Package 'pmxTools'

A transform for ggplot2 with data that may be below the lower limit of quantification

Description

If the lloq is not provided, it will be estimated from the data as the minimum value above zero.

Usage

blq_trans(lloq, x, multiplier = 0.5, lloq_text)

blq_log_trans(lloq, x, multiplier = 0.5, base = 10, lloq_text)

Arguments

lloq	The value of the lower limit of quantification as a numeric scalar
x	(only used if lloq is missing), the data for lloq estimation.
multiplier	When data are < lloq, they are replaced by lloq*multiplier for display.
lloq_text	The text to use on the axis to indicate values < lloq. It will be automatically set to paste0("<", lloq) if missing.
base	The base for the logarithm

Value

A "trans" object based on the scales package for BLQ data.

Functions

• blq_log_trans(): Log-scale transformation with BLQ

See Also

Other BLQ Transformation: breaks_blq_general(), estimate_lloq(), ftrans_blq_linear(), itrans_blq_linear(), label_blq()

Examples

## Not run: 
library(ggplot2)
ggplot(data=data.frame(x=1:10, y=1:10), aes(x=x, y=y)) +
  geom_point()

ggplot(data=data.frame(x=1:10, y=1:10), aes(x=x, y=y)) +
  geom_point() +
  scale_x_continuous(trans=blq_trans(lloq=3))

ggplot(data=data.frame(x=1:10, y=1:10), aes(x=x, y=y)) +
  geom_point() +
  scale_x_continuous(trans=blq_log10_trans(lloq=3))

## End(Not run)

---

Generate breaks for measurements below the limit of quantification

Description

Breaks that are < lloq are removed. If the lowest break is removed if it is too close to the lloq.

Usage

breaks_blq_general(lloq, breakfun, trans = identity, ...)

Arguments

lloq	The value of the lower limit of quantification as a numeric scalar
breakfun	The function used for normal scale breaks if the lloq were not present.
trans	A parameter translation function (typically either identity for linear scale or log for log scale).
...	passed as breakfun(n=n, ...)

Details

For ggplot2 scales. This is not usually used directly. See blq_trans() and blq_log10_trans() for the functions that are more commonly used.

Value

A function for calculating breaks with arguments x and n

See Also

Other BLQ Transformation: blq_trans(), estimate_lloq(), ftrans_blq_linear(), itrans_blq_linear(), label_blq()

Examples

breaks_blq_general(lloq=3, breakfun=scales::breaks_extended)(1:100, n=5)

---

Calculate derived pharmacokinetic parameters for a 1-, 2-, or 3-compartment linear model.

Description

Calculate derived pharmacokinetic parameters for a 1-, 2-, or 3-compartment linear model.

Usage

calc_derived(..., verbose = FALSE)

calc_derived_1cpt(
  CL,
  V = NULL,
  V1 = NULL,
  ka = NULL,
  dur = NULL,
  tlag = NULL,
  tinf = NULL,
  dose = NULL,
  tau = NULL,
  step = 0.1,
  type = "all",
  sigdig = 5
)

calc_derived_2cpt(
  CL,
  V1 = NULL,
  V2,
  Q2 = NULL,
  V = NULL,
  Q = NULL,
  dur = NULL,
  tinf = NULL,
  ka = NULL,
  tlag = NULL,
  dose = NULL,
  tau = NULL,
  step = 0.1,
  type = "all",
  sigdig = 5
)

calc_derived_3cpt(
  CL,
  V1 = NULL,
  V2,
  V3,
  Q2 = NULL,
  Q3,
  V = NULL,
  Q = NULL,
  ka = NULL,
  dur = NULL,
  tinf = NULL,
  tlag = NULL,
  dose = NULL,
  tau = NULL,
  step = 0.1,
  type = "all",
  sigdig = 5
)

Arguments

...	Passed to the other calc_derived_*() functions.
verbose	For calc_derived(), provide a message indicating the type of model detected.
CL	Clearance (volume per time units, e.g. L/h)
V1, V	Central volume of distribution (volume units, e.g. L). Values are synonyms; use only one.
ka	Absorption rate (inverse time units, e.g. 1/h)
dur	Duration of zero-order absorption (time units, e.g. h)
tlag	Absorption lag time (time units, e.g. h)
tinf	Duration of infusion (time units, e.g. h)
dose	Dose (amount units, e.g. mg)
tau	Duration of interdose interval (time units, e.g. h; defaults to 24)
step	Time increment to use when estimating NCA parameters (time units, e.g. h; defaults to 0.1)
type	Parameters to return. Default is "all". If not "all", this may be a vector from the names of the return value list.
sigdig	Number of significant digits to be returned. Default is 5.
V2	First peripheral volume of distribution (volume units, e.g. L)
Q2, Q	Intercompartmental clearance from central to first peripheral compartment (volume per time units, e.g. L/h). Values are synonyms; use only one.
V3	Second peripheral volume of distribution (volume units, e.g. L)
Q3	Intercompartmental clearance from central to second peripheral compartment (volume per time units, e.g. L/h)

Value

Return a list of derived PK parameters for a 1-, 2-, or 3-compartment linear model given provided clearances and volumes based on the type. If a dose is provided, estimated non-compartmental analysis (NCA) parameters will be provided as well, based on simulation of single-dose and (if 'tau' is specified) steady-state time courses.

• Vss: Volume of distribution at steady state, $V_{ss}$ (volume units, e.g. L); 1-, 2-, and 3-compartment

• k10: First-order elimination rate, $k_{10}$ (inverse time units, e.g. 1/h); 1-, 2-, and 3-compartment

• k12: First-order rate of transfer from central to first peripheral compartment, $k_{12}$ (inverse time units, e.g. 1/h); 2- and 3-compartment

• k21: First-order rate of transfer from first peripheral to central compartment, $k_{21}$ (inverse time units, e.g. 1/h); 2- and 3-compartment

• k13: First-order rate of transfer from central to second peripheral compartment, $k_{13}$ (inverse time units, e.g. 1/h); 3-compartment

• k31: First-order rate of transfer from second peripheral to central compartment,$k_{31}$ (inverse time units, e.g. 1/h); 3-compartment

• thalf_alpha: $t_{1/2,\alpha}$ (time units, e.g. h); 1-, 2-, and 3-compartment

• thalf_beta: $t_{1/2,\beta}$ (time units, e.g. h); 2- and 3-compartment

• thalf_gamma: $t_{1/2,\gamma}$ (time units, e.g. h); 3-compartment

• alpha: $\alpha$; 1-, 2-, and 3-compartment

• beta: $\beta$; 2- and 3-compartment

• gamma: $\beta$; 3-compartment

• trueA: true A; 1-, 2-, and 3-compartment

• trueB: true B; 2- and 3-compartment

• trueC: true C; 3-compartment

• fracA: fractional A; 1-, 2-, and 3-compartment

• fracB: fractional B; 2- and 3-compartment

• fracC: fractional C; 3-compartment

• AUCinf: Area under the concentration-time curve to infinity (single dose)

• AUCtau: Area under the concentration-time curve over the dosing interval at steady state

• Cmax: Maximum concentration after a single dose

• Cmaxss: Maximum concentration over the dosing interval at steady state

• Tmax: Time after dose of maximum concentration

• AUCinf_dose_normalized: Dose-normalized area under the concentration-time curve to infinity (single dose)

• AUCtau_dose_normalized: Dose-normalized area under the concentration-time curve over the dosing interval at steady state

• Cmax_dose_normalized: Dose-normalized maximum concentration after a single dose

• Cmaxss_dose_normalized: Dose-normalized maximum concentration over the dosing interval at steady state

• step: Time increment used when estimating NCA parameters.

The input parameters with standardized names (dose, V1, V2, V3, CL, Q2, and Q3) are also returned in the list, and if provided, additional PK parameters of 'ka', 'tlag', 'tinf' and 'dur' are also returned in the list. All inputs may be scalars or vectors.

