contrasts.fit             package:limma             R Documentation

_C_o_n_t_r_a_s_t _I_n_f_o_r_m_a_t_i_o_n _f_r_o_m _L_i_n_e_a_r _M_o_d_e_l _F_i_t

_D_e_s_c_r_i_p_t_i_o_n:

     Given an 'lm.series' fit for a oneway model, compute estimated
     coefficients and standard errors for a given set of contrasts.

_U_s_a_g_e:

     contrasts.fit(fit,contrasts)

_A_r_g_u_m_e_n_t_s:

     fit: object produced by the function 'lm.series' or equivalent. A
          list containing components 'coefficients' and
          'stdev.unscaled'.

contrasts: matrix with columns containing contrasts. May be a vector if
          there is only one contrast.

_D_e_t_a_i_l_s:

     This function accepts input from any of the functions 'lm.series',
     'rlm.series' or 'gls.series'. The design matrix used for this fit
     must have orthogonal columns.

     The idea is to fit a saturated oneway model using of the above
     functions, then use 'contrasts.fit' to obtain coefficients and
     standard errors for any number of contrasts of the coefficients of
     the oneway model.

_V_a_l_u_e:

     An object of the same type as produced by 'lm.series'. This is a
     list components 

coefficients: numeric matrix containing the estimated coefficients for
          each contrasts for each gene.

stdev.unscaled: numeric matrix conformal with 'coef' containing the
          unscaled standard deviations for the coefficient estimators.

     ...: any other components input in 'fit'

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     An overview of linear model functions in limma is given by
     5.LinearModels.

_E_x_a_m_p_l_e_s:

     #  Simulate gene expression data,
     #  6 microarrays and 100 genes with one gene differentially expressed in first 3 arrays
     M <- matrix(rnorm(100*6,sd=0.3),100,6)
     M[1,1:3] <- M[1,1:3] + 2
     #  Design matrix corresponds to oneway layout, columns are orthogonal
     design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
     fit <- lm.series(M,design=design)
     #  Would like to consider original two estimates plus difference between first 3 and last 3 arrays
     contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
     fit2 <- contrasts.fit(fit,contrasts=contrast.matrix)
     eb <- ebayes(fit2)
     #  Large values of eb$t indicate differential expression
     clas <- classifyTests(eb$t,design=design,contrasts=contrast.matrix,df=fit2$df+eb$df)

