How to do a regression of a series of variables without typing each variable name

I want to run a regression with a bunch of independent variables from my dataset. There are a lot of predictors, so I do not want to write them all out. Is there a notation to span multiple columns so I don't have to type each?

My attempt was doing this (where my predictors are column 20 to 43):

modelAllHexSubscales = lm(HHdata$garisktot~HHdata[,20:43])

Obviously, this does not work because HHdata[,20:43] is a matrix of data, whereas I really ne开发者_JAVA百科ed it to see the data as HHdata[,20]+HHdata[,21] etc.

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Here's another alternative:

# if garisktot is in columns 20:43
modelAllHexSubscales <- lm(garisktot ~ ., data=HHdata[,20:43])
# if it isn't
modelData <- data.frame(HHdata["garisktot"],HHdata[,20:43])
modelAllHexSubscales <- lm(garisktot ~ ., data=modelData)

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Generate a formula by pasting column names first.

f <- as.formula(paste('garisktot ~', paste(colnames(HHdata)[20:43], collapse='+')))
modelAllHexSubscales <- lm(f, HHdata)

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Have you tried to do it directly, as in

> y
[1] 10 19 30 42 51 59 72 78

> X
     [,1] [,2]
[1,]    1  1.0
[2,]    2  3.0
[3,]    3  5.5
[4,]    4  7.0
[5,]    5  9.0
[6,]    6 11.0
[7,]    7 13.0
[8,]    8 16.0

> summary(lm(y ~ X))

Call:
lm(formula = y ~ X)

Residuals:
      1       2       3       4       5       6       7       8 
-0.1396 -1.2774  0.9094  1.4472  0.3094 -1.8283  1.0340 -0.4547 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)   -2.647      2.004  -1.321  0.24366   
X1            15.436      3.177   4.859  0.00464 **
X2            -2.649      1.535  -1.726  0.14490   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.363 on 5 degrees of freedom
Multiple R-squared:  0.9978,    Adjusted R-squared:  0.9969 
F-statistic:  1124 on 2 and 5 DF,  p-value: 2.32e-07