Below is my session output. I made a function, funBS(), that generates bootstrapped data from a data frame that has ID as its first column. However, when I try to use the bootstrapped data with nlmixr, I get an error and no object is created. Note that much of this is output from what I just pasted in for Issue #28 but here is the full info. The bootstrap part begins with funBS(). Thank-you for your help.
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> # Preliminaries
> setwd("C:/Users/James/Documents/pharmacometrics/testing")
> library(nlmixr)
Loading required package: nlme
Loading required package: RxODE
> # rxVersion(echo=TRUE)
> rxClean()
[1] TRUE
> rxVersion(echo=TRUE)
_____ ____ _____ ______
| __ \ / __ \| __ \| ____| 0.7-0
| |__) |__ _| | | | | | | |__
| _ / \ \/ / | | | | | | __|
| | \ \ > <| |__| | |__| | |____
|_| \_\/_/\_\\____/|_____/|______|
> # Use some pre-loaded data.
> str(theo_sd) # like Theoph but with ID instead of Subject, also other NONMEM-like
'data.frame': 144 obs. of 7 variables:
$ ID : int 1 1 1 1 1 1 1 1 1 1 ...
$ TIME: num 0 0 0.25 0.57 1.12 2.02 3.82 5.1 7.03 9.05 ...
$ DV : num 0 0.74 2.84 6.57 10.5 9.66 8.58 8.36 7.47 6.89 ...
$ AMT : num 4.02 0 0 0 0 0 0 0 0 0 ...
$ EVID: int 101 0 0 0 0 0 0 0 0 0 ...
$ CMT : int 1 2 2 2 2 2 2 2 2 2 ...
$ WT : num 79.6 79.6 79.6 79.6 79.6 79.6 79.6 79.6 79.6 79.6 ...
> # 'uif' for Unified Interface Function, 1 compartment, lin for linCmt()
> uif.1cm.lin <- function() {
+ ini({
+ logtcl <- -3.2 # log typical value Cl (L/hr)
+ logtka <- 0.5 # log typical value Ka (1/hr)
+ logtv <- -1 # log typical value V (L)
+
+ # error model
+ add.err <- 0.1
+
+ # Initial estimates for IIV variances
+ # Labels work for single parameters.
+ eta.cl ~ 2
+ eta.ka ~ 1 ## BSV Ka
+ eta.v ~ 1
+ })
+ model({
+ cl <- exp(logtcl + eta.cl)
+ ka <- exp(logtka + eta.ka)
+ v <- exp(logtv + eta.v)
+ linCmt() ~ add(add.err)
+ })
+ }
> # NLME fittings
>
> fit.nlme.1cm.lin.theo_sd <- nlmixr(uif.1cm.lin, theo_sd, est="nlme")
**Iteration 1
LME step: Loglik: -182.2318, nlminb iterations: 1
reStruct parameters:
ID1 ID2 ID3
1.0022617 0.2783009 2.0758984
PNLS step: RSS = 63.14922
fixed effects: -3.211937 0.4479979 -0.7859318
iterations: 7
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.272375 3.267309
**Iteration 2
LME step: Loglik: -179.291, nlminb iterations: 9
reStruct parameters:
ID1 ID2 ID3
0.96385527 0.08333435 1.63552295
PNLS step: RSS = 63.28221
fixed effects: -3.211535 0.4441505 -0.7863391
iterations: 7
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.008662381 0.172135330
**Iteration 3
LME step: Loglik: -179.3381, nlminb iterations: 8
reStruct parameters:
ID1 ID2 ID3
0.96129928 0.07085834 1.63885629
PNLS step: RSS = 63.2196
fixed effects: -3.211732 0.4458486 -0.7861743
iterations: 7
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.003808629 0.082919683
**Iteration 4
LME step: Loglik: -179.3201, nlminb iterations: 7
reStruct parameters:
ID1 ID2 ID3
0.96243507 0.07618717 1.63736945
PNLS step: RSS = 63.22991
fixed effects: -3.211823 0.4451494 -0.7862432
iterations: 7
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.001570583 0.013884126
**Iteration 5
LME step: Loglik: -179.3277, nlminb iterations: 4
reStruct parameters:
ID1 ID2 ID3
0.96197111 0.07396744 1.63796900
PNLS step: RSS = 63.24112
fixed effects: -3.211722 0.4454457 -0.7862145
iterations: 6
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.0006651255 0.0147544693
**Iteration 6
LME step: Loglik: -179.3246, nlminb iterations: 1
reStruct parameters:
ID1 ID2 ID3
0.96215433 0.07487426 1.63772430
PNLS step: RSS = 63.24112
fixed effects: -3.211722 0.4454457 -0.7862145
iterations: 1
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.000000e+00 3.910435e-10
Using sympy via SnakeCharmR
## Calculate ETA-based prediction and error derivatives:
Calculate Jacobian...................done.
