similar to: Re: Tests in linear regression

Hi, I wonder if there is a function in (some package of) R which computes marginal effects of the variables in a glm, say, for concretness, a probit model. By marginal effects of the covariate x_j I mean d P(y=1 | x), which is approx g(xB)B_j dx_j where g is the pdf of the normal distribution, x is the vector of covariates (at some points, say, the mean values) and B is the estimated

Hi I have the result of a linear model "lm98" and I want to plot it to analyse the regression but I'm getting the folowing error message > lm98<-lm(log(U+1)~Bq+Gi+Lv+Tb+Bq:Gi+Bq:Lv+Bq:Tb+Gi:Lv+Gi:Tb+Lv:Bq) > plot(lm98) Error in plot.window(xlim, ylim, log, asp, ...) : need finite ylim values In addition: Warning message: NaNs produced in: sqrt(1 - hii) The

Hi I have a code that calculate maximisation using optimx and it is working just fine. I want to extend the code to run several colomns of R_j where j runs from 1 to 200. If I am to run the code in its current state, it means I will have to run it 200 times manually. May you help me adjust it to accomodate several rows of R_j and print the 200 results. ***Please do not get intimidated by the

Hi I have developed the code below. I am worried that the parameters I want to be estimated are "not being found" when I ran my code. Is there a way I can code them so that R recognize that they should be estimated. This is the error I am getting. > out1=optim(llik,par=start.par) Error in pnorm(au_j, mean = b_j * R_m, sd = sigma_j) : object 'au_j' not found #Yet

Hi May you help me correct my loop function. I want optim to estimates al_j; au_j; sigma_j; b_j by looking at 0 to 20, 21 to 40, 41 to 60 data points. The final result should have 4 columns of each of the estimates AND 4 rows of each of 0 to 20, 21 to 40, 41 to 60. ###MY code is n=20 runs=4 out=matrix(0,nrow=runs) llik = function(x) { al_j=x[1]; au_j=x[2]; sigma_j=x[3]; b_j=x[4]

Hello, the more general thing I'd like to learn here is how to compute Function of Data on the basis of grouping determiend by n variables. In terms of the reason why I am interested in this, I need to compute the average of my data based on the value of the month and day across years. I have come up withy the code below which, as far as I can see, does what I need but getting either a more

Hi I need help figure out how to fix my code. When I call into R >optimize(llik,init.params=F) I get this error message ####Error in optimize(llik, init.params = F) : element 1 is empty; the part of the args list of 'min' being evaluated was: (interval)#### My data and my code looks like below. R_j R_m 0.002 0.026567296 0.01 0.003194435 . . . . . . . . 0.0006

Greetings, I have the following? Problem: Given k (=10) discrete independent random variables X_i with n_i (= 5 to 20) values each,compute quantiles of the distribution of the sum X = X_1+...+X_k. Here X has n=n_1 x n_2 ... n_k distinct values which is too large to list them all together with their probabilities. I tried several approaches: (A) Convolution: each X_j is approximated with

Hi, I´m trying to plot a nonlinear regresion with the confidence bands for the curve obtained, similar to what nlintool or nlpredci functions in Matlab does, but I no figure how to. In nls the option is there but not implemented yet. Is there a plan to implement the in a relative near future? Thanks in advance, Florencio La información contenida en este e-mail y sus ficheros adjuntos es

On 10/18/2016 4:54 AM, Renato Golin via llvm-dev wrote: > Unfortunately, we don't have a C-only front-end, nor you can select > some C-only libraries in LLVM to compile C-only code. Such a goal > should have been set from the beginning, and honestly, it would have > made LLVM's code horrendous to work with. An option would be to have a C backend again, and then cross-compile

Dear List, I'm just teaching myself semi-parametric techniques. Apologies in advance for the long post. I've got observational data and a longitudinal, semi-parametric model that I want to fit in GAM (or potentially something equivalent), and I'm not sure how to do it. I'm posting this to ask whether it is possible to do what I want to do using "canned" commands

I want to specify a 2-level mixed model using the lme function in order to test an a priori hypothesis about the between-group values of the slopes but don't know how to do this . Here is the problem. Consider first the case of a single group. The model is: Y_i= a +bX_i + error where I indexes the different values of X and Y in this group . The a priori hypothesis of the slope is: b=K.

Dear everyone, I'm coding the Horowitz-Spokoiny (2001) test [1], and I would be very grateful or some advice regarding the Kernel density (apologies beforehand if my terminology is not fully correct). I have looked into ksmooth and npreg, but with no success. Given a (n x p) matrix of covariates X, I need to construct the following matrix of Kernel densities or weights: w(x_i, x_j) =

patch against recent SVN ... as far as I can tell this trivial typo has been there for 20 years: https://github.com/wch/r-source/blame/ba7920a99fb2fb62b89e404e65f8b132ed4c150a/src/library/base/man/pretty.Rd =================================================================== --- pretty.Rd (revision 74426) +++ pretty.Rd (working copy) @@ -21,8 +21,8 @@ \item{min.n}{nonnegative integer giving

Hello everyone, I have a function here that I wrote but doesn't seem to work quite right. Attached is the code. In the calib funcion under the for loop Bt[i+2]<-(1-m)*Bt[i+1]+Rt[i]*Rerr-Ct[i+1] returns NA's for everything after years 1983 and 1984. However the code works when it reads Bt[i+2]<-(1-m)*Bt[i+1]+Rt[i]*Rerr-Ct[i]. I don't quite understand why since it should be

Hello everyone, I have a function here that I wrote but doesn't seem to work quite right. Attached is the code. In the calib funcion under the for loop Bt[i+2]<-(1-m)*Bt[i+1]+Rt[i]*Rerr-Ct[i+1] returns NA's for everything after years 1983 and 1984. However the code works when it reads Bt[i+2]<-(1-m)*Bt[i+1]+Rt[i]*Rerr-Ct[i]. I don't quite understand why since it should be

I would like to fit the following model: logit(p_{ij}) = \mu + a_i + b_j where a_i ~ N(0, \sigma_a^2) , b_j ~ N(0, \sigma_b^2) and \sigma_a = \sigma_b. Is it possible to fit a model with such a constraint on the variance components in glmer? -- View this message in context: http://r.789695.n4.nabble.com/glmer-question-tp4351829p4351829.html Sent from the R help mailing list archive at

Dear R community, Confused by some of my lab results I ask for the definition of the null hypothesis of a two-way analysis of variance in R (anova() and aov()). Starting with the following model y = a_i + b_j , i in A and j in B is the tested null hypothesis H_0: a_i = 0 for all i in A or H_0: a_m = a_n for any m and n in A? Consequently the same questions for interaction effects.

Hi, I am unable to use the Searcher''s explain method. Anytime I call it, I get Segmentation Faults and it kills the process I have running my Rails site. Has anyone else had this problem? Here is some code I am trying to use it in... search = Search.create(:query => query) @quotations = [] searcher = Ferret::Search::Searcher.new("index") # FerretConfig::INDEX bq =

Hello all, I've been trying to compile metamail on Centos4.2. But no luck so far. rpmbuild --rebuild metamail-2.7-30AS.src.rpm Installing metamail-2.7-30AS.src.rpm Executing(%prep): /bin/sh -e /var/tmp/rpm-tmp.96604 + umask 022 + cd /usr/src/redhat/BUILD + LANG=C + export LANG + unset DISPLAY + cd /usr/src/redhat/BUILD + rm -rf mm2.7 + /bin/gzip -dc /usr/src/redhat/SOURCES/mm2.7.tar.Z + tar