The broom and broom.mixed packages
broom and broom.mixed are packages that
attempt to put standard model outputs into data frames. nlmixr supports
the tidy and glance methods but does not
support augment at this time.
Using a model with a covariance term, the Phenobarbital model, we can explore the different types of output that is used in the tidy functions.
To explore this, first we run the model:
library(nlmixr2)
library(broom.mixed)
pheno <- function() {
# Pheno with covariance
ini({
tcl <- log(0.008) # typical value of clearance
tv <- log(0.6) # typical value of volume
## var(eta.cl)
eta.cl + eta.v ~ c(1,
0.01, 1) ## cov(eta.cl, eta.v), var(eta.v)
# interindividual variability on clearance and volume
add.err <- 0.1 # residual variability
})
model({
cl <- exp(tcl + eta.cl) # individual value of clearance
v <- exp(tv + eta.v) # individual value of volume
ke <- cl / v # elimination rate constant
d/dt(A1) = - ke * A1 # model differential equation
cp = A1 / v # concentration in plasma
cp ~ add(add.err) # define error model
})
}
## We will run it two ways to allow comparisons
fit.s <- nlmixr(pheno, pheno_sd, "saem", control=list(logLik=TRUE, print=0),
table=list(cwres=TRUE, npde=TRUE))
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fit.f <- nlmixr(pheno, pheno_sd, "focei",
control=list(print=0),
table=list(cwres=TRUE, npde=TRUE))
#> calculating covariance matrix
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#> done
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Often in fitting data, you would want to glance at the
fit to see how well it fits. In broom, glance
will give a summary of the fit metrics of goodness of fit:
glance(fit.s)
#> # A tibble: 2 × 6
#> OBJF AIC BIC logLik `Condition#(Cov)` `Condition#(Cor)`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 689. 986. 1004. -487. 6.03 5.25
#> 2 698. 995. 1013. -492. 6.03 5.25Note in nlmixr it is possible to have more than one fit metric (based
on different quadratures, FOCEi approximation etc). However, the
glance only returns the fit metrics that are current.
If you wish you can set the objective function to the focei objective function (which was already calculated with CWRES).
setOfv(fit.s,"gauss3_1.6")
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glance(fit.s)
#> # A tibble: 3 × 6
#> OBJF AIC BIC logLik `Condition#(Cov)` `Condition#(Cor)`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 689. 986. 1004. -487. 6.03 5.25
#> 2 698. 995. 1013. -492. 6.03 5.25
#> 3 698. 995. 1013. -492. 6.03 5.25Of course you can always change the type of objective function that nlmixr uses:
setOfv(fit.s,"FOCEi") # Setting objective function to foceiBy setting it back to the SAEM default objective function of
FOCEi, the glance(fit.s) has the same values
again:
glance(fit.s)
#> # A tibble: 3 × 6
#> OBJF AIC BIC logLik `Condition#(Cov)` `Condition#(Cor)`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 689. 986. 1004. -487. 6.03 5.25
#> 2 698. 995. 1013. -492. 6.03 5.25
#> 3 698. 995. 1013. -492. 6.03 5.25For convenience, you can do this while you glance at the
objects:
glance(fit.s, type="FOCEi")
#> # A tibble: 3 × 6
#> OBJF AIC BIC logLik `Condition#(Cov)` `Condition#(Cor)`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 689. 986. 1004. -487. 6.03 5.25
#> 2 698. 995. 1013. -492. 6.03 5.25
#> 3 698. 995. 1013. -492. 6.03 5.25Tidying the model parameters
Tidying of overall fit parameters
You can also tidy the model estimates into a data frame with broom for processing. This can be useful when integrating into 3rd parting modeling packages. With a consistent parameter format, tasks for multiple types of models can be automated and applied.
The default function for this is tidy, which when
applied to the fit object provides the overall parameter
information in a tidy dataset:
tidy(fit.s)
#> # A tibble: 6 × 7
#> effect group term estimate std.error statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 fixed NA tcl -5.02 0.0750 -66.9 1 e+0
#> 2 fixed NA tv 0.350 0.0548 6.37 1.09e-9
#> 3 ran_pars ID sd__eta.cl 0.488 NA NA NA
#> 4 ran_pars ID sd__eta.v 0.402 NA NA NA
#> 5 ran_pars ID cor__eta.v.eta.cl 0.946 NA NA NA
#> 6 ran_pars Residual(add) add.err 2.76 NA NA NA Note by default these are the parameters that are actually estimated in nlmixr, not the back-transformed values in the table from the printout. Of course, with mu-referenced models, you may want to exponentiate some of the terms. The broom package allows you to apply exponentiation on all the parameters, that is:
## Transformation applied on every parameter
tidy(fit.s, exponentiate=TRUE)
#> # A tibble: 6 × 7
#> effect group term estimate std.error statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 fixed NA tcl 0.00663 0.000498 13.3 1.75e-27
#> 2 fixed NA tv 1.42 0.0778 18.2 4.27e-40
#> 3 ran_pars ID sd__eta.cl 0.488 NA NA NA
#> 4 ran_pars ID sd__eta.v 0.402 NA NA NA
#> 5 ran_pars ID cor__eta.v.eta.… 0.946 NA NA NA
#> 6 ran_pars Residual(add) add.err 2.76 NA NA NA Note:, in accordance with the rest of the broom
package, when the parameters with the exponentiated, the standard errors
are transformed to an approximate standard error by the formula:
.
