I have a dataframe that looks like the following.
consistent admire trust judge
3 3 2 4
5 1 3 6
2 4 5 1
I can run the regressions I need simultaneously using the following code. In the actual dataset, there are many more than 3 variables.
variables <- c("admire", "trust", "judge")
form <- paste("consistent ~ ",variables,"")
model <- form %>%
set_names(variables) %>%
map(~lm(as.formula(.x), data = df))
map(model, summary)
This yields the output for the 3 following regressions.
summary(lm(consistent ~ admire, df))
summary(lm(consistent ~ trust, df))
summary(lm(consistent ~ judge, df))
I would like a list of the variables with significant p-values at p < 0.05. For example, if "admire" was significant and "judge" was significant, the output I am looking for would be something like:
admire, judge
Is there a way to do this that allows me to also run several regressions simultaneously? This question offers a similar answer, but I don't know how to apply it when I have several regressions.
Data:
structure(list(consistent = c(1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1,
0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1,
1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,
1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,
1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1,
0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1,
1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,
0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0,
1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0,
0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,
0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0,
1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0), admire = c(7,
3, 1, 1, 3, 5, 5, 6, 7, 1, 4, 2, 5, 3, 3, 1, 3, 1, 2, 1, 5, 5,
3, 1, 5, 3, 5, 4, 5, 1, 6, 1, 6, 2, 1, 4, 1, 1, 3, 2, 1, 5, 1,
7, 1, 4, 1, 4, 2, 2, 4, 2, 4, 1, 5, 5, 1, 2, 6, 6, 1, 1, 3, 5,
5, 1, 5, 7, 2, 4, 5, 1, 4, 4, 3, 5, 6, 1, 5, 2, 1, 5, 6, 2, 3,
3, 5, 6, 1, 4, 4, 6, 4, 4, 4, 6, 5, 4, 1, 2, 5, 4, 2, 4, 6, 1,
3, 7, 4, 4, 3, 2, 7, 5, 3, 2, 1, 2, 2, 5, 7, 3, 5, 4, 6, 2, 2,
4, 4, 5, 5, 1, 5, 6, 1, 2, 4, 7, 1, 4, 5, 4, 2, 4, 1, 4, 3, 4,
7, 5, 6, 3, 1, 1, 7, 1, 6, 4, 1, 1, 2, 1, 1, 6, 3, 1, 4, 4, 7,
