vignettes/access_xpdb_data.Rmd
access_xpdb_data.RmdThe xpose package was designed as a standardized interface between model outputs and R. Hence model output files can easily be imported in R and used to summarize data or create plots.
The files attached to an xpdb object can be displayed to the console
simply by writing the xpdb name to the console or by using the
print() function. Any of these files can be accessed from
the xpdb using one of the functions listed below.
xpdb # or print(xpdb)run001.lst overview:
- Software: nonmem 7.3.0
- Attached files (memory usage 1.4 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
+ special: <none>
- gg_theme: theme_readable
- xp_theme: theme_xp_default
- Options: dir = data, quiet = FALSE, manual_import = NULLThe get_code() function can be used to access the parsed
model code from the xpdb. This code was used to create the summary and
find table names. The parsed code can be used to get additional
information about the run. If the argument .problem is
specified a subset of the code can be returned based on
$PROBLEM.
Note that general code warnings and PsN outputs appended are listed as problem 0.
code <- get_code(xpdb)
code
[38;5;246m# A tibble: 764 × 5
[39m
problem level subroutine code comment
[38;5;250m*
[39m
[3m
[38;5;246m<int>
[39m
[23m
[3m
[38;5;246m<int>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[38;5;250m 1
[39m 0 0 oth
[38;5;246m"
[39mMon Oct 16 13:34:28 CEST 2017
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;250m 2
[39m 0 0 oth
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m"
[39m; 1. Based on…
[38;5;250m 3
[39m 0 0 oth
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m"
[39m; 2. Descript…
[38;5;250m 4
[39m 0 0 oth
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m"
[39m; NONMEM PK e…
[38;5;250m 5
[39m 1 1 pro
[38;5;246m"
[39mParameter estimation
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;250m 6
[39m 1 2 inp
[38;5;246m"
[39mID DOSE DV SCR AGE SEX CLASS WT ACE MED1 MED2 TAD TIME
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;250m 7
[39m 1 2 inp
[38;5;246m"
[39m CLCR AMT SS II EVID
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;250m 8
[39m 1 3 dat
[38;5;246m"
[39m../../mx19_2.csv IGNORE=@
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;250m 9
[39m 1 4 abb
[38;5;246m"
[39mDERIV2=NO
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;250m10
[39m 1 5 sub
[38;5;246m"
[39mADVAN2 TRANS1
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m"
[39m
[38;5;246m# ℹ 754 more rows
[39mThe get_data() function can be used to access the
imported table files. Tables can be accessed by table name
or by .problem. In the latter a single dataset containing
all aggregated tables is returned. If more than one table
name or .problem number is provided a named list is
returned.
Note when providing a table name it is not guaranteed that the
table will be identical to its file (i.e. the order of the columns may
have been changed and tables with FIRSTONLY will no longer
be deduplicated).
data <- get_data(xpdb, table = 'cotab001')
data
[38;5;246m# A tibble: 550 × 4
[39m
ID CLCR AGE WT
[3m
[38;5;246m<fct>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[38;5;250m 1
[39m 110 66 68 105
[38;5;250m 2
[39m 110 66 68 105
[38;5;250m 3
[39m 110 66 68 105
[38;5;250m 4
[39m 110 66 68 105
[38;5;250m 5
[39m 110 66 68 105
[38;5;250m 6
[39m 110 66 68 105
[38;5;250m 7
[39m 110 66 68 105
[38;5;250m 8
[39m 112 47 73 80
[38;5;250m 9
[39m 112 47 73 80
[38;5;250m10
[39m 112 47 73 80
[38;5;246m# ℹ 540 more rows
[39mThe get_file() function can be used to access the
imported output files. Files can be accessed by file name,
by .problem, .subprob and/or
.method. If more than one file name,
.problem, .subprob, or .method is
provided a named list is returned.
