similar to: cdplot() with 'POSIXct' x

Fairly new at this. Trying to create a conditional density plot. >cdplot(status~harvd.l,data=phy) Error in cdplot.formula(status~harvd.l,data=phy): dependent variable should be a factor What does this error mean? Status is a binary response of infestation (0/1) and harvd.l is the log of timber harvest density per catchment. Thanks. -- View this message in context:

I am having difficulties interpreting a cdplot, I have a binary variable y that I want to cdplot against a continuous variable x,below is the R command cdplot(y~x, data=mydata) you get a plot with a dark shaded area and a light shaded area. what do these areas mean? and how to interpret the plot? you help is greatly appreciated -- View this message in context:

Hi, I have a dataset that I am trying to analyze and plot as an ordered logistic regression (y = ordinal categories 1-3, x = continuous variable with values 3-9). First is a problem with cdplot: Produces a beautiful plot, with the "right" trend, but my independent factor values are transformed. The factor has values from 3-9, but the plot produces an x-axis with values from 20-140.

Dear all, I am quite new with R and I have a problem with plotting a binomial regression line in a plot. This is what I type in: > model<-glm(Para~Size,binomial) > par(mfrow=c(1,1)) > xv<-seq(3.2,4.5,0.01) > yv<-predict(model,list(area=xv),type="response") > plot(Size,Para) > lines(xv,yv) The error message that I get is: > Error in xy.coords(x, y) :

Hello, I plotted a nice tree with "ctree" . It shows 3 nodes with the prediction of my 2 groups. (see picture) Unfortunately I need a larger scale to read the exact prediction of my groups to get the specificity and sensitivity. I tried to change the scale with "axis" but it didn't work, my guess because it's not a normal graph with x and y axis. Has someone an idea

Hi Prof. Williams, thanks for your suggestion. The updated code is below. It turns out it was a matter of displaying the second column in yval to get the number of N and subtracting it from the n column in the frame to get the number of Y remaining in a binary example. once this is added now the function returns the rules along with Y and N count affected by the resulting rule. I am ccing

Hello sir: a data with 2 columns: id x a 1 b 2 c 3 I wanna get such kind of plot: x: a b c y:1 2 3 But the plot command doesn't permit string character as x. How can I get it ? Thanks a lot ! My best

I have noticed that when I iterate permutations of short vectors with the same seed, the cycle lengths are much shorter than I would expect by chance. For example: X <- 1:10 Xorig <- X start <- 112358 N <- 10 for (i in 1:N) { seed <- start + i for (j in 1:1000) { # Maximum cycle length to consider set.seed(seed) # Re-seed RNG to same initial state X <- sample(X)

I try tu use mob() with my data.frame ('data.frame': 288 obs. of 81 variables; factors, numerics and ordered factors) My response is a binary variable and I should use for modelling a logistic regression (family=binomial). I read in the "MOB" Vignette that I could use a formula like this if I would like to have only partitioning variables apart from the response.

The parts of klibc specific to the cris architecture. Signed-off-by: H. Peter Anvin <hpa at zytor.com> --- commit 84f6a72f42cf41e32daa59871a0b5424572093e4 tree 52bad10c1575f773a71cd4f4ea4b8083631f9d82 parent 1eff7c685b36cd0120694fd4150b32a26168d926 author H. Peter Anvin <hpa at zytor.com> Sun, 25 Jun 2006 16:58:18 -0700 committer H. Peter Anvin <hpa at zytor.com> Sun, 25 Jun

Commit-ID: a7a754c66437d4ab26503cdc183fd9e54a9e84d0 Gitweb: http://git.kernel.org/?p=libs/klibc/klibc.git;a=commit;h=a7a754c66437d4ab26503cdc183fd9e54a9e84d0 Author: Ben Hutchings <ben at decadent.org.uk> AuthorDate: Thu, 30 Apr 2020 14:06:32 +0100 Committer: Ben Hutchings <ben at decadent.org.uk> CommitDate: Thu, 30 Apr 2020 14:08:28 +0100 [klibc] arch: Remove cris port

Hi there, I'm new to R but I thought this is the most likely place I could get advice or hints w.r.t the following problem: I have a series of measurements xi with associated uncertainties dxi. I would like to construct the probability density histogram of this data where each density estimate has an associated error that is derived from the dxi. In other words, for large dxi the

Muchísimas gracias Carlos, de verdad que te agradezco la ayuda, pero no es lo que voy buscando. Quiero colocar en el eje de abscisas la secuencia temporal de los meses, es decir, agosto septiembre, octubre, etc? pero no las fechas de las toma de datos, sino que aparezca la marca de un mes, y la siguiente marca sea la del siguiente mes, etc?, y además que las muestras estén separadas de acuerdo con

Hi all, So I've started working on this and I ran into something that I didn't know, namely that for x a multi-dimensional (2+) array, head(x) and tail(x) ignore dimension completely, treat x as an atomic vector, and return an (unclassed) atomic vector: > x = array(100, c(4, 5, 5)) > dim(x) [1] 4 5 5 > head(x, 1) [1] 100 > class(head(x)) [1] "numeric" (For a

Hello: I am using rpart and would like more background on how the splits are made and how to interpret results - also how to properly use text(.rpart). I have looked through Venables and Ripley and through the rpart help and still have some questions. If there is a source (say, Breiman et al) on decision trees that would clear this all up, please let me know. The questions below pertain to a

Muchas gracias Carlos, previo a mi correo, entre las pruebas que hice estaba una parecida a la que apuntas de la siguiente manera: attach (Libro1) plot (xbar~as.Date(fechas,"%d/%m/%y"), ylim=c(400,660), xaxt="n", type="b", pch=19,cex=1) xlabels<-strptime(fecha,format="%d/%m/%Y") axis.Date (1,at=xlabels,format="%b-%y")

Hi Boris, Do a search on "the order of elements of the symmetric group". (This search will also turn up homework questions and solutions.) You will understand why you are seeing this once you understand how a permutation is decomposed into cycles and how the order relates to a partition of n (n=10 in your case). Enjoy! Eric On Fri, Dec 8, 2017 at 6:39 AM, Boris Steipe <boris.steipe

On 10/30/19 04:29, Martin Maechler wrote: >>>>>> Gabriel Becker >>>>>> on Tue, 29 Oct 2019 12:43:15 -0700 writes: > > > Hi all, > > So I've started working on this and I ran into something that I didn't > > know, namely that for x a multi-dimensional (2+) array, head(x) and tail(x) > > ignore dimension

>>>>> Gabriel Becker >>>>> on Tue, 29 Oct 2019 12:43:15 -0700 writes: > Hi all, > So I've started working on this and I ran into something that I didn't > know, namely that for x a multi-dimensional (2+) array, head(x) and tail(x) > ignore dimension completely, treat x as an atomic vector, and return an > (unclassed)

I have a math (and maybe an R) question. I want to find equilibrium population density values for a system of linear population growth equations (e.g., Pimm and Lawton 1977). The eventual goal is to perform stability analysis. I can find the partial differential equations for the Jacobian matrix, but I get stuck trying finding the equilibriuym pop densities (X*). Here is the (incorrect?) R code