similar to: characters, mathematical expressions and computed values

is there a way to use the actual index value for plotting zoo objects this is the way that the index is set up and a sample range of what I would like to plot 01/01/06 00:00:00 - 01/01/06 23:45:00 { library(zoo) # chron library(chron) fmt.chron <- function(x) { chron(sub(" .*", "", x), gsub(".* (.*)", "\\1:00", x)) }} x <- structure(c(15.57, 15.5,

Hola!! tengo un data.frame donde cada fila corresponde a un año y cada columna a un mes (De enero a diciembre) > head(valT) V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 1941 18.0 16.3 15.2 10.1 8.1 8.3 8.8 9.2 7.9 12.2 11.9 14.6 1942 17.2 15.9 13.6 11.6 8.7 6.2 6.4 7.2 9.7 12.0 14.1 16.7 1943 17.6 17.3 13.5 12.5 10.5 7.0 8.2 7.9 -999.9 -999.9

Hi, I am very new to R and I've been trying to work through the R book to gain a better idea of the code (which is also completely new to me). Initially I imputed my data from a text file and that seemed to work ok, but I'm trying to examine linear relationships between gdist and gair, gdist and gsub, m6dist and m6air, etc. This didn't work and I think it might have something to do

Dear R Helpers, I did a search for deleting rows based on conditions but wasn't able to find an example that addressed the error that I am getting. I am hoping that this is a simple syntax phenomenon that somebody else knows off the top of their head. My apologies for not providing a reproducible example but I think that the information given will allow someone to give me a hint. I want to

Some day I may figure out how ggplot2 works. I am trying to plot 5 columns of data on a graph (similar to a simple matplot) =========================================================================== library(ggplot2) bmi <- structure(list(pct = 2:21, P10 = c(14.6, 14.5, 14.2, 13.9, 13.7, 13.7, 13.9, 14.2, 14.5, 14.8, 15.3, 15.9, 16.6, 17.2, 17.8, 18.1, 18.3, 18.4, 18.5, 18.6), P25 =

We are two french students and we have a problem concerning an exercize. We don't know how to resolve it. It would be fantastic if someone can help us. Thanks. Description: This study examined how the metabolic cost of locomotion varied with speed, stride frequency and body mass. Cost was determined by measuring oxygen consumption (?vo2?), analyzing the oxygen content in air inhaled and

Hi everyone, I've run into a bit of strange problem with implicit vs explicit printing and the call stack. I've included an example at the bottom of this email. The basic problem is that I have an S3 object with a print method. When the object is implicitly printed (ie. typed directly into the console) the function arguments in the call stack are exploded out to their actual values,

Dear all, I am attempting to explain patterns of arthropod family richness (count data) using a regression model. It seems to be able to do a pretty good job as an explanatory model (i.e. demonstrating relationships between dependent and independent variables), but it has systematic problems as a predictive model: It is biased high at low observed values of family richness and biased low at

Hi, I have a few questions about glht() and the interpretation of output from Tukey's in multcomp package for lme() model. The main issue is that I noticed that a plot that I produced with code letters seem to contradict the graph itself. I provide data and code below. I end with my questions. A few things about data set. "LMA.vcp" is continuous response variable.

Hi R Users, I am going to run a multiple linear regression with around 57 independent variables. Each time I run the model with just 11 variables, the results are reasonable. With increasing the number of independent variables more than 11, the coefficients will get ?NA? in the output. Is there any limitation for the number of independent variables in multiple linear regressions in R? I attached

I want to use a diallel analysis in R, for some of my own data. I've been through the primary literature and textbooks, and remain stumped as to how to implment this in R. I can illustrate the problem using a published example dataset: [Cockerham and Weir (1977) Quadratic Analyses of Reciprocal Crosses. Biometrics, Vol. 33, No. 1 pp. 187-203] In this study, 8 different individuals were

fun example that shows NCQ lowers wait and %w, but doesn''t have much impact on final speed. [scrubbing, devs reordered for clarity] extended device statistics device r/s w/s kr/s kw/s wait actv svc_t %w %b sd2 454.7 0.0 47168.0 0.0 0.0 5.7 12.6 0 74 sd4 440.7 0.0 45825.9 0.0 0.0 5.5 12.4 0 78 sd6 445.7 0.0

