Estimate posthoc predictions and/or likelihood without doing a full estimation #32
Comments
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Hi Matt (or whoever takes up this issue), Since the ability to calculate post-hoc predictions given only a fully specified model and some data is essential to the ability to do cross-validation (using the testing set), I sure hope that this issue will be bumped up the priority list. I tried to do so using nlme's predict() function as so: This error message is opaque to me. Also unclear to me is nlmixr's prediction() function for gnlmm objects since the example in the documentation has very little in the way of explanation. What would the fit and pred arguments be in my case, just wanting to do cross-validation? I'd like to be able to do a fitting—I don't much care if it's a gnlmm fitting or one more like in the vignette tutorial—and then use the results of that fitting on new data, i.e. to do cross-validation. I'm not sure if it would be better to open a new issue for predict() or prediction() or just comment on this open issue, but for now I opt for the latter. Thanks for hearing me out. James |
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Hi James, The FOCEi branch includes the new posthoc feature you are requesting. However, it is a major rewrite of the nlmixr output object, so it isn't compatible with shinyMixR or xpose.nlmixr right now. I don't know how nlme's predict function works. If I get time, I will look into it. Higher on the priority list for me is this posthoc and a few other issues like multiple end-points. |
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Hi Matt, Just to clarify, the FOCEi branch is still "under construction", correct? If it's working, could you please let us know the syntax it needs? As a major rewrite of the nlmixr output object, my guess is that it will require a new version number to download, no? Thanks for you help! |
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While it is working, the syntax isn't finalized. I would like to review suggestions with the nlmixr team first. |
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Yes. It will require a new version download. |
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With the new version you woud do the following: post <- nlmixr(model,newData,"posthoc")We are working toward allowing you to pip the results of a model to a posthoc prediction possibly with R's |
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I wonder if you could elaborate a bit on this syntax, in particular the word "model". The structural model is given by the UUI, but how does one specify exactly the parameters? For example after a bootstrap run the parameters would be given as fit$theta, fit$omega, etc. and will vary from one bootstrap sample to the next. Using the theo_sd data set included in nlmixr, I fit the data as in the vignette and found this behavior to be strange when trying to update the value of theta: > fit <- nlmixr(cf1cmt.adderr, theo_sd, est="nlme") # for demo and testing
**Iteration 1
LME step: Loglik: -182.2318, nlminb iterations: 1
reStruct parameters:
ID1 ID2 ID3
1.0022617 0.2783009 2.0758984
Beginning PNLS step: .. completed fit_nlme() step.
PNLS step: RSS = 63.1492
fixed effects: -3.211935 0.447998 -0.7859319
iterations: 7
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.2723749 3.2673493
**Iteration 2
LME step: Loglik: -179.291, nlminb iterations: 9
reStruct parameters:
ID1 ID2 ID3
0.96385384 0.08333465 1.63552496
Beginning PNLS step: .. completed fit_nlme() step.
PNLS step: RSS = 63.28224
fixed effects: -3.211529 0.4441499 -0.7863391
iterations: 7
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.008664056 0.172140362
**Iteration 3
LME step: Loglik: -179.337, nlminb iterations: 8
reStruct parameters:
ID1 ID2 ID3
0.96127446 0.07085101 1.63887826
Beginning PNLS step: .. completed fit_nlme() step.
PNLS step: RSS = 63.21956
fixed effects: -3.211695 0.445849 -0.7861748
iterations: 7
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.003810953 0.083041222
**Iteration 4
LME step: Loglik: -179.3201, nlminb iterations: 7
reStruct parameters:
ID1 ID2 ID3
0.96243389 0.07619035 1.63736920
Beginning PNLS step: .. completed fit_nlme() step.
PNLS step: RSS = 63.24586
fixed effects: -3.211622 0.4451181 -0.7862475
iterations: 7
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.001642014 0.034519536
**Iteration 5
LME step: Loglik: -179.328, nlminb iterations: 6
reStruct parameters:
ID1 ID2 ID3
0.96192227 0.07381955 1.63801887
Beginning PNLS step: .. completed fit_nlme() step.
PNLS step: RSS = 63.23423
fixed effects: -3.211654 0.4454379 -0.7862161
iterations: 7
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.0007179909 0.0153297789
**Iteration 6
LME step: Loglik: -179.3247, nlminb iterations: 5
reStruct parameters:
ID1 ID2 ID3
0.96215590 0.07486979 1.63772980
Beginning PNLS step: .. completed fit_nlme() step.