Author(s)

Justin Wilkins, [email protected]

Bill Denney, [email protected]

References

Shafer S. L. CONVERT.XLS

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

params <- calc_derived(CL=29.4, V1=23.4, V2=114, V3=4614, Q2=270, Q3=73)
params <- calc_derived_1cpt(CL=16, V=25)
params <- calc_derived_2cpt(CL=16, V1=25, V2=50, Q=0.5)
params <- calc_derived_3cpt(CL=29.4, V1=23.4, V2=114, V3=4614, Q2=270, Q3=73)

---

Calculate C(t) for a 1-compartment linear model

Description

Calculate C(t) for a 1-compartment linear model

Usage

calc_sd_1cmt(t, dose, dur = NULL, tinf = NULL, ...)

calc_sd_1cmt_linear_bolus(t, dose, ...)

calc_sd_1cmt_linear_oral_1_lag(t, dose, ...)

calc_sd_1cmt_linear_infusion(t, dose, tinf, ...)

calc_sd_1cmt_linear_oral_0(t, dose, dur, ...)

calc_sd_1cmt_linear_oral_1(t, dose, ...)

calc_sd_1cmt_linear_oral_0_lag(t, dose, dur, ...)

Arguments

t	Time after dose (h)
dose	Dose
dur	Duration of zero-order absorption (h)
tinf	Duration of infusion (h)
...	Passed to 'calc_derived_1cpt()'

Value

Concentration of drug at requested time (t) after a single dose, given provided set of parameters and variables.

Functions

• calc_sd_1cmt_linear_bolus(): Calculate C(t) for a 1-compartment linear model after a single IV bolus dose

• calc_sd_1cmt_linear_oral_1_lag(): Calculate C(t) for a 1-compartment linear model with first-order absorption after a single oral dose, with lag time

• calc_sd_1cmt_linear_infusion(): Calculate C(t) for a 1-compartment linear model after a single IV infusion

• calc_sd_1cmt_linear_oral_0(): Calculate C(t) for a 1-compartment linear model with zero-order absorption after a single oral dose

• calc_sd_1cmt_linear_oral_1(): Calculate C(t) for a 1-compartment linear model with first-order absorption after a single oral dose

• calc_sd_1cmt_linear_oral_0_lag(): Calculate C(t) for a 1-compartment linear model with zero-order absorption after a single oral dose, with lag time

Author(s)

Justin Wilkins, [email protected]

Bill Denney, [email protected]

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_sd_1cmt_linear_bolus(t=0:24, CL=6, V=25, dose=600)
Ct <- calc_sd_1cmt_linear_oral_1_lag(t=0:24, CL=6, V=25, ka=1.1, dose=600, tlag=2)
Ct <- calc_sd_1cmt_linear_infusion(t=0:24, CL=6, V=25, dose=600, tinf=1)
Ct <- calc_sd_1cmt_linear_oral_0(t=0:24, CL=6, V=25, dur=1.5, dose=600)
Ct <- calc_sd_1cmt_linear_oral_1(t=0:24, CL=6, V=25, ka=1.1, dose=600)
Ct <- calc_sd_1cmt_linear_oral_0_lag(t=0:24, CL=6, V=25, dur=1.5, dose=600, tlag=1.5)

---

Calculate C(t) for a 2-compartment linear model

Description

Calculate C(t) for a 2-compartment linear model

Usage

calc_sd_2cmt(t, dose, dur = NULL, tinf = NULL, ...)

calc_sd_2cmt_linear_bolus(t, dose, ...)

calc_sd_2cmt_linear_oral_1_lag(t, dose, ...)

calc_sd_2cmt_linear_infusion(t, dose, tinf, ...)

calc_sd_2cmt_linear_oral_0_lag(t, dose, dur, ...)

calc_sd_2cmt_linear_oral_1(t, dose, ...)

calc_sd_2cmt_linear_oral_0(t, dose, dur, ...)

Arguments

t	Time after dose (h)
dose	Dose
dur	Duration of zero-order absorption (h)
tinf	Duration of infusion (h)
...	Passed to 'calc_derived_2cpt()'

Value

Concentration of drug at requested time (t) after a single dose, given provided set of parameters and variables.

Functions

• calc_sd_2cmt_linear_bolus(): Calculate C(t) for a 2-compartment linear model after a single IV bolus dose

• calc_sd_2cmt_linear_oral_1_lag(): Calculate C(t) for a 2-compartment linear model after a single first-order oral dose with a lag time

• calc_sd_2cmt_linear_infusion(): Calculate C(t) for a 2-compartment linear model after a single infusion

• calc_sd_2cmt_linear_oral_0_lag(): Calculate C(t) for a 2-compartment linear model after a single zero-order oral dose, with lag time

• calc_sd_2cmt_linear_oral_1(): Calculate C(t) for a 2-compartment linear model after a single first-order oral dose

• calc_sd_2cmt_linear_oral_0(): Calculate C(t) for a 2-compartment linear model after a single zero-order oral dose

Author(s)

Justin Wilkins, [email protected]

Bill Denney, [email protected]

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_sd_2cmt_linear_bolus(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5, dose = 10)
Ct <- calc_sd_2cmt_linear_oral_1_lag(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, ka = 1, tlag = 2)
Ctrough <- calc_sd_2cmt_linear_infusion(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 10, tinf = 1)
Ctrough <- calc_sd_2cmt_linear_oral_0_lag(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, dur = 1, tlag=2)
Ct <- calc_sd_2cmt_linear_oral_1(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, ka = 1)
Ct <- calc_sd_2cmt_linear_oral_0(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, dur = 1)

---

Calculate C(t) for a 3-compartment linear model

Description

Calculate C(t) for a 3-compartment linear model

Usage

calc_sd_3cmt(t, dose, dur = NULL, tinf = NULL, ...)

calc_sd_3cmt_linear_bolus(t, dose, ...)

calc_sd_3cmt_linear_oral_1_lag(t, dose, ...)

calc_sd_3cmt_linear_infusion(t, dose, tinf, ...)

calc_sd_3cmt_linear_oral_0(t, dose, dur, ...)

calc_sd_3cmt_linear_oral_0_lag(t, dose, dur, ...)

calc_sd_3cmt_linear_oral_1(t, dose, ...)

Arguments

t	Time after dose (h)
dose	Dose
dur	Duration of zero-order absorption (h)
tinf	Duration of infusion (h)
...	Passed to 'calc_derived_3cpt()'

Value

Concentration of drug at requested time (t) after a single dose, given provided set of parameters and variables.

Functions

• calc_sd_3cmt_linear_bolus(): Calculate C(t) for a 3-compartment linear model after a single IV bolus dose

• calc_sd_3cmt_linear_oral_1_lag(): Calculate C(t) for a 3-compartment linear model after a single oral dose

• calc_sd_3cmt_linear_infusion(): Calculate C(t) for a 3-compartment linear model after a single IV infusion

• calc_sd_3cmt_linear_oral_0(): Calculate C(t) for a 3-compartment linear model after a single dose, with zero-order absorption

• calc_sd_3cmt_linear_oral_0_lag(): Calculate C(t) for a 3-compartment linear model after a single dose, with zero-order absorption and a lag time

• calc_sd_3cmt_linear_oral_1(): Calculate C(t) for a 3-compartment linear model after a single oral dose

Author(s)

Justin Wilkins, [email protected]

Bill Denney, [email protected]

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_sd_3cmt_linear_bolus(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dose = 100)
Ct <- calc_sd_3cmt_linear_oral_1_lag(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, ka = 1, dose = 100, tlag = 1.5)
Ct <- calc_sd_3cmt_linear_infusion(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dose = 100, tinf=1)
Ct <- calc_sd_3cmt_linear_oral_0(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dur = 1, dose = 100)
Ct <- calc_sd_3cmt_linear_oral_0_lag(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dur = 1, dose = 100, tlag=1.5)
Ct <- calc_sd_3cmt_linear_oral_1(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, ka = 1, dose = 100)

---

Calculate C(t) for a 1-compartment linear model at steady-state

Description

Calculate C(t) for a 1-compartment linear model at steady-state

Usage

calc_ss_1cmt(tad, tau, dose, dur = NULL, tinf = NULL, ...)

calc_ss_1cmt_linear_bolus(tad, tau, dose, ...)

calc_ss_1cmt_linear_infusion(tad, tau, dose, tinf, ...)

calc_ss_1cmt_linear_oral_0(tad, tau, dose, dur, ...)

calc_ss_1cmt_linear_oral_0_lag(tad, tau, dose, dur, ...)

calc_ss_1cmt_linear_oral_1_lag(tad, tau, dose, ...)

calc_ss_1cmt_linear_oral_1(tad, tau, dose, ...)

