search for: unrounding

Dear users, I am attempting to plot an ols() call from the Design package, by following the procedure explained by Harald Baayen in his 2008 book 'Analyzing linguistic data. A practical introduction to statistics using R', page 175-181. I've attached my data to this e-mail (I hope it's small enough that that's ok). First I paste all the commands I ran, followed by the

I need to report exact p-values in my dissertation. Looking at my lm() results of many regressions with huge datasets I have the feeling that p-values are rounded to the smallest value of "2e-16", because this p-value is very common. Is that true or just chance? If it is true, how do I obtain the "true" unrounded p-values for these regressors? m1 <- lm(y ~ x1+x2+x3+4+x5,

Hi R users. I am new to the community and have got myself into a little problem. I have a dataset of birth weights recorded by nurses at a delivery clinic in an developing country. The weights are entered in KiloGrams with one decimal. However there is substantial heaping at each 500g when looking at the sample in a histogram. Do anyone of you know a easy way to adjust for this and if it exists

Dear UseRs, I'm running a chi-squared test where the expected matrix is the same as the observed, after rounding. R reports a X-squared of zero with a p value of one. I can justify this because any other result will deviate at least as much from the expected because what we observe is the expected, after rounding. But the formula for X-squared, sum (O-E)^2/E gives a positive value. What