PNLS step: RSS = 63.24087
fixed effects: -3.211654 0.4454379 -0.7862161
iterations: 1
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.000000000 0.008689733
**Iteration 7
LME step: Loglik: -179.3247, nlminb iterations: 1
reStruct parameters:
ID1 ID2 ID3
0.96214642 0.07485047 1.63773353
Beginning PNLS step: .. completed fit_nlme() step.
PNLS step: RSS = 63.24087
fixed effects: -3.211654 0.4454379 -0.7862161
iterations: 1
Convergence crit. (must all become <= tolerance = 1e-05):
fixed reStruct
0.000000e+00 1.297069e-11
Calculating residuals/tables
done.
Warning message:
In nlmixrUI.nlme.var(obj) :
Initial condition for additive error ignored with nlme
> fit$theta
logtcl logtka logtv add.err
-3.2116536 0.4454379 -0.7862161 0.6921686
> length(fit$theta)
[1] 4
> fit$theta[2] <- 0.4 # change the second value to something close
> length(fit$theta)
[1] 132
> fit$theta
[1] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[7] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
[13] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[19] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
[25] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[31] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
[37] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[43] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
[49] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[55] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
[61] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[67] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
[73] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[79] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
[85] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[91] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
[97] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[103] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
[109] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[115] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
[121] -3.2116536 0.4000000 -0.7862161 0.6921686 -3.2116536 0.4000000
[127] -0.7862161 0.6921686 -3.2116536 0.4000000 -0.7862161 0.6921686
> Why did the length of fit$theta change? What would be the syntactically best way to set values of theta and omega and their correlation, as e.g. using the results of one fitting? Thanks for your help, again. |
Fundamentally, a fit object is a data.frame. Therefore assigning
Currently the fit has a parsed UI object attached at Note the parameter values and properties are stored in a bounds object Eventually I'm hoping that I can get a general function to do this. In the last revision, I made fit %>% ini({cl=1}) %>% nlmixr(data,"posthoc")or fit %>% ini(list(cl=1)) %>% nlmixr(data,"posthoc")However these details haven't been worked out yet. As a side note, now nlmixr functions now mostly evaluate to the parsed UI object instead of throwing an error. Some information (like parameter labels) are ignored by this approach, however. |
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Note what actually happend with your theta is the following:
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You wrote, "If you modify these parameters in a new ui object, you can then call nlmixr(newParsedUI, data, "estimation")." I've tried various things (below), but nothing seems to work. How would I get newParsedUI, the first argument to nlmixr? Or is this something that I'll just have to wait on (which for me is okay for a bit, as I'll be unavailable to work on nlmixr stuff for the next 10 days)? > fit$uif$ini
Fixed Effects ($theta):
logtcl logtka logtv
-3.2116536 0.4454379 -0.7862161
Omega ($omega):
eta.cl eta.ka eta.v
eta.cl 0.06993812 0.0000000 0.0000000
eta.ka 0.00000000 0.4124862 0.0000000
eta.v 0.00000000 0.0000000 0.0181095
> fit$uif$ini$theta
logtcl logtka logtv
-3.2116536 0.4454379 -0.7862161
> fit$uif$ini$omega
eta.cl eta.ka eta.v
eta.cl 0.06993812 0.0000000 0.0000000
