two.ways.stepfor: Fitting a linear model by forward-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.stepfor fits a linear regression model applying forward-stepwise strategy.

1	two.ways.stepfor(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 all the possible models with only one of the variables included in d. The most statistically significant variable (with the lowest p-value) is included in the model and then it is considered to introduce in the model another variable analysing all the possible models with two variables (the selected variable in the previous step plus a new variable). Again the most statistically significant variable (with lowest p-value) is included in the model. The process is repeated till there are no more statistically significant variables to include. Each time that a variable enters the model, the p-values of the current model vairables is recalculated and non significant variables will be removed.

two.ways.stepfor 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, stepback, stepfor, two.ways.stepback

## 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.stepfor(y = y, d = dis)
summary(s.fit)