Error when fitting negative binomial model #6
Comments
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This comes from (ie, this is a user error and not a nlmixr2 error per se). |
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I believe that |
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Indeed this comes from a You can see the Which means See: https://www.geeksforgeeks.org/int_max-int_min-cc-applications/ |
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It could mean that integers that equal that value should be converted to Real |
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Where would an NA come from in this model? All the values that I see should be positive since they are exponentiated. |
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There are embedded library(rxode2)
#> rxode2 2.0.13.9000 using 4 threads (see ?getRxThreads)
#> no cache: create with `rxCreateCache()`
model_nb_linear_nlmixr <- function() {
ini({
ln0 <- log(3)
sln <- -0.13
lnbsize <- log(1.1)
})
model({
nest <- exp(ln0 + sln*conc)
nbsize <- exp(lnbsize)
voc ~ dnbinomMu(nbsize, nest)
})
}
d_model <-
data.frame(
ID = 0:9,
TIME = 0:9,
DV = rep(0:4, each = 2),
conc = rep(0:4, each = 2)*exp(rnorm(10, mean = 0, sd = 0.1))
)
etTrans(d_model, model_nb_linear_nlmixr)
#> ID TIME EVID AMT II DV
#> 1 0 0 0 NA 0 0
#> 2 1 0 9 NA 0 NA
#> 3 1 1 0 NA 0 0
#> 4 2 0 9 NA 0 NA
#> 5 2 2 0 NA 0 1
#> 6 3 0 9 NA 0 NA
#> 7 3 3 0 NA 0 1
#> 8 4 0 9 NA 0 NA
#> 9 4 4 0 NA 0 2
#> 10 5 0 9 NA 0 NA
#> 11 5 5 0 NA 0 2
#> 12 6 0 9 NA 0 NA
#> 13 6 6 0 NA 0 3
#> 14 7 0 9 NA 0 NA
#> 15 7 7 0 NA 0 3
#> 16 8 0 9 NA 0 NA
#> 17 8 8 0 NA 0 4
#> 18 9 0 9 NA 0 NA
#> 19 9 9 0 NA 0 4
#>
#> Covariates (non time-varying):
#> ID conc
#> 1 1 0.0000000
#> 2 2 0.0000000
#> 3 3 0.9393731
#> 4 4 1.2689009
#> 5 5 1.8698106
#> 6 6 1.6156174
#> 7 7 3.0705462
#> 8 8 2.6240829
#> 9 9 3.7391691
#> 10 10 3.5589855
#>
#> Compartment translation:
#> [1] Compartment Number
#> <0 rows> (or 0-length row.names)
str(etTrans(d_model, model_nb_linear_nlmixr))
#> Classes 'rxEtTran' and 'data.frame': 19 obs. of 6 variables:
#> $ ID : Factor w/ 10 levels "0","1","2","3",..: 1 2 2 3 3 4 4 5 5 6 ...
#> $ TIME: num 0 0 1 0 2 0 3 0 4 0 ...
#> $ EVID: int 0 9 0 9 0 9 0 9 0 9 ...
#> $ AMT : num NA NA NA NA NA NA NA NA NA NA ...
#> $ II : num 0 0 0 0 0 0 0 0 0 0 ...
#> $ DV : num 0 NA 0 NA 1 NA 1 NA 2 NA ...Created on 2023-08-29 with reprex v2.0.2 |
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What is EVID = 9 (it doesn't show up here: https://nlmixr2.github.io/rxode2/articles/rxode2-event-types.html) and why is it inserted into the et table? Should it also have MDV = 1 associated with it so that the calculation is not performed? |
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EVID =9 does show up in the underlying true EVID structure here: https://nlmixr2.github.io/rxode2/articles/rxode2-events-classic.html |
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MDV isn't carried along in the underlying translation. |
It shouldn't be affecting your problem. It has to come from an integer |
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If you simulate from the model, everything is NA because the parameters are not working for the negative binomial distribution as written. library(rxode2)
#> rxode2 2.0.13.9000 using 4 threads (see ?getRxThreads)
#> no cache: create with `rxCreateCache()`
library(nlmixr2)
#> Loading required package: nlmixr2data
model_nb_linear_nlmixr <- function() {
ini({
ln0 <- log(3)
sln <- -0.13
lnbsize <- log(1.1)
})
model({
nest <- exp(ln0 + sln*conc)
nbsize <- exp(lnbsize)
voc ~ dnbinomMu(nbsize, nest)
})
}
d <-et(0:9) %>% et(id=1:10) %>% as.data.frame()
d$conc <- rep(0:4, each = 2)
ds <- rxSolve(model_nb_linear_nlmixr, d)
print(ds)
#> ── Solved rxode2 object ──
#> ── Parameters ($params): ──
#> ln0 sln lnbsize
#> 1.09861229 -0.13000000 0.09531018
#> ── Initial Conditions ($inits): ──
#> named numeric(0)
#> ── First part of data (object): ──
#> # A tibble: 100 × 7
#> id time nest nbsize pred conc DV
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 3 1.1 8 0 NA
#> 2 1 1 3 1.1 11 0 NA
#> 3 1 2 2.63 1.1 0 1 NA
#> 4 1 3 2.63 1.1 1 1 NA
