similar to: conversion factor into numeric

I am attempting to develop a multiple regression model using selected model variables that should all be treated as numeric (mostly real) values. However, R considers one specific variable "mass" automatically to be of class "factor", probably because "mass" consists of integer values that are repeated. I now want to force R to treat "mass" as a numeric

Dear List members I am using the regsubsets function to select a few predictor variables using Mallow's Cp: > sel.proc.regsub.full <- regsubsets(CO2 ~ v + log(v) + v.max + sd.v + tad + no.stops.km + av.stop.T + a + sd.a + a.max + d + sd.d + d.max + RPA + P + perc.stop.T + perc.a.T + perc.d.T + RPS + RPSS + sd.P.acc + P.dec + da.acc.1 + RMSACC + RDI + RPSI + P.acc + cov.v + cov.a +

Hello, The estimate of glm dispersion can be based on the deviance or on the Pearson statistic. I have compared output from R glm() to another statastical package and it appears that R uses the Pearson statistic. I was wondering if it is possible to make use R the deviance instead by modifying the glm(...) function? Thanks for your attention. Kind regards, Robin Smit This e-mail and its

I am hoping to get some advise on the following: I am looking for an automatic variable selection procedure to reduce the number of potential predictor variables (~ 50) in a multiple regression model. I would be interested to use the forward stepwise regression using the partial F test. I have looked into possible R-functions but could not find this particular approach. There is a function

Hello I am able to extract partial regression coefficients from a fitted model object "model", i.e. model <- lm(var.sel.gkm, weights = count.gkm, data = DATA) summary(model) write.table(model$coef, file = "C:/coef_CO_gkm.txt", row.names = TRUE, col.names = TRUE) I was wondering if anyone could advise me how to extract other object items such as std. error, t-values

Hello I am trying to use all subsets regression on a test dataset consisting of 11 trails and 46 potential predictor variables. I would like to use Mallow's Cp as a selection criterion. The leaps function would provide the required output but does not work with this many variables (see below). The alternative function regsubsets should be used, but I am not able to define the function in

Dear list members, I apologize for putting this (probably) very basic question on the mailing list. I have scanned through the R website (using search) but did not found an answer. (code included below) A factor matrix is simply extracted (which can then subsequently be exported using write.table) by FACT$loadings[1:6,]. I would also like to specifically extract and export

Dear all I need to obtain an estimate of the 50% lethal dose (LD50) from a logistic regression model obtained by applying the glm procedure to some binomial data. The model appears to fit the data very well. I used dope.p from MASS to try and find LD50. The following output appears: > dose.p(iso.glm.logit, cf = c(1,3), p = 1:3/4) Error in dose.p(iso.glm.logit, cf = c(1, 3), p = 1:3/4) :

Hello, I have got a simple "cosmetic" question. I have created a bar plot with confidence intervals using: barplot(mean, ylim = c(0,0.2), las = 3, space = 0) arrows(1:17 ,X95p_low, 1:17, X95p_high, length = 0.07, angle = 90, code = 3, lty = 1) Unfortunately, the confidence bars align with the right side of the bars and I would like to shift these to the middle of the bars. Is

Dear mailing list member I have got the following question: The function "cor.test(x1, x2)$p.value" computes the statistical significance of the correlation between vectors x1 and x2. However, I have got a data matrix with many variables. Instead of typing in all possible combinations, I was wondering if there is perhaps an R-function that produces a matrix showing the

Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear. My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~WSf*WSM,

Hi to all, very strange! I tried to debug why imap process takes 100% CPu and it is very slow when I move an email from an imap folder to another imap folder and then I typed: strace -tt -o /log-imap.txt -p <process number> well, during the trace the imap process doesn't crash and it is very fast. If I stop the tracing, I have again the problem and sometimes the crash of the

I have included data at the bottom of this email. It can be read in by highlighting the data and then using this command: dat <- read.table("clipboard", header = TRUE,sep="\t") I can obtain solutions with both of these: library(gee) fit.gee<-gee(score ~ chem + time, id=id, family=gaussian,corstr="exchangeable",data=dat) and library(yags) fit.yags <-

Thanks Bart. Unfortunately "The VIB module does not comply with the security recommendations imposed by VMWare. You lose VMWare support by installing this package." This pretty much rules out this option. :( We need vCenter support, as well. nomad On Thu, May 14, 2020 at 10:06 AM Bart J. Smit <bart at smits.co.uk> wrote: > This shuts down the hosts directly (by-passing

Greetings R Help Group, How does one effect a multiphasic logistic growth model with 4 phases (e.g. Koops 1986; Weigel, Craig, Bidwell and Bates 1992; Grossman and Koops 2003) with R. Before writing to the group, the R help archives were searched, the web was searched with Google, Venables and Ripley 2002 was consulted, Pinheiro and Bates 2000 was consulted, Bates and Watts 2007 was bought and

That's certainly on the list of options. I'm doing data gathering right now to see what options there are. I'd prefer not to rashly decide to run off in a direction only to discover this is already a Solved Problem. nomad On Thu, May 14, 2020 at 1:13 PM Bart J. Smit <bart at smits.co.uk> wrote: > What about the REST API to do a graceful shutdown of your VM’s and the >

I am using barplot, and barplot2 in the gregmisc bundle, in the following way: barplot2(sort(xtabs(expend / 1000 ~ theme)), col = c(mdg7, mdg8, mdg3, mdg1), horiz = T, las = 1, xlab = "$ '000", plot.grid = T) The problem is that the values of 'theme', which is a factor, are in some cases rather long, so that I would like to wrap/split them at a space once they

Dear all, Apologies for this beginner's question. I have a variable Price, which is associated with factors Season and Crop, each of which have several levels. The Price variable contains missing values (NA), which I want to substitute by the mean of the remaining (non-NA) Price values of the same Season-Crop combination of levels. Price Crop Season 10 Rice Summer 12

Thank you for your reply. I am aware of the good reasons not to use the deviance estimate in binomial, Poisson, and gamma families. However, for the inverse Gaussian, the choice seems to me less clear cut. So I just wanted to compare two different options. I have used the dispersion parameter to compute the standardized deviance residuals: summary(model.gamma)$deviance.resid

Hi I would like to plot bar charts in a particular way. The intervals are not evenly distributed and are in a data frame column called size and the relative frequencies are in a second column called mass. Both size and mass are continuous ratio data to all intents and purposes. The data actually represents sieving of sand where $size is sieve aperture and where $mass is the amount remaining on