lm_model_table: Linear Regression Model Table

In jasonmoy28/psycModel: Integrated Toolkit for Psychological Analysis and Modeling in R

Linear Regression Model Table

Description

Generate tables with multiple response, predictor, or two-way interaction variables (only lm models are supported). You can pass multiple variables for one type of variable (either response, pred, or interaction) only. If you want to pass multiple variables for multiple type of variable, try lm_model_explore instead. At the moment, multi-categorical variables are not supported as predictors or interactions (but control is fine). Binary variable should be numeric instead of factor

Usage

lm_model_table(
  data,
  response_variable,
  predictor_variable,
  two_way_interaction_variable = NULL,
  control_variable = NULL,
  other_parameters = NULL,
  marginal_alpha = 0.1,
  return_result = FALSE,
  verbose = TRUE,
  show_p = FALSE
)

Arguments

data	data.frame
response_variable	response variable. Support dplyr::select() syntax.
predictor_variable	predictor variable. Support dplyr::select() syntax. It will automatically remove the response variable from predictor variable, so you can use contains() or start_with() safely.
two_way_interaction_variable	Two-way interaction variable. Each two-way interaction variable will interact with the predictor variable. Support dplyr::select() syntax.
control_variable	control variables. Support dplyr::select() syntax.
other_parameters	catch call for all other parameters that need to be entered (e.g., non-changing interaction terms). Have to be character type.
marginal_alpha	the set marginal_alpha level for marginally significant (denoted by .). Set to 0.05 if do not want marginally significant denotation.
return_result	It set to TRUE, it return the model estimates data frame.
verbose	default is TRUE. Set to FALSE to suppress outputs
show_p	show the p-value in parenthesis

Value

data.frame

Examples

# If you want all varibles to be changing, try lm_model_explore.

test = data.frame(y1 = rnorm(1000,2,3),
y2 = rnorm(1000,10,2),
y3 = rnorm(1000,1,4),
x1 = rnorm(1000,100,10),
x2 = rnorm(1000,10,1),
x3 = rnorm(1000,6,2),
m1 = rnorm(1000,3,1),
m2 = rnorm(1000,2,0.5),
m3 = rnorm(1000,9,0.1),
c1 = rnorm(1000,5,0.4),
c2 = rnorm(1000,2,0.2),
c3 = rnorm(1000,7,0.9)
)

# Changing response variable 
lm_model_table(data = test, 
               response_variable = c(y1,y2,y3),
               predictor_variable = x1,
               control_variable = c(c1,c2,c3))
 
# Changing predictors 
lm_model_table(data = test, 
               response_variable = y1,
               predictor_variable = c(x1,x2,x3),
               control_variable = c(c1,c2,c3))
                 
                 
# Changing interaction terms with a non-changing response variable
lm_model_table(data = test, 
               response_variable = y1,
               predictor_variable = x1,
               two_way_interaction_variable = c(m1,m2,m3),
               control_variable = c(c1,c2,c3))
                 
# A non-changing interaction term with changing response variables                 
lm_model_table(data = test, 
               response_variable = c(y1,y2,y3),
               predictor_variable = x1,
               other_parameters = c('x1*m1'),
               control_variable = c(c1,c2,c3))                  

jasonmoy28/psycModel documentation built on Sept. 6, 2024, 11:45 a.m.