![[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
lm_model_explore(
data,
response_variable,
predictor_variable,
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 = NULL
)Arguments
- data
data.frame- response_variable
Response variable. Support
dplyr::select()syntax.- predictor_variable
Pred variable. Support
dplyr::select()syntax.- 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 = 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)
)
lm_model_explore(data = test,
response_variable = c(y1,y2,y3),
predictor_variable = c(x1,x2,x3),
two_way_interaction_variable = c(m1,m2,m3),
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 0.017 (0.085) + -1.644 (0.088) + -0.045 (0.139) 8.642 (0.014) * 993.000 0.006
#> y1 x1 m2 x1*m2 -0.004 (0.847) 0.114 (0.954) 0.012 (0.771) 3.459 (0.433) 993.000 0.005
#> y1 x1 m3 x1*m3 -0.068 (0.463) 7.077 (0.446) 0.619 (0.460) -60.230 (0.472) 993.000 0.004
#> y1 x2 m1 x2*m1 -0.029 (0.766) 0.301 (0.756) -0.052 (0.865) 4.493 (0.186) 993.000 0.005
#> y1 x2 m2 x2*m2 0.250 (0.183) -2.741 (0.143) -0.643 (0.102) 11.070 (0.010) * 993.000 0.008
#> y1 x2 m3 x2*m3 1.387 (0.188) -13.514 (0.201) -12.617 (0.183) 127.044 (0.182) 993.000 0.007
#> y1 x3 m1 x3*m1 0.103 (0.042) * -0.635 (0.054) + -0.380 (0.020) * 6.551 (0.001) ** 993.000 0.009
#> y1 x3 m2 x3*m2 -0.059 (0.546) 0.090 (0.885) 0.055 (0.788) 4.395 (0.041) * 993.000 0.007
#> y1 x3 m3 x3*m3 0.608 (0.221) -3.382 (0.279) -5.531 (0.216) 34.892 (0.215) 993.000 0.006
#> y2 x1 m1 x1*m1 -0.002 (0.767) 0.271 (0.677) 0.012 (0.556) 9.273 (0.000) *** 993.000 0.003
#> y2 x1 m2 x1*m2 -0.004 (0.741) 0.269 (0.840) 0.015 (0.591) 9.729 (0.001) ** 993.000 0.004
#> y2 x1 m3 x1*m3 -0.076 (0.225) 8.070 (0.199) 0.694 (0.220) -62.546 (0.270) 993.000 0.004
#> y2 x2 m1 x2*m1 0.104 (0.114) -0.948 (0.147) -0.363 (0.079) + 13.940 (0.000) *** 993.000 0.005
#> y2 x2 m2 x2*m2 0.013 (0.917) -0.304 (0.811) -0.073 (0.783) 11.898 (0.000) *** 993.000 0.003
#> y2 x2 m3 x2*m3 -1.092 (0.125) 11.407 (0.111) 9.780 (0.127) -91.428 (0.156) 993.000 0.004
#> y2 x3 m1 x3*m1 0.005 (0.874) 0.045 (0.839) -0.007 (0.952) 10.474 (0.000) *** 993.000 0.002
#> y2 x3 m2 x3*m2 0.045 (0.500) -0.444 (0.293) -0.081 (0.554) 11.666 (0.000) *** 993.000 0.003
#> y2 x3 m3 x3*m3 -0.142 (0.673) 1.331 (0.530) 1.286 (0.671) -1.272 (0.947) 993.000 0.002
#> y3 x1 m1 x1*m1 -0.005 (0.696) 0.417 (0.752) 0.018 (0.670) -0.228 (0.962) 993.000 0.002
#> y3 x1 m2 x1*m2 0.009 (0.744) -1.095 (0.685) -0.016 (0.777) 3.297 (0.586) 993.000 0.002
#> y3 x1 m3 x1*m3 0.161 (0.207) -15.716 (0.218) -1.450 (0.207) 142.647 (0.215) 993.000 0.003
#> y3 x2 m1 x2*m1 -0.461 (0.001) *** 4.438 (0.001) *** 1.596 (0.000) *** -13.781 (0.003) ** 993.000 0.016
#> y3 x2 m2 x2*m2 -0.143 (0.578) 1.196 (0.642) 0.513 (0.342) -3.433 (0.562) 993.000 0.005
#> y3 x2 m3 x2*m3 0.523 (0.718) -4.988 (0.731) -4.486 (0.730) 43.911 (0.737) 993.000 0.004
#> y3 x3 m1 x3*m1 0.056 (0.424) -0.447 (0.323) -0.259 (0.246) 3.422 (0.216) 993.000 0.004
#> y3 x3 m2 x3*m2 0.176 (0.192) -1.284 (0.133) -0.441 (0.114) 4.649 (0.115) 993.000 0.005
#> y3 x3 m3 x3*m3 0.712 (0.297) -4.003 (0.351) -6.487 (0.290) 37.845 (0.327) 993.000 0.004
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 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