file <- get_file(xpdb, file = 'run001.ext')
file
[38;5;246m# A tibble: 28 × 16
[39m
ITERATION THETA1 THETA2 THETA3 THETA4 THETA5 THETA6 THETA7 `SIGMA(1,1)` `OMEGA(1,1)`
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[38;5;250m 1
[39m 0 25.4 1.47 7.45 0.214 0.200 0.009
[4m8
[24m
[4m3
[24m 0.006
[4m0
[24m
[4m1
[24m 1 0.074
[4m0
[24m
[38;5;250m 2
[39m 1 26.3 1.26 7.35 0.219 0.217 0.009
[4m8
[24m
[4m9
[24m 0.006
[4m0
[24m
[4m2
[24m 1 0.074
[4m4
[24m
[38;5;250m 3
[39m 2 25.6 1.47 7.29 0.216 0.212 0.009
[4m8
[24m
[4m7
[24m 0.006
[4m0
[24m
[4m3
[24m 1 0.074
[4m1
[24m
[38;5;250m 4
[39m 3 26.8 1.49 5.76 0.213 0.213 0.009
[4m7
[24m
[4m9
[24m 0.006
[4m2
[24m
[4m8
[24m 1 0.073
[4m0
[24m
[38;5;250m 5
[39m 4 26.7 1.49 5.69 0.213 0.212 0.009
[4m7
[24m
[4m9
[24m 0.006
[4m2
[24m
[4m9
[24m 1 0.073
[4m0
[24m
[38;5;250m 6
[39m 5 26.7 1.49 5.66 0.213 0.212 0.009
[4m7
[24m
[4m9
[24m 0.006
[4m3
[24m
[4m0
[24m 1 0.073
[4m0
[24m
[38;5;250m 7
[39m 6 26.6 1.49 5.03 0.210 0.217 0.010
[4m0
[24m 0.006
[4m5
[24m
[4m2
[24m 1 0.072
[4m4
[24m
[38;5;250m 8
[39m 7 26.6 1.49 4.93 0.205 0.217 0.010
[4m0
[24m 0.006
[4m5
[24m
[4m8
[24m 1 0.071
[4m7
[24m
[38;5;250m 9
[39m 8 26.6 1.48 4.62 0.211 0.217 0.009
[4m5
[24m
[4m1
[24m 0.007
[4m3
[24m
[4m5
[24m 1 0.063
[4m3
[24m
[38;5;250m10
[39m 9 26.6 1.46 4.41 0.209 0.217 0.009
[4m0
[24m
[4m3
[24m 0.008
[4m7
[24m
[4m4
[24m 1 0.076
[4m6
[24m
[38;5;246m# ℹ 18 more rows
[39m
[38;5;246m# ℹ 6 more variables: `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
[39m
[38;5;246m# `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
[39mThe get_prm() function can be used to access the
parameter estimates. To get a nice parameter table printed to the
console use the function prm_table() instead. The arguments
.problem, .subprob and .method
can be used to select the parameter estimates to output.
# Raw output for editing
prm <- get_prm(xpdb, digits = 4)
prm
[38;5;246m# A tibble: 11 × 10
[39m
type name label value se rse fixed diagonal m n
[38;5;250m*
[39m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<lgl>
[39m
[23m
[3m
[38;5;246m<lgl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[38;5;250m 1
[39m the THETA1
[38;5;246m"
[39mTVCL
[38;5;246m"
[39m 26.3 0.892 0.033
[4m9
[24m FALSE
[31mNA
[39m 1
[31mNA
[39m
[38;5;250m 2
[39m the THETA2
[38;5;246m"
[39mTVV
[38;5;246m"
[39m 1.35 0.043
[4m8
[24m 0.032
[4m5
[24m FALSE
[31mNA
[39m 2
[31mNA
[39m
[38;5;250m 3
[39m the THETA3
[38;5;246m"
[39mTVKA
[38;5;246m"
[39m 4.20 0.809 0.192 FALSE
[31mNA
[39m 3
[31mNA
[39m
[38;5;250m 4
[39m the THETA4
[38;5;246m"
[39mLAG
[38;5;246m"
[39m 0.208 0.015
[4m7
[24m 0.075
[4m5
[24m FALSE
[31mNA
[39m 4
[31mNA
[39m
[38;5;250m 5
[39m the THETA5
[38;5;246m"
[39mProp. Err
[38;5;246m"
[39m 0.205 0.022
[4m4
[24m 0.110 FALSE
[31mNA
