如何在R中的1664个外植体变量上拟合多元线性回归模型

Question

I have one response variable, and I'm trying to find a way of fitting a multiple linear regression model using 1664 different explanatory variables. I'm quite new to R and was taught the way of doing this by stating the formula using each of the explanatory variables in the formula. However as I have 1664 variables, it would take too long to do. Is there a quicker way of doing this?

Thank you!

Answer 1

I think you want to select from the 1664 variables a valid model, ie a model that predicts as much of the variability in the data with as few explanatory variables. There are several ways of doing this:

• Using expert knowledge to select variables that are known to be relevant. This can be due to other studies finding this, or due to some underlying process that you now makes that variable relevant.
• Using some kind of stepwise regression approach which selects the variables are relevant based on how well they explain the data. Do note that this method has some serious downsides. Have a look at stepAIC for a way of doing this using the Aikaike Information Criterium.

Correlating 1664 variables with data will yield around 83 significant correlations if you choose a 95% significance level (0.05 * 1664) purely based on randomness. So, tread carefully with the automatic variable selection. Cutting down the amount of variables with expert knowledge or some decorrelation techniques (eg principal component analysis) would help.

For a code example, you first need to include an example of your own (data + code) on which I can build.

Answer 2

I'll answer the programming question, but note that often a regression with that many variables could use some sort of variable selection procedure (eg @PaulHiemstra's suggestions).

1. You can construct a data.frame with only the variables you want to run, then use the formula shortcut: form <- y ~ . , where the dot indicates all variables not yet mentioned.
2. You could instead construct the formula manually. For instance: form <- as.formula( paste( "y ~", paste(myVars,sep="+") ) )

Then run your regression:

lm( form, data=dat )