psycModel (version 0.5.0)

lm_model_summary: Model Summary for Linear Regression

Description

An integrated function for fitting a linear regression model.

Usage

lm_model_summary(
  data,
  response_variable = NULL,
  predictor_variable = NULL,
  two_way_interaction_factor = NULL,
  three_way_interaction_factor = NULL,
  family = NULL,
  cateogrical_var = NULL,
  graph_label_name = NULL,
  model_summary = TRUE,
  interaction_plot = TRUE,
  y_lim = NULL,
  plot_color = FALSE,
  digits = 3,
  simple_slope = FALSE,
  assumption_plot = FALSE,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Value

a list of all requested items in the order of model, model_summary, interaction_plot, simple_slope

Arguments

data

data.frame

response_variable

DV (i.e., outcome variable / response variable). Length of 1. Support dplyr::select() syntax.

predictor_variable

IV. Support dplyr::select() syntax.

two_way_interaction_factor

two-way interaction factors. You need to pass 2+ factor. Support dplyr::select() syntax.

three_way_interaction_factor

three-way interaction factor. You need to pass exactly 3 factors. Specifying three-way interaction factors automatically included all two-way interactions, so please do not specify the two_way_interaction_factor argument. Support dplyr::select() syntax.

family

a GLM family. It will passed to the family argument in glm. See ?glm for possible options.

cateogrical_var

list. Specify the upper bound and lower bound directly instead of using ± 1 SD from the mean. Passed in the form of list(var_name1 = c(upper_bound1, lower_bound1),var_name2 = c(upper_bound2, lower_bound2))

graph_label_name

optional vector or function. vector of length 2 for two-way interaction graph. vector of length 3 for three-way interaction graph. Vector should be passed in the form of c(response_var, predict_var1, predict_var2, ...). Function should be passed as a switch function (see ?two_way_interaction_plot for an example)

model_summary

print model summary. Required to be TRUE if you want assumption_plot.

interaction_plot

generate the interaction plot. Default is TRUE

y_lim

the plot's upper and lower limit for the y-axis. Length of 2. Example: c(lower_limit, upper_limit)

plot_color

If it is set to TRUE (default is FALSE), the interaction plot will plot with color.

digits

number of digits to round to

simple_slope

Slope estimate at +1/-1 SD and the mean of the moderator. Uses interactions::sim_slope() in the background.

assumption_plot

Generate an panel of plots that check major assumptions. It is usually recommended to inspect model assumption violation visually. In the background, it calls performance::check_model()

quite

suppress printing output

streamline

print streamlined output

return_result

If it is set to TRUE (default is FALSE), it will return the model, model_summary, and plot (if the interaction term is included)

Examples

fit <- lm_model_summary(
  data = iris,
  response_variable = "Sepal.Length",
  predictor_variable = dplyr::everything(),
  two_way_interaction_factor = c(Sepal.Width, Species),
  interaction_plot = FALSE, # you can also request the interaction plot
  simple_slope = FALSE, # you can also request simple slope estimate 
  assumption_plot = FALSE, # you can also request assumption plot
  streamline = FALSE #you can change this to get the least amount of info
)