Function reference • visage

Data

Visual inference study data

polynomials: Results of a visual inference study on reading residual plots of misspecified linear regression model caused by missing Hermite polynomial terms

get_polynomials_lineup(): Download the detailed information of lineups used in the polynomials study

P-value

Functions for caculating p-value in a visual test

sim_dist(): Approximate the distribution of number of detections of a lineup with simulation

exact_dist(): Calculate the exact distribution of number of detections of a lineup

calc_p_value(): Calculate p-value for a visual test

calc_p_value_multi(): Calculate p-value for multiple lineups.

eval_p_value(): Evaluate test for given p-value and significance level

Portals to class instantiate methods

Functions to build an instance

rand_var() rand_uniform() rand_uniform_d() rand_normal() rand_lognormal() rand_t() closed_form() vi_model() cubic_model() simple_cubic_model() quartic_model() poly_model() heter_model() ar1_model() non_normal_model(): Portals to class instantiate methods

Random variable class

Random variable class

RAND_VAR: RAND_VAR class environment

RAND_VAR$..init..: Initialization method

RAND_VAR$..str..: String representation of the object

RAND_VAR$dist: Distribution name

RAND_VAR$prm: List of parameters

RAND_VAR$set_prm: Generate random values

RAND_VAR$E: Expectation of the random variable

RAND_VAR$Var: Variance of the random variable

RAND_VAR$gen: Generate random values

Random uniform variable class

Random uniform variable class

RAND_UNIFORM: RAND_UNIFORM class environment

RAND_UNIFORM$..init..: Initialization method

RAND_UNIFORM$gen: Generate random values

Random discrete uniform variable class

Random discrete uniform variable class

RAND_UNIFORM_D: RAND_UNIFORM_D class environment

RAND_UNIFORM_D$..init..: Initialization method

RAND_UNIFORM_D$gen: Generate random values

Random normal variable class

Random normal variable class

RAND_NORMAL: RAND_NORMAL class environment

RAND_NORMAL$..init..: Initialization method

RAND_NORMAL$gen: Generate random values

Random log-normal variable class

Random log-normal variable class

RAND_LOGNORMAL: RAND_LOGNORMAL class environment

RAND_LOGNORMAL$..init..: Initialization method

RAND_LOGNORMAL$gen: Generate random values

Random student’s t variable class

Random student’s t variable class

RAND_T: RAND_T class environment

RAND_T$..init..: Initialization method

RAND_T$gen: Generate random values

Closed form expression class

Closed form expression class

CLOSED_FORM: CLOSED_FORM class environment

CLOSED_FORM$..init..: Initialization method

CLOSED_FORM$..str..: String representation of the object

CLOSED_FORM$..len..: Length of the object

CLOSED_FORM$ast: Abstract syntax tree of the expression

CLOSED_FORM$sym: List of symbols in the abstract syntax tree of the expression

CLOSED_FORM$sym_name: List of symbol names in the abstract syntax tree of the expression

CLOSED_FORM$sym_type: List of symbol types in the abstract syntax tree of the expression

CLOSED_FORM$expr: Expression extracted from the provided formula

CLOSED_FORM$set_sym: Set values for symbols

CLOSED_FORM$set_expr: Set the closed form expression

CLOSED_FORM$compute: Compute the expression without generating any random values

CLOSED_FORM$gen: Generating random values from the expression

CLOSED_FORM$as_dataframe: Transforming list to data frame

Visual inference linear model class

Visual inference linear model class

VI_MODEL: VI_MODEL class environment

VI_MODEL$..init..: Initialization method

VI_MODEL$..str..: String representation of the object

VI_MODEL$..cache..: Cache list, containing the last fitted model, data frame and formula

VI_MODEL$prm: List of parameters

VI_MODEL$prm_type: List of parameter types

VI_MODEL$formula: Closed form expression of y

VI_MODEL$null_formula: Formula for fitting the null model

VI_MODEL$alt_formula: Formula for fitting the alternative model

VI_MODEL$set_formula: Set formula for y, null model or alternative model

VI_MODEL$set_prm: Set parameter for the model

VI_MODEL$test: Test the null model against the alternative model

VI_MODEL$fit: Test the null model against the alternative model

VI_MODEL$average_effect_size: Compute the effect size of the simulated data or the defined model

