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

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

Description

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

Usage

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two.ways.stepfor(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 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.

Value

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.

Author(s)

Ana Conesa, [email protected]; Maria Jose Nueda, [email protected]

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, stepback, stepfor, two.ways.stepback

Examples

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## 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)

maSigPro documentation built on Nov. 1, 2018, 2:35 a.m.