simlm
: The simlm() function quickly simulates data from a basic linear modelEach of the n noise elements are from rnorm
(mean=0,sd=1).
Except for columns 1 and 2, each of the design-matrix elements are from runif
(min=0,max=1).
The outcome y is noise added to the linear combination of the design matrix with 'coef_true'.
simlm(n = 10, p = 4, coef_true = seq(1:p), seed = NULL,
output_meta = FALSE)
Argument |Description
------------- |----------------
n
| integer for number of observations
p
| integer for total number of cols in the design matrix. Note, offset from '3:p'. Since col 1 is reserved for intercept-column of 1s and col 2 is reserved for treatment-column of 1s/0s
coef_true
| vector (length p) for true lm coefficients
seed
| a seed for RNG (default NULL)
output_meta
| a logical (default FALSE) determining if meta info is returned as list elements
If 'output_meta=FALSE' (the default), only the data.frame 'yx' is returned. The column names of yx are names(yx)=c('y',paste0("x",seq(1:p))).
If 'output_meta=TRUE', a 3 element list with yx, coef_true, noise.
```r
simlm()
out = simlm(n=1000,p=10) lm(data=out,y~-1+.)
out = simlm(n=1000,seed=123) lm(data=out,y~-1+.)
out = simlm(n=1000,seed=123,output_meta=TRUE) str(out)
out = simlm(p=3,n=100,coef_true = c(69,23,7),output_meta=TRUE) lm(data=out$yx[,-2], y ~ 1+.)
```
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