Dummy data shown below. I tried amending my loop regression code to account for spaces in the column names, but it still gives an error. Additionally, I'm not sure how to adjust .x to account for spaces in the independent variable column names too.
The purpose of the code is to regress the first column against each of the subsequent columns one at a time. i.e. Canada Price ~ Global Price, then Canada Price ~ Canada Bond Price and so on.
lm.test <- map_dfr(
set_names(names(df)),
~ tidy(lm(as.formula(paste("`",colnames(df[1]),"`","~", .x)),
data = df)),
.id = "predictor")
| Canada Price | Global Price | Canada 2Y Bond Price | US - Canada Inflation | Oil Price |
|---|---|---|---|---|
| -0.030661468 | -0.000207995 | 0.084 | -0.075 | 0.002116 |
| -0.034269304 | -0.140212141 | 0.363 | -1.062 | -0.06122 |
| 0.02371464 | 0.019987 | -0.781 | 0.273 | 0.209895 |
| -0.007971484 | 0.090597341 | -0.221 | 0.068 | -0.10657 |
| 0.005342716 | 0.060627149 | 0.151 | 0.234 | -0.22191 |
| -0.002210376 | 0.046010712 | -0.106 | 0.13 | 0.073975 |
| 0.018338299 | 0.019799534 | 0.036 | 0.005 | 0.058091 |
| 0.000662471 | -0.025356047 | 0.523 | 0.045 | -0.03765 |
| 0.002124411 | 0.045979342 | -0.433 | 0.282 | 0.365933 |
| 0.034191555 | 0.043211347 | -0.08 | 0.147 | 0.113365 |
| -0.011470069 | -0.033773176 | 0.396 | -0.037 | -0.09753 |
| 0.007039847 | 0.050327089 | -0.052 | 0.151 | 0.145487 |
| -0.028872959 | -0.004501371 | 0.324 | -0.214 | 0.064282 |
| 0.009244373 | -0.00092413 | 0.166 | -0.066 | 0.076961 |
| 0.017448201 | -0.010638258 | -0.179 | 0.053 | 0.108548 |
| -0.002379819 | 0.050111674 | 0.352 | -0.161 | -0.11261 |
| -0.002103406 | 0.03094893 | 0.121 | 0.106 | 0.130575 |
| 0.019154969 | 0.08307375 | 0.051 | 0.149 | 0.041074 |
| -0.00124318 | -0.054526954 | 0.345 | 0.032 | 0.079687 |
| -0.001241636 | 0.003319001 | -0.289 | 0.219 | 0.100941 |
Data in dput format.
df1 <-
structure(list(
`Canada Price` = c(-0.030661468, -0.034269304, 0.02371464, -0.007971484,
0.005342716, -0.002210376, 0.018338299, 0.000662471, 0.002124411,
0.034191555, -0.011470069, 0.007039847, -0.028872959, 0.009244373,
0.017448201, -0.002379819, -0.002103406, 0.019154969, -0.00124318, -0.001241636),
`Global Price` = c(-0.000207995, -0.140212141, 0.019987, 0.090597341,
0.060627149, 0.046010712, 0.019799534, -0.025356047, 0.045979342,
0.043211347, -0.033773176, 0.050327089, -0.004501371, -0.00092413,
-0.010638258, 0.050111674, 0.03094893, 0.08307375, -0.054526954, 0.003319001),
`Canada 2Y Bond Price` = c(0.084, 0.363, -0.781, -0.221, 0.151, -0.106,
0.036, 0.523, -0.433, -0.08, 0.396, -0.052, 0.324, 0.166, -0.179, 0.352,
0.121, 0.051, 0.345, -0.289),
`US - Canada Inflation` = c(-0.075, -1.062, 0.273, 0.068,
0.234, 0.13, 0.005, 0.045, 0.282, 0.147, -0.037, 0.151, -0.214,
-0.066, 0.053, -0.161, 0.106, 0.149, 0.032, 0.219),
`Oil Price` = c(0.002116, -0.06122, 0.209895, -0.10657, -0.22191, 0.073975,
0.058091, -0.03765, 0.365933, 0.113365, -0.09753, 0.145487, 0.064282,
0.076961, 0.108548, -0.11261, 0.130575, 0.041074, 0.079687, 0.100941)),
class = "data.frame", row.names = c(NA, -20L))
Here is a solution to run regressions on all other columns but the first.
The formula is created with reformulate.
suppressPackageStartupMessages({
library(magrittr)
library(purrr)
})
# create the formula with 'value' as regressor,
# it will be the column name after pivoting to long format
fmla <- reformulate("value", response = names(df1)[1L] |> as.name())
# run the regressions
lm_fit_list <- df1 %>%
tidyr::pivot_longer(-1L) %>%
split(.$name) %>%
map(\(df) lm(fmla, data = df))
# this gives some statistics
lm_smry_list <- lm_fit_list %>% map(summary)
lm_smry_list %>% map(coef)
#> $`Canada 2Y Bond Price`
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.001736073 0.003667346 0.4733866 0.64162781
#> value -0.025789455 0.011690588 -2.2060015 0.04061964
#>
#> $`Global Price`
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.001515084 0.003712875 -0.4080622 0.68804100
#> value 0.164831106 0.070018144 2.3541199 0.03012634
#>
#> $`Oil Price`
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.001424249 0.004112478 -0.3463238 0.7331147
#> value 0.046411939 0.030404220 1.5264966 0.1442663
#>
#> $`US - Canada Inflation`
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.0001923356 0.003176245 0.06055441 0.952381362
#> value 0.0393945114 0.011325922 3.47826091 0.002683133
Created on 2024-02-23 with reprex v2.0.2
After running summary on the output list, you can extract the values you want from this second list (of summaries).
suppressPackageStartupMessages({
library(magrittr)
library(purrr)
})
# create the formula with 'value' as regressor,
# it will be the column name after pivoting to long format
fmla <- reformulate("value", response = names(df1)[1L] |> as.name())
# run the regressions
lm_fit_list <- df1 %>%
tidyr::pivot_longer(-1L) %>%
split(.$name) %>%
map(\(df) lm(fmla, data = df))
lm_smry_list <- lm_fit_list %>% map(summary)
stats <- lm_smry_list %>%
map(coef) %>%
map2(names(lm_smry_list), \(x, y) {
data.frame(Variable = y) %>% cbind(x)
}) %>%
list_rbind()
row.names(stats) <- NULL
stats
#> Variable Estimate Std. Error t value Pr(>|t|)
#> 1 Canada 2Y Bond Price 0.0017360725 0.003667346 0.47338661 0.641627809
#> 2 Canada 2Y Bond Price -0.0257894550 0.011690588 -2.20600149 0.040619640
#> 3 Global Price -0.0015150839 0.003712875 -0.40806217 0.688041004
#> 4 Global Price 0.1648311056 0.070018144 2.35411990 0.030126342
#> 5 Oil Price -0.0014242490 0.004112478 -0.34632381 0.733114717
#> 6 Oil Price 0.0464119390 0.030404220 1.52649664 0.144266337
#> 7 US - Canada Inflation 0.0001923356 0.003176245 0.06055441 0.952381362
#> 8 US - Canada Inflation 0.0393945114 0.011325922 3.47826091 0.002683133
Created on 2024-02-23 with reprex v2.0.2