Description Usage Arguments Details Value Author(s) References See Also Examples
Plot model results to visualize the effects of mutation and selection along with expression levels. The model can be fitted by MCMC or multinomial logistic regression.
1 2 3 4 5 6 7 8 9  prop.model.roc(b.Init, phi.Obs.lim = c(0.01, 10), phi.Obs.scale = 1,
nclass = 40, x.log10 = TRUE)
plotmodel(ret.model, main = NULL,
xlab = "Production Rate (log10)", ylab = "Proportion",
xlim = NULL, lty = 1, x.log10 = TRUE, ...)
plotaddmodel(ret.model, lty, u.codon = NULL, color = NULL,
x.log10 = TRUE)

b.Init 
a 
phi.Obs.lim 
range of 
phi.Obs.scale 
optional scaling factor. 
nclass 
number of binning classes across the range of 
x.log10 

ret.model 
model results from 
main 
an option passed to 
xlab 
an option passed to 
ylab 
an option passed to 
xlim 
range of Xaxis. 
lty 
line type. 
u.codon 
unique synonymous codon names. 
color 
a color vector for unique codon, typically returns of
the internal function 
... 
options passed to 
The function plotmodel()
plots the fitted curves obtained from
prop.model.roc()
.
The function plotaddmodel()
can append model curves to a binning plot
provided unique synonymous codons and colors are given. This function is
nearly for an internal call within plotmodel()
, but is exported and
useful for workflow.
Currently, only ROC model is supported.
Colors are controlled by .CF.PT
.
A fitted curve plot is drawn.
WeiChen Chen [email protected].
https://github.com/snoweye/cubfits/
plotbin()
, prop.bin.roc()
, and
prop.model.roc()
.
1 2 3 4 
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