Arguments

tad	Time after dose (h)
tau	Dosing interval (h)
dose	Dose
dur	Duration of zero-order absorption (h)
tinf	Duration of infusion (h)
...	Passed to 'calc_derived_1cpt()'

Value

Concentration of drug at requested time (t) after a single dose, given provided set of parameters and variables.

Functions

• calc_ss_1cmt_linear_bolus(): Calculate C(t) for a 1-compartment linear model with IV bolus dosing at steady state

• calc_ss_1cmt_linear_infusion(): Calculate C(t) for a 1-compartment linear model with infusion at steady state

• calc_ss_1cmt_linear_oral_0(): Calculate C(t) for a 1-compartment linear model with zero-order oral absorption at steady state

• calc_ss_1cmt_linear_oral_0_lag(): Calculate C(t) for a 1-compartment linear model with zero-order oral absorption at steady state, with lag time

• calc_ss_1cmt_linear_oral_1_lag(): Calculate C(t) for a 1-compartment linear model with first-order oral absorption at steady state, with lag time

• calc_ss_1cmt_linear_oral_1(): Calculate C(t) for a 1-compartment linear model with first-order oral absorption at steady state

Author(s)

Justin Wilkins, [email protected]

Bill Denney, [email protected]

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_ss_1cmt_linear_bolus(t=0:24, CL=6, V=25, dose=600, tau=24)
Ct <- calc_ss_1cmt_linear_infusion(tad=0:36, CL=2, V=25, dose=600, tinf=1, tau=24)
Ct <- calc_ss_1cmt_linear_oral_0(tad=0:36, CL=2, V=25, dose=600, dur=1, tau=24)
Ct <- calc_ss_1cmt_linear_oral_0_lag(tad=0:36, CL=2, V=25, dose=600, dur=1, tau=24, tlag=1.5)
Ct <- calc_ss_1cmt_linear_oral_1_lag(tad=0:36, CL=2, V=25, dose=600,
    ka=0.25, tlag=0.75, tau=24)
Ct <- calc_ss_1cmt_linear_oral_1(tad=0:36, CL=2, V=25, dose=600, ka=0.25, tau=24)

---

Calculate C(t) for a 2-compartment linear model at steady-state

Description

Calculate C(t) for a 2-compartment linear model at steady-state

Usage

calc_ss_2cmt(tad, tau, dose, dur = NULL, tinf = NULL, ...)

calc_ss_2cmt_linear_bolus(tad, tau, dose, ...)

calc_ss_2cmt_linear_infusion(tad, tau, dose, tinf, ...)

calc_ss_2cmt_linear_oral_0(tad, tau, dose, dur, ...)

calc_ss_2cmt_linear_oral_1_lag(tad, tau, dose, ...)

calc_ss_2cmt_linear_oral_0_lag(tad, tau, dose, dur, ...)

calc_ss_2cmt_linear_oral_1(tad, tau, dose, ...)

Arguments

tad	Time after dose (h)
tau	Dosing interval (h)
dose	Dose
dur	Duration of zero-order absorption (h)
tinf	Duration of infusion (h)
...	Passed to 'calc_derived_2cpt()'

Value

Concentration of drug at requested time (t) at steady-state, given provided set of parameters and variables.

Functions

• calc_ss_2cmt_linear_bolus(): Calculate C(t) for a 2-compartment linear model with IV bolus dosing at steady-state

• calc_ss_2cmt_linear_infusion(): Calculate C(t) for a 2-compartment linear model with infusion at steady state

• calc_ss_2cmt_linear_oral_0(): Calculate C(t) for a 2-compartment linear model at steady-state with zero-order oral dosing

• calc_ss_2cmt_linear_oral_1_lag(): Calculate C(t) for a 2-compartment linear model at steady-state with first-order oral dosing

• calc_ss_2cmt_linear_oral_0_lag(): Calculate C(t) for a 2-compartment linear model at steady-state with zero-order oral dosing and a lag time

• calc_ss_2cmt_linear_oral_1(): Calculate C(t) for a 2-compartment linear model at steady-state with first-order oral dosing

Author(s)

Justin Wilkins, [email protected]

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_ss_2cmt_linear_bolus(tad = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 10, tau=24)
Ct <- calc_ss_2cmt_linear_infusion(tad = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 10, tinf = 1, tau = 12)
Ct <- calc_ss_2cmt_linear_oral_0(tad = 23, CL = 2.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, dur = 1, tau = 24)
Ct <- calc_ss_2cmt_linear_oral_1_lag(tad = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, ka = 1, tau=24, tlag=2)
Ct <- calc_ss_2cmt_linear_oral_0_lag(tad = 23, CL = 2.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, dur = 1, tau = 24, tlag=2)
Ct <- calc_ss_2cmt_linear_oral_1(tad = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, ka = 1, tau=24)

---

Calculate C(t) for a 3-compartment linear model at steady-state

Description

Calculate C(t) for a 3-compartment linear model at steady-state

Usage

calc_ss_3cmt(tad, tau, dose, dur = NULL, tinf = NULL, ...)

calc_ss_3cmt_linear_bolus(tad, tau, dose, ...)

calc_ss_3cmt_linear_oral_1_lag(tad, tau, dose, ...)

calc_ss_3cmt_linear_infusion(tad, tau, dose, tinf, ...)

calc_ss_3cmt_linear_oral_0(tad, tau, dose, dur, ...)

calc_ss_3cmt_linear_oral_0_lag(tad, tau, dose, dur, ...)

calc_ss_3cmt_linear_oral_1(tad, tau, dose, ...)

Arguments

tad	Time after dose (h)
tau	Dosing interval (h)
dose	Dose
dur	Duration of zero-order absorption (h)
tinf	Duration of infusion (h)
...	Passed to 'calc_derived_3cpt()'

Value

Concentration of drug at requested time (t) at steady-state, given provided set of parameters and variables.

Functions

• calc_ss_3cmt_linear_bolus(): Calculate C(t) for a 3-compartment linear model at steady state with IV bolus dosing

• calc_ss_3cmt_linear_oral_1_lag(): Calculate C(t) for a 3-compartment linear model at steady-state with first-order oral dosing with a lag time

• calc_ss_3cmt_linear_infusion(): Calculate C(t) for a 3-compartment linear model at steady state with IV infusions

• calc_ss_3cmt_linear_oral_0(): Calculate C(t) for a 3-compartment linear model at steady state, with zero-order absorption

• calc_ss_3cmt_linear_oral_0_lag(): Calculate C(t) for a 3-compartment linear model at steady state, with zero-order absorption and lag time

• calc_ss_3cmt_linear_oral_1(): Calculate C(t) for a 3-compartment linear model at steady-state with first-order oral dosing

Author(s)

Justin Wilkins, [email protected]

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_ss_3cmt_linear_bolus(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dose = 100, tau=24)
Ctrough <- calc_ss_3cmt_linear_oral_1_lag(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, ka = 1, dose = 100, tau=24, tlag = 1.5)
Ct <- calc_ss_3cmt_linear_infusion(tad = 11.75, CL = 2.5, V1 = 20, V2 = 50,
    V3 = 100, Q2 = 0.5, Q3 = 0.05, dose = 1000, tinf=1, tau=24)
Ct <- calc_ss_3cmt_linear_oral_0(tad = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dur = 1, dose = 100, tau = 24)
Ct <- calc_ss_3cmt_linear_oral_0_lag(tad = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dur = 1, dose = 100, tau = 24, tlag = 1.5)
Ct <- calc_ss_3cmt_linear_oral_1(tad = 11.75, CL = 3.5, V1 = 20,
    V2 = 500, V3 = 200, Q2 = 0.5, Q3 = 0.05, ka = 1, dose = 100, tau = 24)

---

Count the number of NA values in a vector.

Description

Count the number of NA values in a vector.

Usage

count_na(x)

Arguments

x

A vector.

Value

An integer containing the number of NA values in the input vector.

Author(s)

Justin Wilkins, [email protected]

Examples

## Not run: 
 count_na(c(0,5,7,NA,3,3,NA))

## End(Not run)

---

Generate a summary table of descriptive data for every individual in a dataset suitable for tabulation in a report.

Description

Generate a summary table of descriptive data for every individual in a dataset suitable for tabulation in a report.

