similar to: Hidden Markov Models in R

I want to write code that says "If you find an element equal to 4 in this vector for each person in the data set tested separately, then put in 1 for 2 and 2 for 4, else leave the variable as is" u.ppl <- (unique(init.dat1$grid)) l.ppl <- length(u.ppl) for (i in 1:l.ppl) { if (grep("4",init.dat1$Slide1_RESP)) {2 == 1, 4 == 2}; else

I'm am trying to create a vector that has a running total that adds each time a 1 occurs. here's the code and data c(1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,

R - Does anyone know of a cubic gradient descent package? I found grad.desc() but that only allows for a 2d function. I have 3 free parameters and thus am looking for a 3d function. Thank you, -- Edward H. Patzelt Research Assistant – TRiCAM Lab University of Minnesota – Psychology/Psychiatry VA Medical Center S355 Elliot Hall: 612-626-0072 www.psych.umn.edu/research/tricam [[alternative

R help - I am attempting to write a script that has multiple subjects in 1 data file. Each subject has multiple rows with columns as variables. Here is my code, I am having problem executing it on each unique subject id (dat$Subject). getwd() setwd("/Users/edwardpatzelt/Desktop/Neuroimaging/MERIT/SRRT/merge") dat <- read.table("test2.txt", header = TRUE, na.strings

R - I have 3 variables with data below. Variable "Rev" is a vector that changes from 1 to 2, 2 to 3, etc.... Variable "FF" is a binary variable with 1's and 0's. Variable "bin" is a different binary variable with 1's and 0's. I want to calculate the number of elements: 1. Starting with the first element where Rev switches (i.e. 1 to 2) 2. The

R-help - I'm trying to create axis breaks similar to this : http://www.r-bloggers.com/wp-content/uploads/2010/08/bar-chart-natural-axis-split1.png . Is there a way to do this in R? Here's my code thus far: structure(list(condition = structure(c(2L, 1L, 3L), .Label = c("con", "exp", "unedit"), class = "factor"), trial.avg = c(4.04583333333333,

R-help - This code iterates over a function with 2 free parameters to find a list of values (which are the number of incorrect predictions for a computational model). I want to find the values of i,e when there is the minimum number of incorrect predictions. In other words, the value of "i" and "e" when variable "thesum" is at the lowest possible number. The

R-help - I have this set of aggregated tables (sample data below via dput()). And I would like to have delayValue as the column variables with the "temp" (temp1, temp2, temp3) values as the row variables. However I would like to have the temp variables *aggregated into single rows* so that I have the frequency ("Freq" | counts) of each time each "delayValue" occurs

Hi, I am having difficulties estimating the parameters of a HMM using the HMM package. I have simulated a sequence of observations from a known HMM. When I estimate the parameters of a HMM using these simulated observations the parameters are not at all close to the known ones. I realise the estimated parameters are not going to be exactly the same as the known/true parameters, but these are

Hi folks, I'm pleased to introduce a new package called ?aphid?, for analysis with profile hidden Markov models in R. The package contains functions for multiple and pairwise sequence alignment for both nucleic acids and proteins (preferably in the DNAbin or AAbin format), model building, parameter optimization (Baum Welch and Viterbi training), plotting, file import & export,

Hi folks, I'm pleased to introduce a new package called ?aphid?, for analysis with profile hidden Markov models in R. The package contains functions for multiple and pairwise sequence alignment for both nucleic acids and proteins (preferably in the DNAbin or AAbin format), model building, parameter optimization (Baum Welch and Viterbi training), plotting, file import & export,

Hi all, I am using a discrete Hidden Markov Model with discrete observations in order to detect a sequence of integers. I am using the "hmm.discnp" package. I am using the following code: signature <- c(-89, -98, -90, -84, -77, -75, -64, -60, -58, -55, -56, -57, -57, -63, -77, -81, -82, -91, -85, -89, -93) quant <- length(-110:-6) # Initialize and train the hmm with the

Hi R-Help, data at bottom I've been struggling with a problem where I need to order based on 1) the Frequency "Freq" and 2) keeping each group of 3 of the same type together "Var2" but I want across all groups it to go "high to low" based on the earn factor. Thank you! structure(list(Var1 = structure(c(1L, 3L, 2L, 1L, 2L, 3L, 1L, 3L, 2L, 3L, 1L, 2L, 3L, 1L,

Thank you for your prompt answer. The breathing signal observations are the amplitude values as a function of time and phase. According to our model the hidden states are the different breathing types. Subjects, whose respiratiion process is regular, are likely to breathe, keeping the same cycle pattern/type, for many consecutive cycles. therefore dwelling in the same hidden state. The more

Hello Everyone - I want to print a number of results from lme function objects out to a txt file. How could I do this more efficiently than what you see here: out2 <- capture.output(summary(mod2a)) out3 <- capture.output(summary(mod3)) out4 <- capture.output(summary(mod5)) out5 <- capture.output(summary(mod6)) out6 <- capture.output(summary(mod7))

Everyone - What do the NaN's mean here? Is this analysis a problem? Linear mixed-effects model fit by maximum likelihood Data: tmp.dat AIC BIC logLik 1611.251 1638.363 -797.6253 Random effects: Formula: ~1 | group_id (Intercept) Residual StdDev: 0.0003077668 9.236715 Fixed effects: AvgTrials ~ time + factor(group_id) + time * factor(group_id)

Dear helpers, is there any possible that transition (in depmixS4) is in scale of two variable, e.g transition=~scale(x1,x2)? If it can be, how transition of two variable (covariate time) can be worked in depmixS4-hidden markov model for time series. Many thanks, nglthu -- View this message in context: http://r.789695.n4.nabble.com/depmixS4-transition-tp4572726p4572726.html Sent from the R help

depmixS4 has reached some form of maturity and therefore we have bumped its version number to 1.0-0 which is now on CRAN: http://cran.r-project.org/web/packages/depmixS4/index.html depmixS4 fits hidden (latent) Markov models of multivariate, mixed categorical and continuous data, otherwise known as dependent mixture models. Responses or observations can be modeled using GLMs, and additionally

depmixS4 has reached some form of maturity and therefore we have bumped its version number to 1.0-0 which is now on CRAN: http://cran.r-project.org/web/packages/depmixS4/index.html depmixS4 fits hidden (latent) Markov models of multivariate, mixed categorical and continuous data, otherwise known as dependent mixture models. Responses or observations can be modeled using GLMs, and additionally

Hi I am trying to use depmixS4 package. Based on the documentation, it seems that depmix allows one to fit an HMM model based on a training data with time-varying co-variates. However, I did not find any routines which can help test the accuracy on the fitted HMM model on out-of-sample data. Can someone confirm if that is indeed the case? Also are there any alternate packages for the same? Thanks