Package index • webrd3c3

All functions

ctt_table(): ctt_table() produces a table with classical test theory estimates.

fit_grm1(): fit_grm1() it fits a homogenous graded response model (GRM) using MPLUS and MplusAutomation

fit_grm1_plain(): fit_grm1() it fits a homogenous graded response model (GRM) using MPLUS and MplusAutomation

fit_grm1_w(): fit_grm1_w() it fits a graded response model (GRM) using MPLUS and MplusAutomation with weighted observations

fit_grm2(): fit_grm2() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_align(): fit_grm2_align() it fits a between country GRM with alignment method using MPLUS and MplusAutomation

fit_grm2_align_w_wlsmv(): fit_grm2_align_w_wlsmv() it fits a between country GRM with alignment method, with a complex sample design using MPLUS and MplusAutomation. The "w" accounts for survey weights alone.

fit_grm2_align_wj_wlsmv(): fit_grm2_align_wj_wlsmv() it fits a between country GRM with alignment method, with a complex sample design using MPLUS and MplusAutomation. The "wj" version uses taylor series linearization, using clusters and weights.

fit_grm2_align_wjs_wlsmv(): fit_grm2_align_wjs_wlsmv() it fits a between country GRM with alignment method, with a complex sample design using MPLUS and MplusAutomation. The "wjs" version uses taylor series linearization, using strafication, clusters and weights.

fit_grm2_align_wlsmv(): fit_grm2_align_wlsmv() it fits a between country GRM with alignment method, with a complex sample design using MPLUS and MplusAutomation

fit_grm2_clu(): fit_grm2_clu() it fits a graded response model (GRM) using MPLUS and MplusAutomation with clustered errors

fit_grm2_inv1(): fit_grm2_inv1() it fits GRM-configural between countries using MPLUS and MplusAutomation

fit_grm2_inv2(): fit_grm2_inv1() it fits GRM-metric between countries using MPLUS and MplusAutomation

fit_grm2_inv3(): fit_grm2_inv3() it fits GRM-scalar between countries using MPLUS and MplusAutomation

fit_grm2_m01_strict(): fit_grm2_m01_strict() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m01_w_strict(): fit_grm2_m01_w_strict() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m01_wj_strict(): fit_grm2_m01_wj_strict() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m02_scalar(): fit_grm2_m02_scalar() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m02_w_scalar(): fit_grm2_m02_w_scalar() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m02_wj_scalar(): fit_grm2_m02_wj_scalar() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m03_config(): fit_grm2_m03_wj_config() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m03_w_config(): fit_grm2_m03_w_config() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m03_wj_config(): fit_grm2_m03_wj_config() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m04_base(): fit_grm2_m04_base() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m04_w_base(): fit_grm2_m04_w_base() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_m04_wj_base(): fit_grm2_m04_base() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_plain(): fit_grm2() it fits a graded response model (GRM) using MPLUS and MplusAutomation

fit_grm2_w(): fit_grm2_w() it fits a graded response model (GRM) using MPLUS and MplusAutomation with weighted observations

frequency_per_variable(): frequency_per_variable() calculates proportions of category use, from a polytomous scale

get_desc(): get_desc() produces a table with descriptives where items or variables are rows and columns are differerent descriptive values. its largely based on the deprecated function skimr::skim_to_wide

get_descriptives(): get_descriptives() produces a descriptive table from a response data frame

get_empty(): get_empty() estimates the amount of cases with empty rows

get_fit(): get_fit() produces a table displaying obtained fit statistics

get_inv_fit(): get_inv_fit() retrieves model fit indexes from GRM models to generate the invariance table

get_lambda(): get_lambda() retrieves the factor loadings of a latent variable model

get_pattern(): get_pattern() generates a string to represent a response pattern

get_score(): get_score() retrieves the theta score and re-scale these at mean zero, and sd one at the population level

get_theta(): get_theta() retrieves the theta score generated with techr::fit_grm2 or techr::fit_pcm

missing_summary(): missing_summary() generates missing summary

missing_table(): missing_table() generates missing summary

pa_lubbe(): pa_lubbe() calculates a parallel analysis using a response data frame

pa_lubbe_plot(): pa_lubbe_plot() plot the results of a parallel analysis generated with the pa_lubb() function

plot_error(): plot_error() plot a scatter of errors and theta locations with their histograms, using library(ggplot2)

plot_error_plain(): plot_error_plain() plot a scatter of errors and theta locations with their histograms, using library(ggplot2)

plot_infit(): plot_infit() plot infit results from a Rasch model over a response data frame

plot_item_map_grm2(): plot_item_map_grm2() generates an item person map using a GRM model with 2 response categories

plot_item_map_grm3(): plot_item_map_grm3() generates an item person map using a GRM model with 4 response categories

plot_item_map_grm4(): plot_item_map_grm4() generates an item person map using a GRM model with 4 response categories

plot_item_map_grm5(): plot_item_map_grm5() generates an item person map using a GRM model with 5 response categories

plot_missing_pattern(): plot_missing_pattern() generates a plot with missing patterns using VIM::aggr

plot_missing_pattern_plain(): plot_missing_pattern_plain() generates a plot with missing patterns using VIM::aggr

plot_missing_summary(): plot_missing_summary() it plots missing summary

plot_theta_caterpillar(): plot_theta_caterpillar() produces a caterpillar plot for theta scores

plot_theta_caterpillar_plain(): plot_theta_caterpillar_plain() produces a caterpillar plot for theta scores

raw_alpha(): raw_alpha() calculates the alpha coefficient from a response table.

reliability_separation(): reliability_separation() calculates separation reliability of theta scores realizations

scale_infit(): scale_infit() generates a infit table results from a Rasch model over a response data frame

senate_weights(): senate_weights() computes senate weights. These weights are used often to include more than one country with different sample size, yet to scale their weight to a common total.

silent(): silent() suppresses and hides other functions messages and warnings. Its ideal use if to wrapped functions within dynamic reports.