if (!(prot == "h1" && !is.null(splitted) && length(splitted) == 539)){
prot <- "h1"
inpath <- paste(c(getwd(), "/data/"),collapse= "")
prot_data <- read.csv(paste(c(inpath, prot,"_for_LRT.csv"), collapse=""),stringsAsFactors=FALSE)
splitted <- split(prot_data, list(prot_data$site, prot_data$ancestor_node), drop=TRUE)
}
if (is.null(params) || nrow(params) != 142){
params <- parameters(splitted, mutation_position = "middle", filter = TRUE, jack = FALSE, pack = "rootsolve", verbose = FALSE)
}
## too time-consuming
#test_initialize_by_clustering <- function (){
# #arrange
# cluster.number = 3
# #act
# init_values <- initialize_by_clustering(data = splitted, params = params, cluster.number = cluster.number)
# #assert
# checkEquals(class(init_values) == "list")
# checkEquals(class(init_values$iparameters) == "matrix")
# checkEquals(nrow(init_values$iparameters) == cluster.number)
# checkEquals(ncol(init_values$iparameters) == 2)
# checkEquals(length(init_values$iweights) == cluster.number)
# checkEquals(sum(init_values$iweights) == 1)
# checkTrue(all(!is.na(init_values$iparameters)))
# checkTrue(all(!is.na(init_values$iweights)))
#}
#test_initialize_random <- function (){
# cluster.number = 3
#
# init_values <- initialize_by_random(data = splitted, params = parameters, cluster.number = cluster.number)
# #list(iparameters = iparameters, iweights = iweights, irkvector = irkvector)
# checkEquals(class(init_values) == "list")
#
# checkEquals(class(init_values$iparameters) == "matrix")
# checkEquals(nrow(init_values$iparameters) == cluster.number)
# checkEquals(ncol(init_values$iparameters) == 2)
#
# checkEquals(length(init_values$iweights) == cluster.number)
# checkEquals(length(init_values$irkvector) == cluster.number)
#
# checkTrue(all(!is.na(init_values$iparameters)))
# checkTrue(all(!is.na(init_values$iweights)))
# checkTrue(all(!is.na(init_values$irkvector)))
#
#
#}
test_initialize_by <-function (){
cluster.number = 3
init_params = c(9,1,1,1,2,3) #incorrect number of parameters
init_weights = c(0.5, 0.4, 0.1)
model = "exponential"
checkException(initialize_by(init_params =init_params, init_weights= init_weights,model = model, cluster.number = cluster.number),silent = TRUE)
cluster.number = 3
init_params = c(9,1,1) #incorrect number of parameters
init_weights = c(0.5, 0.4, 0.1)
model = "weibull"
checkException(initialize_by(init_params =init_params, init_weights= init_weights,model = model, cluster.number = cluster.number),silent = TRUE)
cluster.number = 3
init_params = c(9,1,1)
init_weights = c(0.5, 0.4, 0.6) #incorrect sum of weights
model = "exponential"
checkException(initialize_by(init_params =init_params, init_weights= init_weights,model = model, cluster.number = cluster.number),silent = TRUE)
}
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