search for: stimulus

Dear all, I have 3 models (from simple to complex) and I want to compare them in order to see if they fit equally well or not. From the R prompt I am not able to see where I can get this information. Let´s do an example: fit1<- lm(response ~ stimulus + condition + stimulus:condition, data=scrd) #EQUIVALE A lm(response ~ stimulus*condition, data=scrd) fit2<- lm(response ~ stimulus + condition, data=scrd) fit3<- lm(response ~ condition, data=scrd) > anova(fit2, fit1) #compare models Analysis of Variance Table Model 1: respons...

...9;numeric','factor','factor','numeric')) This table is the result of a simple experiment. Subjects where exposed to some stimuli and they where asked to evaluate the degree of realism of the stimuli on a 7 point scale (i.e., data in column "response"). Each stimulus was presented in two conditions, "A" and "AH", where AH is the condition A plus another thing (let?s call it "H"). Now, what means exactly in my table the interaction stimulus:condition? I think that if I do the analysis anova(response ~ stimulus*condition) I will...

Dear all, I have got two vectors coding for a stimulus presented in the current trial (mydat$Stimulus) and a prediction in the same trial (mydat$Prediciton), respectively. By applying an if-conditional I want to create a new vector that indicates if there is a match between both vectors in the same trial. That is, if the prediction equals the stimulus....

Hi everyone, I've been bashing my head against this for days now, and can't figure out what to do. I have the following dataframe header appetitive stimulus aversive stimulus chaining contingency discriminative stimulus extinction intermittent reinforcement negative reinforcer operant response place learning positive reinforcer punishment reinforcement schedules response rate secondary reinforcement spontaneous re...

...llows: Subj/Epoch/Stimuli/Time/Temperature There are 8 subjects 9 epochs - 6 of which were just "instruction" blocks, and one "Learning" period. Wrt lme(), I figured out how to use subset too isolate just the Baseline, Learning, and Testing Epochs (and avoid epochs with only 1 stimulus level, such as "instruction"). Data within each epoch are balanced wrt # trials, but not between epochs. Recovery has twice as many trials as Baseline, and Testing has about half. Time for each epoch is roughly that ratio too, although time in each trial differs. Stimuli are the same in...

Hello everybody, I need an help because I don´t know if the command for the ANOVA analysis I am performing in R is correct. Indeed using the function aov I get the following error:"In aov (......) Error() model is singular" The structure of my table is the following: subject, stimulus, condition, sex, response Example: subject stimulus condition sex response subject1 gravel EXP1 M 59.8060 subject2 gravel EXP1 M 49.9880 subject3 gravel EXP1 M 73.7420 subject4 gravel EXP1 M 45.5190 subj...

Dear all, I need to understand a thing in the beheaviour of the two functions aov and anova in the following case involving an analysis of ANOVA with repeated measures: If I use the folowing command I don´t get any problem: >aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), >data=scrd) > summary(aov1) Instead if I try to fit the same model for the regression I get an error: > fit1<- lm(response ~ stimulus*condition + Error(subject/(stimulus*condition)), >data=scrd) > Error in eval(expr, envir, encl...

...xplain the experiment design first: the same 7 subjects were answering a question about 25 linguistic stimuli; the stimuli were the same utterances which were processed in 3 different ways (3 conditions), ie each subject listened 25*3 stimuli. I would like to test the effect of condition and of the stimulus on the subjects' performance. My dependent variable is Nboundaries . I think there is: one random effect :subject . 2 within-subject fixed effects: stimulus(25 levels) and condition (3 levels) . Following the explanations of J. Baron, (Notes onthe use of R for psychology...), I have run:...

.....is there among you someone so kind to give me an hint with a R example please? For example, the aov result of my analysis says that there is a statistical difference between stimuli (there are 7 different stimuli). ...I would like to know which stimuli are involved. > aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), >data=scrd) > summary(aov1) Error: subject Df Sum Sq Mean Sq F value Pr(>F) Residuals 14 227.57 16.255 Error: subject:stimulus Df Sum Sq Mean Sq F value Pr(>F) stimulus 6 11.695 1.94921 2.3009...

