stepback              package:maSigPro              R Documentation

_F_i_t_t_i_n_g _a _l_i_n_e_a_r _m_o_d_e_l _b_y _b_a_c_k_w_a_r_d-_s_t_e_p_w_i_s_e _r_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     stepback(y = y, d = d, alfa = 0.05)

_A_r_g_u_m_e_n_t_s:

       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

_D_e_t_a_i_l_s:

     The strategy begins analysing a model with all the variables
     included in d.  If all variables are statistically significant
     (all 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.

_V_a_l_u_e:

     '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'.

_A_u_t_h_o_r(_s):

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

_R_e_f_e_r_e_n_c_e_s:

     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.

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

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

