use_linear_regression {packDAMipd}

########################################################################### Get the parameter values using the linear regression

Description

########################################################################### Get the parameter values using the linear regression

Usage

use_linear_regression(
  param_to_be_estimated,
  dataset,
  indep_var,
  covariates,
  interaction
)

Arguments

param_to_be_estimated

parameter of interest

dataset

data set to be provided

indep_var

the independent variable (column name in data file)

covariates

list of covariates-calculations to be done before passing

interaction

boolean value to indicate interaction in the case of linear regression, false by default

Details

This function returns the results and plots after doing linear regression Requires param to be estimated, dataset, independent variables and information on covariates, and interaction variables if there are Uses form_expression_lm to create the expression as per R standard for e.g lm(y ~ x ). Returns the fit result,s summary results as returned by summary(), confidence interval for fit coefficients (ci_coeff), variance covariance matrix, cholesky decomposition matrix, Results from correlation test, plot of diagnostic tests and model fit assumptions, plot of model prediction diagnostic include AIC, R2, and BIC. The results of the prediction ie predicted values when each of covariate is fixed will be returned in prediction matrix predicted values will provide the mean value of param_to_to_estimated as calculated by the linear regression formula. ref:https://www.statmethods.net/stats/regression.html

Value

the results of the regression analysis

Examples

results_lm <- use_linear_regression("dist",
  dataset = cars,
  indep_var = "speed", covariates = NA, interaction = FALSE)


library(car)
results_lm <- use_linear_regression("mpg",
  dataset = mtcars,
  indep_var = "disp", covariates = c("hp", "wt", "drat"),
  interaction = FALSE)