[39m 5
[31mNA
[39m
[38;5;250m 6
[39m the THETA6
[38;5;246m"
[39mAdd. Err
[38;5;246m"
[39m 0.010
[4m6
[24m 0.003
[4m6
[24m
[4m6
[24m 0.347 FALSE
[31mNA
[39m 6
[31mNA
[39m
[38;5;250m 7
[39m the THETA7
[38;5;246m"
[39mCRCL on CL
[38;5;246m"
[39m 0.007
[4m1
[24m
[4m7
[24m 0.001
[4m7
[24m
[4m0
[24m 0.237 FALSE
[31mNA
[39m 7
[31mNA
[39m
[38;5;250m 8
[39m ome OMEGA(1,1)
[38;5;246m"
[39mIIV CL
[38;5;246m"
[39m 0.270 0.023
[4m3
[24m 0.086
[4m2
[24m FALSE TRUE 1 1
[38;5;250m 9
[39m ome OMEGA(2,2)
[38;5;246m"
[39mIIV V
[38;5;246m"
[39m 0.195 0.032
[4m0
[24m 0.164 FALSE TRUE 2 2
[38;5;250m10
[39m ome OMEGA(3,3)
[38;5;246m"
[39mIIV KA
[38;5;246m"
[39m 1.38 0.202 0.146 FALSE TRUE 3 3
[38;5;250m11
[39m sig SIGMA(1,1)
[38;5;246m"
[39m
[38;5;246m"
[39m 1
[31mNA
[39m
[31mNA
[39m TRUE TRUE 1 1
# Nicely formated table
prm_table(xpdb, digits = 4)
Reporting transformed parameters:
For the OMEGA and SIGMA matrices, values are reported as standard deviations for the diagonal elements and as correlations for the off-diagonal elements. The relative standard errors (RSE) for OMEGA and SIGMA are reported on the approximate standard deviation scale (SE/variance estimate)/2. Use `transform = FALSE` to report untransformed parameters.
Estimates for $prob no.1, subprob no.1, method foce
Parameter Label Value RSE
THETA1 TVCL 26.29 0.03391
THETA2 TVV 1.348 0.0325
THETA3 TVKA 4.204 0.1925
THETA4 LAG 0.208 0.07554
THETA5 Prop. Err 0.2046 0.1097
THETA6 Add. Err 0.01055 0.3466
THETA7 CRCL on CL 0.007172 0.2366
OMEGA(1,1) IIV CL 0.2701 0.08616
OMEGA(2,2) IIV V 0.195 0.1643
OMEGA(3,3) IIV KA 1.381 0.1463
SIGMA(1,1) 1 fix - The get_summary() function can be used to access the
generated run summary from which the template_titles. If
the argument .problem is specified a subset of the summary
can be returned based on $PROBLEM.
Note that general summary information are listed as problem 0.
run_sum <- get_summary(xpdb, .problem = 0)
run_sum
[38;5;246m# A tibble: 12 × 5
[39m
problem subprob descr label value
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<dbl>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[38;5;250m 1
[39m 0 0 Run description descr NONMEM PK example for xpose
[38;5;250m 2
[39m 0 0 Run directory dir data
[38;5;250m 3
[39m 0 0 Run errors errors na
[38;5;250m 4
[39m 0 0 ESAMPLE seed number esampleseed na
[38;5;250m 5
[39m 0 0 Run file file run001.lst
[38;5;250m 6
[39m 0 0 Number of ESAMPLE nesample na
[38;5;250m 7
[39m 0 0 Reference model ref 000
[38;5;250m 8
[39m 0 0 Run number run run001
[38;5;250m 9
[39m 0 0 Software software nonmem
[38;5;250m10
[39m 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
[38;5;250m11
[39m 0 0 Run stop time timestop Mon Oct 16 13:34:35 CEST 2017
[38;5;250m12
[39m 0 0 Software version version 7.3.0