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##dr4pl
The package dr4pl (Dose Response 4 Parameter Logisitic model) specializes in applying the 4 Parameter Logistic (4PL) model. The 4PL model has been recognized as a major tool to analyze the relationship between a dose and a response in pharmacological experiments. The package dr4pl may be used to model increasing and decreasing curves. The goal of dr4pl is to bring a statistical method which is capable of handeling specific error cases of which other statistical packages produce errors. Examples of Dose Response datasets that will produce errors in other packages may be accessed by name once dr4pl is loaded and these data sets are under the names of drc_error_1, drc_error_2, drc_error_3, and drc_error_4. Along with these error data sets, this package also supplies 13 standard example data sets for the 4PL model under the name sample_data_1, sampel_data_2, etc. The package dr4pl also alows for the user to decide how their theta variable is approximated. The user may choose the default logistic model or use Mead’s Method. Additionally, the user may decide between four loss functions to minimize: Squared, Absolute, Huber, or Tukey’s biweight. Please attempt each of the loss functions and choose the best fit from plotting the dr4pl object.
Installation
You can install dr4pl from github with:
# install.packages("devtools")
devtools::install_bitbucket("dittmerlab/dr4pl")Example
This is a basic example which shows you how to solve a common problem. This example may be used with drc_error_1, drc_error_2, drc_error_3, and drc_error_4:
## basic example code, datasets
## example requires the drc and dr4pl package to be loaded
library(dr4pl)
library(drc)
#> Loading required package: MASS
#>
#> 'drc' has been loaded.
#> Please cite R and 'drc' if used for a publication,
#> for references type 'citation()' and 'citation('drc')'.
#>
#> Attaching package: 'drc'
#> The following objects are masked from 'package:stats':
#>
#> gaussian, getInitial
a <- drc::drm(drc_error_1$Response~drc_error_1$Dose, fct = LL.4())
#> Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
#> non-finite finite-difference value [4]
#> Error in drmOpt(opfct, opdfct1, startVecSc, optMethod, constrained, warnVal, : Convergence failed
plot(a)
#> Error in plot(a): object 'a' not found## basic example code
## example requires the dr4pl package to be loaded
b <- dr4pl(drc_error_1$Response~drc_error_1$Dose, method.init = "logistic", method.robust = "Tukey")
plot(b)
#> Warning: Transformation introduced infinite values in continuous x-axis
#> Warning: Transformation introduced infinite values in continuous x-axissummary(b)
#> Call:
#> dr4pl.formula(formula = drc_error_1$Response ~ drc_error_1$Dose,
#> method.init = "logistic", method.robust = "Tukey")
#>
#> Estimate StdErr 2.5 % 97.5 %
#> UpperLimit 7.9134e+04 6.5396e+01 7.9004e+04 79262.7084
#> Log10(IC50) -1.2371e+01 2.5194e+00 -1.7347e+01 -7.3946
#> Slope -7.3707e-02 2.2747e-02 -1.1864e-01 -0.0288
#> LowerLimit -8.3931e+03 6.5427e+01 -8.5223e+03 -8263.8406Updates
With the release of dr4pl (>= 2.0.0), dr4pl is getting a few quality
improvements as well as more accurate confidence intervals. First and
foremost, prior to dr4pl (>= 2.0.0), dr4pl possessed an error in the
second derivative of the model function with respects to (theta_3)^2.
This has been corrected and a new vignette “dr4pl_derivatives” exists
showing how each derivative was calculated for this package.
The parameters field of the dr4pl object has changed. This field
will now have an object inheriting from dr4pl_param S3 class. This is
likely the biggest change as a lot of internals now dispatch on this
object. The primary function of this object is to track if the theta_2
parameter is in the log10 space. Users were often confused as to which
parameter estimate they were looking at, to remedy this, when printing
the object, it will explicitly tell you the type.
obj <- dr4pl(Response~Dose, sample_data_3)
coef(obj)
#> UpperLimit IC50 Slope LowerLimit
#> 59858.0700194411 5.07949182697285 -0.611737121034004 980.820568608382For backwards compatibility, dr4pl always stores the dr4pl_theta
version of the dr4pl_param object. The user can change the state of
the parameter with the ParmToLog() and LogToParm() functions.