This can be confusing because the confidence intervals (described later)
are using the actual standard error and back-transforming to the
exponentiated scale. This is the reason why the default for nlmixr’s
broom interface is exponentiate=FALSE, that
is:
tidy(fit.s, exponentiate=FALSE) ## No transformation applied
#> # A tibble: 6 × 7
#> effect group term estimate std.error statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 fixed NA tcl -5.02 0.0750 -66.9 1 e+0
#> 2 fixed NA tv 0.350 0.0548 6.37 1.09e-9
#> 3 ran_pars ID sd__eta.cl 0.488 NA NA NA
#> 4 ran_pars ID sd__eta.v 0.402 NA NA NA
#> 5 ran_pars ID cor__eta.v.eta.cl 0.946 NA NA NA
#> 6 ran_pars Residual(add) add.err 2.76 NA NA NA If you want, you can also use the parsed back-transformation that is
used in nlmixr tables (ie fit$parFixedDf). Please
note that this uses the approximate back-transformation for standard
errors on the log-scaled back-transformed values.
This is done by:
## Transformation applied to log-scaled population parameters
tidy(fit.s, exponentiate=NA)
#> # A tibble: 6 × 7
#> effect group term estimate std.error statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 fixed NA tcl 0.00663 0.000498 13.3 1.75e-27
#> 2 fixed NA tv 1.42 0.0778 18.2 4.27e-40
#> 3 ran_pars ID sd__eta.cl 0.488 NA NA NA
#> 4 ran_pars ID sd__eta.v 0.402 NA NA NA
#> 5 ran_pars ID cor__eta.v.eta.… 0.946 NA NA NA
#> 6 ran_pars Residual(add) add.err 2.76 NA NA NA Also note, at the time of this writing the default separator between
variables is ., which doesn’t work well with this model
giving cor__eta.v.eta.cl. You can easily change this
by:
options(broom.mixed.sep2="..")
tidy(fit.s)
#> # A tibble: 6 × 7
#> effect group term estimate std.error statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 fixed NA tcl -5.02 0.0750 -66.9 1 e+0
#> 2 fixed NA tv 0.350 0.0548 6.37 1.09e-9
#> 3 ran_pars ID sd__eta.cl 0.488 NA NA NA
#> 4 ran_pars ID sd__eta.v 0.402 NA NA NA
#> 5 ran_pars ID cor__eta.v..eta.… 0.946 NA NA NA
#> 6 ran_pars Residual(add) add.err 2.76 NA NA NA This gives an easier way to parse value:
cor__eta.v..eta.cl
Adding a confidence interval to the parameters
The default R method confint works with nlmixr fit
objects:
confint(fit.s)
#> model.est estimate 2.5 % 97.5 %
#> tcl -5.0154795 0.00663445 -5.1625159 -4.8684431
#> tv 0.3495036 1.41836329 0.2420439 0.4569632
#> add.err 2.7649251 2.76492510 NA NAThis transforms the variables as described above. You can still use
the exponentiate parameter to control the display of the
confidence interval:
confint(fit.s, exponentiate=FALSE)
#> model.est estimate 2.5 % 97.5 %
#> tcl -5.0154795 0.00663445 -5.1625159 -4.8684431
#> tv 0.3495036 1.41836329 0.2420439 0.4569632
#> add.err 2.7649251 2.76492510 NA NAHowever, broom has also implemented it own way to make these data a tidy dataset. The easiest way to get these values in a nlmixr dataset is to use:
tidy(fit.s, conf.level=0.9)
#> # A tibble: 6 × 9
#> effect group term estimate std.error statistic p.value conf.low conf.high
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fixed NA tcl -5.02 0.0750 -66.9 1 e+0 -5.14 -4.89
#> 2 fixed NA tv 0.350 0.0548 6.37 1.09e-9 0.259 0.440
#> 3 ran_pars ID sd__… 0.488 NA NA NA NA NA
#> 4 ran_pars ID sd__… 0.402 NA NA NA NA NA
#> 5 ran_pars ID cor_… 0.946 NA NA NA NA NA
#> 6 ran_pars Resid… add.… 2.76 NA NA NA NA NAThe confidence interval is on the scale specified by
exponentiate, by default the estimated scale.
