reverse LC_EPS") are numbers very close, but not quite equal, to 0 and 1 respectively

LC_MAXSTDNORM <- 8
LC_MINSTDNORM <- -LC_MAXSTDNORM
	# maximum and minimum values of standard normal random variables; going outside these bounds with a variable that is supposed to be standard normal (e.g. Phi^-1(F(x)) for any random variable x) may lead to instability

LC_ITER_MAX <- 1000
	# default max iterations for EM
LC_ITER_TOL <- 1e-6 # 1e-6 or LC_EPS
	# point below which relative changes in log-likelihood must fall to terminate the algorithm

LC_TOPOLOGY <- c("layered", "chained")
	# topologies for multi-data models

LC_FAMILY <- list(normal      = list(uni="norm",  multi="mvnorm"),
                  pvii        = list(uni="pvii",  multi="mvpvii"),
                  weibull     = list(uni="weisd", multi="mvweisd"),
	              gamma       = list(uni="gamma", multi="mvgamma"),
	              exponential = list(uni="exp",   multi="mvexp"),
	              altpvii     = list(uni="apvii", multi="amvpvii"))
	# names of acceptable model distribution _families_ are names(LC_FAMILY); the $uni and $multi elements of the elements of LC_FAMILY give the names of the applicable univariate and multivariate distributions, respectively, for those families

LC_NONNEGFAM <- c("weibull", "gamma", "exponential")
	# distribution families requiring non-negative values

LC_DISTN <- as.vector(unlist(LC_FAMILY))
	# names of acceptable model _distributions_


LC_SIMPAR = list(
	
	hidden = list(
	
		prob0 = c(0.03, 0.97),
		
		probz = list(
			binding      = c(0.03, 0.97),
			expression   = c(0.05, 0.92, 0.03),
			conservation = c(0.25, 0.75)
		)
	),
	
	observed = list(
	
		binding = list(
			mean = c(0.825, -0.825),
			var  = c(1.50, 0.50),
			sd   = sqrt(c(1.50, 0.50))
		),
		
		expression = list(
			
			mean = matrix(c( 1.5,  1.5,  1.5,
			                 0.00,  0.00,  0.00,
			                -1.5, -1.5, -1.5),
			              nrow=3, byrow=TRUE),
			
			cov = list(
				matrix(c( 3.00,  1.00, -0.50,
				          1.00,  3.00,  0.00,
				         -0.50,  0.00,  3.00),
				        nrow=3, byrow=TRUE),
				diag(3),
				matrix(c( 2.00, -0.20,  0.10,
				         -0.20,  2.00,  0.10,
				          0.10,  0.10,  2.00),
				        nrow=3, byrow=TRUE)
			)
		),
		
		conservation = list(
			mean = c(1.25, -1.25),
			var  = c(1.50, 0.50),
			sd   = sqrt(c(1.50, 0.50))
		)
	)	
) # marginal distribution parameters for simulating data with similar performance to the Ci data

Try the lcmix package in your browser

Any scripts or data that you put into this service are public.

lcmix documentation built on May 31, 2017, 5 a.m.