Description Usage Arguments Details Value Author(s) See Also Examples
Calculates the best fit BRAID response surface given the concentrations of two drugs and the response of a measured target to the combination of those drugs.
1 2 3 4 5 6 7 8 |
model |
a two-column array containing concentrations of Drug 1 and Drug 2 in each dose pair, or a symbolic formula (e.g.
|
data |
if |
getCIs |
determines if confidence intervals will be calculated for all response surface parameters being fit. Parameters are fit using a bootstrapping approach which resamples residuals and refits the new data. |
fixed |
parameter specifying which parameters of the full BRAID model will be fit. See Details for a full description of this highly critical parameter |
startparv |
an optional parameter specifying starting parameter values for the optimization. The relationship between
|
llims |
a vector of lower limits on parameters being fit. May have length 10, or length equal to the number of
free parameters being fit. Any parameters that do not require a limit can have a value of |
ulims |
a vector of upper limits on parameters being fit. Follows same behavior as |
x |
the object of class "braidrm" to be printed |
object |
the object of class "braidrm" to be summarized |
... |
Not used |
This function is designed to give as much control as is reasonably possible over which parameters are optimized and how the optimization behaves. However, implementing this much control can be quite complicated, and despite our efforts to make the function intuitive and transparent, the interface is still quite unwieldy. A great deal can be accomplished with the function simply using the model nicknames "kappa1" and "kappa2" (which constrain the two maximal effects to be equal or allow them to vary independently, respectively); if these are sufficient for you, feel free to ignore the remainder of this section.
The parameter fixed
is used to control which parameters are fit by the optimization, and in the case of the three maximal
effect parameters, how the parameters are constrained with respect to one another. fixed
may be specified in one of three
forms: a raw vector, an index vector, or a model nickname. There are seven model nicknames; using them is equivalent to using
the corresponding index vector as specified in the following table:
kappa1 | 1,2,3,4,6,7,10 |
kappa2 (default) | 1,2,3,4,6,7,8,9 |
kappa3 | 1,2,3,4,6,7,8,9,10 |
delta1 | 1,2,3,4,5,7,10 |
delta2 | 1,2,3,4,5,7,8,9 |
delta3 | 1,2,3,4,5,7,8,9,10 |
ebraid | 1,2,3,4,5,6,7,8,9,10 |
An index vector specifies which parameters vary in the optimization by listing their indices in the full ten parameter vector; a
raw vector specifies which parameters vary by setting the corresponding values equal to NA
. The remaining values in a raw
vector specify the values at which the fixed parameters are fixed, unless these values are overridden by startparv
startparv
specifies the starting values for the optimization; one can input a vector in startparv
that specifies
only the values of varying parameters, but the remaining values must be specified in a raw vector in fixed
. If a full-length
ten-element vector is provided for starparv
, the values of fixed parameters are taken from that vector, regardless of the
type of input in fixed
.
For parameters one through seven, the presence or absence of each parameter in the optimization is quite simple: either the parameter
is fixed at the specified (or calculated) value, or it varies between the specified (or calculated) optimization limits. Parameters
eight through ten, however, which specify the maximal effect parameters, are more complicated. The complication is introduced by the
fact that the parameters can be fixed or constrained to be equal to one another, which introduce the same number of degrees of
freedom, but very different optimization behaviors. How the different possibilities for fixed
and startparv
influence
the optimization behavior (for as many possibilities as we could think of) is described by the following table. In the table W,
X, Y, and Z represent arbitrary but distinct valid values while ~ represents any valid (non-NA
) value; in
addition, all startparv
vectors are assumed to be complete ten-element vectors (incomplete vectors will simply be extended
with the corresponding values in fixed
).
fixed | startparv | Behavior |
Index: (…,8,9,10) or | Any | All three maximal effect parameters vary independently, |
Raw: (…,NA ,NA ,NA ) | with Ef constrained to be a larger effect than EfA and | |
EfB. | ||
Index: (…,8,9) or | NULL | EfA and EfB vary independently and Ef is |
Raw: (…,NA ,NA ,~) | constrained to vary with the larger effect of EfA and | |
EfB. | ||
Index: (…,8,9) or | (…, X, X, X) or | EfA and EfB vary independently and Ef is |
Raw: (…,NA ,NA ,~) | (…, X, Y, X) or | constrained to vary with the larger effect of EfA and |
(…, Y, X, X) | EfB. | |
Index: (…,8,9) or | (…, X, X, Y) or | Ef is fixed at the value Y, while EfA and EfB vary |
Raw: (…,NA ,NA ,~) | (…, X, Z, Y) | independently and are constrained to be smaller effects |
than Y. | ||
Index: (…,10) or | NULL | All maximal parameters are constrained to vary as a |
Raw: (…,X,X,NA ) | single parameter. | |
Raw: (…,X,Y,NA ) | NULL | If X is larger than Y, Ef and EfA are constrained to |
vary as a single parameter above Y, and EfB is fixed | ||
at Y. Otherwise, the roles of A and B are reversed. | ||
Index: (…,10) or | (…, X, X, X) | All maximal parameters are constrained to vary as a |
Raw: (…,~,~,NA ) | single parameter. | |
Index: (…,10) or | (…, X, Y, X) | Ef and EfA are constrained to vary as a single |
Raw: (…,~,~,NA ) | parameter above Y, and EfB is fixed at Y. | |
Index: (…,10) or | (…, Y, X, X) | Ef and EfB are constrained to vary as a single |
Raw: (…,~,~,NA ) | parameter above Y, and EfA is fixed at Y. | |
Index: (…,10) or | (…, X, Y, Z) | Ef is constrained to vary above the larger effect of X |
Raw: (…,~,~,NA ) | and Y, EfA is fixed at X, and EfB is fixed at Y. | |
Index: (…,8,10) or | NULL or | EfA varies freely, and EfB and Ef are constrained to |
Raw: (…,NA ,~,NA ) | (…, ~, X, X) | vary as a single parameter with a larger effect than EfA. |
Index: (…,8,10) or | (…, ~, X, Y) | EfA varies freely, EfB is fixed at X and Ef varies |
Raw: (…,NA ,~,NA ) | freely constrained to be a larger effect than EfA and | |
EfB. | ||
Index: (…,9,10) or | NULL or | EfB varies freely, and EfA and Ef are constrained to |
Raw: (…,~,NA ,NA ) | (…, X, ~, X) | vary as a single parameter with a larger effect than EfB. |
Index: (…,9,10) or | (…, X, ~, Y) | EfB varies freely, EfA is fixed at X and Ef varies |
Raw: (…,~,NA ,NA ) | freely constrained to be a larger effect than EfA and | |
EfB. | ||
Index: (…,8) or | NULL | EfA varies freely, EfB is fixed at X (or calculated |
Raw: (…,NA ,X,X) | starting value), and Ef is constrained to vary with | |
the larger effect of EfA and EfB. | ||
Raw: (…,NA ,X,Y) | NULL | EfB is fixed at X, Ef is fixed at Y, and EfA varies |
freely constrained to be a smaller effect than Y. | ||
Index: (…,8) or | (…, X, X, X) or | EfA varies freely, EfB is fixed at X, and Ef is |
Raw: (…,NA ,~,~) | (…, Y, X, X) or | constrained to vary with the larger effect of EfA |
(…, Y, X, Y) | and EfB. | |
Index: (…,8) or | (…, X, X, Y) or | EfB is fixed at X, Ef is fixed at Y, and EfA varies |
Raw: (…,NA ,~,~) | (…, Z, X, Y) | freely constrained to be a smaller effect than Y. |
Index: (…,9) or | NULL | EfB varies freely, EfA is fixed at X (or calculated |
Raw: (…,X,NA ,X) | starting value), and Ef is constrained to vary with | |
the larger effect of EfA and EfB. | ||
Raw: (…,X,NA ,Y) | NULL | EfA is fixed at X, Ef is fixed at Y, and EfB varies |
freely constrained to be a smaller effect than Y. | ||
Index: (…,9) or | (…, X, X, X) or | EfB varies freely, EfA is fixed at X, and Ef is |
Raw: (…,~,NA ,~) | (…, X, Y, X) or | constrained to vary with the larger effect of EfA |
(…, X, Y, Y) | and EfB. | |
Index: (…,9) or | (…, X, X, Y) or | EfA is fixed at X, Ef is fixed at Y, and EfB varies |
Raw: (…,~,NA ,~) | (…, X, Z, Y) | freely constrained to be a smaller effect than Y. |
Index: (…) or | Any | All three values are fixed, and do not vary. |
Raw: (…,~,~,~) |
An object of class 'braidrm', including the elements
conc1 |
Concentrations of drug 1 used in the fit |
conc2 |
Concentrations of drug 2 used in the fit |
act |
Reponse to drugs 1 and 2 used in the fit |
fitted.values |
Value of best-fit response surface at concentrations of drugs 1 and 2 used in the fit |
residuals |
Difference between actual measurements and fitted values |
ostart |
The full 10-parameter starting parameter vector used in the optimization |
fixed |
Vector describing which parameters were fit |
mlims |
Array containing upper and lower parameter bounds used in the optimization |
coefficients |
Estimates of fitted parameters |
fullpar |
Full parameter vector including best-fit parameters |
convergence |
From |
message |
From |
call |
Function call |
If getCIs
is TRUE, then the following elements are also included
ciPass |
TRUE if a sufficient proportion of bootstrapping trials successfully converged, FALSE otherwise |
ciLevs |
Two-element vector specifying the lower and upper percentiles of the desired confidence interval. Defaults to 0.025 and 0.975 for a 95% confidence interval. |
ciVec |
Assuming 'ciPass' is true, a vector containing the lower and upper bounds on the confidence intervals of all free parameters |
bCoefs |
A matrix containing the best-fit parameter values from all bootstrapping trials. Useful for calculating confidence intervals on other values, such as EC99 |
Nathaniel R. Twarog
getBRAIDbootstrap
, calcBRAIDconfint
, evalBRAIDrsm
1 2 3 4 5 6 7 8 9 | data(es8olatmz)
summary(braidrm(act~conc1+conc2,es8olatmz,getCIs=FALSE))
## Not run:
summary(braidrm(cbind(es8olatmz$conc1,es8olatmz$conc2),es8olatmz$act))
summary(braidrm(act~conc1+conc2,es8olatmz,fixed="delta2"))
summary(braidrm(act~conc1+conc2,es8olatmz,fixed=c(1,2,3,4,6,8,9)))
summary(braidrm(act~conc1+conc2,es8olatmz,llims=c(NA,NA,NA,NA,NA,NA,NA,-4,-4,-4)))
## End(Not run)
|
Call:
braidrm.formula(model = act ~ conc1 + conc2, data = es8olatmz,
getCIs = FALSE)
IDMA IDMB na nb kappa E0 EfA EfB
0.0000 0.0001 1.8572 2.1936 1.4999 0.1816 -3.7797 -4.0421
Call:
braidrm.default(model = cbind(es8olatmz$conc1, es8olatmz$conc2),
data = es8olatmz$act)
CILow Estimate CIHigh
IDMA 0.0000 0.0000 0.0000
IDMB 0.0001 0.0001 0.0001
na 1.5458 1.8572 2.3039
nb 1.7110 2.1936 2.9862
kappa 0.9124 1.4999 2.1218
E0 0.0954 0.1816 0.2935
EfA -3.9374 -3.7797 -3.6419
EfB -4.3564 -4.0421 -3.6850
Call:
braidrm.formula(model = act ~ conc1 + conc2, data = es8olatmz,
fixed = "delta2")
CILow Estimate CIHigh
IDMA 0.0000 0.0000 0.0000
IDMB 0.0001 0.0001 0.0001
na 1.5551 1.8619 2.3194
nb 1.8210 2.1713 2.9499
delta 1.4569 1.7724 2.0102
E0 0.0997 0.1833 0.2708
EfA -3.8999 -3.7755 -3.6285
EfB -4.2803 -4.0593 -3.8036
Call:
braidrm.formula(model = act ~ conc1 + conc2, data = es8olatmz,
fixed = c(1, 2, 3, 4, 6, 8, 9))
CILow Estimate CIHigh
IDMA 0.0000 0.0000 0.0000
IDMB 0.0001 0.0001 0.0001
na 1.2934 1.4745 1.8022
nb 1.2953 1.4541 1.9077
kappa 0.6223 1.2279 1.8653
EfA -3.8478 -3.8043 -3.5308
EfB -4.5125 -4.3354 -3.9095
Call:
braidrm.formula(model = act ~ conc1 + conc2, data = es8olatmz,
llims = c(NA, NA, NA, NA, NA, NA, NA, -4, -4, -4))
CILow Estimate CIHigh
IDMA 0.0000 0.0000 0.0000
IDMB 0.0001 0.0001 0.0001
na 1.5561 1.8436 2.2402
nb 1.8193 2.2038 3.6143
kappa 0.7326 1.4500 1.9969
E0 0.1033 0.1822 0.2630
EfA -3.9355 -3.7769 -3.6205
EfB -4.0000 -4.0000 -3.3197
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