search for: 0.0639

Hi All, I am trying to reproduce this using R instead. [image: Full-size image (38 K)] I tried using the following code *SChla <- read.csv("SM_Chla_data.csv")* *Atlantis <- SChla[16:66,]* *head(Atlantis)* * * Seamount Station Depth Pico Nano Micro Total_Ch dbar Latitude Longitud 16 Atlantis 1217 Surface 0.0639 0.1560 0.0398 0.2597 2.082 -32.71450 57.29733

Hello, I have a data frame like this: > head(SNP) mean var sd FQC.10090295 0.0327 0.002678 0.0517 FQC.10119363 0.0220 0.000978 0.0313 FQC.10132112 0.0275 0.002088 0.0457 FQC.10201128 0.0169 0.000289 0.0170 FQC.10208432 0.0443 0.004081 0.0639 FQC.10218466 0.0116 0.000131 0.0115 ... and I am creating plot like this: s <- ggplot(SNP, mapping = aes(x = mean, y = var))

Would someone be able to help with this question? I'm using the Gcmrec, Survrec, and Design packages to do a power analysis on simulated data. I'm receiving an error after using the Survr function that all data must have a censoring time even after using the gcmrec function: newdata<-addCenTime(olddata). My program is below. I'd greatly appreciate any help!

Hello everybody! I have an ARIMA model for a time series. This model was obtained through an auto.arima function. The resulting model is a ARIMA(2,1,4)(2,0,1)[12] with drift (my time series has monthly data). Then I perform a 12-step ahead forecast to the cited model... so far so good... but when I look the plot of my forecast I see that the result is really far from the behavior of my time

I recommend that you consult with a local statistical expert. Much of what you say (outliers?!?) seems to make little sense, and your statistical knowledge seems minimal. Perhaps more to the point, none of your questions can be properly answered without subject matter context, which this list is not designed to provide. That's why I believe you need local expertise. Bert Gunter "The

Hi Abby, thank you for getting back to me and for this useful information. I'm trying to detect the outliers in my distribution based of mean and variance. Can I see that from the plot I provided? Would outliers be outside of ellipses? If so how do I extract those from my data frame, based on which parameter? So I am trying to connect outliers based on what the plot is showing: s <-

> My understanding is that this represents bivariate normal > approximation of the data which uses the kernel density function to > test for inclusion within a level set. (please correct me) You can fit a bivariate normal distribution by computing five parameters. Two means, two standard deviations (or two variances) and one correlation (or covariance) coefficient. The bivariate normal

Hi Bert, Another confrontational response from you... You might have noticed that I use the word "outlier" carefully in this post and only in relation to the plotted ellipses. I do not know the underlying algorithm of geom_density_2d() and therefore I am having an issue of how to interpret the plot. I was hoping someone here knows that and can help me. Ana On Fri, Oct 9, 2020 at

You could assign a density value to each point. Maybe you've done that already...? Then trim the lowest n (number of) data points Or trim the lowest p (proportion of) data points. e.g. Remove the data points with the 20 lowest density values. Or remove the data points with the lowest 5% of density values. I'll let you decide whether that is a good idea or a bad idea. And if it's a

My understanding is that this represents bivariate normal approximation of the data which uses the kernel density function to test for inclusion within a level set. (please correct me) In order to exclude the outlier to these ellipses/contours is it advisable to do something like this: SNP$density <- get_density(SNP$mean, SNP$var) > summary(SNP$density) Min. 1st Qu. Median Mean 3rd

Dear list, dear Frank, I try to fit a Weibull survival regression model with package Design: sclear <- psm(sobj~V1+V2,dist="weibull") sobj is a one-dimensional survival object (no event indicators), V1 and V2 are factors. I get the following result: Parametric Survival Model: Weibull Distribution psm(formula = sobj ~ V1 + V2, dist = "weibull") Obs Events

Hi Abby, Thanks for getting back to me, yes I believe I did that by doing this: SNP$density <- get_density(SNP$mean, SNP$var) > summary(SNP$density) Min. 1st Qu. Median Mean 3rd Qu. Max. 0 383 696 738 1170 1789 where get_density() is function from here: https://slowkow.com/notes/ggplot2-color-by-density/ and keep only entries with density > 400