Calculate sensitivities.......
done.
## Calculate d(f)/d(eta)
## ...
## done
## ...
## done
C:/RTools/3.4/mingw_64/bin/gcc -I"C:/R/R-34~1.3/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c rx_ebf0e5096f80d0203de27cbc48be0f26_x64.c -o rx_ebf0e5096f80d0203de27cbc48be0f26_x64.o
C:/RTools/3.4/mingw_64/bin/gcc -shared -s -static-libgcc -o rx_ebf0e5096f80d0203de27cbc48be0f26_x64.dll tmp.def rx_ebf0e5096f80d0203de27cbc48be0f26_x64.o -LC:/R/R-34~1.3/bin/x64 -lRblas -LC:/R/R-34~1.3/bin/x64 -lRlapack -lgfortran -lm -lquadmath -LC:/R/R-34~1.3/bin/x64 -lR
C:/RTools/3.4/mingw_64/bin/gcc -I"C:/R/R-34~1.3/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c rx_40eae078bd4f9e0651450c3b310d5e9a_x64.c -o rx_40eae078bd4f9e0651450c3b310d5e9a_x64.o
C:/RTools/3.4/mingw_64/bin/gcc -shared -s -static-libgcc -o rx_40eae078bd4f9e0651450c3b310d5e9a_x64.dll tmp.def rx_40eae078bd4f9e0651450c3b310d5e9a_x64.o -LC:/R/R-34~1.3/bin/x64 -lRblas -LC:/R/R-34~1.3/bin/x64 -lRlapack -lgfortran -lm -lquadmath -LC:/R/R-34~1.3/bin/x64 -lR
The model-based sensitivities have been calculated.
It will be cached for future runs.
Diagonal form: sqrt
[,1]
[1,] 1
Calculate symbolic inverse...done
Calculate symbolic determinant of inverse...done
Calculate d(Omega)/d(Est) and d(Omega^-1)/d(Est)...
.0
...done.
Calculating Table Variables...
done
Warning message:
In nlmixrUI.nlme.var(obj) :
Initial condition for additive error ignored with nlme
nlmixr UI combined dataset and properties
$ par.hist : Parameter history (if available)
$ par.hist.stacked : Parameter history in stacked form for easy plotting (if available)
$ par.fixed : Fixed Effect Parameter Table
$ eta : Individual Parameter Estimates
$ seed : Seed (if applicable)
$ model.name : Model name (from R function)
$ data.name : Name of R data input
> # Only 1 iteration for DFN.
