similar to: Calculation of extremely low p-values (in lm)

Colleagues, Here is my reproducible code for a graph using geom_smooth set.seed(55) scatter_data <- tibble(x_var = runif(100, min = 0, max = 25) ?????????????????????? ,y_var = log2(x_var) + rnorm(100)) library(ggplot2) library(cowplot) ggplot(scatter_data,aes(x=x_var,y=y_var))+ ? geom_point()+ ? geom_smooth(se=TRUE,fill="blue",color="black",linetype="dashed")+

?s 05:17 de 12/08/2023, Thomas Subia via R-help escreveu: > Colleagues, > > Here is my reproducible code for a graph using geom_smooth > set.seed(55) > scatter_data <- tibble(x_var = runif(100, min = 0, max = 25) > ?????????????????????? ,y_var = log2(x_var) + rnorm(100)) > > library(ggplot2) > library(cowplot) > > ggplot(scatter_data,aes(x=x_var,y=y_var))+

Hi Everyone, I just don't know how to extract the information I want from the summary of a linear regression model fitting. For example, I fit the following simple linear regression model: results = lm(y_var ~ x_var) summary(results) gives me: Call: lm(formula = y_var ~ x_var) Residuals: Min 1Q Median 3Q Max -5.9859 -1.5849 0.4574 2.0163 4.6015 Coefficients:

G'day Thomas, On Sat, 12 Aug 2023 04:17:42 +0000 (UTC) Thomas Subia via R-help <r-help at r-project.org> wrote: > Here is my reproducible code for a graph using geom_smooth The call "library(tidyverse)" was missing. :) > I'd like to add a black boundary around the shaded area. I suspect > this can be done with geom_ribbon but I cannot figure this out. Some >

Rhelpers: Having a problem solving this. I have an xyplot call that looks like this: print(xyplot(temp_species_EAM_Pred_Pop$x+temp_species_NULL_Pred_Pop$x~temp_species_EAM_Pred_Pop$Action,main=current_species, xlab="Action",ylab="Predicted Pop", xlim=c(xmin,xmax),ylim=c(ymin,ymax),

Apologies if this has been gone over, but I believe I have checked the intertubes more than a bit..... I am using libvirt and have vms booting under an OVMF.fd to use an efi firmware. I can create vms, linux ubuntu, and they will boot up. However, everytime I reboot am I dropped into the default efi shell provide by the tianocore build. Then I must walk the FS to the booting efi app and run, in

Thanks VERY MUCH for all the info and help! Apologies for the extreme delay. I got distracted by other threads that forced out this work to later date. Also some frustration as well. ;) I completely missed this update. Apologies and thanks Will be diving back into this shortly! On Thu, Mar 24, 2016 at 3:57 PM, Laszlo Ersek <lersek@redhat.com> wrote: > On 03/21/16 19:53, jsl6uy js16uy

On Mon, 2016-03-21 at 13:53 -0500, jsl6uy js16uy wrote: > for this host....my nvram setting look like >   <os> >     <type arch='x86_64' machine='pc-i440fx-2.5'>hvm</type> >     <loader readonly='yes' type='pflash'>/home/xyz/OVMF.fd</loader> >     <nvram

On 03/21/16 19:53, jsl6uy js16uy wrote: > Apologies if this has been gone over, but I believe I have checked the > intertubes more than a bit..... > > I am using libvirt and have vms booting under an OVMF.fd to use an efi > firmware. I can create vms, linux ubuntu, and they will boot up. > However, everytime I reboot am I dropped into the default efi shell > provide by the

Hi, I have a time series problem that I would like some help with if you have the time. I have many data from many sites that look like this: Site.1 date time level temp 2009/10/01 00:01:52.0 2.8797 18.401 2009/10/01 00:16:52.0 2.8769 18.382 2009/10/01 00:31:52.0 2.8708 18.309 2009/10/01 00:46:52.0 2.8728 18.285 2009/10/01 01:01:52.0

Hello, I'm running mixed models in GAMM4 with 2 (non-nested) random intercepts and I want to include a spline term for one of my exposure variables. However, when I include a spline term, I always get reported degrees of freedom of less than 1, even when I know that my spline is using more than 1 degree of freedom. For example, here is the code for my model: >

I am not sure if this is an R-users question, but since most of you here are statisticians, I decided to give it a shot. I am using the lm() function in R to fit a dependent variable to a set of 3 to 5 independent variables. For this, I used the following commands: >model1<-lm(function=PBW~SO4+NO3+NH4) Coefficients: (Intercept) SO4 NO3 NH4 0.01323 0.01968

Dear All, I am working with a generalized linear mixed-effects model with poisson error using the lme4 package in R. I created a model with the lmer function including some main effects, three two way interactions and two random effects. The model works well, but I have troubles when removing on of the two-way interactions. The Warning message: "In mer_finalize(ans) : false convergence

Hi, I have been struggling with this problem for some time now. Internet, books haven't been able to help me. ## I have factorial design with counts (fruits) as response variable. > str(stubb) 'data.frame': 334 obs. of 5 variables: $ id : int 6 23 24 25 26 27 28 29 31 34 ... $ infl.treat : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 2 1 1 ... $ def.treat :

hi, i hope i'm not reporting something well-known; i tried to understand the available bug tracking information. please excuse me if my problem report should not meet your standards, but i want to direct your attention to the following: http://koffein.org/av/rsync-bugreport/ problem report for rsync 2.6.9 on linux/IA32, 09 Dec 2006 Herwig Wittmann

Dear Jim, Here is again exactly what I did and with the output of Rprof (with this reduced dataset and with a simpler function, it is here much faster than in real life). Thanks you again for your help! ## CODE ## mydata1<- structure(list(species = structure(1:8, .Label = c("alsen","gogor", "loalb", "mafas", "pacyn", "patro",