eta.ka 0.00000000 0.4124862 0.0000000
eta.v 0.00000000 0.0000000 0.0181095
> is.matrix(fit$uif$ini$omega)
[1] TRUE
> is.vector(fit$uif$ini$theta)
[1] TRUE
> as.data.frame(fit$uif$ini)
ntheta neta1 neta2 name lower est upper fix err
1 1 NA NA logtcl -Inf -3.21165355 Inf FALSE <NA>
2 2 NA NA logtka -Inf 0.44543795 Inf FALSE <NA>
3 3 NA NA logtv -Inf -0.78621611 Inf FALSE <NA>
4 4 NA NA add.err -Inf 0.69216856 Inf FALSE add
5 NA 1 1 eta.cl -Inf 0.06993812 Inf FALSE <NA>
6 NA 2 2 eta.ka -Inf 0.41248621 Inf FALSE <NA>
7 NA 3 3 eta.v -Inf 0.01810950 Inf FALSE <NA>
label condition
1 log typical value Cl (L/hr) <NA>
2 log typical value Ka (1/hr) <NA>
3 log typical value V (L) <NA>
4 <NA> <NA>
5 <NA> ID
6 <NA> ID
7 <NA> ID
> current.est <- as.data.frame(fit$uif$ini)$est
> current.est
[1] -3.21165355 0.44543795 -0.78621611 0.69216856 0.06993812 0.41248621
[7] 0.01810950
> current.est[2] <- 0.4 ; current.est
[1] -3.21165355 0.40000000 -0.78621611 0.69216856 0.06993812 0.41248621
[7] 0.01810950
> new.est <- current.est
> as.data.frame(fit$uif$ini)
ntheta neta1 neta2 name lower est upper fix err
1 1 NA NA logtcl -Inf -3.21165355 Inf FALSE <NA>
2 2 NA NA logtka -Inf 0.44543795 Inf FALSE <NA>
3 3 NA NA logtv -Inf -0.78621611 Inf FALSE <NA>
4 4 NA NA add.err -Inf 0.69216856 Inf FALSE add
5 NA 1 1 eta.cl -Inf 0.06993812 Inf FALSE <NA>
6 NA 2 2 eta.ka -Inf 0.41248621 Inf FALSE <NA>
7 NA 3 3 eta.v -Inf 0.01810950 Inf FALSE <NA>
label condition
1 log typical value Cl (L/hr) <NA>
2 log typical value Ka (1/hr) <NA>
3 log typical value V (L) <NA>
4 <NA> <NA>
5 <NA> ID
6 <NA> ID
7 <NA> ID
> as.data.frame(fit$uif$ini)$est <- current.est
Error in as.data.frame(fit$uif$ini)$est <- current.est :
could not find function "as.data.frame<-"
> as.data.frame(fit$uif$ini)$est
[1] -3.21165355 0.44543795 -0.78621611 0.69216856 0.06993812 0.41248621
[7] 0.01810950
> as.data.frame(fit$uif$ini)
ntheta neta1 neta2 name lower est upper fix err
1 1 NA NA logtcl -Inf -3.21165355 Inf FALSE <NA>
2 2 NA NA logtka -Inf 0.44543795 Inf FALSE <NA>
3 3 NA NA logtv -Inf -0.78621611 Inf FALSE <NA>
4 4 NA NA add.err -Inf 0.69216856 Inf FALSE add
5 NA 1 1 eta.cl -Inf 0.06993812 Inf FALSE <NA>
6 NA 2 2 eta.ka -Inf 0.41248621 Inf FALSE <NA>
7 NA 3 3 eta.v -Inf 0.01810950 Inf FALSE <NA>
label condition
1 log typical value Cl (L/hr) <NA>
2 log typical value Ka (1/hr) <NA>
3 log typical value V (L) <NA>
4 <NA> <NA>
5 <NA> ID
6 <NA> ID
7 <NA> ID
> (as.data.frame(fit$uif$ini)$est) <- current.est
Error in (as.data.frame(fit$uif$ini)$est) <- current.est :
could not find function "(<-"
> (as.data.frame(fit$uif$ini))$est <- current.est
Error in (as.data.frame(fit$uif$ini))$est <- current.est :
could not find function "(<-"
> df <- as.data.frame(fit$uif$ini)
> df
ntheta neta1 neta2 name lower est upper fix err
1 1 NA NA logtcl -Inf -3.21165355 Inf FALSE <NA>
2 2 NA NA logtka -Inf 0.44543795 Inf FALSE <NA>
3 3 NA NA logtv -Inf -0.78621611 Inf FALSE <NA>
4 4 NA NA add.err -Inf 0.69216856 Inf FALSE add
5 NA 1 1 eta.cl -Inf 0.06993812 Inf FALSE <NA>
6 NA 2 2 eta.ka -Inf 0.41248621 Inf FALSE <NA>
7 NA 3 3 eta.v -Inf 0.01810950 Inf FALSE <NA>
label condition
1 log typical value Cl (L/hr) <NA>
2 log typical value Ka (1/hr) <NA>
3 log typical value V (L) <NA>
4 <NA> <NA>
5 <NA> ID
6 <NA> ID
7 <NA> ID
> df$est <- current.est
> df
ntheta neta1 neta2 name lower est upper fix err
1 1 NA NA logtcl -Inf -3.21165355 Inf FALSE <NA>
2 2 NA NA logtka -Inf 0.40000000 Inf FALSE <NA>
3 3 NA NA logtv -Inf -0.78621611 Inf FALSE <NA>
4 4 NA NA add.err -Inf 0.69216856 Inf FALSE add
5 NA 1 1 eta.cl -Inf 0.06993812 Inf FALSE <NA>
6 NA 2 2 eta.ka -Inf 0.41248621 Inf FALSE <NA>
7 NA 3 3 eta.v -Inf 0.01810950 Inf FALSE <NA>
label condition
1 log typical value Cl (L/hr) <NA>
2 log typical value Ka (1/hr) <NA>
3 log typical value V (L) <NA>
4 <NA> <NA>
5 <NA> ID
6 <NA> ID
7 <NA> ID
> # Why did this work but 'as.data.frame(fit$uif$ini)$est <- current.est' failed?
