similar to: Adjusting two continuous variables by one continuous variable

Graphically, you can use coplot() or lattice to see how the relationship between height and weight changes with age. With a linear model, you can do something like: lm(height ~ weight * age, ...) and work out the interpretation of the coefficients. HTH, Andy > From: Peter Tait > > Hi, > > I want to look at the relationship between 2 > continuous/quantitative variables

Hello, I'm wondering if the 'mosaic' plot of the vcd package (or any other function for that matter) can be used with a continuous variable that should be represented via various categorical variables.  All the documentation I've read lead me to believe that it only works with counts of categories. Alternatively, I've thought of first creating a contingency table where the

What is the quickest way to create many categorical variables (factors) from continuous variables? This is the approach that I have used: # create sample data N <- 20 x <- runif(N,0,1) # setup ranges to define categories x.a <- (x >= 0.0) & (x < 0.4) x.b <- (x >= 0.4) & (x < 0.5) x.c <- (x >= 0.5) & (x < 0.6) x.d <- (x >= 0.6) & (x <

Hi, I am studying using SVM functions of e1071 package to do prediction, and I found during the training data are "factor" type, then svm.predict() can predict data directly by categories; but if response variables are "numerical", the predicted value from svm will be continuous quantitative numbers, then how can I connect these quantitative numbers to categories? (for

Hi I am performing a lmer on count data with several explanatory variables both continuous and categorical. All go in fine apart from two of the continuous variables that have na's in them. They give errors like: Leading minor of order 5 in downdated X'X is not positive definite I used the as.factor function to define my categorical factors as categorical but when i ask is.factor about

I have gotten the this error before: "glm.fit: fitted probabilities numerically 0 or 1 occurred" and the problem was usually solved by combining one or more categories were there were no observations. I am now having this error show up for a variable that is continuous (not categorical). What could be the cause of this for a continuous variable?? Thanks, Nick -- View this message

I am trying to obtain adjusted means and standard errors for a three way ANOVA I have three effects, two continuous; fire frequency and annual precipitation, and one categorical; soil type in an unbalanced design. I am testing the effect of annual precipition (AP), soil type (ST), and fire frequency (FF) on stem count (SCt) My data table looks as such: ST FF AP SCt 3 Coy

Hi, I would like to use the sem package to perform a path analysis (no latent variables) with a mixture of 2 nominal exogenous, 1 continuous exogenous, and 4 continuous endogenous variables. I seek advice as to how to calculate the appropriate covariance matrix for use with the sem package. I have read through the polycor package, and am confused as to the use of "numeric" for

I've used Anova() from the car package to get marginal (aka type II) sum-of-squares and tests for linear models with categorical variables. Is it possible to get marginal SSs also for continuous variables, when the model includes powers of the continuous variables? For instance, if A and B are categorical ("factor"s) and x is continuous ("numeric"), Anova (lm (y ~ A*B +

Hello, I have a data set containing categorical and ordinal factors, as well as sampling weights (i.e., survey weights reflecting unequal probabilities of selection). I want to fit a structural equation model with sem(). I have run sem() on weighted covariance matrices using advice from John Fox (see <http://tolstoy.newcastle.edu.au/R/e5/help/08/12/8773.html> and

Dear all, I have the following problem: I have been using the routine "optim" in order to maximize a joint likelihood (basically a mixture with modeled weights) with quantitative variables..so far so good. Now I need to plug into the model a categorical variable (namely, age classes). Obviously, given the way optim works, it won't allow me to treat it "directly" (i.e.

Dear list I would like to compute a Mantel-Haenszel chi-square in which the matching variable is a continuous variable. The MH chi-square is used to assess the relationship between two categorical variables at each level or strata defined by a third variable. Specifically I would like to know if there is a straightforward way to divide the matching variable into levels, in which each level has a

I've been programming maximum likelihood estimation models using the function "optim." My current research requires modeling a particular parameter as a categorical variable (what R calls a "factor"), not as a continuous parameter. (The research question is, at what level of X does a subject in our experiment choose Y=1 instead of Y=0? So this is a "light switch"

Dear All, I want to plot a heat map with annotated columns. Both functions heatmap (stats) and heatmap.2 (gplots) can plot a horizontal side bar that can be used to visualize a categorical variable. In addition to a categorical variable, I would like to visualize a continuous variable. This could be done by small bars, a curve or simply numbers above the columns. (The Sample names are already

Hi all, I have dataset with 2 independent variable, one (x1) is continuous, the other (x2) is a categorical variable with 2 levels. The dependent variable (y) is continuous. When I run linear regression y~x1*x2, I found that the p value for the continuous independent variable x1 changes when different contrasts was used (helmert vs. treatment), while the p values for the categorical x2 and

Hello everyone, Can anyone suggest a decently documented (with good examples in the documentation) R package/function that performs segmentation (cluster, mixture modeling) of a population using both continuous and categorical input variables? Thank you, Dan [[alternative HTML version deleted]]

I am trying to perform cluster analysis on survey data where each respondent has answered several questions, some of which have categorical answers ("blue" "pink" "green" etc) and some of which have scale answers (rating from 1 to 10 etc).My problem is that certain age groups were over-sampled and I need to weight the data collected in order to accurately reflect the

Hello I have a data base consisting of soil parameters, and tree species densities. The vcd (visualizing categorical data) package includes the ternaryplot function which plots the gravitation center of 3 prameters. I'd like to find a similar function for use with continuous data rather than categorical. Does anyone know of a function suitable for this objective? Thanks Steve

Hello, I have a small statistics question, and as I'm quite new to statistics and R, I'm not sure if I'm doing things correctly. I am looking at two quantitative variables (x,y) that are correlated. When I divide the data set according to a categorical variable z, then x and y are more poorly correlated when z = A than when z = B (see attached figure). In fact x and y are two

Hi all, I want to conduct a cluster analysis with quantitative variables. More precisely, it concerns binary and non-ordered categorical variables. For such data, various similarity measures have been proposed, such as the Jaccard index or the simple matching index. So, is there a package such as mva or multiv in the case of quantitative variables? Could you indicate me reviews, papers or