Stats-Scripts

R scripts useful for statistics

returnAdj

-required for effect_size, get_anova, get_anova_perm and runcontrast_d below

• returns a data.frame with values adjusted for designated covariates
• Requires:
  • data: The overarching data.frame
  • measure: A string for the name of the vector to be adjusted
  • covars: A vector holding the names of the covariates to be used (but not in an interaction term)
  • interacts: A vector holding the names of covariates to be used in an interaction term
  • display: A boolean indicating that the original-adjusted value correlations are to be printed. This defaults to true.
  • method: A string identifying the mean adjusting methods. This defaults to 'iadj'. The other options include 'smean' and 'null'.
  • This feature provides you with the option to fetch sample means adjusted based on: Intercept adjusted (iadj), sample mean (smean) or 'null' adjusted.
  • subset: A conditional expression that returns a vector of booleans the same length as the vector to be adjusted. This can be used to generate the model on only a subset of the data (e.g. Controls only)

effect_size

• calculates Cohen's D between two groups and adjusts for covariates
• requires data frame (data)
• measure ('Left_frontal_thickness')
• name of group variable (eg 'group','PHENOTYPE')
• name of levels to compare within group variable ('SZ','HC')
  • Note can only handle two levels
• list of covariates ( covars=c('age','sex','race'))

get_anova

• calculates Anova contrast (type 3) between two groups and effect sizes (Cohen's D)

• outputs p value and adjusted p values

• requires data frame (data)

• list of measures c('Left_frontal_thickness','Right_frontal_thickness')

• name of group variable (eg 'group','PHENOTYPE')

• name of levels to compare within group variable ('SZ','HC')

  • Note can only handle two levels

• list of covariates ( covars=c('age','sex','race'))

repeated_anova

• Repeated measures anova to calculate a group by time interaction
• outputs F stats and p values for group, time and group by time
• requires your original data frame, a list of measures, list of covariates, list of two interaction terms (eg. Group and Timept) and name of subject id.

get_anova_perm

• run permutation testing for p values
• also run anova pairwise comparisons
• make sure effect_size and returnAdj functions are also sourced.

run_contrast

• returns pairwise contrasts using general linear hypothesis test
• requires list of regions, matrix of group contrasts, data frame, group name, list of covariates
• can set group.adjust for multiple comparisons (adjusts all contrasts per region)

run_contrast_d

• calculates Cohen's D effect Size (adjusted for covariates) for runcontrast output
• dat- main dataframe with all your subject info/data
• results- results from runcontrast
• name of group variable (eg. 'Group')
  • NOTE: Group identifiers CANNOT contain '-' (eg. 'BP-P' must be 'BPP','BP_P',etc)
  • NOTE: Group identifiers in 'contr' column of runcontrast output must match original data frame in Group column
• covars= list of covariates (c('age','sex','site'))