Usage

dgr_table(
  dat,
  fields,
  names,
  cutoff = 7,
  sig = 3,
  by = NULL,
  idvar = "ID",
  navars = c("-99", "-999")
)

Arguments

dat	An input data frame, with one row per unique individual.
fields	A vector of strings containing the names of the fields to be included in the summary table.
names	A vector of strings containing descriptive names for the fields to be included in the summary table.
cutoff	An integer defining the maximum number of unique values a variable should have to be considered categorical. Fields with more than this number of unique values are considered continuous for the purposes of the summary table (defaults to 7).
sig	The number of significant digits summary values should have (defaults to 3).
by	The field to use for grouping (a string). If not NULL (the default), the summary table will contain columns for each unique value of this field, as well as a column summarizing across all fields.
idvar	The field in the dataset identifying each unique individual (defaults to "ID").
navars	A vector containing values that are to be interpreted as missing (defaults to "-99" and "-999"). 'NA' values are always considered to be missing.

Value

A data frame containing a summary of all the fields listed in fields, for each individual in the dataset (the dataset should not contain duplicated individuals), conditioned on the field in by. Continuous values are summarized as median, mean, range and number of missing values. Categorical values are summarized as count and relative percentage.

Author(s)

Justin Wilkins, [email protected]

Examples

## Not run: 
 count_na(c(0,5,7,NA,3,3,NA))

## End(Not run)

---

Estimate the lower limit of quantification (LLOQ) from a vector

Description

Nonnegative values are considered to be above the LLOQ. NA values are ignored.

Usage

estimate_lloq(x)

Arguments

x	The numeric vector to use for estimation of the LLOQ

Value

The lowest, nonzero value from x. If all are NA or zero, 1 is returned, and a warning is issued.

See Also

Other BLQ Transformation: blq_trans(), breaks_blq_general(), ftrans_blq_linear(), itrans_blq_linear(), label_blq()

Examples

estimate_lloq(c(NA, 0, 2, 5))

---

Format a number with the correct number of significant digits and trailing zeroes.

Description

Format a number with the correct number of significant digits and trailing zeroes.

Usage

fmt_signif(x, digits = 3)

Arguments

x	A vector of numeric values.
digits	The number of significant digits values should have (defaults to 3).

Value

A string containing the properly-formatted number.

Author(s)

Justin Wilkins, [email protected]

Examples

## Not run: 
 fmt_signif(c(36.44, 0.0002, 3336.7), digits=3)

## End(Not run)

---

Forward transformation for linear BLQ data

Description

For ggplot2 scales.

Usage

ftrans_blq_linear(lloq, multiplier)

ftrans_blq_log(lloq, multiplier, base = 10)

Arguments

lloq	The value of the lower limit of quantification as a numeric scalar
multiplier	When data are < lloq, they are replaced by lloq*multiplier for display.
base	The base for the logarithm

Value

A function of x that replaces x < lloq with lloq*multiplier

Functions

• ftrans_blq_log(): Log-scale transformation

See Also

Other BLQ Transformation: blq_trans(), breaks_blq_general(), estimate_lloq(), itrans_blq_linear(), label_blq()

---

Calculate a geometric coefficient of variation.

Description

Calculate a geometric coefficient of variation.

Usage

gcv(x, na.rm = F, neg.rm = F)

Arguments

x	A vector.
na.rm	Flag for removing NA values (defaults to FALSE).
neg.rm	Flag for removing negative or zero values (defaults to FALSE).

Value

The geometric coefficient of variation of the input vector. If neg.rm is FALSE and values <= 0 are present, NA will be returned.

Author(s)

Justin Wilkins, [email protected]

Examples

## Not run: 
 gcv(myvector)

## End(Not run)

---

Convert geometric variance or standard deviation to a geometric coefficient of variation

Description

The equation used is: 100*sqrt(exp(gvar)-1)

Usage

gcv_convert(gvar = gsd^2, gsd)

Arguments

gvar	The geometric variance (note that this is the variance not a vector of values to compute the gcv from)
gsd	The geometric standard deviation

Value

Geometric coefficient of variation

Author(s)

Bill Denney

References

http://onbiostatistics.blogspot.com/2008/07/geometric-statistics-geometric-cv-vs.html

Examples

gcv_convert(0.2)
gcv_convert(gsd=0.2)

---

Calculate the area under the curve (AUC) for each subject over the time interval for dependent variables (dv) using the trapezoidal rule.

Description

Calculate the area under the curve (AUC) for each subject over the time interval for dependent variables (dv) using the trapezoidal rule.

Usage

get_auc(data, time = "TIME", id = "ID", dv = "DV")

Arguments

data	A data frame.
time	A string containing the name of the chronologically ordered time variable in data.
id	A string containing the name of the ID column (defining subject level data) in data.
dv	A string containing the name of the dependent variable column in data.

Value

A data frame containing one AUC value for every subject as defined by id.

Based on the AUC function originally written by Leonid Gibiansky in package MIfuns 5.1, from Metrum Institute.

Author(s)

Leonid Gibiansky, [email protected]

References

https://code.google.com/archive/p/mifuns/

Examples

## Not run: 
 AUCs <- get_auc(myAUCdata)

## End(Not run)

---

Create a table of model parameter estimates from a NONMEM output object.

Description

Create a table of model parameter estimates from a NONMEM output object.

Usage

get_est_table(
  x,
  thetaLabels = c(),
  omegaLabels = c(),
  sigmaLabels = c(),
  sigdig = 3
)

Arguments

x	A NONMEM output object generated using read_nm.
thetaLabels	A vector containing labels for THETA parameters.
omegaLabels	A vector containing labels for OMEGA parameters.
sigmaLabels	A vector containing labels for SIGMA parameters.
sigdig	The desired number of significant digits to display.

Value

A named vector of NONMEM model parameter estimates.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput <- read_nm("run315.xml")
 estTab   <- get_est_table(nmOutput)

## End(Not run)

---

Extract variability parameter estimates from a NONMEM output object.

Description

Extract variability parameter estimates from a NONMEM output object.

Usage

get_omega(x, output = "est", sigdig = 6, sep = "-", est.step = NULL)

Arguments

x	A NONMEM output object generated using read_nm.
output	A flag specifying the matrix or matrices to be output. Valid flag values are est (the default), se, rse, cor, cse, 95ci, or all.
sigdig	Specifies the number of significant digits to be provided (default=6).
sep	Specifies the separator character to use for 95% confidence intervals (default="-").
est.step	Specifies which estimation step to return parameters from (default is the last).

Value

A symmetrical matrix, or a list of symmetrical matrices if all is specified.

Author(s)

Justin Wilkins, [email protected]

est returns the estimated OMEGA variance-covariance matrix. se returns the standard errors for the estimated OMEGA variance-covariance matrix. rse returns the relative standard errors for the estimated OMEGA variance-covariance matrix (se/est*100). cor returns the correlation matrix matrix. cse returns the standard errors for the correlation matrix. 95ci returns the asymptotic 95% confidence intervals for the elements of the OMEGA variance-covariance matrix (est +/- 1.96*se). all returns all available OMEGA matrices.

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput  <- read_nm("run315.xml")
 omegas    <- get_omega(nmOutput)
 omegaRSEs <- get_omega(nmOutput, "rse")

## End(Not run)

---

Extract problem and estimation information from a NONMEM output object.

Description

Extract problem and estimation information from a NONMEM output object.

Usage

get_probinfo(x, sigdig = 6, est.step = NULL)

Arguments

x	A NONMEM output object generated using read_nm.
sigdig	Specifies the number of significant digits to be provided (default=6).
est.step	Specifies which estimation step to return parameters from (default is the last).

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput <- read_nm("run315.xml")
 probInfo <- get_probinfo(nmOutput)

## End(Not run)

---

Extract shrinkage estimates from a NONMEM output object.

Description

Extract shrinkage estimates from a NONMEM output object.

Usage

get_shrinkage(x, output = "eta", type = "sd", sigdig = 3, est.step = NULL)

Arguments

x	A NONMEM output object generated using read_nm.
output	A flag specifying the shrinkage estimates to be output. Valid flag values are eta (the default), epsilon, or all.
type	Specifies the type of shrinkage to report. Valid values are sd (standard deviation, the default) or vr (variance, if present in the XML output).
sigdig	Specifies the number of significant digits to be provided (default=3).
est.step	Specifies which estimation step to return parameters from (default is the last).

