similar to: rescale the data into unit square?

Dear Rusers, I have some data in .csv file like "2004-8-1" and "2004-10-1", and i need to convert them into days from the origin (January 1, 1960). I have tried the function date.mmddyyyy(), but cannot get it. Anybody can show me how to handle the date data? Thanks very much! My dataset like: time 2004-8-1 2004-10-1 2001-9-1 2002-9-1 -- With Kind Regards,

Dear Rusers, My question is about "recode variables". First, i'd like to say something about the idea of recoding: My dataset have three variables:type,soiltem and airtem,which means grass type, soil temperature and air temperature. As we all known, the change of air temperature is greater than soil temperature,so the values in those two different temperaturemay represent different

Dear Rusers, The default behavior in R when performing a regression model with missing values is to exclude any case that contains a missing value? How could i set the bahavior that R deal with missing values? e.g.: exclude cases listwise exclude cases pairwise replace with mean Thanks very much! -- Kind Regards, Zhi Jie,Zhang ,PHD Department of Epidemiology School of Public Health Fudan

Dear R users, I hope to explain the concepts of skewness and kurtosis by generating series of distributions with same skewness and different kurtosis or with same kurtosis and different skewness, but it seems that i cannot find the right functions. I have searched the mailing list, but no answers were found. Is it possible to do that in R? Which function could be used? Thanks a lot. --

Dear friends, suppose i want to do the following caulation for 100 times, how to put the results of x , y and z into the same dataframe/dataset? x<-runif(1) y<-x+1 z<-x+y thanks in advance! -- Kind Regards, Zhi Jie,Zhang ,PHD Department of Epidemiology School of Public Health Fudan University Tel:86-21-54237149 [[alternative HTML version deleted]]

Dear R-users, I generate a dataset "d", and want to get a subset from it. ** *z<-rnorm(9) coords<-cbind(x=c(1,1,1,2,2,2,3,3,3),y=c(1,2,3,1,2,3,1,2,3)) d<-SpatialPointsDataFrame(coords, data.frame (z=z[1:9]))* The result*/dataset* is coordinates z 1 (1, 1) 1.41173570 2 (1, 2) 0.18546503 3 (1, 3) -0.04369144 4 (2, 1) -0.21591338 5 (2, 2)

Dear friends, I have a (20*30) matrix,and want to get a subset of it like the following: The original matrix: rows:1,2,3,....20; columns:1,2,3,....30 I want to get my subset of The original matrix and delete others: rows:1,3,5,7,...19; columns:1,3,5.....29 -- Kind Regards,Zhi Jie,Zhang ,PHDDepartment of EpidemiologySchool of Public HealthFudan UniversityTel:86-21-54237149

Dear all, I have two variables, y and x. It seems that the relationship between them is Piecewise Linear Functions. The cutpoint is 20. That is, when x<20, there is a linear relationship between y and x; while x>=20, there is another different linear relationship between them. How can i specify their relationships in R correctly? # glm(y~I(x<20)+I(x>=20),family = binomial, data =

Dear friends, Anybody has ever met the problem to add a legend to a figure generated by image()? I have three variables,x,y and z. x and y are the coordinates, and z is the third values. we can use image(x, y, z,...) to generate a figure according to the z-values, but the problem is the figure legend. How can the legend be added to a figure generated by image()? Note that filled.contour() can

Dear friends, The following is an example to explain my question. I want to get a legend which will show the z-values according to different colors in image() function. x<-sort(runif(10)) #x-coordinates y<-sort(runif(10)) #y-coordinates z<-matrix(runif(100),nrow=10) #attributes values image(x,y,z,col=gray((6:3)/6)) # legend(x,y,legend=z,col=gray((6:3)/6)) #error. the colors should

Dear friends, I'm doing a simulation on logistic regression model, but the programs can't work well,please help me to correct it and give some suggestions. My programs: data<-matrix(rnorm(400),ncol=8) #sample size is 50 data<-data.frame(data) names(data)<-c(paste("x",1:8,sep="")) #8 independent variables,x1-x8; #logistic regression model is

Dear R users, We have some programs for the specific task in our research, but they were very commonly used. We want to make some functions for them, anybody can recommend any good tools to facilitate us to create R functions even without going deep into the theories of R functions ? Any suggestions or help are greatly appreciated. -- With Kind Regards, oooO::::::::: (..):::::::::

Dear Ruser, I use abline() function to add the reference line successfully, but i can't display the values corresponding to the reference line on the x/y axis, anybody knows how to display it? *My simulated programs:* y<-rnorm(50) plot(x,y) abline(v=0.5) *#my question is how to display x=0.5 in the x axis?* Thanks in advance. -- With Kind Regards, oooO::::::::: (..):::::::::

Dear friends, After running the lm() model, we can get summary resluts like the following: Coefficients: Estimate Std. Error t value Pr(>|t|) x1 0.11562 0.10994 1.052 0.2957 x2 -0.13879 0.09674 -1.435 0.1548 x3 0.01051 0.09862 0.107 0.9153 x4 0.14183 0.08471 1.674 0.0975 . x5 0.18995 0.10482 1.812 0.0732 . x6 0.24832 0.10059 2.469 0.0154 * x7

Dear Rusers, I have met a difficult problem on explaining the differences of principal component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't met before. Althought they have got the same eigenvalues, their coeffiecients were different. First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it.

Dear friends, When i clean my dataset , i met a difficulty suppose my data set is : *> data<-data.frame(x=c(1:5,1,2,3)) > data x 1 1 2 2 3 3 4 4 5 5* 6 1 7 2 8 3 Now i need to add the data which are less than 3.5 at the bottom, not including the top data, so the results should be : x 1 1 2 2 3 3 4 4 5 5 *6 6* I tried to use " data[data$x>3.5,]" to do it , but it also

Dear friends, After fitting a generalized linear models ,i hope to get the variance of alpha,variance of beta and their covariance, that is , the variance-covariance matrix/information of alpha and beta , suppose *B* is the object of GLMs, i use attributes(B) to look for the options ,but can't find it, anybody knows how to get it? > attributes(B) $names [1] "coefficients"

Dear friends, In S-PLUS, we can use the following argument, but not in R. mode12 <- gam(score1 ~ lo(latitude) + lo(longitude)) I searched the help in S-PLUS, it says lo() Allows the user to specify a Loess fit in a GAM formula, but i didn't find the correponding function in R. Anybody knows how to do the similar task in R? Thanks very much. -- With Kind Regards, oooO:::::::::

Dear friends, summary() doesn't give a good ouput style,e.g.: grass soiltem airtem gheight humidity altitude diluo :38 Min. :15.90 Min. :17.70 Min. : 8.00 Min. : 0.2360 high: 43 huanghuacai:32 1st Qu.:19.32 1st Qu.:22.60 1st Qu.:40.00 1st Qu.: 0.3190 low :119 hucao :46 Median :20.20 Median :25.30 Median :60.00

Dear all, Some functions like 'ROC(Epi)' can be used to perform ROC analyssi, but it needs us to specify the fitting model in the argument. Now i have got the predicted p-values (0,1) for the 0/1 response variable using some other approach, see the following example dataset: id mark predict.pvalue 1 1 0.927 2 0 0.928 3 1 0.928 ..................