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 |

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

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`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## 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.

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