Package index • LittleHelpers

Multilevel helpers

Explore multilevel data: lmer helpers, centering, exploring parameters

add_term(): Add or remove terms to a lmer formula string
cor_between(): Correlation between group aggregates
cor_within(): Within-group correlations
explained_variance(): Computes pseudo-R2 by subtracting residual variances
grand_center(): Grand mean centering of one or more variables
group_center(): Group-centering of one or more variables
aggr_and_merge(): Creates group-level variables by aggregating individual-level variables and adding it to the df
lmer_table(): Create Regression Table for Multilevel Models
potential_interactions(): Potential cross-level interactions
potential_interactions_ind(): Potential individual-level interactions for lmer model
search_random(): Find which random effects could be added to a lmer model
vif_mer(): Compute variance inflation factor for mer objects

Extract info from Mplus output, beyond MplusAutomation package

check_mplus_model(): Check NDP related to residuals and correlations of models read by 'readModels'
diff_test_mlr(): Loglikelihood test for MLR estimator
diff_test_mlr_manual(): Loglikelihood test for MLR estimator - wrapper
each_param_psr_mplus(): Various versions of PSR
extract_mlefa(): Extracts fit indices of the Multilevel EFA (Mplus output)
gamma_hat_mplus(): Computes Gamma fit index for SEM models fit in Mplus
get_params_mplus(): Extracts parameters from Bayesian models produces by Mplus where 'readModels' cannot help
get_psr(): Get PSR from an Mplus output file
manipulate_mplus(): Manipulate mplus file
partable_mplus(): Summarize parameters from several Mplus models read by 'readModels'
tech11_14(): Extracts Tech11 and Tech14 (Tests for mixture models)

add_all_indir_and_tot(): Add all possible indirect and total effects to the `lavaan` formula
lav_compare(): Combine fit measures form several models and compare
sem_tab(): Table with parameters from a lavaan model

Formatting, showing results, and handling labels

label_book(): Create a codebook, table of labels to explore variables and values in your data
df_to_viewer(): Shows data.frame or model(s) in RStudio viewer
drop_labs(): Get rid of the labels
lab_to_fac(): Convert labelled variable to a factor with corresponding values
unhaven(): Drop the structure added by haven package
untibble(): Get rid of the tibble
eststar(): Format Estimates with Significance Stars and Optional Confidence Intervals
f(): Format the number quickly and remove zero before dot
sig_to_bold(): Conditional formatting
pvalue_to_stars(): Convert P-values to Significance Stars

Visualize multilevel models and SEM

graph_means_ci(): Graph means w/CIs
lav_to_graph(): Create a Graphviz path diagram using lavaan syntax
lav_to_draw(): Convert lavaan to draw.io diagram
plef(): Simple interaction plot with nice defaults
plot_latent_means(): Plot latent means
random_interaction(): Plot cross-level interactions for mer objects
random_plot(): Plot random effects and interactions for lmer objects
scatter_means_ci(): Computes means by group and plots them in as a scatterplot
sig_seg(): Prepares data for significance staples on ggplot
stacked_bar(): Quick stacked bar plot
theme_mr(): Clean and precise theme
traceplots_mplus(): Extract and export Mplus trace and autocorrelation plots into pdf

Convenience functions and shortcuts

cor_table(): Correlation table with stars
compare_cor(): Compare two correlation matrices
crosstab(): Easy cross-tabulation with labels
fill_tri(): Make a matrix full form lower or upper triangle
match_named_vectors(): Combine Named Vectors into Data Frame by Matching Names
replace_by_table(): Replace by table
reverse(): Reverse values and value labels
mean_se_lower_upper(): Means and SEs
se(): Standard Error of Mean
get_net(): Make a quick network graph
na_tab(): Prints the number and percent of NAs in a dataframe
verb(): Convenience function for logging the code
ess_values(): Compute basic values indices as measured in European Social Survey
values: List of Schwartz values