search for: propotional

...ot;blue","green","red"),...) } ) but in plot, x-axis scale ranges from 0 to 30 though I have "Freq" column values in range of 0 to 100. I have tried many ways to get x-axis scale to 0 to 100, including xlim=c(1:100), but still bars in plot are not propotional to the range 0 to 100. hence can somebody let me know how get bars in the plot propotional to x-axis range 0 to 100? Regards, mlsc [[alternative HTML version deleted]]

1. You neglected to cc r-help! 2. Word files are **not** text files. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Fri, Jun 23, 2017 at 12:23 PM, Jay Zola <jayjay.1988 at hotmail.nl> wrote: > Dear sir, > > >

> On Jun 23, 2017, at 5:53 AM, Jay Zola <jayjay.1988 at hotmail.nl> wrote: > > Dear sir/madame, > > > I am currently writing a meta-analysis on the complication and reoperation rates of 5 different treatment modalities after a distal radius fracture. I was able to pool the rates of the 5 different rates using R. Now I have to compare the pooled rates of the 4 treatment

With R and the hist function, is there a way to make a histogram in which the y axis denotes propotion with respect to a separate sample dataset of the same range instead of frequency? [[alternative HTML version deleted]]

Dear sir/madame, I am currently writing a meta-analysis on the complication and reoperation rates of 5 different treatment modalities after a distal radius fracture. I was able to pool the rates of the 5 different rates using R. Now I have to compare the pooled rates of the 4 treatment modalities with the golden standard separately. I though the chi squared test would be the best method. How do

Dear R-users I am trying to make an adjusted Kaplan-Meier curve (using the Survival package) but I am having difficulty with plotting it so that the plot only shows the curves for the adjusted results. My data come from a randomised controlled trial, and I would like the adjusted Kaplan-Meier curve to only show two curves for the adjusted survival: one for those on treatment (Treatment==1)

I am calculating cox propotional hazards models with the coxph function from the survival package. My data relates to failure of various types of endovascular interventions. I can successfully obtain the LR, Wald, and Score test p-values from the coxph.object, as well as the hazard ratio as follows: formula.obj = Surv(days, sta...

First a statistical issue: The survfit routine will produce predicted survival curves for any requested combination of the covariates in the original model. This is not the same thing as an "adjusted" survival curve. Confusion on this is prevalent, however. True adjustment requires a population average over the confounding factors and is closely related to the standardized

Hi all, i know that for each virtual machine i can set up a number of vcpu. But if i want to give only a % of the real CPU? For example: how can i set up a virtual domain for using only 20% of the real CPU? is it possible? Ty in advance D. _______________________________________________ Xen-users mailing list Xen-users@lists.xensource.com http://lists.xensource.com/xen-users

With ggplot2, I can plot the glm stat_smooth for binomial data when the response is binary or a two-level factor as follows: data("Donner", package="vcdExtra") ggplot(Donner, aes(age, survived)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2) But how can I

Hi, I tried this one over on the main wx-users list as well, but thought I would try it here since I am using wxRuby: I am using wxWidgets 2.8.7 on a linux box using the GTK build. I have pared down my issue to the simplest case: I am trying to use a horizontal BoxSizer to split a frame into two sections: a left and right side. I want the left side to stretch proportionally and I want the

Hi list, I want to find the total maximum resources I can spend given a set allocation proportion and some simple budget constraints. However, I get suboptimal results via lp and friends (i.e. lpSolve and simplex in the linprog and boot) . For example: library(lpSolve) proportions = c( 0.46, 0.28, 0.26) constraints = c( 352, 75, 171) lp(objective.in = proportions, const.mat =