Performing multiple regression with purrr and R

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I'm trying to figure out how can I set up purrr to run several multiple regressions like the image below. As you will notice, this dataset describes an intervention program and we are analyzing this data using ANCOVA procedures (TIME 2 ~ TIME 1 + CONDITION). 

om4g**TIME2**01 ~ om4g**TIME1**01 + CONDITION

example:

om4g201 ~ om4g01 + CONDITION

Regression models

Just in case someone want a reproducible code:

dataset <- data.frame(rest201=c(10,20,30,40),
                      rest101=c(5,10,20,24),
                      omgt201=c(40,10,20,10),
                      omgt101=c(10,20,10,05),
                      CONDITION=c(0,1))
lm(rest201~rest101+CONDITION, data=dataset)
lm(omgt201~omgt101+CONDITION, data=dataset)

I found just one similar question than mine here (Making linear models in a for loop using R programming) but the answer was not working.

Thanks!

purrr linear-regression regression r
2 Answers

Similar to @Roman's answer, here is how to do it using map2 from purrr:

library(purrr)

y_var = c("rest201", "omgt201")
x_var = list(c("rest101", "CONDITION"), c("omgt101", "CONDITION"))

map2(x_var, y_var, ~ lm(as.formula(paste(.y, "~", paste(.x, collapse = " + "))), data = dataset))

To get the summary table for each model, you can wrap each lm with summary and extract the coefficients table:

map2(x_var, y_var, ~ {
  lm(as.formula(paste(.y, "~", paste(.x, collapse = " + "))), data = dataset) %>%
    summary() %>%
    `$`("coefficients")
})

Result:

[[1]]
            Estimate Std. Error   t value   Pr(>|t|)
(Intercept) 2.779097 0.76821670  3.617596 0.17169133
rest101     1.377672 0.04750594 29.000000 0.02194371
CONDITION   3.800475 0.72163694  5.266464 0.11945968

[[2]]
                 Estimate Std. Error       t value  Pr(>|t|)
(Intercept)  3.000000e+01  16.666667  1.800000e+00 0.3228289
omgt101     -2.445145e-16   1.333333 -1.833859e-16 1.0000000
CONDITION   -2.000000e+01  14.529663 -1.376494e+00 0.3999753
user_51506290

You could construct a list of formulas for each model and use that to construct a model.

x <- c(101, 102, 103)
mdls <- sprintf("omg4g%s ~ om4g%s + CONDITION",
        as.character(x + 100), 
        as.character(x)
)

out <- sapply(mdls, FUN = function(x) {
  formula(x, data = latino_dataset)
})

$`omg4g201 ~ om4g101 + CONDITION`
omg4g201 ~ om4g101 + CONDITION
<environment: 0x0000000009aff7b8>

$`omg4g202 ~ om4g102 + CONDITION`
omg4g202 ~ om4g102 + CONDITION
<environment: 0x0000000009afda98>

$`omg4g203 ~ om4g103 + CONDITION`
omg4g203 ~ om4g103 + CONDITION
<environment: 0x00000000099b0828>

e.g.

sapply(out, FUN = lm)
user_3229120
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