similar to: relaimpo

Hello! I am trying test my observed data against the null-hypothesis that different items from a psychological questionnaire contribute equally to the metric dependent variable that measures problems (sum score of a questionnaire). That is, I am interested in relative strength of the predictors. Predictor items of the questionnaire are on a scale from 0-3, and technically ordinal, although most

Hi, I have a question related to the function "boot.relimp" (package relaimpo). I have a model with numerical predictors and one dummy (class) variable. I would like to calculate the relative importance of the predictors but I have some problems with the dummy variable. The name of the linear model is "lmbroadst.lc" and the dummy variable is "datacon.landcover_gb"

I am currently using the relaimpo package to estimate the relative importance of regressors (N= 4000): > m1 <- lm(y ~ x1+x2+x3+x4+x5+, data=data) > calc.relimp(m1, rela=TRUE) > m2=boot.relimp(m1, boot = 500, rela=TRUE, type="lmg") > booteval.relimp(m2) > plot(booteval.relimp(m2)) In a new dataset with 3 measurement points (0,6,12 weeks), I want to perform a similar

I'm having trouble getting the relaimpo package to use a covariance matrix as input. I'm getting an error message that reads as follows: Error in eval(m$weights, data, parent.frame()) : numeric 'envir' arg not of length one I'm guessing there is something wrong with the structure of my covariance matrix, but it looks fine to me. Pardon my R ignorance if this is an easy

Dears, Unfortunatelly, the packages relaimpo and relimp do not seem to work with plm function (plm package) or gls function (in nlm package). I've been studying on how to adapt one of them for this pourpose. In that sense, I have two questions regarding to this work: 1) have anyone hard of any workaround for those incompatibilities, or at least of any ideas on that - especially for plm? 2)

Hei Group, I want to compare the relative importance of predictors in a multiple linear regression y~a+bx1+cx2... However, bptest indicates heteroskedasticity of my model. I therefore perform a robust regression (rlm), in combination with bootstrapping (as outlined in J. Fox, Bootstrapping Regression Models). Now I want to compare the relative importance of my predictors. Can I rely on the

Hi R-Users, I have a student doing work with lionfish and she has been trying to analyse a multivariate dataset to see what variables/factors are influencing the behaviour of lionfish. We have attempted a number of analyses, including rpart, relimpo and standard linear regression but we are not having much luck with quality output. The data is very non-normal and we would appreciate some advice

1) Packages to be used- For smaller datasets use these 1. CAR Package http://cran.r-project.org/web/packages/car/index.html 2. GVLMA Package http://cran.r-project.org/web/packages/gvlma/index.html 3. ROCR Package http://rocr.bioinf.mpi-sb.mpg.de/ 4. Relaimpo Package 5. DAAG package 6. MASS package 7. Bootstrap package 8. Leaps package Also see

?? If I haven't misunderstood, they are completely different! 1) NIR must be a matrix, or poly(NIR,...) will fail. 2) Due to the previously identified bug in poly, degree must be explicitly given as poly(NIR, degree =2,raw = TRUE). Now consider the following example: > df <-matrix(runif(60),ncol=3) > y <- runif(20) > mdl1 <-lm(y~df*I(df^2)) > mdl2

Dear all, I am stuck with tracking down an error I get from R CMD check (Windows, R 2.4.0, same error for R 2.2.1): when running R CMD check over a modified version of package relaimpo, I get the following output in install.out: preparing package relaimpo for lazy loading Loading required package: MASS Loading required package: boot Error in parse(file, n, text, prompt) : syntax error at 2193:

Have you tried web searching on " R constrained linear regression" or similar. There seemed to be resources related to your issues when I looked. You might also search on rseek.org . There are apparently several packages that do regression with constraints, but I don't know if they fit your situation. Cheers, Bert Bert Gunter "The trouble with having an open mind is that

I am trying to automatically construct a distance function from a training set in order to use it to cluster another data set. The variables are nominal. One variable is a "class" variable having two values; it is kept separate from the others. I have a method which constructs a distance matrix for the levels of a nominal variable in the context of the other variables. I want to

Hi Everyone, I just don't know how to extract the information I want from the summary of a linear regression model fitting. For example, I fit the following simple linear regression model: results = lm(y_var ~ x_var) summary(results) gives me: Call: lm(formula = y_var ~ x_var) Residuals: Min 1Q Median 3Q Max -5.9859 -1.5849 0.4574 2.0163 4.6015 Coefficients:

I scanned the website with linkchecker and found quite a lot of dead links. This commit fixes or removes them. --- developers.html | 2 +- documentation_tasks.html | 2 +- download.html | 12 ++++++------ faq.html | 2 +- features.html | 2 +- feeds/feed.xml | 8 ++++++++ format.html | 8 ++++----

This update adds an intercept option (by popular request) - now one can fit a model without an intercept Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net" regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast. The current list of models covered are:

This update adds an intercept option (by popular request) - now one can fit a model without an intercept Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net" regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast. The current list of models covered are:

Readers, Using version 251 I tried the following command: lm(y~a+b,data=datafile) Resulting in, inter alia: ... coefficients (intercept) a 1.2 3.4 Packages installed: acepack ace() and avas() for selecting regression transformations adlift An adaptive lifting scheme algorithm akima Interpolation of irregularly spaced

R-packages leaps and subselect implement various methods of selecting best or good subsets of predictor variables for linear regression models, but they do not seem to be applicable to logistic regression models. Does anyone know of software for finding good subsets of predictor variables for linear regression models? Thanks. -Ben p.s., The leaps package references "Subset Selection

Hello, I've written a simple (although probably overly roundabout) function to test whether two regression slope coefficients from two linear models on independent data sets are significantly different. I'm a bit concerned, because when I test it on simulated data with different sample sizes and variances, the function seems to be extremely sensitive both of these. I am wondering if

I scanned the website with linkchecker and found quite a lot of dead links. This commit fixes or removes them. --- developers.html | 2 +- documentation_tasks.html | 2 +- download.html | 14 +++++++------- faq.html | 2 +- features.html | 2 +- feeds/feed.xml | 8 ++++++++ format.html | 8 ++++----