two.ways.stepback {maSigPro} | R Documentation

## Fitting a linear model by backward-stepwise regression

### Description

`two.ways.stepback` fits a linear regression model applying backward-stepwise strategy.

### Usage

    two.ways.stepback(y = y, d = d, alfa = 0.05, family = gaussian(), epsilon=0.00001)

### Arguments

`y` | dependent variable
`d` | data frame containing by columns the set of variables that could be in the selected model
`alfa` | significance level to decide if a variable stays or not in the model
`family` | the distribution function to be used in the glm model
`epsilon` | argument to pass to `glm.control`, convergence tolerance in the iterative process to estimate de glm model

### Details

The strategy begins analysing a model with all the variables included in d. If all the variables are statistically significant (all the variables have a p-value less than alfa) this model will be the result. If not, the less statistically significant variable will be removed and the model is re-calculated. The process is repeated up to find a model with all the variables statistically significant (p-value < alpha). Each time that a variable is removed from the model, it is considered the possibility of one or more removed variables to come in again.

### Value

`two.ways.stepback` returns an object of the class `lm`, where the model uses `y` as dependent variable and all the selected variables from `d` as independent variables.

The function `summary` are used to obtain a summary and analysis of variance table of the results. The generic accessor functions `coefficients`, `effects`, `fitted.values` and `residuals` extract various useful features of the value returned by `lm`.

### Author(s)

Ana Conesa and Maria Jose Nueda, mj.nueda@ua.es

### References

Conesa, A., Nueda M.J., Alberto Ferrer, A., Talon, T. 2005. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments.

### See Also

`lm`, `step`, `stepfor`, `stepback`, `two.ways.stepfor`

### Examples

    ## create design matrix
    Time <- rep(c(rep(c(1:3), each = 3)), 4)
    Replicates <- rep(c(1:12), each = 3)
    Control <- c(rep(1, 9), rep(0, 27))
    Treat1 <- c(rep(0, 9), rep(1, 9), rep(0, 18))
    Treat2 <- c(rep(0, 18), rep(1, 9), rep(0,9))
    Treat3 <- c(rep(0, 27), rep(1, 9))
    edesign <- cbind(Time, Replicates, Control, Treat1, Treat2, Treat3)
    rownames(edesign) <- paste("Array", c(1:36), sep = "")
    dise <- make.design.matrix(edesign)
    dis <- as.data.frame(dise$dis)

    ## expression vector
    y <- c(0.082, 0.021, 0.010, 0.113, 0.013, 0.077, 0.068, 0.042, -0.056, -0.232, -0.014, -0.040,
    -0.055, 0.150, -0.027, 0.064, -0.108, -0.220, 0.275, -0.130, 0.130, 1.018, 1.005, 0.931,
     -1.009, -1.101, -1.014, -0.045, -0.110, -0.128, -0.643, -0.785, -1.077, -1.187, -1.249, -1.463)

    s.fit <- two.ways.stepback(y = y, d = dis)
    summary(s.fit)

[Package maSigPro version 1.64.0 Index]