VI_MODEL$sample_effect_size: Compute the sample based effect size of the simulated data of the defined model

VI_MODEL$gen: Generating random values from the expression of y

VI_MODEL$gen_lineup: Generating random values from the expression of y, and forms a lineup

VI_MODEL$null_resid: Generate null residuals from a null model

VI_MODEL$plot: Plot the fitted model

VI_MODEL$plot_resid: Plot the residuals vs fitted values plot

VI_MODEL$plot_qq: Plot the residual Q-Q plot

VI_MODEL$plot_lineup: Plot the lineup

VI_MODEL$rss: Residual sum of square of a fitted model

Visual inference cubic linear model class

Visual inference cubic linear model class

CUBIC_MODEL: CUBIC_MODEL class environment

CUBIC_MODEL$..init..: Initialization method

CUBIC_MODEL$formula: Closed form expression of y

CUBIC_MODEL$null_formula: Formula for fitting the null model

CUBIC_MODEL$alt_formula: Formula for fitting the alternative model

CUBIC_MODEL$set_prm: Set parameter for the model

CUBIC_MODEL$E: Expectation of the residuals

CUBIC_MODEL$sample_effect_size: Compute the sample baased effect size of the simulated data

Visual inference simple cubic linear model class

Visual inference simple cubic linear model class

SIMPLE_CUBIC_MODEL: SIMPLE_CUBIC_MODEL class environment

SIMPLE_CUBIC_MODEL$..init..: Initialization method

SIMPLE_CUBIC_MODEL$formula: Closed form expression of y

SIMPLE_CUBIC_MODEL$null_formula: Formula for fitting the null model

SIMPLE_CUBIC_MODEL$alt_formula: Formula for fitting the alternative model

SIMPLE_CUBIC_MODEL$set_prm: Set parameter for the model

SIMPLE_CUBIC_MODEL$E: Expectation of the residuals

SIMPLE_CUBIC_MODEL$sample_effect_size: Compute the sample based effect size of the simulated data

Visual inference quartic linear model class

Visual inference quartic linear model class

QUARTIC_MODEL: QUARTIC_MODEL class environment

QUARTIC_MODEL$..init..: Initialization method

QUARTIC_MODEL$formula: Closed form expression of y

QUARTIC_MODEL$null_formula: Formula for fitting the null model

QUARTIC_MODEL$alt_formula: Formula for fitting the alternative model

QUARTIC_MODEL$set_prm: Set parameter for the model

QUARTIC_MODEL$E: Expectation of the residuals

QUARTIC_MODEL$sample_effect_size: Compute the sample based effect size of the simulated data

Visual inference orthogonal polynomial model class

Visual inference orthogonal polynomial linear model class

POLY_MODEL: POLY_MODEL class environment

POLY_MODEL$..init..: Initialization method

POLY_MODEL$formula: Closed form expression of y

POLY_MODEL$null_formula: Formula for fitting the null model

POLY_MODEL$alt_formula: Formula for fitting the alternative model

POLY_MODEL$raw_z_formula: Formula for the raw orthogonal polynomial term raw_z

POLY_MODEL$z_formula: Formula for the scaled orthogonal polynomial term z

POLY_MODEL$set_prm: Set parameter for the model

POLY_MODEL$test: Test the null model

POLY_MODEL$E: Expectation of the residuals

POLY_MODEL$sample_effect_size: Compute the sample based effect size of the simulated data

POLY_MODEL$hermite: Hermite polynomial functions

Visual inference heteroskedasticity linear model class

Visual inference heteroskedasticity linear model class

HETER_MODEL: HETER_MODEL class environment

HETER_MODEL$..init..: Initialization method

HETER_MODEL$formula: Closed form expression of y

HETER_MODEL$null_formula: Formula for fitting the null model

HETER_MODEL$alt_formula: Formula for fitting the alternative model

HETER_MODEL$test: Test the null model

HETER_MODEL$sample_effect_size: Compute the sample based effect size of the simulated data

Visual inference autoregressive (1) linear model class

Visual inference autoregressive (1) linear model class

AR1_MODEL: AR1_MODEL class environment

AR1_MODEL$..init..: Initialization method

Visual inference non-normal linear model class

Visual inference non-normal linear model class

NON_NORMAL_MODEL: NON_NORMAL_MODEL class environment

NON_NORMAL_MODEL$..init..: Initialization method