DWin
• Member for 11 years, 1 month
• Last seen this week
• Alameda, CA

The exponentiated numberofdrugs coefficient is the multiplicative term to use for the goal of calculating the estimated healthvalue when numberofdrugs increases by 1 unit. In the case of categorical (...

The KS test is premised on testing the "sameness" of two independent samples from a continuous distribution (as the help page states). If that is the case then the probability of ties should be ...

The default error family for a glm model in (the language) R is Gaussian, so with the code submitted you are getting ordinary linear regression where $R^2$ is a widely accepted measure of "goodness of ...

Zeros in tables are sometimes classified as structural, i.e.zero by design or by definition, or as random, i.e. a possible value that was observed. In the case of a study where no instances were ...

Q: " ... how do I interpret the x2 value "High"? For example, what effect does "High" x2s have on the response variable in the example given here?? A: You have no doubt noticed that there is no ...

So if you did three studies of similar sizes and got a p-value of 0.05 on all three occasions, your intuition is that the "true value" should be 0.05? My intuition is different. Multiple similar ...

Regarding fitting the exponential model with glm: When using the glm function with family=Gamma one needs to also use the supporting facilities of summary.glm in order to fix the dispersion parameter ...

df_split<- strsplit(as.character(df$position), split=":") df <- transform(df, seq_name= sapply(df_split, "[[", 1),pos2= sapply(df_split, "[[", 2)) > > df name position pos seq_name ... View answer 9 votes They are not ignored. If they were 'ignored' they would not be in the model. The estimate of the explanatory variable of interest is conditional on the other variables. The estimate is formed "in the ... View answer 8 votes @mpiktas came close in offering a feasible model, however the term that needs to be used for the quadratic in time=t would be I(t^2)) . This is so because in R the formula interpretation of "^" ... View answer Accepted answer 7 votes The rms package allows you to model interactions between continuous variables very flexibly. This is a demonstration of modeling crossed regression splines with that data: (m <- cph(Surv(time, ... View answer Accepted answer 7 votes Generally tables are handled as matrices or arrays and matrix indexing allows two column arguments as (i,j)-indexing (and if the object being indexed has higher dimensions then matrices with more ... View answer 7 votes You should focus on the function that is passed to boot as the "statistic" parameter and notice how it is constructed. f <- function(data, i) { require(pscl) m <- zeroinfl(count ~ child + ... View answer 7 votes The answer to your last question is a flat NO. The magnitude of coefficients are in no way a measure of importance. The lasso can be used for logistic regression. You need to study the area more ... View answer Accepted answer 7 votes (You should probably cite the source for your naming conventions and explain in more detail why this question is being posed. If this a case of trying to match the documentation for SAS or SPSS we ... View answer Accepted answer 7 votes Creating dummy variables should not be necessary. You should just use factors when modeling in R. admityear <- factor(admityear) m4 <- lrm(Outcome ~ relGPA + mcAvgGPA + Interview_Z + ... View answer Accepted answer 6 votes The CDC uses the epidemic threshold of 1.645 standard deviations above the baseline for that time of year. The definition may have multiple sorts of detection or mortality endpoints. (The one you ... View answer 6 votes The two-group t-test balances (by computing their ratios) two aspects of the distributions of the values, one: the difference in the means, which your "disturbance" did increase somewhat, and two: the ... View answer 6 votes It seems likely that you could fairly easily construct a counter-example by assigning the negative roots of$Y^2$to$Y$for$X\leq1$and to the positive roots for$X > 1$. Y <- rnorm(1000)^2 X ... View answer 6 votes (Some moderator must have a warped sense of what is R and what is statistics. This is a coding question if I ever saw one.) Since the columns are of necessity "character" the values will be "character"... View answer Accepted answer 6 votes Use density and fields::colorbar.plot require(fields) plot(1:10, rep(1,10), ylim=c(0,10)) colorbar.plot( 2, 4, 800*density(rgamma(100, shape=2))$y) colorbar.plot( 2, 5, 800*density(rexp(100))\$y) ...

Only when the counts in all groups are the same will the mean of means equal the global mean,

This really does look to be more appropriate to an [r] tagged question in SO but there seems to be a surprisingly wide degree of tolerance for such questions in CV, so here goes. The answer also ...

You can prevent the loss of the dimension attribute when using "[" with drop=FALSE: tapply(colnames(myMA), c(1,1,1,2,2,2,3,3,4,4), list) myMAmean <- sapply(myList, function(x) rowMeans(myMA[,x, ...

Copy of my response to identical cross-posted question on Rhelp: You have an offset that is not described. And gam suppresses the Intercept. These would seem to be likely sources of confusion. For ...

Look at ?contrasts and ?lm. The default contrasts in R are different than those of SAS, but the difference is explained and you can specify the alternate form. (Am I correct in thinking you wanted to ...

You may not get a simple response to residuals(npk.aovE) but that does not mean there are no residuals in that object. Do str and see that within the levels there are still residuals. I would imagine ...

One method (the easiest to grasp in one sentence) is to look at the increment in sums of squares due to regression when a covariate is added. This is R's ANOVA (or AOV) strategy, which implies that ...