2, 1, 5, 3, 3, 7, 4, 5, 1, 3, 7, 5, 4, 1, 1, 1, 5, 2, 1, 1, 4,
1, 5, 4, 5, 1, 4, 4, 4, 7, 1, 1, 2, 5, 2, 4, 2, 4, 6, 4, 2, 6,
5, 6, 7, 4, 4, 5, 1, 5, 7, 1, 7, 2, 7, 3, 6, 2, 5, 7, 3, 5, 4,
1, 4, 1, 5, 1, 1, 6, 6, 7, 3, 4, 1, 6, 4, 1, 6, 7, 5, 4, 2, 6,
5, 5, 4, 1, 2, 6, 1, 5, 3, 1, 1, 1, 7, 7, 3, 5, 1, 5, 1, 7, 2,
5, 4, 2, 1, 4, 1, 1, 5, 5, 4, 5, 2, 4, 5, 5, 1, 4, 4, 1, 3, 4,
2, 7, 6, 6, 4, 3, 6, 1, 6, 1, 1, 4, 7, 7, 1, 3, 1, 4, 2, 2, 6,
1, 2, 1, 1, 1, 4, 2, 5, 4, 1, 4, 2, 5, 5, 2, 1, 6, 1, 2, 3, 4,
1, 7, 2, 2, 4, 5, 1, 6, 2, 5, 1, 5, 6, 2, 5, 1, 1, 7, 4, 5, 6,
1, 4, 5, 2, 4, 4, 6, 4, 4, 2, 6, 1, 1, 2, 6, 1, 3, 5, 5, 3, 7,
5, 6, 4, 3, 4, 7, 5, 4, 2, 1, 5, 7, 2, 6, 3, 1, 2, 4, 3, 5, 4,
1, 6, 1, 3, 1, 1, 1, 4, 3, 3, 1, 1, 1, 6, 4, 1, 1, 1, 1, 4, 1,
6, 4, 4, 4, 4, 1, 5, 2, 4, 5, 4, 4, 3, 3, 6, 7, 3, 2, 4, 2, 5,
1, 4, 5, 4, 1, 2, 4, 1), trust = c(7, 4, 2, 2, 3, 4, 6, 6, 7,
1, 4, 5, 5, 4, 1, 1, 2, 2, 1, 1, 6, 6, 4, 1, 3, 6, 5, 4, 6, 1,
5, 1, 6, 1, 2, 5, 1, 1, 4, 1, 1, 5, 1, 7, 1, 4, 4, 5, 3, 4, 5,
3, 5, 2, 6, 5, 3, 2, 6, 6, 1, 1, 3, 5, 5, 1, 5, 7, 2, 4, 6, 1,
4, 4, 4, 6, 6, 3, 5, 6, 1, 6, 5, 2, 2, 2, 5, 7, 1, 5, 3, 7, 3,
5, 4, 6, 6, 5, 2, 1, 6, 5, 2, 6, 5, 1, 2, 7, 6, 5, 3, 3, 4, 7,
4, 2, 1, 3, 4, 7, 6, 2, 6, 5, 7, 3, 2, 4, 5, 5, 5, 1, 2, 7, 1,
1, 5, 4, 1, 4, 6, 6, 2, 4, 2, 4, 1, 5, 7, 6, 7, 3, 2, 1, 7, 1,
4, 4, 1, 2, 4, 1, 1, 6, 3, 1, 4, 3, 7, 2, 2, 6, 4, 5, 7, 5, 7,
2, 4, 7, 4, 3, 1, 1, 1, 5, 2, 4, 1, 4, 1, 5, 4, 5, 1, 6, 5, 4,
6, 1, 1, 2, 6, 2, 4, 4, 4, 5, 6, 1, 5, 5, 5, 6, 4, 4, 5, 5, 6,
7, 1, 7, 3, 7, 5, 6, 3, 5, 7, 4, 5, 4, 2, 3, 1, 4, 5, 1, 5, 4,
7, 3, 5, 1, 6, 6, 1, 4, 6, 5, 4, 3, 7, 6, 5, 4, 1, 1, 6, 1, 5,
3, 1, 1, 1, 7, 7, 3, 4, 1, 4, 1, 7, 2, 4, 2, 2, 2, 4, 1, 1, 5,
4, 6, 5, 2, 4, 5, 4, 1, 6, 4, 1, 4, 4, 3, 7, 5, 6, 4, 4, 6, 2,
6, 1, 2, 4, 7, 7, 1, 1, 1, 4, 2, 2, 6, 2, 4, 1, 2, 1, 6, 2, 6,
4, 1, 6, 3, 5, 4, 3, 1, 6, 1, 2, 3, 5, 1, 6, 1, 3, 4, 5, 2, 6,
2, 5, 1, 3, 7, 1, 4, 1, 1, 7, 5, 6, 5, 1, 5, 5, 1, 4, 3, 7, 4,
4, 1, 7, 1, 1, 4, 6, 1, 4, 5, 5, 4, 7, 6, 7, 4, 4, 4, 4, 4, 4,
1, 1, 5, 6, 2, 7, 4, 2, 4, 5, 4, 5, 4, 1, 5, 1, 2, 1, 1, 4, 4,
3, 4, 3, 1, 2, 6, 5, 1, 1, 1, 2, 4, 1, 7, 4, 4, 5, 6, 2, 5, 3,
4, 5, 4, 4, 3, 3, 6, 7, 4, 4, 3, 2, 5, 1, 5, 5, 5, 2, 2, 3, 1
), judge = c(1, 5, 6, 3, 6, 3, 4, 5, 4, 1, 3, 2, 3, 2, 4, 3,