> nlmixr(df, theo_sd, "estimation")
Error in UseMethod("nlmixr") :
no applicable method for 'nlmixr' applied to an object of class "data.frame"
> fit$uif$ini$est
[1] -3.21165355 0.44543795 -0.78621611 0.69216856 0.06993812 0.41248621
[7] 0.01810950
> fit$uif$ini$est <- current.est
Error in `$<-.data.frame`(`*tmp*`, uif, value = list(ini = list(ntheta = c(1, :
replacement has 5 rows, data has 132
> length(current.est)
[1] 7
> ## Huh- "replacement has 5 rows"?
> model(df)
> model.df <- model(df)
> model.df
NULL
> model(df)
> df
ntheta neta1 neta2 name lower est upper fix err
1 1 NA NA logtcl -Inf -3.21165355 Inf FALSE <NA>
2 2 NA NA logtka -Inf 0.40000000 Inf FALSE <NA>
3 3 NA NA logtv -Inf -0.78621611 Inf FALSE <NA>
4 4 NA NA add.err -Inf 0.69216856 Inf FALSE add
5 NA 1 1 eta.cl -Inf 0.06993812 Inf FALSE <NA>
6 NA 2 2 eta.ka -Inf 0.41248621 Inf FALSE <NA>
7 NA 3 3 eta.v -Inf 0.01810950 Inf FALSE <NA>
label condition
1 log typical value Cl (L/hr) <NA>
2 log typical value Ka (1/hr) <NA>
3 log typical value V (L) <NA>
4 <NA> <NA>
5 <NA> ID
6 <NA> ID
7 <NA> ID
> ini.df <- ini(df)
> ini.df
NULL
> # So how to reparse it?Thanks again for all your help. I really look forward to this functionality! :) James |
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Hi Matt, You wrote, "Note the parameter values and properties are stored in a bounds object fit$uif$ini. You can sort of see the structure by executing as.data.frame(fit$uif$ini). If you modify these parameters in a new ui object, you can then call nlmixr(newParsedUI, data, "estimation")." While fit$uif$ini may indeed be a data frame as you wrote it is (in my case with 7 observations of 11 variables), it doesn't seem to be an ordinary data frame since it also has other fields associated with it: > fit$uif$ini
Fixed Effects ($theta):
logtcl logtka logtv
-3.2116536 0.4454379 -0.7862161
Omega ($omega):
eta.cl eta.ka eta.v
eta.cl 0.06993812 0.0000000 0.0000000
eta.ka 0.00000000 0.4124862 0.0000000
eta.v 0.00000000 0.0000000 0.0181095
> str(fit$uif$ini)
'data.frame': 7 obs. of 11 variables:
$ ntheta : num 1 2 3 4 NA NA NA
$ neta1 : num NA NA NA NA 1 2 3
$ neta2 : num NA NA NA NA 1 2 3
$ name : Factor w/ 7 levels "logtcl","logtka",..: 1 2 3 4 5 6 7
$ lower : num -Inf -Inf -Inf -Inf -Inf ...
$ est : num -3.2117 0.4454 -0.7862 0.6922 0.0699 ...
$ upper : num Inf Inf Inf Inf Inf ...
$ fix : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ err : chr NA NA NA "add" ...
$ label : chr "log typical value Cl (L/hr)" "log typical value Ka (1/hr)" "log typical value V (L)" NA ...
$ condition: chr NA NA NA "" ...
$ theta : num ... (theta estimates)
$ theta.full: num ... (theta estimates, including error terms)
$ omega : matrix ... (omega matrix)
$ random : matrix class ... (Based on Between Subject Random effects)
$ fixed.form: formula ... (Fixed effect parameters based on theta.)
