Plotmodel {cubfits} | R Documentation |
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.
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 X-axis. |
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.
Wei-Chen Chen wccsnow@gmail.com.
https://github.com/snoweye/cubfits/
plotbin()
, prop.bin.roc()
, and
prop.model.roc()
.
## Not run: demo(plotbin, 'cubfits', ask = F, echo = F) ## End(Not run)