similar to: How to predict probabilities after using lmer

Dear R-users, I'm using lmer to fit two-level logistic models and I'm interested in predicted probabilities that I get in this way (using "fitted"): glm1 = lmer(XY$T1~X1 + X2 + X3 + (1|Cind), family=binomial) #estimation of a two-level logit model fit1=fitted(glm1) # I get the fitted linear predictor ilog = function(x) { 1/(1 + exp(-x)) } ps1=ilog(fit1) # In

Hi, I am trying to construct a svmpoly model using the "caret" package (please see code below). Using the same data, without changing any setting, I am just changing the seed value. Sometimes it constructs the model successfully, and sometimes I get an ?Error in indexes[[j]] : subscript out of bounds?. For example when I set seed to 357 following code produced result only for 8

Dear R Helpers, I have noticed that when I use lmer to analyse data, the summary function gives different values for the AIC, BIC and log-likelihood compared with the anova function. Here is a sample program #make some data set.seed(1); datx=data.frame(array(runif(720),c(240,3),dimnames=list(NULL,c('x1','x2','y' )))) id=rep(1:120,2); datx=cbind(id,datx) #give x1 a

Hello, I start trying to use a USB dongle and a Bluetooth GSM phone to make GSM call with asterisk using the BLT channel provided by the GSM phone. Unfortunately I get a "Everyone is busy/congested at this time" whenever I try to Dial("IAX2/****@****/2", "BLT/MotorolaLara/3474501***") For sure I make some mistake in the configuration. Unfortunately I don't find

Problems with predict and lines in plotting binomial glm Dear R-helpers I have found quite a lot of tips on how to work with glm through this mailing list, but still have a problem that I can't solve. I have got a data set of which the x-variable is count data and the y-variable is proportional data, and I want to know what the relationship between the variables are. The data was

Dear all, the help file for predict.gbm states that "The predictions from gbm do not include the offset term. The user may add the value of the offset to the predicted value if desired." I am just not sure how exactly, especially for a Poisson model, where I believe the offset is multiplicative ? For example: library(MASS) fit1 <- glm(Claims ~ District + Group + Age +

I have compiled chan_bluetooth and I have used it many times before... but now I have a problem and I can?t find a solution. I have a bluetooth dongle (MSI) connected to my asterisk and I use a V3 phone to make calls to another cell phones... the thing is that I just get one way audio.. I can hear people from my IAX softphone but they can?t hear me... I don?t know what could be.. anyone had

Hi, I would like to tabulate the likelihood for an affection. For this, I retrieve indices of affected people and controls for my data set and proceed as follows: flags <- c(rep(1, length(patient_indices)), rep(0, length(control_indices))) # dataset is a data.frame and param the parameter to be analysed: data1 <- dataset[,param][c(patient_indices, control_indices)] fit1 <- glm(flags ~

Hello I'm using function gam from package mgcv to fit splines. ?When I try to make a prediction slightly beyond the original 'x' range, I get this error: > A = runif(50,1,149) > B = sqrt(A) + rnorm(50) > range(A) [1] 3.289136 145.342961 > > > fit1 = gam(B ~ s(A, bs="ps"), outer.ok=TRUE) > predict(fit1, newdata=data.frame(A=149.9), outer.ok=TRUE) Error

Hi - How can I 'manually' reproduce the results in 'pred1' below? My attempt is pred_manual, but is not correct. Any help is much appreciated. library(splines) set.seed(12345) y <- rgamma(1000, shape =0.5) age <- rnorm(1000, 45, 10) glm1 <- glm(y ~ ns(age, 4), family=Gamma(link=log)) dd <- data.frame(age = 16:80) mm <- model.matrix( ~ ns(dd$age, 4)) pred1 <-

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) :

R 2.8.1 Windows XP I am trying to analyze repeated measures data (the data are listed at the end of this Email message) and I need help to make sure that I have properly specified my model, and would like to know why lmer does not return a p value for Group, my fixed effect. My subjects are divided into two groups (variable GROUP), individual subjects are indicated by the variable SS, Value is

Dear List, I'm trying to install Rcplex on a ubuntu 64 bit machine, and have not been successful. After installing cplex latest version from ibm's website (everything as standard, except had it installed unto ~/ (the home directory) rather than the default directory). i type (from inside the directory where Rcplex_0.3-0.tar.gz is): >CPLEX_INCLUDE_PATH=~/ILOG/CPLEX/cplex

Ted Harding said: > I can get the estimated RRs from > RRs <- exp(summary(GLM)$coef[,1]) > but do not see how to implement confidence intervals based > on "robust error variances" using the output in GLM. Thanks for the link to the data. Here's my best guess. If you use the following approach, with the HC0 type of robust standard errors in the

Estimados amigos, Estoy familiarizándome con los modelos lineales generalizados en R. Estoy interesado en realizar un análisis lig linear y me gustaría saber cuáles son o como extraer los valores correspondientes al chi cuadrado en el análisis para cada grupo y para las interacciones. Desde ya muchas gracias y disculpas si la pregunta es muy básica, adjunto los comandos que estoy utilizando. Si

Estimados amigos, Estoy familiarizándome con los modelos lineales generalizados en R. Estoy interesado en realizar un análisis lig linear y me gustaría saber cuáles son o como extraer los valores correspondientes al chi cuadrado en el análisis para cada grupo y para las interacciones. Desde ya muchas gracias y disculpas si la pregunta es muy básica, adjunto los comandos que estoy utilizando. Si

Estimados amigos, Estoy familiarizándome con los modelos lineales generalizados en R. Estoy interesado en realizar un análisis lig linear y me gustaría saber cuáles son o como extraer los valores correspondientes al chi cuadrado en el análisis para cada grupo y para las interacciones. Desde ya muchas gracias y disculpas si la pregunta es muy básica, adjunto los comandos que estoy utilizando. Si

Found this code: ftp://ftp.samba.org/pub/unpacked/ntdb/lib/ccan/ilog/ilog.c Tests show that it's faster to use the following code in FLAC__bitmath_ilog2_wide(): static const unsigned char DEBRUIJN_IDX32[32]={ 0, 1,28, 2,29,14,24, 3,30,22,20,15,25,17, 4, 8, 31,27,13,23,21,19,16, 7,26,12,18, 6,11, 5,10, 9 }; FLAC__uint32 v; int m;

I have undertaken a small project of writing a Vorbis decoder completely from the spec, with the goal of catching any errors, both typographically and algorithmically, in the spec. My "reference" decoder is also being written in an extremely methodical style, with practically no algorithmic optimization, such that it will be clear that every step has been copied exactly from the spec.

Hi all, I have a problem when running lmer. In my data set, Agree is a binary(0/1) response. WalkerID and ObsID is the identification number of the subjects. the description of the other variables are as follows: > levels(regdat$Display) [1] "Dynamic" "Static" > levels(regdat$Survey) [1] "HM1_A" "HM1_B" "HM1_C" "HM2_A"