two.ways.stepback: Fitting a linear model by backward-stepwise regression
In maSigPro: Significant Gene Expression Profile Differences in Time Course Gene Expression Data

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`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

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.

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.

Ana Conesa, aconesa@cipf.es; Maria Jose Nueda, mj.nueda@ua.es

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.

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

## 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)