similar to: How to combine matrices?

Dear All, Let: dp: depth of the?river tp: temperature with respect to depth We can?have a simple scatter plot, between depth as y-axis and temperature as x-axis,?by using a?plot function as shown?below. #####################? dp <- c(1,4,3,2,5,7,9,8,9,2) tp <- 1:10 plot(tp,dp, type= 'l') ##################### ? Could someone advice me on?the way to?plot the same pair of

Dear All, I was looking for the color index in?image function,?such as from topo.colors(n) and etc. but still never found it. For instance, from the help menu. ########################################### # Volcano data visualized as matrix. Need to transpose and flip # matrix horizontally. image(t(volcano)[ncol(volcano):1,]) # A prettier display of the volcano x <- 10*(1:nrow(volcano)) y

Dear All, I have a series of data in which the first column consist of a combination of date and time, for instance 17 April 2008 at 4.01pm, such data is recorded as: 4/17/2008 16:01 I'd like to seperate it into four different columns which consist of Day, Month,Year and Time, respectively. Could someone please advice me on this mater? Thank you, Fir

Dear All, I've been?searching for?appropriate codes to compute the rate of change and the curvature?of ?nonparametric regression model whish was denoted by a smooth function?but?unfortunately?don't manage to?do?it. I presume that such characteristics from a smooth curve can be determined by the first and second derivative operators. The following are the example of fitting a

Dear All, Could anyone?give me a hand?to suggest few packages in R to running Kalman prediction and filtration ? Thanks Fir

Dear All, I'm sorry if my question does not suit with this R group. I have recently installed?R software with version 2.9.2, but i found the program took almost 1 minute as soon as it was opened, before it can be used. However,?the previous version 2.9.1 only take few seconds?after the menu bar was clicked. This circumstance has caused me to wait for couple of minutes as several R windows

Dear All, ? Let: dp: depth of the river tp: temperature with respect to depth These pair of observations are in 3 different groups i.e: Obs. 1,3,5,7?from the first group Obs. 2,4 and?10?from second group Obs 6,8 and 9?from third group. We can have a simple scatter plot, between depth as y-axis and temperature as x-axis, with each pairs are denoted by a red dot, by using a plot function shown

Hi,   I have an original data frame with 8 columns of variables, which are stored in 'data1' frame.   data1 <- read.csv("E:\\PHD GLASGOW UNIVERSITY\\Data\\R\\Colin\\Cailness21.csv") attach(data1) names(data1)   [1] "Date"       "d"          "m"          "y"          "Time"      [6] "Depth"      "Temp"      

Dear All, Let? 499 piece-wise lines were buit up by 500 pair of observations, via R code below. x <- 1:500 y <- rnorm(500) plot(x, y, type = 'b') I was trying to compute all the slopes?for the ?lines which were connected between two adjacent points. For instance,?slopes?of lines between first and second points, second and third points, and so on, until?between??points 499 and 500,

Dear All, Let have 10 pair of observations, as shown below. ###################### x <- 1:10 y <- c(1,3,2,4,5,10,13,15,19,22) plot(x,y) ###################### Two fitted? models, with ranges of [1,5] and [5,10],?can be easily fitted separately by lm function as shown below: ####################### mod1 <- lm(y[1:5] ~ x[1:5]) mod2 <- lm(y[5:10] ~ x[5:10]) #######################

Hi, I tried to use tapply function to find the mean of the data?in each group as the following command, but the result are NA, as there are several missing values in each group. tapply(data,group,mean) Could someone please advice me the way to ?ignore the missing data in order for the fucntion to run successfully? Thanks Fir

Hi everyone, I'm trying to calculate correlation coefficients between corresponding columns in two matrices with identical dimensions but different data. The problem is that the matrices contain NAs in different locations. I am using the following code to try to calculate correlations between complete sets of data: #Code start maxcol<-ncol(mat1) for (i in 1:maxcol) {

Hi, the standard errors of the coefficients in two regressions that I computed by hand and using lm() differ by about 1%. Can somebody help me to identify the source of this difference? The coefficient estimates are the same, but the standard errors differ. ####Simulate data happiness=0 income=0 gender=(rep(c(0,1,1,0),25)) for(i in 1:100){ happiness[i]=1000+i+rnorm(1,0,40)

Hi, I hope somebody can help me with this issue: I am doing population pyramids using the barplot command, so in the left side I have male age structure and in the right side the female age structure. To plot the male age structure I put the data in negative numbers. Now, I want to change the sign in the bar plot in such way that I have no-sign numbers, both in left and right side of the graph. I

I have a matrix containing means and CIs (lower and upper in two columns, so three columns for every data point) for several points. I have to build a graph of these means accompained by the CIs (as wiskers). No problems with making the graph of means, but I don't know how to introduce CIs. Can anybody advise? -- View this message in context:

Dear All, I'm looking an appropriate way in R to compute/estimate? points of intersection between a line and a curve and will really appreciate for any suggestion or ideas? Thank you, Fir

Hi all, Is there any package in R which fits binary regression models with a probit (or logit) link function related to a nonlinear predictor with both fixed and random effects? Something like that: Y ~ Bernoulli(p) probit(p) = h(X*Beta + Z*b), where ``h'' is a nonlinear function, X and Z are known design matrices, Beta are fixed effects and b are random effects. Thanks all in

Dear All,   Could someone please give some advice the way to do linear modelling via best subset regression in R? I'd really appreciate for your kindness.   Thanks, Kagba [[alternative HTML version deleted]]

Hello, I'm trying to do cor(x1,x2) and I get the following error: Error in cor.default(x1, x2) : missing observations in cov/cor A few things: 1. I've used cor() many times and have never encountered this error. 2. length(x1) = length(x2) 3. is.numeric(x1) = is.numeric(x2) = TRUE 4. which(is.na(x1)) = which(is.na(x2)) = integer(0) {the same goes for is.nan()} 5. I also try

The myoglobin sequence, with reference number NM_005368 in Gen bank, has the following frequencies of DNA nucleotides: A C G T 237 278 309 242 Do these data provide sufficient evidence, at the 1% level of significance, that the DNA nucleotides have an unequal distribution, that is the DNA nucleotides are not evenly utilised? Clearly state your hypothesis, test statistic and conclusion.