similar to: Non-Linear Regression with two Predictors

Hi to all I found an formular of an ** ***p-Value Calculator for the Chi-Square test* *http://www.danielsoper.com/statcalc/calc11.aspx* *with the formula* *http://www.danielsoper.com/statkb/topic11.aspx* *what's the gamma function of this formula in r?* *df=5* *ch2=25.50878* *the following code does not give the result <0.001 for the values above * *p=

Dear R help list, I am modeling some survival data with coxph and survreg (dist='weibull') using package survival. I have 2 problems: 1) I do not understand how to interpret the regression coefficients in the survreg output and it is not clear, for me, from ?survreg.objects how to. Here is an example of the codes that points out my problem: - data is stc1 - the factor is dichotomous

Thanks for sharing the questions and responses! Is it possible to appreciate how much the coefficients matter in one or the other model? Say, using Biau's example, using coxph, as.factor(grade2 == "high")TRUE gives hazard ratio 1.27 (rounded). As clinician I can grasp this HR as 27% relative increase. I can relate with other published results. With survreg the Weibull model gives a

Hello, 1) What is the difference between a "data frame" (J H Maindonald, Using R, p. 12) and a "vector"? In Using R, the author asks the reader to enter the following data in a data frame, which I will call "mydata": year snow.cover 1970 6.5 1971 12.0 1972 14.9 1973 10.0 1974 10.7 1975 7.9 ... mydata=data.frame(year=c(1970,...),snow.cover=c(6.5,...)) 2) How to

> -----Message d'origine----- > De : Andrew Bartlett [mailto:abartlet at samba.org] > Envoyé : samedi 26 août 2017 12:40 > À : HB; samba at lists.samba.org > Objet : Re: [Samba] DC Upgrade from 4.1.7 to 4.6.7 > > On Sat, 2017-08-26 at 12:32 +0400, HB via samba wrote: > > > > > Here is the output of samba-tool dbcheck : > > # samba-tool dbcheck >

Hello, I use asterisk to to IAX2 trunking between London POP & Reunion Island pop. I would like to know if it's possible to do a kind of call-limit (i.e. restrict to XX) channels but on a per dialcode and / or destination basis. For example: [trunk] ; reunion proper, i want to send no more than 24 channels exten => _0262XXXXXX,1,Dial(IAX2/mytrunk/${EXTEN}) ; reunion mobile, i want

Dear all, I am trying to fit a double gaussian to some data using the mixdist package: --- begin code --- ? library(mixdist) ? time <- seq(673,723) ??counts <- c(3,12,8,12,18,24,39,48,64,88,101,132,198,253,331,419,563,781,1134,1423,1842,2505,374,6099,9343,13009, ? ? ? ? ? ? ? ? ? ?15097,13712,9969,6785,4742,3626,3794,4737,5494,5656,4806,3474,2165,1290,799,431,213,137,66,57,41,35,27,27,27) ?

Hello Helpers, I have some problems with fitting the model for my data... -->my Literatur says (crawley testbook)= Non-normality of errors-->I get a banana shape Q-Q plot with opening of banana downwards Structure of data: origin wt pes gender 1 wild 5.35 147.0 male 2 wild 5.90 148.0 male 3 wild 6.00 156.0 male 4 wild 7.50 157.0 male 5 wild 5.90

I am trying to understand what is the difference between linear and polynomial kernel: linear: u'*v polynomial: (gamma*u'*v + coef0)^degree It would seem that polynomial kernel with gamma = 1; coef0 = 0 and degree = 1 should be identical to linear kernel, however it gives me significantly different results for very simple data set, with linear kernel

Hi! I would like to perform an F-Test over more than one variable within a generalized mixed model with Gamma-distribution and log-link function. For this purpose, I use the package mgcv. Similar tests may be done using the function "anova", as for example in the case of a normal distributed response. However, if I do so, the error message "error in eval(expr, envir, enclos) :

Dear R-users, I would like to use optim( ) to minimize a function which depends on 4 parameters: 2 vectors, a scalar, and a matrix. And I have a hard to define the parameters at the beginning of the function, and then to call optim. Indeed, all the examples I have seen dont treat cases where parameters are not all real. Here is my code, it doesnt work but its just to show you where is exactly my

I am trying to fit a curve to a cumulative mortality curve (logistic) where y is the cumulative proportion of mortalities, and t is the time in hours (see below). Asym. at 0 and 1 > y [1] 0.00000000 0.04853859 0.08303777 0.15201970 0.40995074 0.46444992 0.62862069 0.95885057 1.00000000 [10] 1.00000000 1.00000000 > t [1] 0 13 20 24 37 42 48 61 72 86 90 I tried to find starting values for

Hi, Use offset variables if count occurrences of an event and you want to model the observation time. glm(count ~ predictors + offset(log(observation_time)), family=poisson) If you want to compare durations, look at library(survival), ?coxph If tnoise_sqrt is the square root of tourist noise, your example seems incorrect, because it is a predictor, not the dependent variable tnoise_sqrt ~

Hi everybody, I'm using the method described here to make a linear regression: http://www.apsnet.org/education/advancedplantpath/topics/Rmodules/Doc1/05_Nonlinear_regression.html > ## Input the data that include the variables time, plant ID, and severity > time <- c(seq(0,10),seq(0,10),seq(0,10)) > plant <- c(rep(1,11),rep(2,11),rep(3,11)) > > ## Severity

Dear R-help, I am using R-3.3.2 on Windows 10. As per my previous post today, I teach on a course which has 4 computer practical sessions related to the development and validation of clinical prediction models. These are currently written for Stata and I am in the process of writing them for use in R too (as I far prefer R to Stata!) Part of the practical requires the student to fit a flexible

> -----Message d'origine----- > De : samba [mailto:samba-bounces at lists.samba.org] De la part de Rowland > Penny via samba > Envoyé : samedi 26 août 2017 12:00 > À : samba at lists.samba.org > Objet : Re: [Samba] DC Upgrade from 4.1.7 to 4.6.7 > ... > On Sat, 26 Aug 2017 11:28:00 +0400 > > Hi, > > > > I have begun to add a new 4.6.7 DC (following

Hi, I want to fit a linear model (without any random effect) with method "ml". I tried to use "glm" I found that there is no option for "ml" or "reml" and the default one is "reml". THen I tried to use "lme" but it requires a random effect. How can I fix this problem? Of course, it's not necessary to be "glm" or

Hi, I'm new to R and am hoping someone might be able to help with the following lme problem. I am trying to fit an ellipse equation to some spatial human factors data, varying the major and minor axes randomly and specifying an exp~ variogram for errors. Using normally-distributed random effects produces some -ve minor/major axes. I am hoping to be able to specify a gamma distribution to

I checked GlmmML package. However, it can only do binomial and poisson distribution. How about others such as gamma or neg binomial? Thank you so much! wensui

I would like to be able to construct hazard rates (or unconditional death prob) for many subjects from a given survfit. This will involve adjusting the ( n.event/n.risk) with (coxph object )$linear.predictors I must be having another silly day as I cannot reproduce the linear predictor: fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian) fit$linear.predictors[1] [1] 2.612756