If you want to have the confidence on the adaptive back-transformed scale, you would simply use the following:
tidy(fit.s, conf.level=0.9, exponentiate=NA)
#> # A tibble: 6 × 9
#> effect group term estimate std.error statistic p.value conf.low conf.high
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fixed NA tcl 0.00663 0.000498 13.3 1.75e-27 0.00586 0.00751
#> 2 fixed NA tv 1.42 0.0778 18.2 4.27e-40 1.30 1.55
#> 3 ran_pars ID sd__… 0.488 NA NA NA NA NA
#> 4 ran_pars ID sd__… 0.402 NA NA NA NA NA
#> 5 ran_pars ID cor_… 0.946 NA NA NA NA NA
#> 6 ran_pars Resi… add.… 2.76 NA NA NA NA NAExtracting other model information with tidy
The type of information that is extracted can be controlled by the
effects argument.
Extracting only fixed effect parameters
The fixed effect parameters can be extracted by
effects="fixed"
tidy(fit.s, effects="fixed")
#> # A tibble: 2 × 6
#> effect term estimate std.error statistic p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 fixed tcl -5.02 0.0750 -66.9 1
#> 2 fixed tv 0.350 0.0548 6.37 0.00000000109Extracting only random parameters
The random standard deviations can be extracted by
effects="ran_pars":
tidy(fit.s, effects="ran_pars")
#> # A tibble: 4 × 4
#> effect group term estimate
#> <chr> <chr> <chr> <dbl>
#> 1 ran_pars ID sd__eta.cl 0.488
#> 2 ran_pars ID sd__eta.v 0.402
#> 3 ran_pars ID cor__eta.v..eta.cl 0.946
#> 4 ran_pars Residual(add) add.err 2.76Extracting random values (also called ETAs)
The random values, or in NONMEM the ETAs, can be extracted by
effects="ran_vals" or effects="random"
head(tidy(fit.s, effects="ran_vals"))
#> # A tibble: 6 × 5
#> effect group level term estimate
#> <chr> <chr> <fct> <fct> <dbl>
#> 1 ran_vals ID 1 eta.cl -0.0854
#> 2 ran_vals ID 2 eta.cl -0.232
#> 3 ran_vals ID 3 eta.cl 0.257
#> 4 ran_vals ID 4 eta.cl -0.522
#> 5 ran_vals ID 5 eta.cl 0.316
#> 6 ran_vals ID 6 eta.cl -0.163This duplicate method of running effects is because the
broom package supports effects="random" while
the broom.mixed package supports
effects="ran_vals".
Extracting random coefficients
Random coefficients are the population fixed effect parameter + the random effect parameter, possibly transformed to the correct scale.
In this case we can extract this information from a nlmixr fit object by:
head(tidy(fit.s, effects="ran_coef"))
#> # A tibble: 6 × 5
#> effect group level term estimate
#> <chr> <chr> <fct> <fct> <dbl>
#> 1 ran_coef ID 1 tcl -5.10
#> 2 ran_coef ID 2 tcl -5.25
#> 3 ran_coef ID 3 tcl -4.76
#> 4 ran_coef ID 4 tcl -5.54
#> 5 ran_coef ID 5 tcl -4.70
#> 6 ran_coef ID 6 tcl -5.18This can also be changed by the exponentiate
argument:
head(tidy(fit.s, effects="ran_coef", exponentiate=NA))
#> # A tibble: 6 × 5
#> effect group level term estimate
#> <chr> <chr> <fct> <fct> <dbl>
#> 1 ran_coef ID 1 tcl 0.00609
#> 2 ran_coef ID 2 tcl 0.00526
#> 3 ran_coef ID 3 tcl 0.00858
#> 4 ran_coef ID 4 tcl 0.00393
#> 5 ran_coef ID 5 tcl 0.00910
#> 6 ran_coef ID 6 tcl 0.00564
head(tidy(fit.s, effects="ran_coef", exponentiate=TRUE))
#> # A tibble: 6 × 5
#> effect group level term estimate
#> <chr> <chr> <fct> <fct> <dbl>
#> 1 ran_coef ID 1 tcl 0.00609
#> 2 ran_coef ID 2 tcl 0.00526
#> 3 ran_coef ID 3 tcl 0.00858
#> 4 ran_coef ID 4 tcl 0.00393
#> 5 ran_coef ID 5 tcl 0.00910
#> 6 ran_coef ID 6 tcl 0.00564