similar to: glm with Student t for error distribution

I wonder how one in R can fit a 3rd degree polynomial to some data? Say the data is: y <- c(15.51, 12.44, 31.5, 21.5, 17.89, 27.09, 15.02, 13.43, 18.18, 11.32) x <- seq(3.75, 6, 0.25) And resulting degrees of polynomial are: 5.8007 -91.6339 472.1726 -774.2584 THanks in advance! -- Jonas Malmros Stockholm University Stockholm, Sweden

I have a data frame named "database" with panel data, a little piece of which looks like this: Symbol Name Trial Factor1 Factor2 External 1 548140 A 1 -3.87 -0.32 0.01 2 547400 B 1 12.11 -0.68 0.40 3 547173 C 1

Hello, I have a vector of names, say : names <- c("Factor 1", "Factor 2", Factor 3") I am creating a dataframe and I want the column names to be mixed like this: "Factor 1" " Sign Factor 1" "Factor 2" "Sign Factor 2" "Factor 3" "Sign Factor 3" How can I automate the creation of such a mixed vector? I tried

Bill, Alberto, Gabor, Thank you for answering my question. Now I learned about outer() function. That was a straightforward example. But what if I had a matrix, where the last column was filled with values first (again, a for loop), and the rest was filled by using a double loop? OVal <- matrix(0, n+1, n+1) for(i in 0:n){ OVal[i+1, n+1] <- max(Val[i+1, n+1]-K, 0) } for(i in seq(n,1,

Dear list members, I have written a function, called say Analysis. I supply an Excel file name as an argument, it does analysis on this file and returns a pdf file with specific plots, and a text file with several statistical models' output (I extract certain values from the output and create my own custom dataframe with output). As of now I have to open the script file and load the function

Dear R users, I have a huge database and I need to adjust it somewhat. Here is a very little cut out from database: CODE NAME DATE DATA1 4813 ADVANCED TELECOM 1987 0.013 3845 ADVANCED THERAPEUTIC SYS LTD 1987 10.1 3845 ADVANCED THERAPEUTIC SYS LTD 1989 2.463 3845 ADVANCED THERAPEUTIC SYS LTD 1988 1.563 2836 ADVANCED TISSUE

Hello, I am using textplot function in gplots package to put some model output inside a PDF file, but it does not seem to work properly with PDF. I am doing follwing: pdf(file="C:/...", paper="a4", width=8, height=12) .model <- lm(.model.formula, data=database) textplot(capture.output(summary(.model)), valign="top", halign="left") I am getting these

Hi, All: assume I need to generate X from inverse gamma with parameter (k, beta). should I generate from Y from gamma(-k, beta), then take X=1/Y? Thanks pat

Hi everybody, I wonder if there is a built-in function similar to Matlab's "normfit" which computes 95% CI based on the normality assumption. So, I have a vector of values and I want to calculate 95% normal CI. Of course, I could write my own function, no problem, but I still wonder if built-in functionality exists. (I wish quantile() had this functionality included). Anyone knows?

Hello everyone. I use double for loops to fill in matrices, but there are surely better (and computationally faster) ways to perform that task. Could someone show me, given the following example of a double for loop, how this could be done? It is much easier to learn by examples. Val <- matrix(0, nrow=n+1, ncol=n+1) for( i in 0:n){ for(j in 0:i){ Val[j+1, i+1] <- u^j*d^(i-j)

Hi, all How can generate a sample from truncated inverse gamma distribution in R? thanks

http://r.789695.n4.nabble.com/file/n3216865/Inverse_Gamma.png Hello, I am seeking help in estimating the parameters of an inverse gamma distribution (from the 'actuar' package) using a function like 'fitdistr'. Unfortunately I haven't found such a package using findFn('fit Inverse Gamma') from the 'sos' package and was therefore hoping someone might be aware

Dear users; I am doing GLMs with the Gamma distribution, and I always get errors ("no valid set of coefficients: please supply starting values") or warnings ("NaNs produced in log(x)") when I use the links identity or inverse, but I don´t get them if I use the log link. For example: >

Hi, guys, I am trying to sample from a truncated normal/gamma distribution. But only the far end of the distribution (where the probability is very low) is left. e.g. mu = - 4; sigma = 0.1; The distribution is Normal(mu,sigma^2) truncated on [0,+Inf]; How can I get a sample? I tried to use inverse CDF method, but got Inf as answers. Please help me out. Also, pls help me on the similar

Hi all, I am also confused about the manual: a. The input arguments: wt.method are the weights case weights (giving the relative importance of case, so a weight of 2 means there are two of these) or the inverse of the variances, so a weight of two means this error is half as variable? w (optional) initial down-weighting for each case. init (optional) initial values for the

Hello! I am trying with this question again: I would like to test few distributional assumptions for some behavioral response data. There are few theories about true distribution of those data, like: normal, lognormal, gamma, ex-Gaussian (exponential-Gaussian), Wald (inverse Gaussian) etc. The best way would be via qq-plot, to show to students differences. First two are trivial: qqnorm(dat$X)

Hello, I'm Emanuela, I'm implemented a survival analysis and I'm trying to use a frailty model with inverse gaussian distribution, but I'm not able to find the right code, because it seems to be only a gamma and a gaussian distribution. Is there also the inverse gaussian distribution? Thanks a lot Emanuela Rossi

R 2.5 windows XP I am getting an error from glm() that I don't understand. Any help or suggestions would be appreciated. N.B. 1<=AAMTCAREJ<=327900 > summary(data$AAMTCAREJ) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.0 404.3 1430.0 6567.0 5457.0 327900.0 > fitglm<-glm(AAMTCAREJ~sexcat+H_AGE+SmokeCat+InsuranceCat+MedicadeCat+ +

Hi, I have a problem with the df. I read in a big csv file. Tabelle <- read.csv("C:\\Users\\Public\\Documents\\Bachelorarbeit\\eingabe8_durchnummeriert.csv" , header = T , sep=";") then I try this: > ygamma <- glm(Tabelle$sb_ek_ber ~1+ Tabelle$FAHRL_C + Tabelle$NUTZKREIS + Tabelle$schw_drittel_c   , family = Gamma) >  anova(ygamma, test="Chisq")

Dear all, I would be very grateful if you could help me with: Given the regularized gamma function Reg=int_0^r (x^(k-1)e^(-x))dx/int_0^Inf (x^(k-1)e^(-x))dx ; 0<r<Inf (which is eventually the ratio of the Incomplete gamma function by the gamma function), does anyone know of a package in R that would evaluate the derivative of the inverse of Reg with respect to k? I am aware that the