Value

A named vector of NONMEM shrinkage estimates, or in the case of all, a list of named vectors.

eta returns a vector of ETA shrinkages, as reported by NONMEM. epsilon returns EPSILON shrinkage, as reported by NONMEM. all returns both ETA and EPSILON shrinkage estimates as a list of vectors.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput <- read_nm("run315.xml")
 shr <- get_shrinkage(nmOutput, output="all")

## End(Not run)

---

Extract residual variability parameter estimates from a NONMEM output object.

Description

Extract residual variability parameter estimates from a NONMEM output object.

Usage

get_sigma(x, output = "est", sigdig = 6, sep = "-", est.step = NULL)

Arguments

x	A NONMEM output object generated using read_nm.
output	A flag specifying the matrix or matrices to be output. Valid flag values are est (the default), se, rse, cor, cse, 95ci, or all.
sigdig	Specifies the number of significant digits to be provided (default=6).
sep	Specifies the separator character to use for 95% confidence intervals (default="-").
est.step	Specifies which estimation step to return parameters from (default is the last).

Value

A symmetrical matrix, or a list of symmetrical matrices if all is specified.

est returns the estimated SIGMA variance-covariance matrix. se returns the standard errors for the estimated SIGMA variance-covariance matrix. rse returns the relative standard errors for the estimated SIGMA variance-covariance matrix (se/est*100). cor returns the correlation matrix matrix. cse returns the standard errors for the correlation matrix. 95ci returns the asymptotic 95% confidence intervals for the elements of the SIGMA variance-covariance matrix (est +/- 1.96*se). all returns all available SIGMA matrices.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput <- read_nm("run315.xml")
 sigmas   <- get_sigma(nmOutput)
 sigmaRSEs <- get_sigma(nmOutput, "rse")

## End(Not run)

---

Extract structural model parameter estimates and associated information from a NONMEM output object.

Description

Extract structural model parameter estimates and associated information from a NONMEM output object.

Usage

get_theta(x, output = "est", sigdig = 6, sep = "-", est.step = NULL)

Arguments

x	A NONMEM output object generated using read_nm.
output	A flag specifying the matrix or matrices to be output. Valid flag values are est (the default), se, rse, 95ci, or all.
sigdig	Specifies the number of significant digits to be provided (default=6).
sep	Specifies the separator character to use for 95% confidence intervals (default="-").
est.step	Specifies which estimation step to return parameters from (default is the last).

Value

A named vector of NONMEM model parameter estimates, or in the case of all, a list of named vectors.

est returns a vector of THETA values. se returns a vector of THETA standard errors. rse returns a vector of THETA relative standard errors (se/est*100). 95ci returns a vector of the asymptotic 95% confidence intervals for the elements of THETA (est +/- 1.96*se). all returns all available THETA information as a list of named vectors.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput <- read_nm("run315.xml")
 thetas <- get_theta(nmOutput)

## End(Not run)

---

Calculate geometric mean

Description

Calculate geometric mean

Usage

gm(x)

Arguments

x	Numeric vector.

Value

The geometric mean. NA is returned if there are any non-positive elements in x.

Author(s)

Justin Wilkins, [email protected]

Examples

gm(c(0.5, 7, 8, 5))

---

Inverse transformation for linear BLQ data

Description

For ggplot2 scales.

Usage

itrans_blq_linear(lloq)

itrans_blq_log(lloq, base)

Arguments

lloq	The value of the lower limit of quantification as a numeric scalar
base	The base for the logarithm

Value

A function of x that replaces x < lloq with lloq

Functions

• itrans_blq_log(): Log-scale inverse transform

See Also

Other BLQ Transformation: blq_trans(), breaks_blq_general(), estimate_lloq(), ftrans_blq_linear(), label_blq()

---

Label axes with censoring labels for BLQ

Description

For ggplot2 scales.

Usage

label_blq(lloq, lloq_text)

Arguments

lloq	The value of the lower limit of quantification as a numeric scalar
lloq_text	The text to use on the axis to indicate values < lloq. It will be automatically set to paste0("<", lloq) if missing.

Value

A function of x which returns the formatted values.

See Also

Other BLQ Transformation: blq_trans(), breaks_blq_general(), estimate_lloq(), ftrans_blq_linear(), itrans_blq_linear()

---

Calculate percentage coefficient of variation

Description

Calculate percentage coefficient of variation

Usage

pcv(x, na.rm = FALSE)

Arguments

x	Numeric vector.
na.rm	A logical value indicating whether NA values should be stripped before the computation proceeds.

Value

The percentage coefficient of variation.

Author(s)

Justin Wilkins, [email protected]

Examples

pcv(rnorm(50, 5, 7.56))

---

Provide concentration-time curves.

Description

Provide concentration-time curves.

Usage

pk_curve(
  t,
  model = "1cmt_oral",
  params = list(ka = 2.77, CL = 2.5, V = 25),
  dose = 600,
  ii = 24,
  addl = 0,
  ss = F
)

Arguments

t	Observation time in h, specified as a vector.
model	The model to use. Must be one of "1cmt_bolus", "1cmt_infusion", "1cmt_oral", "2cmt_bolus", "2cmt_infusion", "2cmt_oral", "3cmt_bolus", "3cmt_infusion", "3cmt_oral". The default is "1cmt_oral".
params	A named list containing parameter values for the selected model type.
dose	Dose amount.
ii	Interdose interval (or tau), in hours (default 24).
addl	Number of additional doses (default 0).
ss	Assume steady state concentration (default FALSE).

Value

A data frame containing times (t) and concentrations (cp).

Author(s)

Justin Wilkins, [email protected]

Examples

plot(pk_curve(t=seq(0,72,by=0.1), model="3cmt_oral", ii=12, addl=5,
  params=list(CL=2.5, V1=25, V2=2, V3=5, Q2=0.5, Q3=0.25, ka=1)), type="l")

---

Plot a distribution as a hybrid containing a halfeye, a boxplot and jittered points.

Description

Plot a distribution as a hybrid containing a halfeye, a boxplot and jittered points.

Usage

plot_dist(
  dat,
  yvar,
  xvar = NULL,
  ylim = NULL,
  xlb = "",
  ylb = "",
  identity_line = FALSE,
  identity_value = 0,
  he_adjust = 0.5,
  he_width = 0.6,
  he_justification = -0.2,
  he_col = "black",
  he_fill = "#F8766D",
  he_alpha = 0.9,
  he_slab_type = "pdf",
  he_breaks = "Sturges",
  he_outline_bars = FALSE,
  he_point_interval = "median_qi",
  bxp_width = 0.12,
  bxp_outlier_col = NA,
  bxp_outlier_fill = NA,
  bxp_outlier_shape = 19,
  bxp_outlier_size = 1.5,
  bxp_col = "black",
  bxp_fill = "#F8766D",
  bxp_alpha = 0.9,
  bxp_notch = FALSE,
  bxp_notchwidth = 0.5,
  hp_range_scale = 0.4,
  hp_alpha = 0.25,
  hp_col = "#F8766D",
  hp_transformation = position_jitter(),
  na.rm = FALSE
)