ParmToLog(coef(obj))
#> UpperLimit Log10(IC50) Slope LowerLimit
#> 59858.0700194411 0.705820265870361 -0.611737121034004 980.820568608382dr4pl’s convention was to always report the values in linear space
(even if the Confidence Intervals reflected the log10 parameter), but
this understandably so only increased confusion around how the values
were calculated. To remedy this, various S3 functions that dispatch with
dr4pl have an optional theta argument in which you can force a
calculation on a parameter set. To stay somewhat backwards compatible,
dr4pl will use the log10 space by default since parameters are
always estimated in that space.
summary(obj) #uses log10 space by default
#> Call:
#> dr4pl.formula(formula = Response ~ Dose, data = sample_data_3)
#>
#> Estimate StdErr 2.5 % 97.5 %
#> UpperLimit 5.9858e+04 6.1255e+02 5.8616e+04 61100.3693
#> Log10(IC50) 7.0582e-01 8.1390e-02 5.4075e-01 0.8709
#> Slope -6.1174e-01 6.4982e-02 -7.4353e-01 -0.4799
#> LowerLimit 9.8082e+02 6.2518e+02 -2.8710e+02 2248.7443
summary(obj, theta = coef(obj)) #grab linear space of dr4pl_param
#> Call:
#> dr4pl.formula(formula = Response ~ Dose, data = sample_data_3)
#>
#> Estimate StdErr 2.5 % 97.5 %
#> UpperLimit 5.9858e+04 6.1255e+02 5.8616e+04 61100.3693
#> Log10(IC50) 7.0582e-01 8.1390e-02 5.4075e-01 0.8709
#> Slope -6.1174e-01 6.4982e-02 -7.4353e-01 -0.4799
#> LowerLimit 9.8082e+02 6.2518e+02 -2.8710e+02 2248.7443Since the dr4pl_param object’s displayed names may change, the user is
able to programmatically select elements with theta_1, theta_2,
theta_3, theta_4 or by integer index.
ParmToLog(coef(obj))["theta_2"]
#> theta_2
#> 0.7058203
coef(obj)[2]
#> theta_2
#> 5.079492dr4pl now imports the rlang package to allow for tidy evaluation
when using dr4pl.data.frame. This is not a major dependency as dr4pl
already imports ggplot2 which imports rlang.
dr4pl(sample_data_3, dose = Dose, response = Response)
#> Call:
#> dr4pl.data.frame(data = sample_data_3, dose = Dose, response = Response)
#>
#> Coefficients:
#> UpperLimit IC50 Slope LowerLimit
#> 59858.0700194411 5.07949182697285 -0.611737121034004 980.820568608382
dr4pl(sample_data_3, dose = Dose/100000, response = Response)
#> Call:
#> dr4pl.data.frame(data = sample_data_3, dose = Dose/1e+05, response = Response)
#>
#> Coefficients:
#> UpperLimit IC50 Slope LowerLimit
#> 56553.5168965252 0.000167802698462147 -0.880333688166774 -753.724715235523Prior to dr4pl (>= 2.0.0), users were required to specify a limit for
each parameter even if they wanted to only constrain one. Now the users
can supply a named numeric vector for ease of use.
dr4pl(sample_data_1, dose = Dose, response = Response, lowerl = c(theta_4 = 0)) #make lowerlimit positive
#> Call:
#> dr4pl.data.frame(data = sample_data_1, dose = Dose, response = Response,
#> lowerl = c(theta_4 = 0))
#>
#> Coefficients:
#> UpperLimit IC50 Slope LowerLimit
#> 112247.294407694 17.2973238818617 -0.384948923142149 0.00189050905753972In addition, users will receive a more helpful error message if they
ever attempt to constrain the fit with upperl and lowerl.
dr4pl(Response~Dose, sample_data_4, lowerl = c(theta_4 = 0)) #make lowerlimit positive
#> Error: Initial parameter values are not in the interior of the feasible region.
#> Estimated Parameters:
#> UpperLimit Log10(IC50) Slope LowerLimit
#> 31295.0216027415 4.67986724187866 -8.2357409544985 -57.218
#> Failed Constraints:
#> theta_4 = - 57.218 >= 0You may also pass a named numeric vector to init.parm, but you will
also receive a warning message as init.parm expects a dr4pl_param
object constructed from dr4pl_theta(). This warning will only appear
every 8 hours though.
dr4pl(Response~Dose, sample_data_4, init.parm = c(theta_4 = 0.1),lowerl = c(theta_4 = 0))
#> Warning: A numeric object is being coerced to a "dr4pl_param" object.`theta_2` is assumed to be in linear space. Please use `dr4pl_theta()` to construct the theta parameter.
#> This warning is displayed once every 8 hours.
#> Call:
#> dr4pl.formula(formula = Response ~ Dose, data = sample_data_4,
#> init.parm = c(theta_4 = 0.1), lowerl = c(theta_4 = 0))
#>
#> Coefficients:
#> UpperLimit IC50 Slope LowerLimit
#> 30599.6079444198 1020.20601140621 -8.03521868073897 3716.67380596223dr4pl_theta() function allows the user to specify if the object is in
log10 space or not:
parm_lin <- dr4pl_theta(theta_2 = 2)
parm_log <- dr4pl_theta(theta_2 = 2, isLog10 = T)
parm_lin
#> UpperLimit IC50 Slope LowerLimit
#> NA 2 NA NA
parm_log
#> UpperLimit Log10(IC50) Slope LowerLimit
#> NA 2 NA NA
identical(parm_lin, LogToParm(parm_log))
#> [1] FALSE