autonpde {npde} | R Documentation |
These functions compute normalised prediction distribution errors (npde) and
optionally prediction discrepancies (pd). npde
asks the
user the name and structure of the files containing the data, using
pdemenu
, while autonpde
takes these variables and others as
arguments.
autonpde(namobs, namsim, iid = 1, ix = 2, iy = 3, imdv = 0, namsav = "output", boolsave = TRUE, type.graph = "eps", output = TRUE, verbose = FALSE, calc.npde=TRUE, calc.pd=FALSE) npde()
namobs |
name of the file containing the observed data, or a dataframe
containing the observed data (in both cases, the column containing the various
data required for the computation of the pde can be set using the arguments
iid ,ix and iy below) |
namsim |
name of the file containing the simulated data, or a dataframe containing the simulated data (the program will assume that subject ID are in column 1 and simulated Y in column 3, see User Guide) |
iid |
number of the column in the observed data containing the patient ID, defaults to 1 (will be ignored if the dataframe contains a column named id) |
ix |
number of the column in the observed data containing the independent variable (X), defaults to 2 (will be ignored if the dataframe contains a column named xobs) |
iy |
number of the column in the observed data containing the dependent variable (Y), defaults to 3 (will be ignored if the dataframe contains a column named yobs) |
imdv |
number of the column containing information about missing data (MDV), defaults to 0 (column not present) |
namsav |
name of the files to which results are to be saved (defaults to "output", which will produce a file called output.eps (if the default format of postscript is kept, see type.graph) for the graphs and a file called output.npde for the numerical results (see value) |
boolsave |
a boolean (T if graphs and results are to be saved to a file, F otherwise), defaults to T |
type.graph |
type of graph (one of "eps","jpeg","png","pdf"), defaults to postscript ("eps") |
output |
a boolean (T if the function should return values, F otherwise), defaults to T |
verbose |
a boolean (T if messages are to be printed as each subject is processed, F otherwise), defaults to F |
calc.npde |
a boolean (T if npde are to be computed, F otherwise), defaults to T |
calc.pd |
a boolean (T if pd are to be computed, F otherwise), defaults to F |
Both functions compute the normalised prediction distribution errors (and/or
prediction discrepancies) in the same way. npde
is an
interactive function whereas autonpde
takes all required input as
arguments.
When the computation of npde fails because of numerical problems, error messages are printed out, then pd are computed instead and graphs of pd are plotted so that the user may evaluate why the computation failed.
A list containing the following components:
obsdat |
observed data, a dataframe containing 3 columns (id=patient ID, xobs=independent variable (X), yobs=dependent variable (Y) |
simdat |
simulated data, a dataframe containing 4 columns (idsim=patient ID, irsim=integer identifying the replicate; xsim=independent variable (X), ysim=dependent variable (Y) |
ydobs |
the vector of observed dependent variable (Y), decorrelated |
ydsim |
the vector of simulated dependent variable (Y), decorrelated |
ypred |
a vector giving the mean of the predicted distribution for each observation |
xerr |
an integer code to keep track of errors during the computation;
after a successful computation the value of xerr should be 0. A value
of 1 or 2 signals errors during the computation. |
npde |
the vector of normalised prediction distribution errors, if calc.npde=T (NA otherwise) |
pd |
the vector of prediction discrepancies, if calc.npd=T (NA otherwise) |
results file |
the numerical results are saved in a file with extension .npde (the name of which is given by the user). The file contains the components id, xobs, ypred, npde, pd stored in columns |
graph file |
the graphs are saved to a file with the same name as the
results file, and with extension depending on the format. See also
graphnpde |
Emmanuelle Comets <emmanuelle.comets@bichat.inserm.fr>
K. Brendel, E. Comets, C. Laffont, C. Laveille, and F. MentrĂ©. Metrics for external model evaluation with an application to the population pharmacokinetics of gliclazide. Pharmaceutical Research, 23:2036–49, 2006.
data(theopp) data(simtheopp) # Calling autonpde with dataframes x<-autonpde(theopp,simtheopp,1,3,4,boolsave=FALSE) x$npde # Calling autonpde with names of files to be read from disk write.table(theopp,"theopp.tab",quote=FALSE,row.names=FALSE) write.table(simtheopp,"simtheopp.tab",quote=FALSE,row.names=FALSE) x<-autonpde(namobs="theopp.tab", namsim="simtheopp.tab", iid = 1, ix = 3, iy = 4, imdv=0, boolsave = FALSE) x$npde