![[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 0.001 (0.935) -0.216 (0.819) -0.008 (0.787) 5.840 (0.105) 993.000 0.007
#> y1 x1 m2 x1*m2 -0.028 (0.190) 2.856 (0.176) 0.048 (0.258) -0.559 (0.904) 993.000 0.007
#> y1 x1 m3 x1*m3 -0.027 (0.769) 2.818 (0.761) 0.238 (0.774) -20.259 (0.808) 993.000 0.005
#> y1 x2 m1 x2*m1 0.017 (0.869) -0.314 (0.766) -0.002 (0.995) 4.985 (0.186) 993.000 0.007
#> y1 x2 m2 x2*m2 -0.062 (0.760) 0.720 (0.724) 0.172 (0.675) 2.539 (0.565) 993.000 0.005
#> y1 x2 m3 x2*m3 0.543 (0.552) -5.352 (0.564) -4.837 (0.557) 52.082 (0.533) 993.000 0.005
#> y1 x3 m1 x3*m1 -0.124 (0.019) * 0.602 (0.069) + 0.440 (0.010) ** 2.295 (0.262) 993.000 0.013
#> y1 x3 m2 x3*m2 0.079 (0.412) -0.387 (0.532) -0.097 (0.620) 4.819 (0.027) * 993.000 0.007
#> y1 x3 m3 x3*m3 -0.338 (0.478) 2.209 (0.464) 3.103 (0.470) -15.835 (0.560) 993.000 0.006
#> y2 x1 m1 x1*m1 -0.002 (0.712) 0.211 (0.743) 0.017 (0.408) 7.851 (0.001) ** 993.000 0.003
#> y2 x1 m2 x1*m2 0.008 (0.569) -0.839 (0.561) -0.006 (0.833) 10.161 (0.001) ** 993.000 0.003
#> y2 x1 m3 x1*m3 0.099 (0.118) -10.149 (0.108) -0.878 (0.122) 99.793 (0.079) + 993.000 0.006
#> y2 x2 m1 x2*m1 0.068 (0.339) -0.710 (0.324) -0.290 (0.198) 12.604 (0.000) *** 993.000 0.003
#> y2 x2 m2 x2*m2 0.147 (0.290) -1.488 (0.287) -0.373 (0.183) 13.299 (0.000) *** 993.000 0.003
#> y2 x2 m3 x2*m3 0.645 (0.302) -6.860 (0.279) -5.890 (0.295) 72.145 (0.206) 993.000 0.003
#> y2 x3 m1 x3*m1 -0.071 (0.050) + 0.399 (0.079) + 0.177 (0.128) 8.673 (0.000) *** 993.000 0.006
#> y2 x3 m2 x3*m2 -0.056 (0.395) 0.330 (0.436) 0.069 (0.606) 9.194 (0.000) *** 993.000 0.003
#> y2 x3 m3 x3*m3 -0.512 (0.117) 2.729 (0.186) 4.564 (0.120) -14.688 (0.429) 993.000 0.005
#> y3 x1 m1 x1*m1 -0.004 (0.743) 0.253 (0.839) 0.013 (0.740) -2.040 (0.667) 993.000 0.004
#> y3 x1 m2 x1*m2 -0.011 (0.679) 0.964 (0.729) 0.024 (0.674) -3.283 (0.592) 993.000 0.003
#> y3 x1 m3 x1*m3 -0.049 (0.690) 6.678 (0.583) 0.437 (0.690) -61.206 (0.576) 993.000 0.005
#> y3 x2 m1 x2*m1 -0.032 (0.815) 0.169 (0.903) 0.179 (0.680) -2.484 (0.617) 993.000 0.004
#> y3 x2 m2 x2*m2 -0.069 (0.796) 0.511 (0.849) 0.216 (0.690) -3.054 (0.599) 993.000 0.003
#> y3 x2 m3 x2*m3 -1.223 (0.310) 14.250 (0.243) 11.097 (0.306) -130.255 (0.236) 993.000 0.006
#> y3 x3 m1 x3*m1 -0.012 (0.865) -0.083 (0.849) 0.022 (0.923) -0.784 (0.772) 993.000 0.004
#> y3 x3 m2 x3*m2 -0.038 (0.766) 0.048 (0.953) 0.061 (0.813) -1.280 (0.655) 993.000 0.003
#> y3 x3 m3 x3*m3 0.301 (0.632) 0.029 (0.994) -2.713 (0.631) -1.321 (0.971) 993.000 0.005
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 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