use_generalised_linear_model {packDAMipd} | R Documentation |
############################################################################ Get the parameter values using logistic regression
Description
############################################################################ Get the parameter values using logistic regression
Usage
use_generalised_linear_model(
param_to_be_estimated,
dataset,
indep_var,
family,
covariates,
interaction,
naaction,
link = NA
)
Arguments
param_to_be_estimated |
parameter of interest |
dataset |
data set to be provided |
indep_var |
the independent variable (column name in data file) |
family |
distribution name eg. for logistic regression -binomial |
covariates |
list of covariates-calculations to be done before passing |
interaction |
boolean value to indicate interaction in the case of linear regression |
naaction |
action to be taken with the missing values |
link |
link function if not the default for each family |
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_glm to create the expression as per R standard for e.g glm(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 for fixed other variables will be returned in prediction matrix
Value
the results of the regression analysis
Examples
gm_result <- use_generalised_linear_model(
param_to_be_estimated = "Direction",
dataset = ISLR::Smarket, indep_var = "Lag1", family = "binomial",
covariates = c("Lag2", "Lag3"),
interaction = FALSE, naaction = "na.omit", link = NA)