search for: inferential

...to modify the strucchange package to suit my purposes, or should i use be using another package, or is this a tough nut to crack? :) my application is detecting the onset of a flu outbreak as new daily data trickles in from each morning from local hospitals. seems to me like the same sort of inferential goal that strucchange refers to as "monitoring of structural change." thank you in advance. cheers, alexis

...variable Y using a weighted mixed effects GLM with weights W (using the glmmPQL function). I applied a blocking approach using factor F2 nested in factor F1 as a grouping structure on a random intercept in the GLMM . The fixed effect are the 4 independent variables simultaneously fitted in the inferential model (Z1, ..., Z4). Three out of the four variables have a significant effect on Y (Z1, Z2, Z3). My problem is that I want to show graphically my results. If I plot Y according to the Z1, this won't take into account the effect of e.g. Z2 and the fact that the analysis was weighted by W (s...

Hi, Im using lme to calculate a mixed factors ANOVA according to: px_anova = anova(lme(dep~music*time*group, random = ~1|id, data = px_data)) where dep is a threshold, time is a repeated measures variable (2 levels) group is a between subjects variable (2 levels) id is a random factor (subject id) music is a between subjects variable (2 levels) indicating if a person has a musical experience,

...error, which raises concerns in my mind that this may not be a proper model. I have no experience with the use of plsr or its underlying theory, so the fact that this is not throwing an error is no guarantee of validity. Using this construction in ordinary least squares regression has dangers with inferential statistics because of the correlation of the linear and squared terms as well as likely violation of homoscedasticity. -- David. > > May I have some help please? > > Thanks, > > Kelvin > > [[alternative HTML version deleted]] > > _____________________________...

...error, which raises concerns in my mind that this may not be a proper model. I have no experience with the use of plsr or its underlying theory, so the fact that this is not throwing an error is no guarantee of validity. Using this construction in ordinary least squares regression has dangers with inferential statistics because of the correlation of the linear and squared terms as well as likely violation of homoscedasticity. > > -- > David. > > >> >> May I have some help please? >> >> Thanks, >> >> Kelvin >> >> [[alternative HTML vers...

I apologize if this question is not completely appropriate for this list. I have been using SAS for a while and am now in the process of learning some C and R as a part of my graduate studies. All of the statistical packages I have used generally yield p-values as a default output to standard procedures. This week I have been reading "Testing Precise Hypotheses" by J.O. Berger

Dear all, I am using the pls package of R to perform partial least square on a set of multivariate data. Instead of fitting a linear model, I want to fit my data with a quadratic function with interaction terms. But I am not sure how. I will use an example to illustrate my problem: Following the example in the PLS manual: ## Read data data(gasoline) gasTrain <- gasoline[1:50,] ## Perform

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

...error, which raises concerns in my mind that this may not be a proper model. I have no experience with the use of plsr or its underlying theory, so the fact that this is not throwing an error is no guarantee of validity. Using this construction in ordinary least squares regression has dangers with inferential statistics because of the correlation of the linear and squared terms as well as likely violation of homoscedasticity. >> >> -- >> David. >> >> >>> >>> May I have some help please? >>> >>> Thanks, >>> >>> Kelv...

...error, which raises concerns in my mind that this may not be a proper model. I have no experience with the use of plsr or its underlying theory, so the fact that this is not throwing an error is no guarantee of validity. Using this construction in ordinary least squares regression has dangers with inferential statistics because of the correlation of the linear and squared terms as well as likely violation of homoscedasticity. >> >> -- >> David. >> >> >>> >>> May I have some help please? >>> >>> Thanks, >>> >>> Kelv...

The Chisquare test is based upon a normal approx of the (essentially) binomial distribution for the data in question. Small EXPECTED (not observed) values (<5) suggest a asymetric distribution and potential errors in inferential conclusions. The alternative is the exact test, which calculates the exact probabilities of the observed distribution, or a more extreme one, given the constraining expectations. It is usually much more useful to make the statistics fit the data question than to assume or force the vice versa. -...

...tation tests, jackknife, and other resampling procedures, and is lead author of "Bootstrap Methods and Permutation Tests" (2003), W. H. Freeman, and numerous technical articles on resampling. See www.insightful.com/Hesterberg/ This course will be suitable for any statistician faced with inferential problems where the use of standard results may be questionable or not available. Further information on the course is available at: http://www.insightful.com/services/training/bootstrap_UK.asp To register for the course: http://www.insightful.com/services/register_european.asp If you need any...

...tic regression and the generalized linear model (multiple regression and logistic regression illustrated as special cases). The Poisson model for count data, and the concept of overdispersion are also covered. You learn how to analyze longitudinal data using straightforward graphics and simple inferential approaches, then mixed-effects models and the generalized estimating approach for such data. The course emphasizes how to fit the models listed and interpret results, rather than how to derive the theoretical background of the models. Brian Everitt and Torsten Hothorn are the authors of "A...

...d the between-group values of the slopes and is: b_j=Kj where Kj is specified a priori for each group j based on theoretical considerations but whose values differ between groups. This is clearly a mixed-model problem. I know how to specify the model in lme but I don't know how to set up the inferential test that b_j=Kj for all j groups versus the alternative hypothesis that b_j is not equal to Kj for at least one group. Any help in explaining how to do this using the mle function in R is appreciated. Thanks. Bill Shipley D?partement de biologie Universit? de Sherbrooke Sherbrooke (Qu?bec) J1K...

Hi, I am wondering if anybody ever come across any implementation of product integral in R? As far as I googled, I haven't come across any package. Is there any? Thank you. http://en.wikipedia.org/wiki/Product_integral Regards, Robert

...ffs, synthetic control methods, etc. Visualizing data refers to the production of highly customized figures and charts, derived from complex datasets. PhD or Masters preferred. Prior publication history preferred. Mastery of "R" required, as is prior experience with at least some of the inferential methods described above. Candidates for the position will be asked to complete a task that tests technical competence. INDIVIDUAL / FIRM PROFILE The consultant should be an individual. SUBMISSION REQUIREMENTS The World Bank now invites eligible consultants to indicate their interest in providing...

Am I correct in assuming that the output below essentially translates to "Males have a mean time that is significantly lower than Females"? Is this the correct way to interpret the fact that the coefficient is negative? Assume the variale sex is treated as a factor with Female =0 and Male=1. survmodel<-survreg(survobj~sex,data=data1, dist="weibull")

I am relatively new to R, but am intrigued by its flexibility. I am interested in quantile regression and quantile estimation as regards to cotton fiber length distributions. The length distribution affects spinning and weaving properties, so it is desirable to select for certain distribution types. The AFIS fiber testing machinery outputs a vector for each sample of type c(12, 235, 355, . . .

Hi All, I have a set of features of size p and I would like to separate my feature space into two sets so that p = p1 + p2, p1 is a set of features and p2 is another set of features and I want to fit a glm model for each sets of features separately. Then I want to combine the results of two glm models with a parameter beta. For example, beta * F(p1) + (1-beta) * F(p2) where F(p1) is a learned

dear list, i am running a within-design ANOVA with 4 factors (4,4,2 and 2 levels each). the last one is a time factor comprising two different treatment timepoints. i fit a mixed-effects model using lme and apply the anova function to the outcome. according to this analysis, there are highly significant main effect on the first and the time factor. i then checked the boxplots for the two 4-level