> fit.nlme.1cm.lin.theo_sd.init <- nlmixr(uif.1cm.lin, theo_sd, est="nlme",
+ control = nlmeControl(maxIter = 1,
+ pnlsMaxIter = 1,
+ msMaxIter = 1)
+ )
**Iteration 1
LME step: Loglik: -182.2318, nlminb iterations: 1
reStruct parameters:
ID1 ID2 ID3
1.0022617 0.2783009 2.0758984
PNLS step: RSS = 87.52968
fixed effects: -3.230002 0.5346704 -0.7523971
iterations: 1
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
3.290853e-01 4.201928e-06
Error in nlme.formula(model = DV ~ (nlmixr::nlmeModList("user_fn"))(logtcl, :
maximum number of iterations (maxIter = 1) reached without convergence
In addition: Warning message:
In nlmixrUI.nlme.var(obj) :
Initial condition for additive error ignored with nlme
> # No object created.
> # So try to expand the tolerance:
> fit.nlme.lin.theo_sd.init <- nlmixr(uif.1cm.lin, theo_sd, est="nlme",
+ control = nlmeControl(maxIter = 1,
+ pnlsMaxIter = 1,
+ msMaxIter = 1),
+ tolerance = 1 # deliberately a huge tolerance
+ )
Error in (function (model, data = sys.frame(sys.parent()), fixed, random = fixed, :
unused argument (tolerance = 1)
In addition: Warning message:
In nlmixrUI.nlme.var(obj) :
Initial condition for additive error ignored with nlme
> # No object created.
> # Just curious, will nlmixrUI(fun) work? It's been removed from latest documentation.
> nlmixrUI(uif.1cm.lin)
## 1-compartment model with first-order absorption in terms of Cl
## Initialization:
################################################################################
Fixed Effects ($theta):
logtcl logtka logtv
-3.2 0.5 -1.0
Omega ($omega):
[,1] [,2] [,3]
[1,] 2 0 0
[2,] 0 1 0
[3,] 0 0 1
## Model:
################################################################################
cl <- exp(logtcl + eta.cl)
ka <- exp(logtka + eta.ka)
v <- exp(logtv + eta.v)
> # Seems to still work, not sure why its documentation was removed from nlmixr.pdf.
>
> # Can nlmixr work on bootstrapped data?
> funBS <- function(df, B) {
+ # df is a data frame with column 1 called ID.
+ # B is the number of bootstrapped data sets.
+ subjects <- sort(as.integer(unique(df$ID)))
+
+ # Initialize an empty data frame to hold bootstrapped values.
+ BsIteration.and.Subject <- data.frame(BsIteration=numeric(),
+ ID=character(),
+ stringsAsFactors=FALSE)
+ temp <- setNames(data.frame(matrix(ncol = 2, nrow = 0), stringsAsFactors=FALSE),
+ c("BsIteration", "ID"))
+ for (b in 1:B){
+ # Create a vector of bootstrapped subject IDs.
+ BSsubjects <- sample(1:length(subjects), length(subjects), replace=TRUE)
+ for (i in BSsubjects){
+ # The next line relies on ID being column 1. Index b gets recycled.
+ temp <- as.data.frame( c(b, df[df$ID==i,][1]), stringsAsFactors=FALSE)
+ names(temp) <- c("BsIteration", "ID")
+ BsIteration.and.Subject <- rbind(BsIteration.and.Subject, temp)
+ }
+ }
+ # Create the rest of the empty vector (to avoid lots of rbind on lots of data).
+ other <- setNames(data.frame(matrix(nrow=nrow(BsIteration.and.Subject),
+ ncol=ncol(df[-1])),
+ stringsAsFactors=FALSE),
+ names(df[-1]) )
+ # 'BSDS' for bootstrapped data set
+ BSDS <- cbind(BsIteration.and.Subject, other)
+
+ # Fill in BSDS.
