![[Experimental]](figures/lifecycle-experimental.svg)
Exploratory analyses for linear regression models with multiple response, predictor, and two-way interaction variables. (lm models).
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
glm_model_explore(
data,
response_variable,
predictor_variable,
family,
two_way_interaction_variable = NULL,
three_way_interaction_variable = NULL,
control_variable = NULL,
marginal_alpha = 0.1,
verbose = TRUE,
show_p = TRUE,
return_result = FALSE,
print_control = FALSE,
plot_interaction = FALSE,
file_dir = NULL,
device = "jpeg",
width = 8.5,
height = 5,
units = "in",
y_lim = c(0, 1)
)Arguments
- data
data.frame- response_variable
Response variable. Support
dplyr::select()syntax.- predictor_variable
Pred variable. Support
dplyr::select()syntax.- family
a GLM family. It will passed to the family argument in glm See
?glmfor possible options.- two_way_interaction_variable
Two-way interaction variable. Each two-way interaction variable will interact with each pred variable. Support
dplyr::select()syntax.- three_way_interaction_variable
Three-way interaction variable. Each three-way interaction variable will interact with each pred and two-way interaction variables. Support
dplyr::select()syntax.- control_variable
Control variables. Support
dplyr::select()syntax.- marginal_alpha
Set marginal_alpha level for marginally significant (denoted by
.). Set to 0.05 if do not want marginally significant denotation.- verbose
Default is
TRUE. Set toFALSEto suppress outputs- show_p
Default is
TRUE. WhenTRUE, show the p-value in parenthesis.- return_result
Default is
FALSE. IfTRUE, it returns the model estimates as a data frame.- print_control
Default is
FALSE. IfTRUE, print coefficients of control variables.- plot_interaction
Default is
FALSE. IfTRUE, interactions will be plotted and saved on your laptop.- file_dir
Path of the directory to save plot to
- device
Default is "jpeg". See ggplot2::ggsave() for all options.
- width
Default is 8.5 (i.e., letter size width).
- height
Default is 5.
- units
Default is inches. Options are "in", "cm", "mm" or "px".
- y_lim
the plot's upper and lower limit for the y-axis. Length of 2. Example:
c(lower_limit, upper_limit)
Value
data.frame
Examples
test = data.frame(y1 = sample(c(0, 1), size = 1000, replace = TRUE),
y2 = sample(c(0, 1), size = 1000, replace = TRUE),
y3 = sample(c(0, 1), size = 1000, replace = TRUE),
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)
)
glm_model_explore(data = test,
response_variable = c(y1,y2,y3),
predictor_variable = c(x1,x2,x3),
two_way_interaction_variable = c(m1,m2,m3),
family = binomial(link = 'logit'),
control_variable = c(c1,c2,c3))
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Response Pred Interact_pred Interact_term Interact_term_coef Interact_pred_coef Pred_coef (Intercept) df r2
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> y1 x1 m1 x1*m1 0.006 (0.370) -0.640 (0.318) -0.020 (0.325) 3.300 (0.173) 993.000 0.008
#> y1 x1 m2 x1*m2 -0.015 (0.253) 1.601 (0.231) 0.028 (0.312) -2.020 (0.508) 993.000 0.007
#> y1 x1 m3 x1*m3 0.001 (0.988) -0.851 (0.902) -0.012 (0.984) 8.867 (0.886) 993.000 0.007
#> y1 x2 m1 x2*m1 -0.051 (0.426) 0.447 (0.490) 0.078 (0.690) 0.383 (0.867) 993.000 0.008
#> y1 x2 m2 x2*m2 -0.149 (0.245) 1.565 (0.226) 0.231 (0.384) -1.584 (0.593) 993.000 0.008
#> y1 x2 m3 x2*m3 0.457 (0.507) -5.310 (0.443) -4.179 (0.500) 49.356 (0.427) 993.000 0.008
#> y1 x3 m1 x3*m1 0.038 (0.215) -0.294 (0.130) -0.133 (0.167) 1.968 (0.123) 993.000 0.009
#> y1 x3 m2 x3*m2 0.062 (0.328) -0.298 (0.460) -0.144 (0.271) 1.691 (0.234) 993.000 0.007
#> y1 x3 m3 x3*m3 -0.029 (0.928) -0.554 (0.782) 0.245 (0.933) 6.008 (0.738) 993.000 0.007
#> y2 x1 m1 x1*m1 -0.009 (0.165) 0.801 (0.213) 0.030 (0.144) -2.160 (0.372) 993.000 0.005
#> y2 x1 m2 x1*m2 -0.009 (0.500) 0.777 (0.559) 0.022 (0.439) -1.228 (0.686) 993.000 0.003
#> y2 x1 m3 x1*m3 0.070 (0.307) -6.125 (0.375) -0.625 (0.309) 55.636 (0.371) 993.000 0.004
#> y2 x2 m1 x2*m1 0.013 (0.843) -0.214 (0.740) -0.124 (0.527) 2.179 (0.339) 993.000 0.005
#> y2 x2 m2 x2*m2 0.128 (0.315) -1.413 (0.273) -0.352 (0.186) 4.550 (0.126) 993.000 0.005
#> y2 x2 m3 x2*m3 -1.452 (0.038) * 15.418 (0.028) * 12.971 (0.039) * -136.941 (0.030) * 993.000 0.009
#> y2 x3 m1 x3*m1 0.001 (0.970) -0.095 (0.621) -0.014 (0.883) 1.033 (0.416) 993.000 0.003
#> y2 x3 m2 x3*m2 0.020 (0.748) -0.236 (0.557) -0.051 (0.692) 1.262 (0.372) 993.000 0.002
#> y2 x3 m3 x3*m3 -0.446 (0.170) 3.526 (0.080) + 3.989 (0.171) -30.809 (0.089) + 993.000 0.005
#> y3 x1 m1 x1*m1 -0.004 (0.495) 0.338 (0.598) 0.006 (0.774) -0.855 (0.723) 993.000 0.005
#> y3 x1 m2 x1*m2 0.022 (0.097) + -2.104 (0.117) -0.053 (0.061) + 4.535 (0.140) 993.000 0.006
#> y3 x1 m3 x1*m3 0.011 (0.875) -0.571 (0.934) -0.104 (0.865) 5.399 (0.930) 993.000 0.003
#> y3 x2 m1 x2*m1 -0.050 (0.437) 0.407 (0.533) 0.206 (0.299) -2.323 (0.312) 993.000 0.005
#> y3 x2 m2 x2*m2 0.153 (0.231) -1.418 (0.271) -0.246 (0.354) 1.731 (0.559) 993.000 0.004
#> y3 x2 m3 x2*m3 -1.136 (0.101) 11.850 (0.088) + 10.278 (0.099) + -107.628 (0.085) + 993.000 0.005
#> y3 x3 m1 x3*m1 0.007 (0.828) -0.136 (0.480) -0.004 (0.965) -0.231 (0.855) 993.000 0.004
#> y3 x3 m2 x3*m2 -0.003 (0.958) 0.132 (0.743) 0.023 (0.862) -0.940 (0.506) 993.000 0.002
#> y3 x3 m3 x3*m3 0.317 (0.325) -1.368 (0.493) -2.834 (0.327) 11.666 (0.514) 993.000 0.003
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> You can drag and resize the R console to view the entire table
#> Note: Coefficient (p-value): + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001