...ellow(3) green(2) red(4) blue(1) 4 7 blue(4) red(1) green(3) yellow(2) 4 8 blue(4) red(1) green(3) yellow(2) There are 4 factors: factor levels type ----------------------------------------------------------------- responseFinger index, middle, ring, little within-subject stimulusName green, yellow, blue, red within-subject oom 1, 2, 3, 4 within-subject mapping.code 1, 2, 3, 4 between-subjects Subject.n 1, 2, 3, 4, 5, 6, 7, 8 nested within mapping.code DV = asin.Stimulus.ER There are 32 observations and 31 total dfs. I fit the following...

...the data is stored in a dataframe, and i finally managed to order the factor correctly! Each column is a variable and contains integers for the same set of values in the column that contains the headers for each row (graphLabels). So, I get the data and my first call is: app <- qplot(appetitive.stimulus, graphLabels, data=related.differences, size=variance, colour="Appetitive Stimulus", xlim=c(-20,20), main="Title here", xlab="Differences", ylab="Header Concepts") which works great. Now, there are 16 columns in my dataframe that I want to output together, s...

Dear list, Following up on an earlier post, I would like to reorder a dataset and compute pairwise correlations. But I'm having some real problems getting this done. My data looks something like: Participant Stimulus Measurement p1 s`1 5 p1 s`2 6.1 p1 s`3 7 p2 s`1 4.8 p2 s`2 6 p2 s`3 6.5 p3 s`1 4 p3 s`...

...e I am really not able to find over internet a good example in R to analyze an unbalanced table with Anova with repeated measures. For unbalanced table I mean that the questions are not answered all by the same number of subjects. For a balanced case I would use the command aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), data=scrd) Does the same code still work for unbalanced design? Can anyone provide a R example of code in order to get the same analysis? Thanks in advance for any suggestion. Best regards [[alternative HTML version deleted]]

...rm the analysis in the right way, as I get different beheaviors using t-test and two ways ANOVA. In what follow I post the table, my goal and the strange results I got. I kindly ask you an help because I really don´t know how to solve this problem. So the table is this: number stimulus condition response 1 flat_550_W_realism A 3 2 flat_550_W_realism A 3 3 flat_550_W_realism A 5 4 flat_550_W_realism A 3 5 flat_550_W_realism A 3 6 flat_5...

..., and C Stimuli A, B and C are randomly interleaved in the experiment, does this matter in my ANOVA? I am interested in making a between and within group comparison of responses to A, B, and C <he>Here is what I am doing: My data is arranged in the following way Group Subject Condition Stimulus Response One S1 Alert A _Value_ One S1 Alert B _Value_ One S1 Alert C _Value_ One S1 Passive A _Value_ One S1 Passive B _Value_ One S1 Passive...

Dear Colleagues, I hope to know how ordinal logistic regression with a mixed model is made in R. We (My colleague and I) are studying the behavior of a beetle. The attraction of beetles to a stimulus are recorded: the response is Slow, Mid, or Fast. They are based on the time after the presentation of the stimulus to the beetles. Because we do not observe the behavior continuously but do record the number of beetles near the stimulus at the pre-determined two timings. The beetles that are...

...nd a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function relating the frequency a subject's binary response (stimulus present / absent) to the strength of a physical stimulus. Such data is often modeled using a cumulative gaussian function. I have tried to implement such a fitting algorithm in R, but unfortunately, I was not successful. Maybe anyone on the list already coded a script for such purposes or could...

This is a question about the use of the Cox proportional hazards model to analyze event histories. I am looking at the responses of sympathetic nervous system activity to a stimulus. The activity I observe is a burst that can only occur once per heart beat cycle (e.g., a binary count). Typically bursts occur in 60-80% of the heart cycles * sensory stimuli can modify these burst probabilities. I give 48 stimuli-trials at random intervals and count the number of bursts associ...

Dear R-help, I have fitted a glm logistic function to dichotomous forced choices responses varying according to time interval between two stimulus. x values are time separation in miliseconds, and the y values are proportion responses for one of the stimulus. Now I am trying to extrapolate x values for the y value (proportion) at .25, .5, and .75. I have tried several predict parameters, and they don't appear to be working. Is this correc...

...with crossed random intercepts and factor by variable Hello, (I'm sorry if this has been discussed elsewhere; I may not have been looking in the right places.) I ran a binomial GAM in which "Correct" is modelled in terms of the participant's age and the modality in which the stimulus is presented (written vs spoken). Participants ("Subject") and stimuli are also included as crossed random intercepts. age.gam <- bam(Correct ~ Modality + s(Age, by=Modality) + s(Subject, bs="re") + s(Stimulus, bs="re"), data = dat, family=&q...