Fstat                  package:SAGx                  R Documentation

_C_a_l_c_u_l_a_t_i_o_n _o_f _F _s_t_a_t_i_s_t_i_c _b_y _g_e_n_e _g_i_v_e_n _a _l_i_n_e_a_r _m_o_d_e_l

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

     Calculates F statistic.

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

     Fstat(indata =  M, formula1 = ~as.factor(g), formula0 = "mean", design1 = NULL, design0 = NULL, B = NULL)

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

  indata: The data matrix

formula1: a formula descibing the alternative linear model

formula0: a formula describing the nullmodel. Use linear models syntax,
          except for one-way ANOVA ("mean")

 design1: the alternaive design matrix. If not NULL it overrides the
          formula argument

 design0: the null design matrix. If not NULL it overrides the formula
          argument

       B: the number of bootstrap replicates

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

     A list with the components 

   Fstat: the value of the F statistic

    fnum: the numerator degrees of freedom

  fdenom: the denominator degrees og freedom

 design1: the alternative design matrix

 design0: the null design matrix

     SS1: the sum of squares in the denominator of the F-statistic

     SS0: the sum of squares in the numerator  of the F-statistic

  pvalue: the p-value for testing the alternative vs the null model

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

     Per Broberg

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

     ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". 
     ## Page 9: Plant Weight Data. 
      ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) 
      trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) 
      group <- gl(2,10,20, labels=c("Ctl","Trt")) 
      weight <- c(ctl, trt) 
      anova(lm.D9 <- lm(weight ~ group)) 
     # Analysis of Variance Table

     # Response: weight
     #          Df Sum Sq Mean Sq F value Pr(>F)
     #group      1 0.6882  0.6882  1.4191  0.249
     #Residuals 18 8.7292  0.4850               

      Fstat(indata = rbind(weight,weight),formula1=~group) # Fstat will need at least two genes to work with #
     #$Fstat
     #  weight   weight 
     #1.419101 1.419101 

     #$fnum
     #[1] 18

     #$fdenom
     #[1] 1

     #$design1
     #   (Intercept) groupTrt
     #1            1        0
     #2            1        0
     #3            1        0
     #4            1        0
     #5            1        0
     #6            1        0
     #7            1        0
     #8            1        0
     #9            1        0
     #10           1        0
     #11           1        1
     #12           1        1
     #13           1        1
     #14           1        1
     #15           1        1
     #16           1        1
     #17           1        1
     #18           1        1
     #19           1        1
     #20           1        1
     #attr(,"assign")
     #[1] 0 1

     # $design0
     # NULL

     # $SS1
     # weight  weight 
     #8.72925 8.72925 

     #$SS0
     #  weight   weight 
     #0.688205 0.688205 

