Re: [R] about a p-value < 2.2e-16

thanks a lot, Vivek ! in other words, assuming that we work with 1000 data points, shall we use EXACT = TRUE, it uses the normal approximation, while if EXACT=FALSE (for these large samples), it does not ? On Thu, Mar 18, 2021 at 10:47 PM Vivek Das wrote: > Hi Bogdan, > > You can also get the

Re: [R] about a p-value < 2.2e-16

Hi Bogdan, You can also get the information from the link of the Wilcox.test function page. “By default (if exact is not specified), an exact p-value is computed if the samples contain less than 50 finite values and there are no ties. Otherwise, a normal approximation is used.” For more:

Re: [R] about a p-value < 2.2e-16

Dear Peter, thanks a lot. yes, we can see a very precise p-value, and that was the request from the journal. if I may ask another question please : what is the meaning of "exact=TRUE" or "exact=FALSE" in wilcox.test ? i can see that the "numerically precise" p-values are different. thanks a lot

Re: [R] about a p-value < 2.2e-16

Dear Spencer, thank you very much for your prompt email and help. When using : > wilcox.test(rnorm(100), rnorm(100, 2), exact=TRUE) W = 698, p-value < 2.2e-16 > wilcox.test(rnorm(100), rnorm(100, 2), exact=FALSE) W = 1443, p-value < 2.2e-16 and in both cases p-value < 2.2e-16. By "exact"

Re: [R] about a p-value < 2.2e-16

I thinnk the answer is much simpler. The print method for hypothesis tests (class htest) truncates the p-values. In the above example, instead of using wilcox.test(rnorm(100), rnorm(100, 2), exact=TRUE) and copying the output, just print the p-value: tst = wilcox.test(rnorm(100), rnorm(100, 2),

Re: [R] about a p-value < 2.2e-16

  I would push back on that from two perspectives:         1.  I would study exactly what the journal said very carefully.  If they mandated "wilcox.test", that function has an argument called "exact".  If that's what they are asking, then using that argument gives the exact p-value,

[R] about a p-value < 2.2e-16

Dear all, i would appreciate having your advice on the following please : in R, the wilcox.test() provides "a p-value < 2.2e-16", when we compare sets of 1000 genes expression (in the genomics field). however, the

[R] How to plot dates

Greg Based upon you last email I suspect this is what you were looking for. # create the data.frame datetime <- c("2021-03-12 05:16:46","2021-03-12 09:17:02","2021-03-12 13:31:43","2021-03-12 22:00:32", "2021-03-13 09:21:43","2021-03-13 13:51:12","2021-03-13

Re: [R] Plot_ly bar plots - bars span sevral x values when there are missing values.

Hi Rasmus, thanks for that suggestion. It does indeed fix the column widths in this example. the reason it does this is becuase it means that the additonal 0 values added result in values for two adjacent columns. so if adjacent columns are present the "correct" column width is maintained

Re: [R] Failure in predicting parameters

Hello, Maybe a bit late but there is a contributed package [1] for quantitative PCR fitting non-linear models with the Levenberg-Marquardt algorithm. estim and vector R below are your model and your fitted values vector. The RMSE of this fit is smaller than your model's. Isn't this

Re: [R] Failure in predicting parameters

It worked. I re-written the equation as: ``` rutledge_param <- function(p, x, y) ( (p$M / ( 1 + exp(-(x-p$m)/p$s)) ) + p$B ) - y ``` and used Desmos to estimate the slope, so: ``` estim <- nls.lm(par = list(m = halfCycle, s = 2.77, M = MaxFluo, B = high[1]), fn = rutledge_param, x =

Re: [R] modelling 4-parameter curve in R does not match data - how to proceed?

Oh, I got it! I was sending the fluorescence instead of the cycles x. Thank you ``` desmos <- rutledge(list(half_fluorescence = 27.1102, slope = 2.76798, max_fluorescence = 11839.8, back_fluorescence = -138.864) , 1:45) ``` On Wed, Mar 17, 2021 at 8:58 PM Duncan Murdoch