similar to: Need suggestions for finding dose response using nls

Hi, I have a SystemStackError. Here is the stack (sorry if it''s a little long) : app/models/line_item.rb:14:in `product'' app/models/line_item.rb:14:in `product'' app/controllers/store_controller.rb:188:in `redirect_to_paypal'' /opt/local/lib/ruby/gems/1.8/gems/activerecord-1.15.3/lib/ active_record/associations/association_proxy .rb:123:in `each''

Hi, First my apologies for a non-working piece of code in a previous submission, I have corrected this error. I'm doing is individual based modelling of a pathogen and it's host. The way I've thought of doing this is with two dataframes, one of the pathogen and it's genes and effector genes, and one of the host and it's resistance genes. During the simulation, these things

Dear all, I would like to know if it is possible to fit in R a Cox ph model with time-dependent covariates and to account for hierarchical effects at the same time. Additionally, I'd like also to know if it would be possible to perform any feature selection on this model fit. I have a data set that is composed by multiple marker measurements (and hundreds of covariates) at different time

Dear R users & Experts, This is just a curiousity, I was wondering why the dominant eigenvetor and eigenvalue of the following matrix is given as the third. I guess this could complicate automatic selection procedures. 0 0 0 0 0 5 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 Please

Hi I'm trying to use ggplot2 to chart a dataset that I've drawn in from excel.? When I run the qplot command, I get the following error message: Error: ggplot2 doesn't know how to deal with data of class character It feels like I need to coerce one of the fields in my data frame. Here's the code, which is short: #draw data in from network location

I'm working on a program that loads several large data files. I'm using ddply (plyr is really awesome) but I want to minimize the amount of times a large data file is read in. One solution is to keep track of which data file is open with a global variable and then change the currently open data file globally as needed. However, I'm unclear on how to alter a global variable

Dovecot version dovecot-1.0.rc14-7 (opensuse) Postfix is using Dovecot LDA. I'm getting this error message, and I guess it's going to be a problem on my next task, which is to set up sieve scripts. Is there something wrong/contraditory on my config or I forgot something? I want: /var/dovecot-tests/: where user's inbox resides /var/dovecot-tests/home/: home dir (users will not be

Dear all, I have a quasipoisson glm for which I need confidence bands in a graphic: gm6 <- glm(num_leaves ~ b_dist_min_new, family = quasipoisson, data = beva) summary(gm6) library('VIM') b_dist_min_new <- as.numeric(prepare(beva$dist_min, scaling="classical", transformation="logarithm")). My first steps for the solution are following: range(b_dist_min_new)

Hi The cor function in the stats package calculates the correlation between columns of data, does anyone know if it is at all possible to calculate the correlation between rows instead ? Or is there an appropriate package or function that is more appropriate I'd like to calculate spearman & pearson correlations between rows. Many thanks Jason -- --------------------------------

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Hello, I would like to know if there is a function in R that will test for normality and handle censored data sets. Currently, I evaluate each censored data set by the extent to which a normal scores plot approximate a straight line. For complete data sets I use shapiro.test(). Below is an example of a censored data set. data1<-c(0.00, 0.00, 0.00, 5.86, 5.17, 8.17, 5.12, 4.92, 7.08,

Dear All, Apologies for sending this email to both list, but at this point I'm not sure which one could help me the most. I have 4 sets of data, 1 test and 3 different sets of controls. The measurements are binary, with a matrix of 0 and 1 I'm measuring across time (rows, ~815) the behaviour of organelles in the cell by microscopy in response to different stimuli (several measurements

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

Dear Alina If I understand you correctly you cannot just have a single predicted curve but one for each level of your factor. On 09/08/2017 16:24, Alina Vodonos Zilberg wrote: > Hi, > > I am performing meta-regression using linear mixed-effect model with the > lme() function that has two fixed effect variables;one as a log > transformed variable (x) and one as factor (y)

Hi All, I'm trying to run a simulation of host-pathogen evolution based around individuals. What I need to have is a dataframe or table of some description - describing all the individuals of a pathogen population (so far I've implemented this as a matrix): ID No_of_Effectors Effectors (Sequences) [1,] 0001 3 ## 3

Dear colleagues, following John Fox' advice in this article (http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf), I'm trying to create a new data frame to examine the differential survival curves from a combination of covariates. These are derived from a Cox Proportional Hazards model I fit to data about the diffusion of a particular policy across American

Hello again, It appears that get_all_vars() incorrectly handles model formulae that use a right-hand side (rhs) matrix. For example, consider these two substantively identical models: # model using named variables mpg <- mtcars$mpg wt <- mtcars$wt hp <- mtcars$hp m1 <- lm(mpg ~ wt + hp) # model using matrix y <- mtcars$mpg x <- cbind(mtcars$wt, mtcars$hp) m2 <- lm(y ~ x)

Thank you Michael, Curves for each level of the factor sounds very interesting, Do you have a suggestion how to plot them? Thank you! Alina *Alina Vodonos Zilberg* On Thu, Aug 10, 2017 at 7:39 AM, Michael Dewey <lists at dewey.myzen.co.uk> wrote: > Dear Alina > > If I understand you correctly you cannot just have a single predicted > curve but one for each level of your

Hi, I am performing meta-regression using linear mixed-effect model with the lme() function that has two fixed effect variables;one as a log transformed variable (x) and one as factor (y) variable, and two nested random intercept terms. I want to save the predicted values from that model and show the log curve in a plot ; predicted~log(x) mod<-lme(B~log(x)+as.factor(y),

I'm working with a few functions (e.g. do.base.descriptions in the netstat package) that, in addition to returning an object with variables I want to extract, also print output. There is no way to turn this default printing behavior off in many of the functions. Is there a blanket way to suppress such printing, say, within a loop or a ddply statement? Thanks! -Jarrett