similar to: Plotting fit marginals, multiple plots on same x-axis

Hola, Bueno, puedes hacer el cálculo de una forma mucho más compacta y rápida. Esta forma es especialmente recomendable cuando tienes muchas columnas y muchas filas. > library(data.table) > myDT <- as.data.table(mtcars) > myDTlong <- melt(myDT, measure.vars=1:ncol(myDT)) > myDTlong[ , list(p_value = shapiro.test(value)$p.value, v_stat = shapiro.test(value)$statistic) , by

Hi, I am wondering if anyone knows of an easy way to fit a saturated model using the sem package on raw data? Say the data were: mtcars[, c("mpg", "hp", "wt")] The model would estimate the three means (intercepts) of c("mpg", "hp", "wt"). The variances of c("mpg", "hp", "wt"). The covariance of mpg with

Hi everyone, This is about documentation for the model.frame() page. The get_all_vars() function (added in R 2.5.0) is a great addition, but the behavior of its '...' argument is different from that of model.frame() with which it is documented and this creates ambiguity. The current docs read: \item{\dots}{further arguments such as \code{data}, \code{na.action}, \code{subset}. Any

This syntax is already implemented in the {purrr} package, more or less -- you need to add a tilde before your function call for it to work exactly as written: purrr::map_dbl(split(mtcars, mtcars$cyl), ~ summary(lm(wt ~ mpg, .))$r.squared) is equivalent to sapply(split(mtcars, mtcars$cyl), function(d) summary(lm(mpg ~ wt, d))$r.squared) Seems like using this package is probably an easier

I have applied the same linear model to several different subsets of a dataset. I recently read that in R, code should never be repeated. I feel my code as it currently stands has a lot of repetition, which could be condensed into fewer lines. I will use the mtcars dataset to replicate what I have done. My question is: how can I use fewer lines of code (for example using a for loop, a function or

On Wed, May 10, 2017 at 2:36 AM, William Dunlap via R-devel <r-devel at r-project.org> wrote: > Some formula methods for S3 generic functions use the idiom > returnValue$call <- sys.call(sys.parent()) > to show how to recreate the returned object or to use as a label on a > plot. It is often followed by > returnValue$call[[1]] <- quote(myName) > E.g., I see

Hello again, It appears that get_all_vars() incorrectly handles model formulae that use a right-hand side (rhs) matrix. For example, consider these two substantively identical models: # model using named variables mpg <- mtcars$mpg wt <- mtcars$wt hp <- mtcars$hp m1 <- lm(mpg ~ wt + hp) # model using matrix y <- mtcars$mpg x <- cbind(mtcars$wt, mtcars$hp) m2 <- lm(y ~ x)

I am trying to plot a graph but the points on the graph should be different symbols and colors. It should represent what is in the legend. I tried using the points command but this does not work. Is there another command in R that would allow me to use different symbols and colors for the points? Thank you kindly. data(mtcars) plot(mtcars$wt,mtcars$mpg,xlab= "Weight(lbs/1000)",

Serguei, > On 17/04/2020, at 2:24 AM, Sokol Serguei <sokol at insa-toulouse.fr> wrote: > > Hi, > > I would like to make a suggestion for a small syntactic modification of FUN argument in the family of functions [lsv]apply(). The idea is to allow one-liner expressions without typing "function(item) {...}" to surround them. The argument to the anonymous function is

Passing in a function passes not only an argument list but also an environment from which to get free variables. Since your function doesn't pay attention to the environment you get things like the following. > wsapply(list(1,2:3), paste(., ":", deparse(s))) [[1]] [1] "1 : paste(., \":\", deparse(s))" [[2]] [1] "2 : paste(., \":\",

I'm sure this exists elsewhere, but, as a trade-off, could you achieve what you want with a separate helper function F(expr) that constructs the function you want to pass to [lsv]apply()? Something that would allow you to write: sapply(split(mtcars, mtcars$cyl), F(summary(lm(mpg ~ wt,.))$r.squared)) Such an F() function would apply elsewhere too. /Henrik On Thu, Apr 16, 2020 at 9:30 AM

You can get pretty close to that already using fn$ in the gsubfn package: > library(gsubfn) > fn$sapply(split(mtcars, mtcars$cyl), x ~ summary(lm(mpg ~ wt, x))$r.squared) 4 6 8 0.5086326 0.4645102 0.4229655 It is not specific to sapply but rather fn$ can preface most functions. If the only free variables are the arguments to the function then you can omit the left

Dear all, I'm attaching a patch that should fix the issue mentioned above. It simply makes the same check seen in the same file for global variables: emitPTXAddressSpace(PTy->getAddressSpace(), O); if (GVar->getAlignment() == 0) O << " .align " << (int) TD->getPrefTypeAlignment(ETy); else O << " .align " <<

Here is a case where the current scheme fails: > with(datasets::mtcars, xyplot(mpg~wt|gear)$call) xyplot(substitute(expr), data, enclos = parent.frame()) Bill Dunlap TIBCO Software wdunlap tibco.com On Thu, May 11, 2017 at 1:09 AM, Deepayan Sarkar <deepayan.sarkar at gmail.com> wrote: > On Wed, May 10, 2017 at 2:36 AM, William Dunlap via R-devel > <r-devel at

Full_Name: Liming Liang Version: 1.8.1 OS: windows2000 professional Submission from: (NULL) (67.172.81.139) I was looking at the variable 'mpg' of the data file 'mtcars' and make a stem plot, the following is the commend I entered. The stem plot shows the largest observation is 32.9 but actually in the data the largest observation is 33.9, here might be a problem. >

In ggplot2, I would like to make a boxplot that has the following properties: (1) Contrary to default, the meaningful axis should be the horizontal axis. Lattice does this, for instance, by library(lattice);bwplot(~mtcars$mpg) (2) It is *univariate*, i.e., of a single vector, say mtcars$mpg. I do not wish to make separate plots for the different values of mtcars$cyl. (3) Nothing on the

On Thu, 1 Jun 2017, POLWART, Calum (COUNTY DURHAM AND DARLINGTON NHS FOUNDATION TRUST) via R-help wrote: > Before I go and do this another way - can I check if anyone has a way of > looping through data in odfWeave (or possibly sweave) to do a repeating > analysis on subsets of data? > > For simplicity lets use mtcars dataset in R to explain. Dataset looks like this: >

Sergei, the main problem that I was pointing out is is that there is no way you can introduce the new syntax without breaking the old one. The expression is evaluated to obtain a function, so by definition using anything that results in a valid expression for your syntax will break. E.g., using sapply(x, (foo)) is completely valid so you can't just change the evaluation of the expression to

Before I go and do this another way - can I check if anyone has a way of looping through data in odfWeave (or possibly sweave) to do a repeating analysis on subsets of data? For simplicity lets use mtcars dataset in R to explain. Dataset looks like this: > mtcars mpg cyl disp hp drat wt ... Mazda RX4 21.0 6 160 110 3.90 2.62 ... Mazda RX4 Wag 21.0 6 160 110 3.90

Dear R users, Does the results below make any sense? Can the the interval of the correlation coefficient be between *-1.0185* and -0.8265 at 95% confidence level? Liviu > library(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="spearman")) Spearman's rank correlation rho data: mpg and wt S = 10292, p-value = 1.488e-11 alternative hypothesis: true rho is not