To make predictions using ecvr results
cmf_krr_ecvr_pred(ecvr_fname = "ligands-ecvr.RData", kernels_train_fname = "ligands-kernels-train.RData", kernels_pred_fname = "ligands-kernels-pred.RData", act_colnum = 2, sep = ",", act_pred_fname = "activity-pred.txt", pred_fname = "ligands-pred.RData", ...)
ecvr_fname | |
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kernels_train_fname | |
kernels_pred_fname | |
act_colnum | |
sep | |
act_pred_fname | |
pred_fname | |
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##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (ecvr_fname = "ligands-ecvr.RData", kernels_train_fname = "ligands-kernels-train.RData", kernels_pred_fname = "ligands-kernels-pred.RData", act_colnum = 2, sep = ",", act_pred_fname = "activity-pred.txt", pred_fname = "ligands-pred.RData", ...) { load(ecvr_fname) load(kernels_train_fname) load(kernels_pred_fname) iprop <- act_colnum if (iprop > 0) { act <- read.table(act_pred_fname, header = TRUE, sep = sep) y_exp <- act[, iprop] } else { y_exp <- NA } ecvr_pred <- cmf_krr_ecvr_pred_mem(ecvr = ecvr, kernels = kernels, kernels_pred = kernels_pred, y_exp = y_exp, ...) save(ecvr_pred, file = pred_fname) }#> function (ecvr_fname = "ligands-ecvr.RData", kernels_train_fname = "ligands-kernels-train.RData", #> kernels_pred_fname = "ligands-kernels-pred.RData", act_colnum = 2, #> sep = ",", act_pred_fname = "activity-pred.txt", pred_fname = "ligands-pred.RData", #> ...) #> { #> load(ecvr_fname) #> load(kernels_train_fname) #> load(kernels_pred_fname) #> iprop <- act_colnum #> if (iprop > 0) { #> act <- read.table(act_pred_fname, header = TRUE, sep = sep) #> y_exp <- act[, iprop] #> } #> else { #> y_exp <- NA #> } #> ecvr_pred <- cmf_krr_ecvr_pred_mem(ecvr = ecvr, kernels = kernels, #> kernels_pred = kernels_pred, y_exp = y_exp, ...) #> save(ecvr_pred, file = pred_fname) #> } #> <environment: 0x11144e8e0>