$ focei.upper: Upper bounds for FOCEi
$ focei.lower: Lower bounds for FOCEi
$ focei.err.type: Residual Error type for FOCEi thetas
$ eta.names: Eta names
$ focei.names: Theta names for FOCEi
> print.default(fit$uif$ini)
$`ntheta`
[1] 1 2 3 4 NA NA NA
$neta1
[1] NA NA NA NA 1 2 3
$neta2
[1] NA NA NA NA 1 2 3
$name
[1] logtcl logtka logtv add.err eta.cl eta.ka eta.v
Levels: logtcl logtka logtv add.err eta.cl eta.ka eta.v
$lower
[1] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
$est
[1] -3.21165355 0.44543795 -0.78621611 0.69216856 0.06993812 0.41248621
[7] 0.01810950
$upper
[1] Inf Inf Inf Inf Inf Inf Inf
$fix
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
$err
[1] NA NA NA "add" NA NA NA
$label
[1] "log typical value Cl (L/hr)" "log typical value Ka (1/hr)"
[3] "log typical value V (L)" NA
[5] NA NA
[7] NA
$condition
[1] NA NA NA "" "ID" "ID" "ID"
attr(,"class")
[1] "nlmixrBounds" "data.frame"
> Can you please elaborate a little bit on what sort of data frame this is (how it's different from a simple textbook example of a data frame), or perhaps provide a reference where I could learn more? You also wrote, "Currently the fit has a parsed UI object attached at fit$uif. Not only does this have the last fit information but that is what should be modified to run a new fit." How exactly should this be modified? Are there any accessor or mutator functions we can use for such an object? It would be really great if you could supply us with a preferred syntax for changing the fit$uif$ini parameters so one can then call ' nlmixr(newParsedUI, data, "estimation") ' as you wrote above, and also use something like ' post <- nlmixr(model, newData, "posthoc") ' for cross-validation while keeping the model and its parameters the same as for fitting on a training data set. Much thanks! James |
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I'll opten a new feature request for this. This feature didn't make 1.0 |
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Hello Matt, Almost 4 years ago I wrote in Issue # 28, "I am interested in seeing the likelihood (or log-likelihood) for a combination of model, known parameter values, and a data set; this would be the value prior to any parameter optimization." On 2018-09-22 you closed issue # 32 noting that this feature request didn't make it into version 1.0. It seems that a work-around for Bill Denney's issue was adequate for his needs (i.e. set EVID = 2), and that this feature request may have fallen by the wayside. Since it was essential for my interest in cross-validation, I had to drop nlmixr as a viable option for my work on using bootstrap cross-validation for model selection and used Monolix' R API instead. (This has since been published at https://www.tandfonline.com/doi/full/10.1080/19466315.2020.1828159 in 2020.) For cross-validation (and for using cross-validation for model selection), it is necessary to have a way to compare likelihoods for testing data. In NONMEM this would be with the MAXEVAL=0 option. So now, nearly 4 years later, I would again like to take a serious interest in nlmixr and see its capabilities. I'm glad to see that it's matured in the interim. Can an equivalent to MAXEVAL=0 please be included? Thank-you, |
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Hi James, This has been present in nlmixr for awhile. The easiest way to use this is: fit <- nlmixr(model, data, "posthoc")However you can technically do the following too: fit <- nlmixr(model, data, "focei", control=foceiControl(maxOuterIterations=0))This will find the empirical Bayes estimates based on the prior model through optimizing the "inner" problem. This is what You can even take it a step further than NONMEM does by supplying the etas (in the order of the UI and in matrix form): fit <- nlmixr(model, data, "focei", control=foceiControl(maxOuterIterations=0, maxInnerIterations=0, etaMat=etas))which evaluates an objective function at the ETAs that were pre-specified. This feature has been there for a very long time, near the time of 1.0. It is used to get the |
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Matt, I appreciate your reply. I was unaware of this, so thank-you!
-James
…________________________________
From: Matthew Fidler ***@***.***>
Sent: Monday, January 17, 2022 5:41 AM
To: nlmixrdevelopment/nlmixr ***@***.***>
Cc: JSC67 ***@***.***>; Comment ***@***.***>
Subject: Re: [nlmixrdevelopment/nlmixr] Estimate posthoc predictions and/or likelihood without doing a full estimation (#32)
Hi James,
This has been present in nlmixr for awhile.
The easiest way to use this is:
fit <- nlmixr(model, data, "posthoc")
However you can technically do the following too:
fit <- nlmixr(model, data, "focei", control=foceiControl(maxOuterIterations=0))
This will find the empirical Bayes estimates based on the prior model through optimizing the "inner" problem. This is what maxevals=0 does.
You can even take it a step further than NONMEM does by supplying the etas (in the order of the UI and in matrix form):
fit <- nlmixr(model, data, "focei", control=foceiControl(maxOuterIterations=0, maxInnerIterations=0, etaMat=etas))
which evaluates an objective function at the ETAs that were pre-specified.
This feature has been there for a very long time, near the time of 1.0. It is used to get the focei likelihood for saem
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Based on a model and data, many people want to:
This is disccussed in Issue #28.
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