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Update predictpbpk.R #1

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b7ea64d
Update predictpbpk.R
periklis91 Feb 25, 2019
3f49ebe
Update predictpbpk.R
periklis91 Feb 25, 2019
1e19cb9
Update predictpbpk.R
periklis91 Feb 25, 2019
2339f74
Update predictpbpk.R
periklis91 Apr 1, 2019
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49 changes: 30 additions & 19 deletions R/predictpbpk.R
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Original file line number Diff line number Diff line change
Expand Up @@ -6,14 +6,24 @@
predictpbpk <- function(dataset, rawModel, additionalInfo){

n_comp <- length(additionalInfo$predictedFeatures) - 1
feats <- colnames(dataset$dataEntry[,2])
# feats <- colnames(dataset$dataEntry)
# Get feature keys (a key number that points to the url)
feat.keys <- dataset$features$key
# Get feature names (actual name)
feat.names <- dataset$features$names
# Convert names from a factor list to a vector of characters
feat.names <- as.vector(unlist(lapply(feat.names, as.character)))
# Create a dataframe that includes the feature key and the corresponding name
key.match <- data.frame(cbind(feat.keys, feat.names), stringAsFactors = FALSE)

# Initialize a dataframe with as many rows as the number of values per feature
rows_data <- length(dataset$dataEntry$values[,2])
df <- data.frame(matrix(0, ncol = 0, nrow = rows_data))
for(i in feats){
fe <- additionalInfo$independentFeatures[i][[i]]

for(key in feat.keys){
# For each key (feature) get the vector of values (of length 'row_data')
feval <- dataset$dataEntry$values[i][,1]
df[fe] <- feval
# Name the column with the corresponding name that is connected with the key
df[key.match[key.match$feat.keys == i, 2]] <- feval
}

mod <- unserialize(base64_dec(rawModel))
Expand All @@ -23,9 +33,10 @@ predictpbpk <- function(dataset, rawModel, additionalInfo){
# predFeat <- additionalInfo$predictedFeatures[1][[1]]
predFeat <- additionalInfo$predictedFeatures

initial_concentration = rep(0, n_comp)
for(i in 1:length(initial_concentration)){
con = paste("y", i, sep="")
comp <- additionalInfo$comp
initial_concentration <- rep(0,length(comp))
for(i in length(comp)){
con = paste("C0_", comp[i], sep="")
initial_concentration[i] = df[[con]]
}
weight = df$weight
Expand All @@ -37,22 +48,22 @@ predictpbpk <- function(dataset, rawModel, additionalInfo){
sample_time <- c(0, 5/60, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 6, 8, 10, 12, 24, 36, 48, 72) # in hours
solution <- ode(y = initial_concentration, times = sample_time, func = odemodel, parms = params)

comp_names <- rep(0, n_comp + 1)
comp_short <- rep(0, n_comp + 1)
pred_names <- rep(0, n_comp + 1)
for(i in 1:length(comp_names)){
comp_names[i] <- additionalInfo$predictedFeatures[[i]]
comp_short[i] <- as.integer(strsplit(additionalInfo$predictedFeatures[[i]], "_")[[1]])
pred_names[i] <- names(additionalInfo$predictedFeatures)[i]
}
comp_all <- data.frame(cbind(comp_names, comp_short, pred_names))
comp_all_s <- comp_all[order(comp_short),][,1]
#comp_names <- rep(0, n_comp + 1)
#comp_short <- rep(0, n_comp + 1)
#pred_names <- rep(0, n_comp + 1)
#for(i in 1:length(comp_names)){
# comp_names[i] <- additionalInfo$predictedFeatures[[i]]
# comp_short[i] <- as.integer(strsplit(additionalInfo$predictedFeatures[[i]], "_")[[1]])
# pred_names[i] <- names(additionalInfo$predictedFeatures)[i]
#}
#comp_all <- data.frame(cbind(comp_names, comp_short, pred_names))
#comp_all_s <- comp_all[order(comp_short),][,1]



for(i in 1:dim(solution)[1]){
prediction<- data.frame(t(solution[i,]))
colnames(prediction)<- comp_all_s
colnames(prediction)<- c("time", comp)
if(i==1){lh_preds<- list(unbox(prediction))
}else{
lh_preds[[i]]<- unbox(prediction)
Expand Down