jmRtools

Description

This is an R package with personal tools and functions.

Installation

Because this package is not available on CRAN, to install it, first install the devtools package using install.packages("devtools"), followed by the function devtools::install_github("jrosen48/jmRtools"). After installing the package, use library(jmRtools) to load it in each session.

Functions

• convert_log_odds(vec) where vec is a vector of values in log odds units, to be converted into odds
• convert_odds(vec) where vec is a vector of values in odds units, to be converted into probabilities
• fix_missing(vec, missing_val) where vec is a vector, and missing_val is a character or number (i.e., a scalar of numericorintegertype); this returns avectorwithmissing_valvalues replaced withNA` (adapted from Wickham's Advanced R)
• l_unique(vec) where vec is a vector; returns the number of unique values
• composite_matrix_maker(df, ...) where df is a data.frame, and ... are any number of columns evaluated using non-standard evaluation (so use unquoted column names); a matrix with the columns specified is returned
• composite_mean_maker(df, ...) where df is a data.frame, and ... are any number of columns evaluated using non-standard evaluation (so use unquoted column names); a vector with the mean of the columns specified is returned
• composite_stat_maker(df, ...) where df is a data.frame, and ... are any number of columns evaluated using non-standard evaluation (so use unquoted column names); a character scalar is returned with M(SD), Cronbach's alpha, and split-half reliability.
• to_compare(network1, network2, to_combine = F) where network1 and network2 are data.frames with row.names and names for the two modes (or matrices with row.names and col.names for the two modes) representing two-mode adjacency matrices; modified networks with structural zeroes added for the row.names or names not present in the other; to_combine (optional) adds together the networks
• center_vector(v) center the values in the vector v to have mean = 0
• scale_vector(v) standardize the values in the vector v to have SD = 1
• center_and_scale_vector(v) center and standardize the values in the vector v to have mean = 0 and SD = 1
• t_tester() takes the dv (for the dependent variable), fac (for the factor), and df (for the data frame) using raw (unquoted) variable names. Returns the test statistic, p-value, and effect size.
• tidy_t_test() a simple wrapper around t.test() with the broom::tidy() function around it