dstat               package:quantchem               R Documentation

_D_e_s_c_r_i_p_t_i_v_e _s_t_a_t_i_s_t_i_c_s _o_f _q_u_a_n_t_i_t_a_t_i_v_e _a_n_a_l_y_s_i_s _r_e_s_u_l_t_s

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

     Performs comprehensive statistical evaluation of quantitative
     analysis results.

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

     dstat(x, expected = median(unlist(x)), sort = TRUE, inverse.f = TRUE,
       na.rm = FALSE, conf.level = 0.95, alternative = c("two.sided", "less", "greater"), ansari = FALSE)

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

       x: a vector of results, of a dataframe with results to compare 

expected: expected reference value 

    sort: if TRUE, the matrices are sorted by means, variances or
          p-values. 

inverse.f: if F value in variance comparison is below 1, the inverse is
          taken (has no effect on p-value, but there are sometimes need
          to have such F 

   na.rm: logical: should NA values be removed? 

conf.level: level for calculate confidence intervals 

alternative: alternative for all tests performed. 

  ansari: due to reports of errors on some datasets, the ansari.test()
          on data is turned off by default since 0.12. you can turn it
          on by setting this variable to TRUE 

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

     If argument is vector, several one-row matrices are produced (see
     below). If argument is a data.frame, there are also additional
     matrices with pairwise comparisons. The comparison of all groups
     by appropriate test are also calculated. This function prints its
     results with significance stars and returns a list invisibly.

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

     A list containing following matrices (if data is a vector, only 5
     first are returned):

    mean: mean, its confidence interval and t-test for expected value

  median: median, its confidence interval and Wilcoxon test for
          expected value

     var: variance, standard deviation, standard error and Dixon test
          for outlier

     rsd: relative standard deviation, its confidence interval and
          Grubbs test for outlier

   range: minimum and maximum value, range, IQR, MAD and Shapiro-Wilk
          test for normality

 vartest: ratios of variances, their confidence intervals and F test
          with p-value

   ttest: differences between means, their confidence intervals and t
          test with p-value

   atest: nonparametric differences in scale, their confidence
          intervals  and Ansari-Bradley test with p-value 

   atest: nonparametric differences in location, their confidence
          intervals  and Wilcoxon test with p-value 

   anova: ANOVA between all data

 kruskal: Kruskal-Wallis test (nonparametric equivalent for ANOVA)

bartlett: Bartlett test for homogeneity of all variances

 fligner: Fligner-Killeen test for equal variances (nonparametric
          alternative to Bartlett)

_N_o_t_e:

     This function calculates always *both* parametric and
     nonparametric tests, and choosing a test to take into account
     should be also decision of analyst, based on the other tests
     results.

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

     Lukasz Komsta

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

     'vstat'

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

     set.seed(1234)
     a = data.frame(x=rnorm(10),y=runif(10),z=rt(10,1))
     dstat(a,0)

