two.ways.stepback: Fitting a linear model by backward-stepwise regression

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

Description

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

Usage

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

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.stepback(y = y, d = dis)
summary(s.fit)

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