similar to: 3D Scatter Plot

Hii, I will create boxplots from matrices. I have the following data sets: 5.0 1.78 2.99 2.019 0 10.0 1.79 3.00 1.744 0 15.0 1.78 2.98 1.936 0 20.0 1.78 2.99 1.975 0 25.0 1.73 2.91 3.591 0 30.0 1.79 3.00 1.966 0 35.0 1.79 3.00 2.451 0 40.0 1.79 3.00

Hello, my test.R file contains two huge arrays (>3000 entries), from which R needs to calculate the Pearson Correlation, if I look at the file the numbers look correct. if I run R R < test.R --no-save I see things like this: 0.723, 0.838, 1.002, 0.364, 0.357, 0.227, 0.982+ , 0.963, 0.535, 1.214, 1.270, 0.832, 1.033, 0.632, 2.482, 1.239, 0.743, 1.077, 0.962, 1.052, 1.075, 1.427, 1.395,

I try to get working together vlan and bonding both for dom0 and domU. I lost packets sent to dom0 while domU is OK. Nightly stats for dom0: 52879 packets transmitted, 45293 received, 14% packet loss, time 52879599ms rtt min/avg/max/mdev = 0.144/0.224/717.306/5.129 ms Nightly stats for domU: 52952 packets transmitted, 52952 received, 0% packet loss, time 52952554ms rtt min/avg/max/mdev =

Hi, I receive the following warning message when I run a poisson GLM in R: "glm.fit: fitted rates numerically 0 occurred" The model summary is shown below. The variable 'Species' consists of counts of different species ranging from 0 to 4. I suspect this may have something to do with the warning message but I'm not sure. Can anybody help? Thank you! Anna Call:

Dear Sir/Madam, How to use getSymbols() to get annual data? For example, I need the annual stock price of APPLE from the year 2000 to 2016. How to write the command? I only know how to get the daily data. It is: getSymbols("AAPL",from="2000-01-01",to="2016-12-31") Thank you very much. Have a good week! Best regards, Yingrui Liu [[alternative HTML

Problem: I am sorting through model selection process for first time and want to make sure that I have used glm, stepAIC, and update correctly. Something is strange because I get a different result between: 1) a glm of 12 predictor variables followed by a stepAIC where all interactions are considered and then an update to remove one specific interaction. vs. 2) entering all the terms

The box has a fairly heavy UDP load. Its RELENG_7 as of today and took 3hrs for it to dump core. Fatal trap 12: page fault while in kernel mode cpuid = 1; apic id = 01 fault virtual address = 0x68 fault code = supervisor read, page not present instruction pointer = 0x20:0xc0637146 stack pointer = 0x28:0xe766eaac frame pointer = 0x28:0xe766eb54 code segment

Dear all I am trying to fit Variable Coefficients Models on Unbalanced Panel Data. I managed to fit such models on balanced panel data (the example from the "plm" vignette), but I failed to do so on my real, unbalanced panel data. I can reproduce the error on a modified example from the vignette: > require(plm) > data("Hedonic") > Hed <- pvcm(mv ~ crim + zn + indus

Hi, I'm a newbie to R. My knowledge of statistics is mostly self-taught. My problem is how to measure the effect of users in groups. I can calculate a particular attribute for a user in a group. But my hypothesis is that the user's attribute is not independent of each other and that the user's attribute depends on the group ie that user's behaviour change based on the group. Let

Hello all! I have a problem to extract values greater that for example 1820. I try this code: x[x[,1]>1820,]->x1 Please help me! Thank you! The data structure is: structure(c(2.576, 1.728, 3.434, 2.187, 1.928, 1.886, 1.2425, 1.23, 1.075, 1.1785, 1.186, 1.165, 1.732, 1.517, 1.4095, 1.074, 1.618, 1.677, 1.845, 1.594, 1.6655, 1.1605, 1.425, 1.099, 1.007, 1.1795, 1.3855, 1.4065, 1.138, 1.514,

Good afternoon, In the code below, I have a set of functions (m1,m2,m3,s1,s2, and s3) which represent response surface designs for the mean and variance for three response variables, followed by an objective function that uses the "Big M" method to minimize variance (that is, push s1, s2, and s3 as close to 0 as possible) and hit targets for each of the three means (which are 0, 10,

Hello all! I have a problem with R. I try to merge data like this: structure(c(2.1785, 1.868, 2.1855, 2.5175, 2.025, 2.435, 1.809, 1.628, 1.327, 1.3485, 1.4335, 2.052, 2.2465, 2.151, 1.7945, 1.79, 1.6055, 1.616, 1.633, 1.665, 2.002, 2.152, 1.736, 1.7985, 1.9155, 1.7135, 1.548, 1.568, 1.713, 2.079, 1.875, 2.12, 2.072, 1.906, 1.4645, 1.3025, 1.407, 1.5445, 1.437, 1.463, 1.5235, 1.609, 1.738, 1.478,

Hola eRReros. Os lo explico con un ejemplo: # Cargamos los paquetes y el ejemplo install.packages("SPEI") library("SPEI") data(wichita) # los primeros 12 datos head(wichita,12) # mi subset de los primeros 12 datos meu<-wichita[c(1:12),] meu # como veis los valores de TMED son iguales en ambos dataframes. # ahora viene el problema # calculamos

Package: logcheck-database Version: 1.2.68 Severity: normal Hi, I use postfix, amavisd-new and logcheck on a lenny server, and the mails I receive seem a little to verbose. For example, I get in the report all mails which are received, for example: Sep 11 08:35:11 heracles amavis[19788]: (19788-02) Passed CLEAN, [xxx.xxx.xxx.xxx] [xxx.xxx.xxx.xxx] <user at domain> -> <user at

Dear R-Users, Does anyone know how to flatten, i.e. convert to a table, a return value of tapply when its INDEX argument is a list? Here is an example of what I need > x <- rnorm(100) > f1 <- rep(c(T,F),50) > f2 <- c(rep(T,50), rep(F,50)) > y <- tapply(x, list(f1=f1,f2=f2), summary) > y f2 f1 FALSE TRUE FALSE "Numeric,6"

How do I allow traceroute to reach my server? Pings work fine but traceroute stops at the last hop before my server. If I shut off the firewall it reaches it fine. PING danicar.net (24.222.246.120): 56 data bytes 64 bytes from 24.222.246.120: icmp_seq=0 ttl=237 time=104.0 ms 64 bytes from 24.222.246.120: icmp_seq=1 ttl=237 time=74.9 ms 64 bytes from 24.222.246.120: icmp_seq=2 ttl=237 time=90.6

Dear all, Even after a couple of hours looking at old messages I still haven't found a solution for my problem. I'm trying to fit an additive linear regression model with 2 effects, both fixed, to some dataset. The function contrasts(effectA) <- contr.sum can gaurantee that the coefficients per parameter sum to one, and the function dummy.coef provices the estimates of all

Please help me interpret the following results. The full model (Schwa~Dialect*Prediction*Reduction) was reduced via both update() and step(). The minimal adequate model is: ancova<-lm(Schwa~Dialect+Prediction+Reduction+Dialect:Prediction) Schwa is response variable Dialect is factor, two levels ("QF","SF") Prediction is factor, two levels ("High","Low")

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

Dear all here is my data called "rglp" structure(list(vzorek = structure(1:17, .Label = c("179/1/1", "179/2/1", "180/1", "181/1", "182/1", "183/1", "184/1", "185/1", "186/1", "187/1", "188/1", "189/1", "190/1", "191/1", "192/1",