Arguments

dat	A data frame.
yvar	The name of the field containing values to be plotted.
xvar	The name of the field containing the grouping variable (defaults to 'NULL').
ylim	Limits for the y-axis. Defaults to NULL. If provided, should be a 2-element vector containing the upper and lower limits.
xlb	Label for the x-axis.
ylb	Label for the y-axis.
identity_line	Show a line of identity? Default FALSE.
identity_value	If an identity line is shown, it will be drawn horizontally at this y-value (default 0).
he_adjust	If he_slab_type is "pdf", bandwidth for the density estimator is adjusted by multiplying it by this value.
he_width	Width of the halfeye component of the plot (default 0.6).
he_justification	Justification of the halfeye component of the plot (default -0.2).
he_col	Color for the halfeye component of the plot.
he_fill	Fill color for the halfeye component of the plot.
he_alpha	Alpha for the halfeye component of the plot (default 0.9).
he_slab_type	The type of slab function to calculate for the halfeye component of the plot: probability density (or mass) function ("pdf", the default), cumulative distribution function ("cdf"), complementary CDF ("ccdf") or histogram ("histogram").
he_breaks	If slab_type is "histogram", the breaks parameter that is passed to hist() to determine where to put breaks in the histogram.
he_outline_bars	If slab_type is "histogram", determines if outlines in between the bars are drawn when the slab_color aesthetic is used. If FALSE (the default), the outline is drawn only along the tops of the bars; if TRUE, outlines in between bars are also drawn.
he_point_interval	A function from the ggdist::point_interval family (e.g., median_qi, mean_qi, mode_hdi, etc), or a string giving the name of a function from that family (e.g., "median_qi", "mean_qi", "mode_hdi", etc. This function determines the point summary (typically mean, median, or mode) and interval type (quantile interval, qi; highest-density interval, hdi; or highest-density continuous interval, hdci). Output will be converted to the appropriate x- or y-based aesthetics depending on the value of orientation.
bxp_width	Width of the boxplot component (default 0.12).
bxp_outlier_col	Color for outliers in the boxplot component.
bxp_outlier_fill	Fill color for outliers in the boxplot component.
bxp_outlier_shape	Shape for outliers in the boxplot component.
bxp_outlier_size	Size for outliers in the boxplot component.
bxp_col	Color for the boxplot component.
bxp_fill	Fill color for the boxplot component.
bxp_alpha	Alpha for the boxplot component.
bxp_notch	If FALSE (default) make a standard box plot. If TRUE, make a notched box plot. Notches are used to compare groups; if the notches of two boxes do not overlap, this suggests that the medians are significantly different.
bxp_notchwidth	For a notched box plot, width of the notch relative to the body (default 0.5).
hp_range_scale	If no 'width' argument is specified in hp_transformation, used to determine the width of the jitter. Defaults to 0.75, which is half of the allotted space for the jitter-points, whereas 1 would use all of the alotted space.
hp_alpha	Alpha for the jitter.
hp_col	Color for the jitter.
hp_transformation	An evaluated position_*() function yielding a 'Position' object with specified parameters to calculate the transformation of the points. Defaults to ggplot2::position_jitter.
na.rm	If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

Value

A plot containing jittered points, a boxplot and a density plot or histogram illustrating the distribution of every group of the data under evaluation.

Author(s)

Justin Wilkins, [email protected]

Examples

## Not run: 
 plot_dist(dat, "ETA1", identity_line = T, he_slab_type = "histogram", he_breaks = 30)

## End(Not run)

---

Plot NONMEM parameter estimation by iteration.

Description

plot_nmprogress returns a plot or set of plots showing the evolution of parameter estimates by iteration.

Usage

plot_nmprogress(
  fileName,
  fileExt = ".lst",
  metric = "perc",
  lineCol = "#902C10",
  idlineCol = "black"
)

Arguments

fileName	A NONMEM output file prefix, without extension (e.g. 'run315').
fileExt	The file extension for NONMEM output, set to '.lst' by default.
metric	What to show in the plot. Allowed options are 'est' (the actual estimate) or 'perc' (the percentage change in the estimated or OFV since estimation began). Default is 'perc'.
lineCol	Line color. Default is '#902C10'.
idlineCol	Identity line color (only used if 'perc' metric is selected). Default is black.

Value

A set of plots.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
plot_nmprogress("run315")
plot_nmprogress("run315", ".nmlst")

## End(Not run)

---

Visualize PsN SCM output.

Description

plot_scm returns a visualization of a Perl-speaks-NONMEM (PsN, https://uupharmacometrics.github.io/PsN/) SCM (stepwise covariate modeling) procedure. It depends on the presence of scmlog.txt and short_scmlog.txt files in the specified directory.

Usage

plot_scm(
  dir,
  startPhase = "forward",
  fwdSuccessCol = "#66C2A5",
  fwdFailCol = "black",
  bwdSuccessCol = "#FC8D62",
  bwdFailCol = "black",
  defCol = "black",
  fwdSuccessFillCol = "#B3E2CD",
  fwdFailFillCol = "white",
  bwdSuccessFillCol = "#FDCDAC",
  bwdFailFillCol = "white",
  defFillCol = "white",
  fwdSuccessFontCol = "black",
  fwdFailFontCol = "black",
  bwdSuccessFontCol = "black",
  bwdFailFontCol = "black",
  defFontCol = "black",
  fullFwdCol = "#8DA0CB",
  finalCol = "#E78AC3",
  fullFwdFillCol = "#CBD5E8",
  finalFillCol = "#F4CAE4",
  fullFwdFontCol = "black",
  finalFontCol = "black",
  fullFwdWidth = "2px",
  finalWidth = "2px",
  defWidth = "1px",
  nodeStyle = "filled,rounded",
  nodeShape = "box",
  fontname = "helvetica",
  rankdir = "TB",
  layout = "dot",
  lookupDF = NULL,
  ...
)

Arguments

dir	A PsN SCM folder (containing scmlog.txt and short_scmlog.txt).
startPhase	Where to start collating the output; can be "forward" (the default) or "backward".
fwdSuccessCol	Node outline color for a model fit matching the forward inclusion criterion.
fwdFailCol	Node outline color for a model fit not matching the forward inclusion criterion.
bwdSuccessCol	Node outline color for a model fit matching the backward elimination criterion.
bwdFailCol	Node outline color for a model fit not matching the backward elimination criterion.
defCol	Default node outline color.
fwdSuccessFillCol	Node fill color for a model fit matching the forward inclusion criterion.
fwdFailFillCol	Node fill color for a model fit not matching the forward inclusion criterion.
bwdSuccessFillCol	Node fill color for a model fit matching the backward elimination criterion.
bwdFailFillCol	Node fill color for a model fit not matching the backward elimination criterion.
defFillCol	Default node fill color.
fwdSuccessFontCol	Node font color for a model fit matching the forward inclusion criterion.
fwdFailFontCol	Node font color for a model fit not matching the forward inclusion criterion.
bwdSuccessFontCol	Node font color for a model fit matching the backward elimination criterion.
bwdFailFontCol	Node font color for a model fit not matching the backward elimination criterion.
defFontCol	Default node font color.
fullFwdCol	Node outline color for the full forward model (i.e. the final model before the backward elimination procedure in SCM).
finalCol	Node outline color for the final reduced model (i.e. the final model reached after the backward elimination procedure in SCM).
fullFwdFillCol	Node fill color for the full forward model (i.e. the final model before the backward elimination procedure in SCM).
finalFillCol	Node fill color for the final reduced model (i.e. the final model reached after the backward elimination procedure in SCM).
fullFwdFontCol	Node font color for the full forward model (i.e. the final model before the backward elimination procedure in SCM).
finalFontCol	Node font color for the final reduced model (i.e. the final model reached after the backward elimination procedure in SCM).
fullFwdWidth	Node outline width for the full forward model (i.e. the final model before the backward elimination procedure in SCM).
finalWidth	Node outline width for the final reduced model (i.e. the final model reached after the backward elimination procedure in SCM).
defWidth	Default node outline width.
nodeStyle	Node style. A string containing a comma-separated list of options (which include "filled", "striped", "wedged", "diagonals" and "rounded"). See the GraphViz documentation for further details.
nodeShape	Node shape. Options include "box" (the default), "oval", "diamond", "egg", "plaintext", "point", "square", "triangle" and many more. See the GraphViz documentation for further details.
fontname	Font for nodes. Options depend heavily on the local system - see the GraphViz documentation for further details.
rankdir	Direction of graph layout. Possible values are "TB" (the default), "LR", "BT", "RL", corresponding to directed graphs drawn from top to bottom, from left to right, from bottom to top, and from right to left, respectively.
layout	Graph layout. Possible values are "dot" (the default), "neato", "twopi", and "circo". Note that of these, "dot" is the easiest to interpret and the others may produce odd results.
lookupDF	A data frame containing a lookup table for node labels. By default, plot_scm will use the PSN model names. If a lookup table containing the fields 'Model' and 'Alias' is provided, model names in 'Model' will be replaced in the output plots by mtaching labels in 'Alias'.
...	Additional parameters passed to the underlying SetNodeStyle and SetEdgeStyle functions, which in turn rely on DiagrammeR.

