I have these regressions being run 1000 times
y ~ x1y ~ x1 + x2y ~ x1 + x2 + x3y ~ x1 + x2 + x3 + x4y ~ x1 + x2 + x3 + x4 + x5y ~ x1 + x2 + x3 + x4 + x5 + x6y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9I have the for loop calculating the RMSE for all of them which turns into 9 vectors with 1000 rows. How do I tell R to tell me which regression has the lowest RMSE for each simulation?
In doing stepwise addition of covariates like this it helps to write a function such as
do_regr <- function(xs) {lm(y ~ xs, data = my_data}
then create a list of vectors, l
v1 <- `x1`
...
v9 <- c(`x1` ... `x9`)
then use purrr:map to apply do_regr over the vector list into a list of lists and sweep that with another function with
sqrt(mean(res$residuals^2))
summary(t(as.matrix(mtcars)))[1,]
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> "Min. : 0.00 " "Min. : 0.000 " "Min. : 1.00 " "Min. : 0.000 "
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> "Min. : 0.00 " "Min. : 0.00 " "Min. : 0.000 " "Min. : 0.000 "
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> "Min. : 0.000 " "Min. : 0.00 " "Min. : 0.00 " "Min. : 0.00 "
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> "Min. : 0.00 " "Min. : 0.00 " "Min. : 0.000 " "Min. : 0.000 "
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> "Min. : 0.000 " "Min. : 1.00 " "Min. : 1.000 " "Min. : 1.000 "
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> "Min. : 0.000 " "Min. : 0.00 " "Min. : 0.000 " "Min. : 0.000 "
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> "Min. : 0.000 " "Min. : 1.000 " "Min. : 0.00 " "Min. : 1.000 "
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> "Min. : 0.000 " "Min. : 0.000 " "Min. : 0.000 " "Min. : 1.00 "
Created on 2020-03-23 by the reprex package (v0.3.0)
Is there any way i can make this easier for you?
That's something we all experience. The best way to get help getting through it is to set up the problem with limited data sets, fewer variables and fewer simulations. The next step is to create the proper objects, the a workflow
I have been doing that but I've just been stuck on getting the index number of the column with the minimum value 
have you tried adapting
summary(t(as.matrix(mtcars)))[1,]
to your data?
yes, ive done something like this however it just gives the values of the minimum not value of the column it is in.
apply(train_selections, 1, FUN=min) this gives me the same thing. (Sorry I wasn't clear though I am going for minimums not maximums)
train_selections is my data.frame
That doesnt work though
library(tidyverse)
library(purrr)
set.seed(42)
df <- tibble( a = rnorm(12),
b = rnorm(12),
c = rnorm(12)
)
#get miniminum values of the columns
varnames <- colnames(df)
min_vals_each_row <- df %>% mutate(minval = pmin(!!!syms(varnames)))
min_vals_each_row$minorigin <- map_int( 1:nrow(min_vals_each_row),
#
# > min_vals_each_row
# # A tibble: 12 x 5
# a b c minval minorigin
# <dbl> <dbl> <dbl> <dbl> <int>
# 1 1.37 -1.39 1.90 -1.39 2
# 2 -0.565 -0.279 -0.430 -0.565 1
# 3 0.363 -0.133 -0.257 -0.257 3
# 4 0.633 0.636 -1.76 -1.76 3
# 5 0.404 -0.284 0.460 -0.284 2
# 6 -0.106 -2.66 -0.640 -2.66 2
# 7 1.51 -2.44 0.455 -2.44 2
# 8 -0.0947 1.32 0.705 -0.0947 1
# 9 2.02 -0.307 1.04 -0.307 2
# 10 -0.0627 -1.78 -0.609 -1.78 2
# 11 1.30 -0.172 0.505 -0.172 2
# 12 2.29 1.21 -1.72 -1.72 3Im so sorry to be annoying but @nirgrahamuk my professor won't have those packages when grading our homework :(. is there another way using just R to do this?
i tried writing an else if tree
if (train_selections_min[i] == train_rmse_x1_1_sigma_1[i]){
train_lowest[i] = 1
} else if (train_selections_min[i] == train_rmse_x1_2_sigma_1[i]){
train_lowest[i] = 2
} else if (train_selections_min[i] == train_rmse_x1_3_sigma_1[i]){
train_lowest[i] = 3
} else if (train_selections_min[i] == train_rmse_x1_4_sigma_1[i]){
train_lowest[i] = 4
} else if (train_selections_min[i] == train_rmse_x1_5_sigma_1[i]){
train_lowest[i] = 5
} else if (train_selections_min[i] == train_rmse_x1_6_sigma_1[i]){
train_lowest[i] = 6
} else if (train_selections_min[i] == train_rmse_x1_7_sigma_1[i]){
train_lowest[i] = 7
} else if (train_selections_min[i] == train_rmse_x1_8_sigma_1[i]){
train_lowest[i] = 8
} else if (train_selections_min[i] == train_rmse_x1_9_sigma_1[i]){
train_lowest[i] = 9
}
where train_selections_min holds the minimum for each row and train_rmse_x1_x_sigma_1 is the value its checking against to see if its equal. It goes from side to side scanning but for some reason this doesnt work either
train_lowest is supposed to be a storage vector for the value of each column
So, I got the else if tree to work now when I try and input the else if results into train_lowest[i], nothing happens. What is wrong here?
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Another question, how to i output the column number of the minimum value of each row in a data frame?
I tried using which.min(apply(train_selections,MARGIN=2,min)) but this just seems to give me the first column. I want the one with the min value and it to give it to me as the index number not the column name.
Time for a reprex, I think
Im sorry, I'm just stuck on this. Ive been working on it for so long and I can't figure out a way to do it 