CODEWITHSUNDEEP
rstatisticsregression
Dev P
Dev P

Reputation: 449

Is there a way to check the regression on all variables(dependent) in the data

is there a way to make all variables a target variable and check regression results against other independent variables. For example

df
Date         Var1       Var2    Var3
27/9/2019     12         45      59
28/9/2019     34         43      54
29/9/2019     45         23      40

Usually if want to see the relationship between Var1 and Var2 i use below code

lm(Var1 ~ Var2, data = myData)

In case I want to see the results for all variables (Var1 , Var2 and Var3) like, in one instance, Var1 is dependent variable and rest(Var2 and Var3) are independent. Then 2 instance, Var2 is dependent variable and rest(Var1 and Var3) are independent and so on. Is there a way to do this?

Upvotes: 1

Views: 633

Answers (3)

Rui Barradas
Rui Barradas

Reputation: 76402

The following is based on the answers to these questions: 1, 2 and 3. See the explanations therein.

The main difference is that it loops (lapply) through the columns of the input data set and constructs full models with each of those column-vectors as response and all others as predictors. Then dredges the full model fit.

library(MuMIn)

model_list <- lapply(names(df1), function(resp){
  fmla <- as.formula(paste(resp, "~ ."))
  print(fmla)
  full <- lm(fmla, data = df1, na.action = na.fail)
  dredge(full)
})

model_list

Test data creation code. 

set.seed(1234)
df1 <- replicate(3, sample(10:99, 100, TRUE))
df1 <- as.data.frame(df1)
names(df1) <- paste0("Var", 1:3)

Upvotes: 0

user10191355
user10191355

Reputation:

You could use something like this to get the formulas you need:

vars <- names(df)[-1] # we can eliminate the dates

forms <- lapply(1:length(vars),
       function(i) formula(paste(vars[i], "~", paste(vars[-i], collapse = "+")))
       )

Output:

[[1]]
Var1 ~ Var2 + Var3
<environment: 0x7fdaaa63abd0>

[[2]]
Var2 ~ Var1 + Var3
<environment: 0x7fdaaa63c508>

[[3]]
Var3 ~ Var1 + Var2
<environment: 0x7fdaaec0d2a8>

Then you just need to pass each formula into lm in lapply:

mods <- lapply(forms, lm, data = df)

Output:

[[1]]

Call:
FUN(formula = X[[i]], data = ..1)

Coefficients:
(Intercept)         Var2         Var3  
    196.403        3.514       -5.806  


[[2]]

Call:
FUN(formula = X[[i]], data = ..1)

Coefficients:
(Intercept)         Var1         Var3  
   -55.8933       0.2846       1.6522  


[[3]]

Call:
FUN(formula = X[[i]], data = ..1)

Coefficients:
(Intercept)         Var1         Var2  
    33.8301      -0.1722       0.6053

Upvotes: 2

Cettt
Cettt

Reputation: 11981

If you want to regress Var1 against all other variables you can do the following :

 lm(Var1 ~. , data = myData)

If you just want to select more tab one variable than you can also use:

lm(Var1 ~ Var2 + Var3, data = myData)

Upvotes: 0

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