Details

This function parses PsN SCM output and displays it as a GraphViz graph (effectively, an HTML widget). It is built on plot.Node - please refer to doucmentation for this function for a more detailed overview of what is possible (a lot). For more specific details, see http://rich-iannone.github.io/DiagrammeR/docs.html.

Value

A grViz object.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

GraphViz (https://graphviz.org/Documentation.php)

Lindbom L, Ribbing J & Jonsson EN (2004). Perl-speaks-NONMEM (PsN) - A Perl module for NONMEM related programming. Computer Methods and Programs in Biomedicine, 75(2), 85-94. doi:10.1016/j.cmpb.2003.11.003

Lindbom L, Pihlgren P & Jonsson N (2005). PsN-Toolkit - A collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Computer Methods and Programs in Biomedicine, 79(3), 241-257. doi:10.1016/j.cmpb.2005.04.005

Other NONMEM reading: read_nm_all(), read_nm_multi_table(), read_nmcov(), read_nmext(), read_nmtables(), read_nm(), read_scm()

Examples

## Not run: 
scm <- plot_scm("E:/DrugX/ModelDevelopment/scm310")

## End(Not run)

---

Read NONMEM 7.2+ output into a list of lists.

Description

Read NONMEM 7.2+ output into a list of lists.

Usage

read_nm(fileName, directory = NULL, quiet = FALSE, ...)

Arguments

fileName	A NONMEM XML output file (e.g. "run315.xml").
directory	The directory to look for files within. If NULL, uses the current directory.
quiet	Flag for displaying intermediate output.
...	Passed to each of the read functions (ignored in the functions).

Value

A list of lists corresponding to a NONMEM output object.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Other NONMEM reading: plot_scm(), read_nm_all(), read_nm_multi_table(), read_nmcov(), read_nmext(), read_nmtables(), read_scm()

Examples

## Not run: 
nmOutput <- read_nm("run315.xml")

## End(Not run)

---

Read all NONMEM files for a single NONMEM run.

Description

Read all NONMEM files for a single NONMEM run.

Usage

read_nm_all(runNo, run_prefix = "run", directory = NULL, quiet = FALSE, ...)

Arguments

runNo	Run number.
run_prefix	The start to the name of the run.
directory	The directory to look for files within. If NULL, uses the current directory.
quiet	Flag for displaying intermediate output.
...	Passed to each of the read functions (ignored in the functions).

Details

The filename for loading is constructed as paste(run_prefix, runNo). To load a nonstandard file, simply set one of those values to NULL.

See Also

Other NONMEM reading: plot_scm(), read_nm_multi_table(), read_nmcov(), read_nmext(), read_nmtables(), read_nm(), read_scm()

---

Read (single or) multiple NONMEM tables from a single file

Description

Read (single or) multiple NONMEM tables from a single file

Usage

read_nm_multi_table(
  fileName,
  header = TRUE,
  ...,
  simplify = TRUE,
  table_start_pattern = "^TABLE NO"
)

Arguments

fileName	The filename to read from
header, ...	Arguments passed to read.table
simplify	If a single table is present, return a data.frame instead of a list of data.frames?
table_start_pattern	What should be found to start a new table?

Value

A list of data.frames, or if only one is present and simplify=TRUE, a data.frame.

Author(s)

Bill Denney

See Also

Other NONMEM reading: plot_scm(), read_nm_all(), read_nmcov(), read_nmext(), read_nmtables(), read_nm(), read_scm()

Examples

## Not run: 
read_nm_multi_table("run1.cov", row.names=1)

## End(Not run)

---

Read a standard NONMEM extension file

Description

Read a standard NONMEM extension file

Usage

read_nm_std_ext(fileName, extension, directory = NULL, ...)

Arguments

fileName	The filename (with directory name, if applicable) to read (with or without the extension)
extension	The file extension to optionally append (preferably starting with a ".")
directory	The directory to look for files within. If NULL, uses the current directory.
...	Passed to read_nm_multi_table()

Value

NULL if the file does not exist or the value of read_nm_multi_table() if it does exist.

Examples

## Not run: 
read_nm_std_ext("run1", "phi")

## End(Not run)

---

Read in the NONMEM variance-covariance matrix.

Description

Read in the NONMEM variance-covariance matrix.

Usage

read_nmcov(fileName, quiet = FALSE, directory = NULL, ...)

Arguments

fileName	Root filename for the NONMEM run (e.g. "run315"). This function reads the ".cov" NONMEM output table, and will return an error if this is missing.
quiet	Flag for displaying intermediate output.
directory	The directory to look for files within. If NULL, uses the current directory.
...	Passed to each of the read functions (ignored in the functions).

Value

A symmetrical variance-covariance matrix covering all model parameters.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Other NONMEM reading: plot_scm(), read_nm_all(), read_nm_multi_table(), read_nmext(), read_nmtables(), read_nm(), read_scm()

Examples

## Not run: 
nmVcov <- read_nmcov("run315")

## End(Not run)

---

Read NONMEM output into a list.

Description

read_nmext returns a summary of a given NONMEM run, including termination messages, parameter estimates, and precision estimates. Minimally, the NONMEM output and '.ext' files must be available.

Usage

read_nmext(
  fileName,
  fileExt = ".lst",
  directory = NULL,
  quiet = FALSE,
  estNo = NULL,
  ...
)

Arguments

fileName	A NONMEM output file prefix, without extension (e.g. "run315").
fileExt	The file extension for NONMEM output, set to ".lst" by default.
directory	The directory to look for files within. If NULL, uses the current directory.
quiet	Flag for displaying intermediate output.
estNo	The estimation number to report (by default, if only one estimation step is present, that will be reported; if multiple are reported, the last will be reported by default).
...	Passed to each of the read functions (ignored in the functions).

Value

A list of lists, containing 'Termination' (summary of NONMEM's termination output, including shrinkages and ETABAR estimates), 'OFV' (the objective function value), 'Thetas' (a vector of structural parameter estimates, or THETAs), 'Omega', a list of lists containing the OMEGA matrix, 'Sigma', a list of lists containing the SIGMA matrix, 'seThetas', a vector of standard errors for THETAs, 'seOmega', a list of lists containing standard errors for the OMEGA matrix, and 'seSigma', a list of lists containing standard errors for the SIGMA matrix.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Other NONMEM reading: plot_scm(), read_nm_all(), read_nm_multi_table(), read_nmcov(), read_nmtables(), read_nm(), read_scm()

Examples

## Not run: 
read_nmext("run315")
read_nmext("run315", ".nmlst")

## End(Not run)

---

Reads NONMEM output tables.

Description

Reads NONMEM output tables.

Usage

read_nmtables(
  tableFiles = NULL,
  runNo = NULL,
  tabSuffix = "",
  tableNames = c("sdtab", "mutab", "patab", "catab", "cotab", "mytab", "extra", "xptab"),
  quiet = FALSE,
  directory = NULL,
  output_type = c("data.frame", "list"),
  ...
)

Arguments

tableFiles	NONMEM table files to be read.
runNo	Run number.
tabSuffix	Table file suffix.
tableNames	List of root table names, using the Xpose naming convention as the default.
quiet	Flag for displaying intermediate output.
directory	The directory to look for files within. If NULL, uses the current directory.
output_type	Should output be a "data.frame" where all results are merged or a "list" of data.frames.
...	Passed to each of the read functions (ignored in the functions).

Value

A data.frame or list of data.frames depending on the output_type argument.

Note

Adapted from Xpose 4 (https://CRAN.R-project.org/package=xpose4).

Author(s)

Bill Denney, Justin Wilkins, Niclas Jonsson, Andrew Hooker

References

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Jonsson EN, Karlsson MO. Xpose–an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput Methods Programs Biomed. 1999 Jan;58(1):51-64

See Also

Other NONMEM reading: plot_scm(), read_nm_all(), read_nm_multi_table(), read_nmcov(), read_nmext(), read_nm(), read_scm()

Examples

## Not run: 
tables <- read_nmtables(runNo=315)

## End(Not run)

---

Read PsN SCM output into a format suitable for further use.

Description

read_scm returns a summary of a Perl-speaks-NONMEM (PsN, https://uupharmacometrics.github.io/PsN/) SCM (stepwise covariate modeling) procedure. It depends on the presence of scmlog.txt and short_scmlog.txt files in the specified directory.

