similar to: complex matrix manipulation question

Sometimes I print out a package and read about it and there are sometimes nice examples that I would like to run myself. Is there a way to bring them into R from the package or are they only meant to be typed in manually ? If manual is the only way, that's fine. I was just checking whether there was a quicker way. Thanks. Mark

I think I did enough reading on my Own about startup ( part of the morning And most of this afternoon ) to not feel uncomfortable asking for confirmation of my understanding of this startup stuff. Obviously, the startup process is more complicated Than below but, for my R newbie purposes, It seems like I can think of the startup process as follows : Suppose my home directory =

Does anyone know of a simple test in any R package that given a series of negative ones and positive ones ( no other values are possible in the series ) returns a test of whether the series is random or not. ( a test at each point would be good but I can use the apply function to implement that ) ? thanks.

I'm sorry to bother everyone with a stupid question but, when I am at an R prompt in Windows, is there a way to see what packages you already have installed from the R site so that you can just do library(name_of_package) and it will work. I've looked at help etc but I can't find a command like this. Maybe there isn't one which is fine.

I thought r-help let you attach asci files but I don't think it does now so below is a sample of my data set. Thanks again. 09:40:08.5238,67.00,33 09:40:09.1968,67.00,2 09:40:09.7945,67.00,2 09:40:09.7975,67.00,2 09:40:09.8318,66.99,-3 09:40:17.6335,66.95,3 09:41:09.3393,66.95,6 09:41:11.1482,66.95,-1 09:42:07.4552,66.90,-5 09:42:12.5823,66.85,-5 09:42:14.4329,66.80,-2

Hi Folks, I am dealing with data which have been presented as at each x_i, mean m_i of the y-values at x_i, sd s_i of the y-values at x_i number n_i of the y-values at x_i and I want to linearly regress y on x. There does not seem to be an option to 'lm' which can deal with such data directly, though the regression problem could be algebraically

Thanks a lot. setHook is Currently not in my knowledge set But it's great to save these Thing so I can look them up When I feel more comfortable. Just to add to that Stata versus R discussion : I believe, anyone who uses any other package than R, is probably missing out in the long run. It's truly unbelievable what has been done here. I feel like I fell asleep for 5 years ( by not using

Hi the list, According to what I know, the Canberra distance between X et Y is : sum[ (|x_i - y_i|) / (|x_i|+|y_i|) ] (with | | denoting the function 'absolute value') In the source code of the canberra distance in the file distance.c, we find : sum = fabs(x[i1] + x[i2]); diff = fabs(x[i1] - x[i2]); dev = diff/sum; which correspond to the formula : sum[ (|x_i - y_i|) /

Hello, Based on yesterday's R-help thread (help: program efficiency), and following Bill's suggestions, it appeared that sequence: > sequence function (nvec) unlist(lapply(nvec, seq_len)) <environment: namespace:base> could benefit from being written in C to avoid unnecessary memory allocations. I made this version using inline: require( inline ) sequence_c <- local( {

Dear All, I am recycling a previous email of mine where I asked some questions about clustering mixed numerical/categorical data. This time I am more into data mining. I am given a set of known statistical indexes {s_i}, i=1,2...N for a N countries. These indexes in general are a both numerical and categorical variables. For each country, I also have a property x_i whose value is known, but

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions

Dear R, I have a covariates matrix with 10 observations, e.g. > X <- matrix(rnorm(50), 10, 5) > X [,1] [,2] [,3] [,4] [,5] [1,] 0.24857135 0.30880745 -1.44118657 1.10229027 1.0526010 [2,] 1.24316806 0.36275370 -0.40096866 -0.24387888 -1.5324384 [3,] -0.33504014 0.42996246 0.03902479 -0.84778875 -2.4754644 [4,] 0.06710229 1.01950917

Greetings. For R gurus this may be a no brainer, but I could not find pointers to efficient computation of this beast in past help files. Background - I wish to implement a Cramer-von Mises type test statistic which involves double sums of max(X_i,Y_j) where X and Y are vectors of differing length. I am currently using ifelse pointwise in a vector, but have a nagging suspicion that there is a

Canberra distance is defined in function `dist' (standard library `mva') as sum(|x_i - y_i| / |x_i + y_i|) Obviously this is undefined for cases where both x_i and y_i are zeros. Since double zeros are common in many data sets, this is a nuisance. In our field (from which the distance is coming), it is customary to remove double zeros: contribution to distance is zero when both x_i

Canberra distance is defined in function `dist' (standard library `mva') as sum(|x_i - y_i| / |x_i + y_i|) Obviously this is undefined for cases where both x_i and y_i are zeros. Since double zeros are common in many data sets, this is a nuisance. In our field (from which the distance is coming), it is customary to remove double zeros: contribution to distance is zero when both x_i

I am trying to test out several mgcv::gam models in a scalar-on-function regression analysis. The following is the 'hierarchy' of models I would like to test: (1) Y_i = a + integral[ X_i(t)*Beta(t) dt ] (2) Y_i = a + integral[ F{X_i(t)}*Beta(t) dt ] (3) Y_i = a + integral[ F{X_i(t),t} dt ] equivalents for discrete data might be: 1) Y_i = a + sum_t[ L_t * X_it * Beta_t ] (2) Y_i

Dear All, Here is what I am trying to achieve: I would like to plot some data in 3D. Usually, one has a matrix of the kind y_1(x_1) , y_1(x_2).....y_1(x_i) y_2(x_1) , y_2(x_2).....y_2(x_i) ........................................... y_n(x_1) , y_n(x_2)......y_n(x_i) where e.g. y_2(x_1) is the value of y at time 2 at point x_1 (see that the grid in x is the same for the y values at all times).

Suppose one wanted to consider random variables X_1,...X_n and from each subtract off the piece which is correlated with the previous variables in the list. i.e. make new variables Z_i so that Z_1=X_1 and Z_i=X_i-cov(X_i,Z_1)Z_1/var(Z_1)-...- cov(X_i,Z__{i-1})Z__{i-1}/var(Z_{i-1}) I have code to do this but I keep getting a "non-conformable array" error in the line with the covariance.

Dear All, I'm having just a little terminology problem, relating the language used in the Hosmer and Lemeshow text on Applied Survival Analysis to that of the help that comes with the survival package. I am trying to back out the values for the baseline hazard, h_o(t_i), for each event time or observation time. Now survfit(fit)$surv gives me the value of the survival function, S(t_i|X_i,B),

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (