replist {qpcR} | R Documentation |
Amalgamation of single data models into a model containing replicates
Description
Starting from a 'modlist' containing qPCR models from single data, replist
amalgamates the models according to the grouping structure as defined in group
. The result is a 'replist' with models obtained from fitting the replicates by pcrfit
. A kinetic outlier detection and removal option is included.
Usage
replist(object, group = NULL, check = "none",
checkPAR = parKOD(), remove = c("none", "KOD"),
names = c("group", "first"), doFit = TRUE, opt = FALSE,
optPAR = list(sig.level = 0.05, crit = "ftest"),
verbose = TRUE, ...)
Arguments
object |
an object of class 'modlist'. |
group |
a vector defining the replicates for each group. |
check |
which method to use for kinetic outlier detection. Either |
checkPAR |
parameters to be supplied to the |
remove |
which runs to remove. Either |
names |
how to name the grouped fit. Either 'group_1, ...' or the first name of the replicates. |
doFit |
logical. If set to |
opt |
logical. Should model selection be applied to the final model? |
optPAR |
parameters to be supplied to |
verbose |
if |
... |
other parameters to be supplied to |
Details
As being defined by group
, the 'modlist' is split into groups of runs and these amalgamated into a nonlinear model. Runs which have failed to be fitted by modlist
are automatically removed and group
is updated (that is, the correpsonding entries also removed) prior to fitting the replicate model by pcrfit
. Model selection can be applied to the final replicate model by setting opt = TRUE
and changing the parameters in optPAR
. If check
is set to any of the methods in "KOD"
, kinetic outliers are identified and optionally removed, if remove
is set to "KOD"
.
If doFit = FALSE
, the replicate data is only aggregated and no refitting is done. This is useful when plotting replicate data by some grouping vector. See 'Examples'.
Value
An object of class 'replist' containing the replicate models of class 'nls'/'pcrfit'.
Author(s)
Andrej-Nikolai Spiess
See Also
Examples
## Convert 'modlist' into 'replist'.
ml1 <- modlist(reps, model = l4)
rl1 <- replist(ml1, group = gl(7, 4))
plot(rl1)
summary(rl1[[1]])
## Optimize model based on Akaike weights.
rl2 <- replist(ml1, group = gl(7, 4), opt = TRUE,
optPARS = list(crit = "weights"))
plot(rl2)
## Not run:
## Remove kinetic outliers,
## use first replicate name for output.
ml3 <- modlist(reps, model = l4)
rl3 <- replist(ml3, group = gl(7, 4), check = "uni1",
remove = "KOD", names = "first")
plot(rl3, which = "single")
## Just aggregation and no refitting.
ml4 <- modlist(reps, model = l4)
rl4 <- replist(ml4, group = gl(7, 4), doFit = FALSE)
plot(rl4, which = "single")
## Scenario 1:
## automatic removal of runs that failed to
## fit during 'modlist' by using 'testdat' set.
ml5 <- modlist(testdat, model = l5)
rl5 <- replist(ml5, gl(6, 4))
plot(rl5, which = "single")
## Scenario 2:
## automatic removal of runs that failed to
## fit during 'replist':
## samples F3.1-F3.4 is set to 1.
dat1 <- reps
ml6 <- modlist(dat1)
ml6[[9]]$DATA[, 2] <- 1
ml6[[10]]$DATA[, 2] <- 1
ml6[[11]]$DATA[, 2] <- 1
ml6[[12]]$DATA[, 2] <- 1
rl6 <- replist(ml6, gl(7, 4))
plot(rl6, which = "single")
## End(Not run)