search for: proportionality

Hello, we want to plot a proportional symbol map with ggplot. Symbols' area should have the same proportions as the scaled variable. Hereby an example we found on http://www.r-bloggers.com/bubble-chart-by-using-ggplot2/ . In this example we see the proportions of the symbols' area are different from the proportions of the scaled variable: crime <-

Dear list, I have very little experience in dealing with proportions, i am sure this is a very simple question but i could find no suitable answer beyond doing a chi-sq test and then using the Marascuilo procedure as a post-hoc analysis. I am simply wanting to know if the proportions ( i.e the number of Yes / No) significantly differ between the cases and if so which cases are significantly

Hi , SO i have been on a role of asking simple questions lately. So much for feeling like im getting this R business. I wrote a script 2 weeks ago that utilized "proportion" to turn values in a table (from "table") into proportions to then graph. I now get an error that proportion is not a function so im confused. I ran the script a few times and im thinking maybe i had

Hello. My question is in the subject line. Using R 4.1.3 on Windows 10. Commented MWE below. Thanks. --Chris Ryan library(dplyr) library(lattice) ## fabricate a dataframe dd <- data.frame(agency = sample(LETTERS, size = 5), total = sample(100:200, size = 5), las = sample(20:40, size = 5)) dd <- dd %>% mutate(proportion = las/total, bubble = total/100) ## attempt to make a dotplot

Hello, I want to determine the regression relationship between a proportion (y) and a continuous variable (x). Reading a number of sources (e.g. The R Book, Quick R,help), I believe I should be able to designate the model as: model<-glm(formula=proportion~x, family=binomial(link="logit")) this runs but gives me error messages: Warning message: In eval(expr, envir, enclos) :

Dear Sir, I am using the following cos which creates a 3 panel figure on one image. However the legend function i am using only allows the legend to be placed within the axes of one of the graphs. The plots however do not allow for a logend to be places within the axes do to the location of points on the graph. I can add the legend later using imaging software such as paint but i lose

Hello. I must fit a logistic regression to data in the form of proportions, but in which some of the proportions are zero. I therefore cannot use the glm function with a binomial link since the link function is not defined for p=0 or 1. What other solutions are available? Any references to this specific problem (i.e. regression using proportions, of which some are zero) would be welcome.

Hi listers, I am working on a program of statistical analysis of simulated data and I've been searching the error at the program, but I didn't find it! It is something about the WHILE procedure, the error says: Error in while (ecart >= d) { : missing value where TRUE/FALSE needed Thanks in advance! M?rcio k<-100 d<-0.00112 z<-rnorm(100, 0, 1) prop<-rep(0,100) for (i in

BODY { font-family:Arial, Helvetica, sans-serif;font-size:12px; }I am looking to draw what I hoped was a simple plot of proportion WITHIN a strata, save % males by site. I seem to be able to get proportion of males, by site, where the proportion is across the whole dataset, but not the proportion within each site. thanks in advance, Rob [[alternative HTML version deleted]]

Handling NSE in these kinds of examples is a pain in lattice. I would suggest using with() and dropping the data argument for simple examples, e.g., dd |> mutate(new.proportion = las/total, new.bubble = total/100) |> with(dotplot(agency ~ new.proportion, pch = 16, cex = new.bubble)) But if you care about multi-panel plots, you also need to be careful about making sure that the

Following from the R-help thread of March 22 on "Memory usage in prcomp", I've started looking into adding an optional 'rank.' argument to prcomp allowing to more efficiently get only a few PCs instead of the full p PCs, say when p = 1000 and you know you only want 5 PCs. (https://stat.ethz.ch/pipermail/r-help/2016-March/437228.html As it was mentioned, we already

Dear all, I am trying to find a suitable R-function for parametric proportional hazard regressions. The package survival contains the coxph() function which performs a Cox regression which leaves the base hazard unspecified, i.e. it is a semi-parametric method. The package Design contains the function pphsm() which is good for parametric proportional hazard regressions when the underlying base

Dear R-Helpers, I've looked high and low for a function that provides frequencies, proportions and cumulative proportions side-by-side. Below is the table I need. Is there a function that already does it? Thanks, Bob > # Generate some test scores > myValues <- c(70:95) > Score <- ( sample( myValues, size=1000, replace=TRUE) ) > head(Score) [1] 77 71 81 88 83 93 > >

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 =

I agree with Kasper, this is a 'big' issue. Does your method of taking only n PCs reduce the load on memory? The new addition to the summary looks like a good idea, but Proportion of Variance as you describe it may be confusing to new users. Am I correct in saying Proportion of variance describes the amount of variance with respect to the number of components the user chooses to show? So

Dear Dushanthi, Please keep your e-mails on the R-Help list, where Michael has already given you some excellent advice. As Michael already explained, metafor can handle proportions, but does not have any specific functionality for categorical variables with more than 2 levels (at the moment). So, if it is logical and possible to do so, you could collapse the levels of the categorical outcome to 2

R-experts: I would like to explore the partial proportional odds models of Peterson and Harrell (Applied Statistics 1990, 39(2): 205-217) for a dataset that I am analyzing. I have not been able to locate a R package that implements these models. Is anyone aware of existing R functions, packages, etc... that might be used to implement the partial proportional odds models? Brant Inman

Hello metafor users, I'm using metafor to perform a single-effect summary estimate of the raw proportion of patients experiencing a post-operative complication, and I'm interested in seeing if this proportion differs between the three most commonly used surgical techniques. The software is working as expected, but I would like to double check on the interpretation of my mixed-effect model

Hello, I am trying to compute the density of a variable k that is either (1) Normally distributed; (2) Log-Normally distributed; or (3) follows proportional error distribution. I tried to search R-help and the answer for normal distribution was easy to find (please see 1c). I am not sure if my formula for dlnorm is correct (please see 2c below)? I really don't know what function to use for the

Good morning, I am using R to try to model the proportion of burned area in Portugal. The dependent variable is the proportion. The family used is binomial and the epsilon would be binary. I am not able to find the package to be used when the proportion (%) has to be used in glm. Could someone help me? I am using normal commands of glm.. for example: glm_5<- glm(formula=p~Precipitation,