Returns a dataframe corresponding to a descriptive table.
covsum(
data,
covs,
maincov = NULL,
digits = 1,
numobs = NULL,
markup = TRUE,
sanitize = TRUE,
nicenames = TRUE,
IQR = FALSE,
all.stats = FALSE,
pvalue = TRUE,
effSize = FALSE,
show.tests = FALSE,
dropLevels = TRUE,
excludeLevels = NULL,
full = TRUE,
digits.cat = 0,
testcont = c("rank-sum test", "ANOVA"),
testcat = c("Chi-squared", "Fisher"),
include_missing = FALSE,
percentage = c("column", "row")
)dataframe containing data
character vector with the names of columns to include in table
covariate to stratify table by
number of digits for summarizing mean data, does not affect p-values
named list overriding the number of people you expect to have the covariate
boolean indicating if you want latex markup
boolean indicating if you want to sanitize all strings to not break LaTeX
boolean indicating if you want to replace . and _ in strings with a space
boolean indicating if you want to display the inter quantile range (Q1,Q3) as opposed to (min,max) in the summary for continuous variables
boolean indicating if all summary statistics (Q1,Q3 + min,max on a separate line) should be displayed. Overrides IQR.
boolean indicating if you want p-values included in the table
boolean indicating if you want effect sizes included in the table. Can only be obtained if pvalue is also requested. Effect sizes calculated include Cramer's V for categorical variables, Cohen's d, Wilcoxon r, or Eta-squared for numeric/continuous variables.
boolean indicating if the type of statistical test and effect size used should be shown in a column beside the pvalues. Ignored if pvalue=FALSE.
logical, indicating if empty factor levels be dropped from the output, default is TRUE.
a named list of covariate levels to exclude from statistical tests in the form list(varname =c('level1','level2')). These levels will be excluded from association tests, but not the table. This can be useful for levels where there is a logical skip (ie not missing, but not presented). Ignored if pvalue=FALSE.
boolean indicating if you want the full sample included in the table, ignored if maincov is NULL
number of digits for the proportions when summarizing categorical data (default: 0)
test of choice for continuous variables,one of rank-sum (default) or ANOVA
test of choice for categorical variables,one of Chi-squared (default) or Fisher
Option to include NA values of maincov. NAs will not be included in statistical tests
choice of how percentages are presented ,one of column (default) or row
Comparisons for categorical variables default to chi-square tests, but if there are counts of <5 then the Fisher Exact test will be used and if this is unsuccessful then a second attempt will be made computing p-values using MC simulation. If testcont='ANOVA' then the t-test with unequal variance will be used for two groups and an ANOVA will be used for three or more. The statistical test used can be displayed by specifying show.tests=TRUE.
The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.
Ellis, P.D. (2010) The essential guide to effect sizes: statistical power, meta-analysis, and the interpretation of research results. Cambridge: Cambridge University Press.tools:::Rd_expr_doi("10.1017/CBO9780511761676")
Lakens, D. (2013) Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4; 863:1-12. tools:::Rd_expr_doi("10.3389/fpsyg.2013.00863")