search for: beta_2

Hi, I'm trying to fit the following model to data using 'nls': y = alpha_1 * beta_1 * exp(-beta_1 * x) + alpha_2 * beta_2 * exp(-beta_2 * x) and the call I've been using is: nls(y ~ alpha_1 * beta_1 * exp(-beta_1 * x) + alpha_2 * beta_2 * exp(-beta_2 * x), start=list(alpha_1=4, alpha_2=2, beta_1=3.5, beta_2=2.5), trace=TRUE, control=nls.control(maxiter = 200)) So the model has 4 parameters (a...

Hello, I would like to create a matrix with one of the columns named $\delta$. I have also created columns $\beta_1$ , $\beta_2$, etc. However, it seems like \d is an escape sequence which gets automatically removed. (Using these names such that they work right in xtable -> latex) colnames(simpleReg.mat) <- c("$\beta_1$","$SE(\beta_1)$", "$\beta_2$", "$SE(\beta_2)$", "$\de...

...n't get the results. I get the following message: BHHH maximisation Number of iterations: 0 Return code: 100 Initial value out of range. I don't know this is so because of the way i have written my loglikelihood or what. The loglikelihood LogLik<-function(param){ beta_1<-param[1] beta_2<-param[2] beta_3<-param[3] beta_4<-param[4] lambda.plus<-beta_1*Iplusless1 + beta_2*Iminusless1 lambda.minus<-beta_3*Iplusless1 + beta_4*Iminusless1 sum(Iplus_new*log(lambda.plus))-sum(log(lambda.plus)*x_new) + sum(Iminus_new*log(lambda.minus))-sum(log(lambda.minus)*x_new) } The g...

I want to estimate the following fixed effect model: y_i,t = alpha_i + beta_1 x1_t + beta_2 x2_i,tx2_i,t = gamma_i + gamma_1 x1_t + gamma_2 Z1_i + gamma_3 Z2_i I can use ivreg from AER to do the iv regression. fm <- ivreg(y_i,t ~ x1_t + x2_i,t | x1_t + Z1_i + Z2_i, data = DataSet) But, I'm not sure how can I add the fixed effects. Thanks! [[alternative HT...

producing a multi-figure plot, i am try to add beta_1, beta_2,.. beta_9 to ylab using expression or substitution, but cannot work out like for (i in 1:9){ plot(seq(1/m, 1-1/m, 1/m), beta.q[,i], type="l", col=1, ylim=range(beta.q), xlab="quantile", ylab=expresion(beta[i])) } any suggestions will be greatly appreciated. DL [[al...

...; is a more accurate description.) In order to ascribe unique values to the parameters, one must apply a "constraint". With the "treatment contrasts" the constraint is that beta_1 = 0. As a result the mean for the first treatment is mu, that for the second treatment is mu + beta_2, and so on. Consequently the SE corresponding to "(Intercept)" is the SE of estimated mean for treatment 1. The SE corresponding to beta_2 is the SE of the estimated *difference* between the mean for treatment 2 and that for treatment 1, and so on. Frequently the constraint beta_1 +...

This is really a statistical issue. What do you think the Intercept term represents? See ?contrasts. Cheers, 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 Thu, Feb 15, 2018 at 5:27 PM, Marc Girondot via R-help < r-help at

Dear all, I'm running a coxph model of the form: coxph(Surv(Start, End, Death.ID) ~ x1 + x2 + a1 + a2 + a3) Within this model, I would like to compare the influence of x1 and x2 on the hazard rate. Specifically I am interested in testing whether the estimated coefficient for x1 is equal (or not) to the estimated coefficient for x2. I was thinking of using a Chow-test for this but the Chow

Dear Sir/Madam, I apologize for any cross-posting. I got a simple question, which I thought the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~ Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t) are needed. y=c(12,5,17,14) n=4 What about a Hierarchal B ayes

Dear R-er, I try to get the standard error of fitted parameters for factors with a glm, even the reference one: a <- runif(100) b <- sample(x=c("0", "1", "2"), size=100, replace = TRUE) df <- data.frame(A=a, B=b, stringsAsFactors = FALSE) g <- glm(a ~ b, data=df) summary(g)$coefficients # I don't get SE for the reference factor, here 0:

