search for: beta_1

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...

Hello, I'm having trouble figuring out how to calculate a p-value for a 1-tailed test of beta_1 in a linear model fit using command lm. My model has only 1 continuous, predictor variable. I want to test the null hypothesis beta_1 is >= 0. I can calculate the p-value for a 2-tailed test using the code "2*pt(-abs(t-value), df=degrees.freedom)", where t-value and degrees.freedom...

...a. library(MASS) mu <- c(0,1) Sigma <- matrix(c(1,.8,.8,1), ncol=2) set.seed(123) x <- mvrnorm(50,mu,Sigma) plot(x) abline(lm(x[,2] ~ x[,1])) Say I want to add a normal distribution surrounding the predicted values at the x-values of -1, 0, and 1. That is, at the points \hat{y} = \mu + \beta_1(-1) \hat{y} = \mu + \beta_1(0) \hat{y} = \mu + \beta_1(1) How might I go about doing this? Harold > version _ platform i386-pc-mingw32 arch i386 os mingw32 system...

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("$\beta_1$","$SE(\beta_1)$", "$\beta_2$", "$SE(\beta_2)$"...

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits...

Hi, as i suppose to know in a stationary GARCH(1,1) model the sum of alpha and beta has to be smaller than 1. But if i use the garchfit() function from the package fGarch for my timeseries the sum is bigger than 1. The adf.test tells me a p-value smaller than 0.01 instead. What does this mean for me? Can i trust in the coefficients in this case? mfg user84 -- View this message in context:

In the logistic regression model, there is no residual log (pi/(1-pi)) = beta_0 + beta_1*X_1 + ..... But glm model will return residuals What is that? How to understand this? Can we put some residual in the logistic regression model by replacing pi with pi' (the estimated pi)? log (pi'/(1-pi')) = beta_0 + beta_1*X_1 + .....+ ei Thanks! [[alternative HTML version de...

...es run well but i don'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.mi...

...[1:18, 1:4] -0.170 -0.304 -2.617 2.025 -1.610 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:18] "Amount: CP for VF" "Amount: CP for F" "Amount: CP for M" "Amount: RD for VF" ... ..$ : chr [1:4] "beta_0" "P-value" "beta_1" "P-value" It contains the following: > model.mat[1:2,] beta_0 P-value beta_1 P-value Amount: CP for VF -0.1702877 0.7716153 0.5148616 2.020602e-03 Amount: CP for F -0.3042522 0.1966133 0.8795982 6.058299e-12 That's fine. Now to produce...

...max) - p.hat(x.mean) using predict() (with type="response"). And I can get the standard error for p.hat (x.max) and p.hat(x.mean) by specifiying se.fit=TRUE. No problem there. But how can I get a handle on the standard error of the difference? In a linear model this would just be SE(beta_1.hat)*(x.max-x.mean) (where beta_1.hat is specific to the particular level of `a' being considered). If I am not mistaken. (Please correct me if I am!) But in the logistic model, everything is entangled in the inverse link function (the ``expit'' function as it is called by some), an...

Hola, Tengo una base de datos con estructura jerárquica en la que quiero clasificar observaciones en distintas categorías. En el caso más simple, tengo una variable con dos categorías (variable Y1) y dentro de cada una de ellas hay otras dos categorías (variable Y2). Además tengo una variable explicativa cuantitativa discreta X. El banco de datos sería de este tipo: X Y1 Y2 5 0 1 9 0 0 2

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! [[...

Hi Listers, seems I have forgotten some basics re offset in glm: data: counts (y) from locations off different size (area), explanatory variable: x Model: y ~ x+offset(area) Predictions (pred) using Poisson errors plot(x,y) and points(x,pred) gives neat "line" of estimated values. However, for ease of understanding graphs are better using plot(x,y/area). Question: How to display

...gless! (The usual jargon is to say that they are "not estimable", but "meaningless" 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* bet...

...boot[,e,p,2]) g1 <- ggplot(data, aes(x, y)) g1 + geom_errorbar(classiclimits, colour = "red") + geom_errorbar(ownlimits, colour = "green") + geom_errorbar(rbootlimits, colour = "blue") + geom_hline(yintercept = betas[p]) + xlab("Simulation") + ylab("beta_1") + opts(title = "CI for error 'normal' and beta_1") + opts(legend.position = c(10,2.5)) Many thanks in advance, Best regards, Giovanni [[alternative HTML version deleted]]

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. D...

...hort answer to the original question: > On 16 Feb 2018, at 05:02 , Rolf Turner <r.turner at auckland.ac.nz> wrote: > > 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. ...and consequently, being a constant, has an s.e. of 0. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com

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

...n', I received a floating point exception even when compiled with -DFIXED_POINT. The reason is as follows: <original beta calculation within speex_preprocess_run> ... st->nb_adapt++; st->min_count++; beta = MAX16(QCONST16(.03,15),DIV32_16(Q15_ONE,st->nb_adapt)); beta_1 = Q15_ONE-beta; ... </> On my architecture(x86) at least the DIV32_16 is defined as follows. typedef short spx_int16_t; typedef spx_int16_t spx_word16_t; #define DIV32_16(a,b) ((spx_word16_t)(((spx_word32_t)(a))/((spx_word16_t)(b)))) The nb_adapt overflow occurred when its value reached 0x...

...including a "fixed effect" in the econometrics sense of the term -- an individual-specific intercept. I also want to model the continuous variables flexibly -- I have no good priors on the proper specification for the function form. The model is the following: y_{it} = \alpha_i + \beta_1(T_{it}) + f(continuous.vars_{it}) + e_{it} To control for unobserved time-invariant heterogeneity, I want to de-mean the data as follows: y_{it}-\bar{y_i} = \beta_1(T_{it}-\bar{T_i}) + f(continuous.vars_{it}-\bar{continuous.vars_i}) + e_{it} - \bar{e}_i Fitting the demeaned model should give m...