Hi there I thought this would be of particular interest to people using 'optim' functions and perhaps people involved with R development. I've been beaten down by R trying to get it to perform an optimization on a mass-balance model. I've written the same program in excel, and using the 'solver' function, it comes up with an answer for my variables (p, ACT, which

Greetings, I am trying the get the standard errors of multiple comparisons using Tukey's HSD. These are not reported by the function TukeyHSD. When I apply the following code to the data, which I store as PROLE4.TXT, several unexpected things happen. First, the function TukeyHSD works for all the comparisons but the function simint doesn't. Second, after the application of na.omit

I'm trying to solve a homework problem using R. The problem gives a list of cricket chirps per second and corresponding temperature, and asks to give the equation for the linear model and then predict the temperature to produce 18 chirps per second. So far, I have: > # Homework 11.2.1 and 11.3.3 > chirps <- scan() 1: 20 2: 16 3: 19.8 4: 18.4 5: 17.1 6: 15.5 7: 14.7 8: 17.1 9: 15.4

Dear Community, I have been using two types of survival programs to analyse a data set. The first one is an R function called aftreg. The second one an STATA function called streg. Both of them include the same analyisis with a weibull distribution. Yet, results are very different. Shouldn't the results be the same? Kind regards, J -- View this message in context:

Hello R experts, what follows is my reproducible example: mydata<-structure(list(ped.avg = c(335.9, 110.8, 645.7, 638.9, 1468.1, 126.4, 4811.1, 88.5, 868.5, 656.6, 723.6, 654, 2.8, 15, 14.2, 17.5, 15.4, 112.1, 424.7, 18.3, 19.9, 28.6, 25.6, 23.5, 15.4, 27, 62.1, 15.6, 74.6), ped.erst = c(96, 53.2, 615.2, 616.5, 512.9, 56.2, 1851.8, 57.1, 579.5, 613.2, 601.1, 613.6, 1.3, 6.3, 6.5, 6.1,

Hello, It's an alternative to use SAS by function in R? I want to plot d histograms by plot.from example bellow: Thank you! plot d 1 1 16.3 2 1 25.0 3 1 57.8 4 1 17.0 5 2 10.8 13 2 96.4 17 3 76.0 18 3 32.0 19 3 11.0 20 3 11.0 24 3 106.0 25 3 12.5 21 4 19.3 22 4 12.0 26 4 15.0 27 5 99.3 32 7 11.0 36

Greetings, I would like to plot three lines on the same figure, and I am lost. There is an answer to a similar thread… but I tried matplot and it is beyond me. An example of the data follows: Year EM IM BM 1983 9.1 16.8 -7.7 1984 12.0 18.0 -6.0 1985 13.6 19.1 -5.5 1986 12.4 17.3 -4.9 1987 14.6 20.3 -5.7 1988 20.6 23.3 -2.6 1989 25.0 27.2 -2.2 1990 28.4 30.2 -1.8 1991 33.3 31.2 2.1 1992 40.6

Colleagues, I am fitting an Emax model using nls. The code is: START <- list(EMAX=INITEMAX, EFFECT=INITEFFECT, C50=INITC50) CONTROL <- list(maxiter=1000, warnOnly=T) #FORMULA <- as.formula(YVAR ~ EMAX - EFFECT * XVAR^GAMMA / (XVAR^GAMMA + C50^GAMMA)) ## alternate version of formula FORMULA <- as.formula(YVAR ~ EMAX - EFFECT / (1 + (C50/XVAR)^GAMMA)) FIT <-