4, 2, 5, 4, 3, 3, 4, 4, 7, 5, 4, 4, 1, 3, 6, 2, 3, 2, 5, 2, 3,
4, 2, 4, 4, 3, 4, 4, 1, 4, 1, 2, 3, 1, 2, 2, 3, 5, 3, 5, 5, 3,
1, 4, 4, 2, 5, 4, 3, 1, 5, 4, 4, 5, 2, 2, 2, 7, 3, 3, 1, 1, 5,
3, 3, 1, 2, 5, 2, 3, 5, 4, 3, 4, 3, 2, 1, 3, 4, 4, 5, 5, 3, 2,
2, 3, 2, 4, 1, 1, 4, 2, 2, 3, 3, 2, 4, 4, 6, 1, 7, 4, 2, 3, 4,
1, 2, 4, 4, 5, 2, 1, 3, 2, 2, 1, 1, 7, 2, 3, 5, 5, 1, 2, 2, 5,
6, 5, 1, 1, 1, 4, 1, 5, 4, 3, 6, 1, 4, 1, 3, 4, 6, 1, 2, 4, 3,
3, 4, 7, 1, 3, 1, 2, 2, 3, 2, 3, 5, 3, 4, 2, 6, 3, 1, 1, 1, 1,
4, 2, 2, 4, 4, 5, 4, 2, 1, 6, 7, 5, 2, 2, 4, 5, 6, 1, 5, 2, 4,
5, 5, 2, 2, 3, 4, 5, 2, 2, 4, 1, 3, 4, 4, 4, 2, 3, 1, 4, 4, 3,
2, 3, 1, 4, 2, 4, 4, 1, 5, 4, 4, 4, 4, 6, 1, 3, 5, 7, 2, 6, 1,
5, 7, 5, 4, 2, 3, 6, 3, 1, 1, 2, 2, 5, 5, 2, 5, 4, 4, 5, 4, 4,
3, 7, 4, 4, 4, 2, 5, 3, 6, 5, 4, 4, 4, 6, 4, 5, 5, 1, 5, 2, 6,
4, 4, 1, 1, 4, 6, 1, 7, 1, 5, 2, 5, 4, 2, 3, 2, 6, 3, 2, 2, 1,
1, 5, 4, 1, 1, 4, 1, 5, 1, 4, 3, 2, 3, 4, 1, 6, 1, 2, 1, 3, 5,
5, 2, 1, 3, 4, 2, 4, 5, 4, 6, 3, 4, 6, 7, 6, 2, 4, 6, 2, 4, 5,
1, 4, 1, 3, 2, 4, 1, 6, 4, 3, 1, 3, 4, 5, 1, 6, 1, 5, 1, 3, 3,
1, 3, 4, 2, 4, 1, 1, 2, 2, 2, 3, 1, 6, 5, 4, 1, 7, 5, 6, 5, 2,
3, 5, 4, 3, 4, 5, 7, 1, 5, 2, 5, 1, 3, 4, 3, 5, 1, 4, 2, 3, 4,
1, 7, 5, 5, 2, 1, 2, 5, 6, 5, 5, 3, 1, 3, 1, 4, 1, 5, 2, 3, 5,
6, 4, 4, 3, 2, 4, 1, 3, 4, 3, 4, 4, 1, 5)), row.names = c(NA,
-450L), class = c("tbl_df", "tbl", "data.frame"))
To fit many many simple linear regression models, I recommend Fast pairwise simple linear regression between variables in a data frame. Hmm... looks like I need to collect those functions in an R package...
## suppose your data frame is `df`
## response variable (LHS) in column 1
## independent variable (RHS) in other columns
out <- general_paired_simpleLM(df[1], df[-1])
# LHS RHS alpha beta beta.se beta.tv beta.pv
#1 consistent admire -0.1458455 0.18754326 0.008324192 22.529906 1.040756e-75
#2 consistent trust -0.2211250 0.19565589 0.007721387 25.339475 1.531499e-88
#3 consistent judge 0.3484851 0.04824981 0.014182420 3.402086 7.287372e-04
# sig R2 F.fv F.pv
#1 0.3430602 0.53118295 507.59665 1.040756e-75
#2 0.3212008 0.58902439 642.08902 1.531499e-88
#3 0.4946862 0.02518459 11.57419 7.287372e-04
To get what you want:
with(out, RHS[beta.pv < 0.05])
#[1] "admire" "trust" "judge"