+ for (i in BSDS$ID){
+ BSDS[,c(-1,-2)] <- df[df$ID==i,2:ncol(df)]
+ }
+
+ return(list(BSsubjects, BSDS))
+ }
> BSDS002 <- funBS(theo_sd, 2) # 002 suffix is number of BS samples
> BSDS002[[1]]
[1] 10 12 8 11 1 7 5 10 5 9 2 7
> BSDS002[[2]]$ID
[1] 2 2 2 2 2 2 2 2 2 2 2 2 11 11 11 11 11 11 11 11 11 11 11 11 10 10 10
[28] 10 10 10 10 10 10 10 10 10 2 2 2 2 2 2 2 2 2 2 2 2 9 9 9 9 9 9
[55] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6 6 6 6 6 6 6 6 6
[82] 6 6 6 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 1 1 1 1 1 1 1
[109] 10 10 10 10 10 10 10 10 10 10 10 10 4 4 4 4 4 4 4 4 4 4 4 4 7 7 7
[136] 7 7 7 7 7 7 7 7 7 10 10 10 10 10 10 10 10 10 10 10 10 12 12 12 12 12 12
[163] 12 12 12 12 12 12 8 8 8 8 8 8 8 8 8 8 8 8 11 11 11 11 11 11 11 11 11
[190] 11 11 11 1 1 1 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 7 7 7 7 7
[217] 5 5 5 5 5 5 5 5 5 5 5 5 10 10 10 10 10 10 10 10 10 10 10 10 5 5 5
[244] 5 5 5 5 5 5 5 5 5 9 9 9 9 9 9 9 9 9 9 9 9 2 2 2 2 2 2
[271] 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7
> head(BSDS002[[2]]$ID, 15)
[1] 2 2 2 2 2 2 2 2 2 2 2 2 11 11 11
> head(BSDS002[[2]], 15)
BsIteration ID TIME DV AMT EVID CMT WT
1 1 2 0.00 0.00 4.95 101 1 64.6
2 1 2 0.00 0.15 0.00 0 2 64.6
3 1 2 0.25 0.85 0.00 0 2 64.6
4 1 2 0.50 2.35 0.00 0 2 64.6
5 1 2 1.02 5.02 0.00 0 2 64.6
6 1 2 2.02 6.58 0.00 0 2 64.6
7 1 2 3.48 7.09 0.00 0 2 64.6
8 1 2 5.00 6.66 0.00 0 2 64.6
9 1 2 6.98 5.25 0.00 0 2 64.6
10 1 2 9.00 4.39 0.00 0 2 64.6
11 1 2 12.05 3.53 0.00 0 2 64.6
12 1 2 24.22 1.15 0.00 0 2 64.6
13 1 11 0.00 0.00 4.95 101 1 64.6
14 1 11 0.00 0.15 0.00 0 2 64.6
15 1 11 0.25 0.85 0.00 0 2 64.6
> tail(BSDS002[[2]], 15)
BsIteration ID TIME DV AMT EVID CMT WT
274 2 2 9.00 4.39 0.00 0 2 64.6
275 2 2 12.05 3.53 0.00 0 2 64.6
276 2 2 24.22 1.15 0.00 0 2 64.6
277 2 7 0.00 0.00 4.95 101 1 64.6
278 2 7 0.00 0.15 0.00 0 2 64.6
279 2 7 0.25 0.85 0.00 0 2 64.6
280 2 7 0.50 2.35 0.00 0 2 64.6
281 2 7 1.02 5.02 0.00 0 2 64.6
282 2 7 2.02 6.58 0.00 0 2 64.6
283 2 7 3.48 7.09 0.00 0 2 64.6
284 2 7 5.00 6.66 0.00 0 2 64.6
285 2 7 6.98 5.25 0.00 0 2 64.6
286 2 7 9.00 4.39 0.00 0 2 64.6
287 2 7 12.05 3.53 0.00 0 2 64.6
288 2 7 24.22 1.15 0.00 0 2 64.6
> # Bootstrapped data set BSDS002 seems reasonable.
> # nlmixr on bootstrapped data unfortunately fails:
> fit.nlme.1cm.lin.BSDS002.full <- nlmixr(uif.1cm.lin, BSDS002, est="nlme")
Error in apply(data, 1, function(x) { :
dim(X) must have a positive length
> 
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