Integrated Function for Linear Regression

[Stable]
It will first compute the linear regression. Then, it will graph the interaction using the two_way_interaction_plot or the three_way_interaction_plot function. If you requested simple slope summary, it will calls the interaction::sim_slopes()

integrated_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
)

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. [Experimental]

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)

Value

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

Examples

fit <- integrated_model_summary(
  data = iris,
  response_variable = "Sepal.Length",
  predictor_variable = tidyselect::everything(),
  two_way_interaction_factor = c(Sepal.Width, Species)
)
#> Warning: The following columns are coerced into numeric: Species
#> 
#>  
#> Model Summary
#> Model Type = Linear regression
#> Outcome = Sepal.Length
#> Predictors = Sepal.Width, Petal.Length, Petal.Width, Species
#> 
#> Model Estimates
#> ───────────────────────────────────────────────────────────────────────────────────
#>             Parameter  Coefficient       t   df     SE          p            95% CI
#> ───────────────────────────────────────────────────────────────────────────────────
#>           (Intercept)        1.552   2.649  144  0.586  0.009 **   [ 0.394,  2.711]
#>           Sepal.Width        0.795   4.401  144  0.181  0.000 ***  [ 0.438,  1.152]
#>          Petal.Length        0.750  12.662  144  0.059  0.000 ***  [ 0.633,  0.867]
#>           Petal.Width       -0.364  -2.370  144  0.154  0.019 *    [-0.668, -0.060]
#>               Species        0.029   0.105  144  0.278  0.917      [-0.520,  0.578]
#>   Sepal.Width:Species       -0.097  -1.013  144  0.096  0.313      [-0.287,  0.092]
#> ───────────────────────────────────────────────────────────────────────────────────
#> 
#> Goodness of Fit
#> ───────────────────────────────────────────────────
#>      AIC      BIC     R²  R²_adjusted   RMSE      σ
#> ───────────────────────────────────────────────────
#>   83.729  104.803  0.863        0.858  0.305  0.312
#> ───────────────────────────────────────────────────
#> 
#> Model Assumption Check
#> OK: Residuals appear to be independent and not autocorrelated (p = 0.998).
#> OK: residuals appear as normally distributed (p = 0.949).
#> Unable to check autocorrelation. Try changing na.action to na.omit.
#> Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.036).
#> 
#> Warning: Model has interaction terms. VIFs might be inflated. You may check
#>   multicollinearity among predictors of a model without interaction terms.
#> Warning: Severe multicolinearity detected (VIF > 10). Please inspect the following table to identify high correlation factors.
#> Multicollinearity Table 
#> ────────────────────────────────────────
#>                  Term     VIF  SE_factor
#> ────────────────────────────────────────
#>           Sepal.Width   9.518      3.085
#>          Petal.Length  16.785      4.097
#>           Petal.Width  21.093      4.593
#>               Species  79.480      8.915
#>   Sepal.Width:Species  79.743      8.930
#> ────────────────────────────────────────
#> 

# \donttest{
fit <- integrated_model_summary(
  data = iris,
  response_variable = "Sepal.Length",
  predictor_variable = tidyselect::everything(),
  two_way_interaction_factor = c(Sepal.Width, Species),
  simple_slope = TRUE, # you can request simple slope
  assumption_plot = TRUE, # you can also request assumption plot
  plot_color = TRUE # you can also request the plot in color
)
#> Warning: The following columns are coerced into numeric: Species
#> 
#>  
#> Model Summary
#> Model Type = Linear regression
#> Outcome = Sepal.Length
#> Predictors = Sepal.Width, Petal.Length, Petal.Width, Species
#> 
#> Model Estimates
#> ───────────────────────────────────────────────────────────────────────────────────
#>             Parameter  Coefficient       t   df     SE          p            95% CI
#> ───────────────────────────────────────────────────────────────────────────────────
#>           (Intercept)        1.552   2.649  144  0.586  0.009 **   [ 0.394,  2.711]
#>           Sepal.Width        0.795   4.401  144  0.181  0.000 ***  [ 0.438,  1.152]
#>          Petal.Length        0.750  12.662  144  0.059  0.000 ***  [ 0.633,  0.867]
#>           Petal.Width       -0.364  -2.370  144  0.154  0.019 *    [-0.668, -0.060]
#>               Species        0.029   0.105  144  0.278  0.917      [-0.520,  0.578]
#>   Sepal.Width:Species       -0.097  -1.013  144  0.096  0.313      [-0.287,  0.092]
#> ───────────────────────────────────────────────────────────────────────────────────
#> 
#> Goodness of Fit
#> ───────────────────────────────────────────────────
#>      AIC      BIC     R²  R²_adjusted   RMSE      σ
#> ───────────────────────────────────────────────────
#>   83.729  104.803  0.863        0.858  0.305  0.312
#> ───────────────────────────────────────────────────
#> 
#> Model Assumption Check
#> OK: Residuals appear to be independent and not autocorrelated (p = 0.996).
#> OK: residuals appear as normally distributed (p = 0.949).
#> Unable to check autocorrelation. Try changing na.action to na.omit.
#> Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.036).
#> 
#> Warning: Model has interaction terms. VIFs might be inflated. You may check
#>   multicollinearity among predictors of a model without interaction terms.
#> Warning: Severe multicolinearity detected (VIF > 10). Please inspect the following table to identify high correlation factors.
#> Multicollinearity Table 
#> ────────────────────────────────────────
#>                  Term     VIF  SE_factor
#> ────────────────────────────────────────
#>           Sepal.Width   9.518      3.085
#>          Petal.Length  16.785      4.097
#>           Petal.Width  21.093      4.593
#>               Species  79.480      8.915
#>   Sepal.Width:Species  79.743      8.930
#> ────────────────────────────────────────
#> Loading required namespace: qqplotr
#> Loading required namespace: see

#> 
#> Slope Estimates at Each Level of Moderators
#> ────────────────────────────────────────────────────────────────
#>   Species Level   Est.   S.E.  t val.          p          95% CI
#> ────────────────────────────────────────────────────────────────
#>             Low  0.680  0.087   7.864  0.000 ***  [0.509, 0.851]
#>            Mean  0.601  0.072   8.392  0.000 ***  [0.459, 0.742]
#>            High  0.521  0.123   4.236  0.000 ***  [0.278, 0.764]
#> ────────────────────────────────────────────────────────────────
#> Note: For continuous variable, low and high represent -1 and +1 SD from the mean, respectively.

#> Warning: You requested > 2 plots. Since 1 plot can be displayed at a time, considering using Rmd for better viewing experience.

# }