Dear lmer-ers, My thanks for all of you who are sharing your trials and tribulations publicly. I was hoping to elicit some feedback on my thoughts on denominator degrees of freedom for F ratios in mixed models. These thoughts and practices result from my reading of previous postings by Doug Bates and others. - I start by assuming that the appropriate denominator degrees lies between n

...had to use "for" loop, which is quite slow. The loop is necessary, since you need to calculate recursive sequence. Is there a faster way to do this in R, without using loops? The model is such: r_t = \mu + \alpha_2 r_{t-2} + a_t a_t = \sigma_t\varepsilon_t \sigma_t^2 = \beta_1a_{t-1}^2+\beta_2\sigma_{t-1}^2+ 1_{\{a_{t-1}>0\}}(\gamma_0+ \gamma_1a_{t-1}^2+\gamma_2\sigma^2_{t-1}) It is asummed that \varepsilon_t are iid and normal with zero mean and variance one. The data given is r_t, and you have to estimate variables, \mu, \alpha, \beta and \gamma. Since \varepsilon_t=\frac{a_t}{\sq...

...Now, by changing my data set(final), I repeat the process 100 times and obtain 100 such vectors which I store as 100 rows in a 100X5 matrix: matrix[i,]=coef(reg)[best,] (i varying from 1 to 100) Now my final estimates for the beta's are: Beta_0=median(matrix[,1]) Beta_1=median(matrix[,2]) Beta_2=median(matrix[,3]) Beta_3=median(matrix[,4]) Beta_4=median(matrix[,5]) I want to find the p-values of each of the estimated beta's. I am confused how to extract these p values in R (may be we need to go back to the reg= lm.ridge model corresponding to each final beta estimate, but I am not sur...

Hi everybody! I'm looking some way to do in R a polynomial fit, say like polyfit function of Octave/MATLAB. For who don't know, c = polyfit(x,y,m) finds the coefficients of a polynomial p(x) of degree m that fits the data, p(x[i]) to y[i], in a least squares sense. The result c is a vector of length m+1 containing the polynomial coefficients in descending powers: p(x) = c[1]*x^n +

..._1=(\alpha\beta)_{11}= (\alpha\beta)_{12}=(\alpha\beta)_{12}=(\alpha\beta)_{31}=0\] in the model $E[Y_{jkl}]=\mu+\alpha_j+\beta_k+(\alpha\beta)_{jk}$ $j=1,2,3$, $k=1,2$, $l=1,2$, Dobson, page 102. My question is: how can I incorporate restrictions like $\alpha_1+\alpha_2+\alpha_3=0$, $\beta_1+\beta_2=0$, $(\alpha\beta)_{21}+\alpha\beta)_{22}=0$, $(\alpha\beta)_{31}+(\alpha\beta)_{32}=0$ and $(\alpha\beta)_{11}+(\alpha\beta)_{21}+(\alpha\beta)_{31}=0$ from the outset? Or put another way: Why is it that lm() uses the corner point constraints by default? Where can I find a documentation for thi...

Dear list: I am attempting to use what I thought would be a pretty straightforward practical application of Cox regression. I figure users of the survival package must have come across this problem before, so I would like to ask you how you dealt with it. I have set up an illustrative example and included it at the end of this post. I took a sample of 100 data points from each of two populations

Hola a todos. Estoy utilizando la función lmer del paquete lme4 para ajustar un modelo mixto. Tengo varias variables en mi data.frame, unas son a nivel individual y otras a nivel de comarcas. Listo algunas. ingre_6 : Ingresos (nivel individual) iscs_a : un indicador sintético resumen de otras variables, calculado mediante componentes principales. sau_com : superficie agraria útil de cada una

...@table @option @item --help ====================================================================== [-as-] {+as:+} ====================================================================== --- R-intro.texi~ Mon Aug 23 13:24:18 2004 +++ R-intro.texi Mon Aug 23 13:24:18 2004 @@ -4945,7 +4945,7 @@ $\beta_2=\theta_2/\theta_1$. @end tex Supposing a suitable data frame to be set up we could fit this -non-linear regression as +non-linear regression as: @example > nlfit <- glm(y ~ x1 + x2 - 1, ====================================================================== [-below.-] {+below:+} ======...