#> 5 1 4 2.31 1.1 4 2 NA
#> 6 1 5 2.31 1.1 2 2 NA
#> # ℹ 94 more rows
print(summary(ds))
#> ── Summary of Solved rxode2 object ──
#> ── Model ($model): ──
#> rxode2({
#> param(ln0, sln, lnbsize, conc)
#> nest = exp(ln0 + sln * conc)
#> nbsize = exp(lnbsize)
#> rx_yj_ ~ 182
#> rx_lambda_ ~ 1
#> rx_low_ ~ 0
#> rx_hi_ ~ 1
#> rx_r_ ~ 0
#> rx_pred_ ~ llikNbinomMu(DV, nbsize, nest)
#> sim = rxnbinomMu(nbsize, nest)
#> cmt(voc)
#> dvid(1)
#> })
#> ── Parameters (ds$params): ──
#> ln0 sln lnbsize
#> 1.09861229 -0.13000000 0.09531018
#> ── Initial Conditions (ds$inits): ──
#> named numeric(0)
#> ── Summary of data (object): ──
#> id time nest nbsize pred
#> Min. : 1.0 Min. :0.0 Min. :1.784 Min. :1.1 Min. : 0.00
#> 1st Qu.: 3.0 1st Qu.:2.0 1st Qu.:2.031 1st Qu.:1.1 1st Qu.: 0.00
#> Median : 5.5 Median :4.5 Median :2.313 Median :1.1 Median : 1.00
#> Mean : 5.5 Mean :4.5 Mean :2.352 Mean :1.1 Mean : 2.32
#> 3rd Qu.: 8.0 3rd Qu.:7.0 3rd Qu.:2.634 3rd Qu.:1.1 3rd Qu.: 3.25
#> Max. :10.0 Max. :9.0 Max. :3.000 Max. :1.1 Max. :14.00
#>
#> conc DV
#> Min. :0 Min. : NA
#> 1st Qu.:1 1st Qu.: NA
#> Median :2 Median : NA
#> Mean :2 Mean :NaN
#> 3rd Qu.:3 3rd Qu.: NA
#> Max. :4 Max. : NA
#> NA's :100
#> id time nest nbsize pred
#> Min. : 1.0 Min. :0.0 Min. :1.784 Min. :1.1 Min. : 0.00
#> 1st Qu.: 3.0 1st Qu.:2.0 1st Qu.:2.031 1st Qu.:1.1 1st Qu.: 0.00
#> Median : 5.5 Median :4.5 Median :2.313 Median :1.1 Median : 1.00
#> Mean : 5.5 Mean :4.5 Mean :2.352 Mean :1.1 Mean : 2.32
#> 3rd Qu.: 8.0 3rd Qu.:7.0 3rd Qu.:2.634 3rd Qu.:1.1 3rd Qu.: 3.25
#> Max. :10.0 Max. :9.0 Max. :3.000 Max. :1.1 Max. :14.00
#>
#> conc DV
#> Min. :0 Min. : NA
#> 1st Qu.:1 1st Qu.: NA
#> Median :2 Median : NA
#> Mean :2 Mean :NaN
#> 3rd Qu.:3 3rd Qu.: NA
#> Max. :4 Max. : NA
#> NA's :100Created on 2023-08-29 with reprex v2.0.2 You could see if it not matching what you expect by looking at the source https://github.com/nlmixr2/rxode2ll/blob/main/src/llikNbinom2.cpp And the description of the distribution from stan: https://mc-stan.org/docs/2_20/functions-reference/nbalt.html It could be that I'm implementing this incorrectly, but I'm only pulling things from stan right now for likelihoods. |
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When I tested my parameters in R with what I think is the same parameterization, I got what I expected out: |
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library(rxode2)
#> rxode2 2.0.13.9000 using 4 threads (see ?getRxThreads)
#> no cache: create with `rxCreateCache()`
model_nb_linear_nlmixr <- function() {
ini({
ln0 <- log(3)
sln <- -0.13
lnbsize <- log(1.1)
})
model({
nest <- exp(ln0 + sln*conc)
nbsize <- exp(lnbsize)
voc ~ dnbinomMu(nbsize, nest)
})
}
f <- model_nb_linear_nlmixr()
summary(f$simulationModel)
#> rxode2 2.0.13.9000 model named rx_a2d71a872d72c8620009fbf2ee6c6790_x6 model (✔ ready).
#> DLL: C:\Users\fidlema3\AppData\Local\Temp\1\RTMPCA~1\rxode2\RX_A2D~1.RXD/rx_a2d71a872d72c8620009fbf2ee6c6790_x64.dll
#> NULL
#>
#> Calculated Variables:
#> [1] "nest" "nbsize" "sim"
#> ── rxode2 Model Syntax ──
#> rxode2({
#> param(ln0, sln, lnbsize, conc)
#> nest = exp(ln0 + sln * conc)
#> nbsize = exp(lnbsize)
#> rx_yj_ ~ 182
#> rx_lambda_ ~ 1
#> rx_low_ ~ 0
#> rx_hi_ ~ 1
#> rx_r_ ~ 0
#> rx_pred_ ~ llikNbinomMu(DV, nbsize, nest)
#> sim = rxnbinomMu(nbsize, nest)
#> cmt(voc)
#> dvid(1)
#> })Created on 2023-08-30 with reprex v2.0.2 |
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It is likely because size is not an integer, which seems to be required. rxode2random::rxnbinomMu(1.1, 3, n=100)
#> Error in rxode2random::rxnbinomMu(1.1, 3, n = 100): Assertion on 'size' failed: Must be of type 'count', not 'double'.
rxode2random::rxnbinomMu(1, 3, n=100)
#> [1] 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0
#> [38] 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 0 0
#> [75] 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1Created on 2023-08-30 with reprex v2.0.2 |
In the model below, I'm getting an error about the precision parameter, but I don't think I'm setting anything to -2147483648.
Created on 2023-08-29 with reprex v2.0.2
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