Usage

read_scm(dir, startPhase = "forward")

Arguments

dir	A PsN SCM folder (containing scmlog.txt and short_scmlog.txt).
startPhase	Where to start collating the output; can be "forward" (the default) or "backward".

Value

A list of data frames, containing

forward	all models evaluated during the forward inclusion step of covariate model building
forwardSummary	the covariate relationships selected at each forward step
forwardP	the P-value used for inclusion during the forward inclusion step
backward	all models evaluated during the backward elimination step of covariate model building
backwardSummary	the covariate relationships eliminated at each backward step
backwardP	the P-value used for exclusion during the backward elimination step

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Lindbom L, Ribbing J & Jonsson EN (2004). Perl-speaks-NONMEM (PsN) - A Perl module for NONMEM related programming. Computer Methods and Programs in Biomedicine, 75(2), 85-94. doi:10.1016/j.cmpb.2003.11.003

Lindbom L, Pihlgren P & Jonsson N (2005). PsN-Toolkit - A collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Computer Methods and Programs in Biomedicine, 79(3), 241-257. doi:10.1016/j.cmpb.2005.04.005

Other NONMEM reading: plot_scm(), read_nm_all(), read_nm_multi_table(), read_nmcov(), read_nmext(), read_nmtables(), read_nm()

Examples

## Not run: 
scm <- read_scm("E:/DrugX/ModelDevelopment/scm310")

## End(Not run)

---

Read NONMEM 7.2+ output into an R object.

Description

Read NONMEM 7.2+ output into an R object.

Usage

rnm(
  index,
  prefix = "run",
  pathNM,
  ndig = 3,
  ndigB = 3,
  ndigP = 1,
  Pci = 95,
  ext = ".lst",
  extmod = ".mod",
  Pvalues = TRUE,
  RawCI = FALSE,
  ...
)

Arguments

index	The NONMEM model index, i.e. the numeric part of the filename assuming it follows the convention 'run123.mod'.
prefix	The NONMEM model prefix, assuming it follows the convention 'run123.mod'. The default is "run".
pathNM	The path to the NONMEM output. This should not contain a trailing slash.
ndig	Number of significant digits to use. The default is 3.
ndigB	Number of significant digits to use. The default is 3.
ndigP	Number of digits after the decimal point to use for percentages. The default is 1.
Pci	Asymptotic confidence interval to apply when reporting parameter uncertainty. The default is 95.
ext	NONMEM output file extension. The default is ".lst".
extmod	NONMEM control stream file extension. The default is "mod".
Pvalues	Report P-values for parameters? The default is TRUE.
RawCI	Report confidence intervals without estimate? The default is FALSE.
...	Additional arguments.

Details

The output list is composed of the following objects:

• "Theta"A data frame describing the structural (fixed-effect) parameters, containing parameter name, estimated value, standard error (SE), coefficient of variation (CV), lower and upper confidence limits (CIL and CIU, based on Pci), and P-value, calculated as 2 * (1 - pnorm(abs(theta/theta.se))).

• "Eta"A data frame describing the interindividual random-effects parameters, containing estimated value, standard error (SE), coefficient of variation (CV, calculated as abs(100*(SE/OMEGA))), coefficient of variation (EtaCV, calculated as 100*sqrt(OMEGA)), and shrinkage.

• "Epsilon"A data frame describing the residual random-effects parameters, containing estimated value, standard error (SE), coefficient of variation (CV, calculated as abs(100*(SE/OMEGA))), coefficient of variation (EtaCV, calculated as 100*sqrt(SIGMA)), and shrinkage.

• "CorTheta"A data frame containing the correlation matrix for fixed effects ("THETA").

• "CorOmega"A data frame containing the correlation matrix for interindividual random effects ("OMEGA").

• "CorSigma"A data frame containing the correlation matrix for residual random effects ("OMEGA").

• "OmegaMatrix"A data frame containing the "OMEGA" matrix.

• "SigmaMatrix"A data frame containing the "OMEGA" matrix.

• "CovMatrixTheta"A data frame containing the variance-covariance matrix for structural parameters (THETA).

• "CovMatrix"A data frame containing the complete variance-covariance matrix.

• "OFV"The objective function value.

• "ThetaString"A data frame containing all relevant fixed-effects parameter information, suitable for use in a table of parameter estimates. Contains parameter name, estimate, standard error, coefficient of variation, combined estimate and asymptotic confidence interval, and P-value.

• "EtaString"A data frame containing all relevant interindivudal random-effects parameter information, suitable for use in a table of parameter estimates. Contains parameter name, estimate (variance), standard error, coefficient of variation, percentage value (calculated as 100*sqrt(OMEGA)), and shrinkage.

• "EpsString"A data frame containing all relevant residual random-effects parameter information, suitable for use in a table of parameter estimates. Contains parameter name, estimate (variance), standard error, coefficient of variation, percentage value (calculated as 100*sqrt(SIGMA)), and shrinkage.

• "RunTime"Run time.

• "ConditionN"Condition number.

Value

A list containing information extracted from the NONMEM output.

Author(s)

Rik Schoemaker, [email protected]

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
nmOutput <- rnm("run315.lst")

## End(Not run)

---

Sample from the multivariate normal distribution using the OMEGA variance-covariance matrix to generate new sets of simulated ETAs from NONMEM output.

Description

Sample from the multivariate normal distribution using the OMEGA variance-covariance matrix to generate new sets of simulated ETAs from NONMEM output.

Usage

sample_omega(nmRun, n, seed)

Arguments

nmRun	Root filename for the NONMEM run (e.g. "run315").
n	Number of samples required.
seed	Random seed.

Value

A data frame containing n samples from the multivariate normal distribution, using the estimated NONMEM OMEGA variance-covariance matrix. This provides n sets of ETA estimates suitable for simulation of new patients.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 omDist <- sample_omega("run315", 5000, seed=740727)

## End(Not run)

---

Sample from the multivariate normal distribution using the SIGMA variance-covariance matrix to generate new sets of simulated EPSILONs from NONMEM output.

Description

Sample from the multivariate normal distribution using the SIGMA variance-covariance matrix to generate new sets of simulated EPSILONs from NONMEM output.

Usage

sample_sigma(nmRun, n, seed)

Arguments

nmRun	Root filename for the NONMEM run (e.g. "run315").
n	Number of samples required.
seed	Random seed.

Value

A data frame containing n samples from the multivariate normal distribution, using the estimated NONMEM SIGMA variance-covariance matrix. This provides n sets of EPSILON estimates suitable for simulation of new datasets.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 sigDist <- sample_sigma("run315", 5000, seed=740727)

## End(Not run)

---

Sample from the multivariate normal distribution to generate new sets of parameters from NONMEM output.

Description

Sample from the multivariate normal distribution to generate new sets of parameters from NONMEM output.

Usage

sample_uncert(nmRun, n, seed)

Arguments

nmRun	Root filename for the NONMEM run (e.g. "run315.xml").
n	Number of samples required.
seed	Random seed.

Value

A data frame containing n samples from the multivariate normal distribution, using NONMEM typical parameter estimates the NONMEM variance-covariance matrix (from the *.cov file). This provides n sets of parameter estimates sampled from the uncertainty distribution, suitable for simulation under model uncertainty.

Author(s)

Justin Wilkins, [email protected]

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmMatrix <- sample_uncert("run315.xml", 5000, seed=740727)

## End(Not run)

---

Read NONMEM output into a list.

Description

table_rtf generates an RTF table from a data frame.

Usage

table_rtf(
  df,
  outFile = NULL,
  rtfFile = TRUE,
  boldHeader = TRUE,
  rowNames = FALSE,
  ...
)

Arguments

df	A data frame.
outFile	A filename for writing the table to. If NULL, writes to console.
rtfFile	If TRUE (the default), then add RTF tabs to generate a fully formatted RTF file.
boldHeader	If TRUE, make the header bold.
rowNames	If TRUE, include row names in the table. Default is FALSE.
...	Other formatting options for the table body.

Value

An RTF table based on the data frame provided.

Author(s)

John Johnson, [email protected]

References

https://www.r-bloggers.com/2008/10/another-solution-to-the-r-to-word-table-problem/

Examples

## Not run: 
scm <- read_scm("E:/DrugX/ModelDevelopment/scm310")
myRTF <- table_rtf(scm$forwardSummary)

## End(Not run)

---