Performs a regression fit for each gene taking all variables present in the model.

```
sc.p.vector(
scmpObj,
p_value = 0.05,
mt_correction = "BH",
min_na = 6,
family = negative.binomial(theta = 10),
epsilon = 1e-08,
verbose = TRUE,
offset = TRUE,
parallel = FALSE,
n_cores = availableCores() - 2,
log_offset = FALSE,
max_it = 100,
link = "log"
)
```

- scmpObj
An object of class

`ScMaSigPro`

.- p_value
Significance level used for variable selection in the stepwise regression.

- mt_correction
A character string specifying the p-value correction method.

- min_na
Minimum values needed per gene across cells to estimate the model.

- family
Distribution of the error term.

- epsilon
Model convergence tolerance.

- verbose
Print detailed output in the console. (Default is TRUE)

- offset
logical value specifying whether to use offset during fitting.

- parallel
Use forking process to run parallelly. (Default is FALSE) (Currently, Windows is not supported)

- n_cores
Explicitly specify the number of cores to use for parallel model fitting. (Default is inferred from the system using availableCores()-2)

- log_offset
A logical value specifying whether to take the logarithm of the offsets.

- max_it
Maximum number of iterations to fit the model.

- link
Type of link function to use in the model. Default is "log".

An object of class `ScMaSigPro`

, with updated `Profile`
slot.

Conesa, A., Nueda M.J., Alberto Ferrer, A., Talon, T. 2006. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics 22, 1096-1102