similar to: boxplot hinge customization

Hello all- I would appreciate any guidance that can be provided. I am new to R and am using it exclusively in a statistics program I am undertaking that mainly references Minitab. My focus is on data modeling and further more multivariate data analysis as much of my work in involves chemical measurements from custom sensors using all sorts of transduction methods. I am looking for a

R users: I am trying to replicate the boxplot output I achieve with Minitab in R. I realize that R gives the user many more options on the algorithm used to calculate the IQR than Minitab, so I concentrated on type=6 when using the quantile() function in R. The problem I am having is setting the upper and lower limit of the whisker based on the nearest actual data that should be included. If

Good Afternoon- I was wanting to alter the boxplot to remove the lower whisker, both the whisker line and staple just on the lower end. Is there a way to do this? As my code is currently: boxplot(log_loads~ind,data=nfmaum, horizontal=TRUE, notch=T, outline=FALSE, whisker=0, main="Maumee River Near Future Climate Scenarios", ylab="Log Load",xlab="Climate Scenarios")

Dear r-help, Unfortunately I cannot find in the documentation what determines ranges of a 'box' in the box-and-whisker plot. It is said in "Simple R" (http://www.math.csi.cuny.edu/Statistics/R/simpleR) that they are 1st and 3rd Qus usually. I tried to add to boxplot lines with (quantile(x,probs=0.25)), but lines do not coincide with edges of boxes.

I am trying to draw a plot like Matlab does: The upper extreme whisker represents 95% of the data; The upper hinge represents 75% of the data; The median represents 50% of the data; The lower hinge represents 25% of the data; The lower extreme whisker represents 5% of the data. It looks like: --- 95% | | ------- 75% | | |-----| 50% | | | | -------

Hello R Users! I have a question about the output of ARMAtoMA when used to calculate the variance of a model. I have a mixed model of the form ARMA(1,1). The actual model takes the form: X(t) = 0.75X(t-12) + a(t) - 0.4a(t-1) Given that gamma(0) takes the form [(1 + theta^2 - 2*theta*phi)/(1-phi^2)]*sigma(a), I would expect a process variance of 4.02*sigma(a) when I substitute 0.75 for phi and

Hi R-help. I am trying to run a linear mixed model with nested factors with either lme or lmer and I am having no luck obtaining the same results as Minitab. Here is Minitab's code: MTB > GLM 'count' = site year replicate(site year) site*year; SUBC> Random 'year' 'replicate'; Can you tell me how to code this in R? The settings are typeII, Tukey,

I'm having trouble using the read.mtp function in the foreign package to import datasets from minitab (.mtw) format. Specifically, each file I try to import fails to load any data beyond a header row stating the version of Minitab that saved the dataset. I get this error: incomplete final line found by readtableHeader on 'income.mtw' The dataset appears to be complete (ie, using

Hi I am performing an analysis of variance with two factors, each with two levels. I have differing numbers of observations in each of the four combinations, but all four combinations *are* present (2 of the factor combinations have 3 observations, 1 has 4 and 1 has 5) I have used both anova(aov(...)) and anova(lm(...)) in R and it gave the same result - as expected. I then plugged this into

Dear All, Would like to ask the inconsistency in the autocorrelation from R with SPSS/Minitab. I have tried a dataset x with 20 data (1-20) and ask R to give the autocorrelation of different lags using the command < acf(x, lag.max=100, type = "correlation"), However while SPSS and Minitab give the same answers (0.85 for lag1), R gives 0.3688 which is much smaller. Obviously, the

Dear R list, I have been trying to do a linear model, extracting the effect of a covariate.... and the results do not match, when I do it with other programs (e.g. minitab).... so it is obvious that I was doing something wrong. Whan I do it with minitab, I have this results: (sector is a factor and depth is the covariate): Source DF Seq SS Adj SS Adj MS F P

Hi, I ran a small data set from a factorial experiment through R, Minitab and JMP... the result from R is significantly different from what Minitab or JMP give... The data set is at the following link: http://www.personal.psu.edu/nug107/Uploads/2x3_16repsANOVA.txt The first 5 columns are the factors and the next three are responses. In particular, for the response beta11MSE, two of the

Hello, Given a probability function: p(x) = 12 / (25(x+1)) , x=0, 1, 2, 3 we generate the following values: C1 C2 0 0.48 1 0.24 2 0.16 3 0.12 Now, I'm supposed to create 50 random values using this table. In MiniTab, I simply selected Calc -> Random Data -> Discrete, and selected the columns, and it created 50 random values in a new column.[1] How do I do the

A strange thing happened. After installing the most recent RC version of 2.7 (2008-04-20 r45403) I compiled it as usual with no errors. After installing it I ran "make fullcheck". I got as far as testing recommended packages and I got the following: ... -------- Testing package foreign -------- Running examples in 'foreign-Ex.R' ... Comparing `foreign-Ex.Rout' to

I recently downloaded R for Windows, running on Win XP. I'm trying to create a perspective plot but not having any luck after reading the R manual and several examples found on the Internet. I have a 100 x 100 matrix of Z data as a tab-delimited text file exported from Minitab. I read this in to R using read.delim; this seemed to go ok. I created X and Y using seq() to get 100 divisions

Does anyone else miss email-friendly character graphics such as the following example, produced using Minitab? Histogram of C6 N = 478 N* = 21 Each * represents 2 observation(s) Midpoint Count -12 16 ******** -11 53 *************************** -10 63 ******************************** -9 83

I want to specify a two-factor model in lme, which should be easy? Here's what I have: factor 1 - treatment FIXED (two levels) factor 2 - genotype RANDOM (160 genotypes in total) I need a model that tells me whether the treatment, genotype and interaction terms are significant. I have been reading 'Mixed effects models in S' but in all examples the random factor is not in the main

hi folks, Bit of a newbie, but I've spent a fair bit of time looking for an answer on this, with no joy. Can anyone help me? Dataset: A single column of values in a csv file (eg. 52, 53, 54, 85, etc) Goal: In Minitab, you have what they call a dot plot. It's a histogram, where a single dot represents a set of identical values (eg. 57, 57, 57 would be one dot). Multiple dots are

Hola, LLevo buscando desde hace tiempo como hacer el Goodness of fit test en R. Es decir, me explico, intento hacer una cosa parecida que se hace en Minitab, por ejemplo, yo tengo un conjunto de datos, y lo que quiero es sabes que tipo de distibución es, en minitab se hace un histograma para ver si se ajusta bien o no a la campana de Gauss, luego vemos si aproximar la distribución de la muestra

Dear R-Friends, Sorry to bother everyone with my earlier question: "Do we have similar R function to work like Minitab's INDICATOR?" I found the way to make things work: e.g., > x <- c(2,2,5,3,6,5,NA) > model.matrix(~ factor(x) - 1) factor(x)2 factor(x)3 factor(x)5 factor(x)6 1 1 0 0 0 2 1 0 0 0