search for: newd

Hi R Fans, I stumbled across a strange (I think) bug in R 2.9.1. I have read in a data file with 5934 rows and 9 columns with the commands: daten = data.frame(read.table("C:/fussball.dat",header=TRUE)) Then I needed a subset of the data file: newd = daten[daten[,1]!=daten[,2],] --> two values do not meet the logical specification and are dropped. The strange thing about it: When I print the newd in the R Console, the output still shows 5934 rows. When I check the number of rows with NROW(newd) , I get 5932 as output. When I print newd...

...2/lib/readdir.c +++ b/com32/lib/readdir.c @@ -33,21 +33,16 @@ struct dirent *readdir(DIR *dir) /* Don't do this as we won't be able to rewind. dir->dd_fd = regs.esi.w[0]; /* Shouldn't be needed? */ if ((!(regs.eflags.l & EFLAGS_CF)) && (regs.esi.w[0] != 0)) { - newde = calloc(1, sizeof(newde)); - if (newde != NULL) { - strcpy(newde->d_name, __com32.cs_bounce); - newde->d_mode = regs.edx.w[0]; - newde->d_type = (newde->d_mode & S_IFMT) >> 12; - newde->d_size = regs.eax.l; - newde->d_ino = regs.ebx.l; - dir->dd_...

...), weightData,               start = c(alpha = 0.0, beta = 1, gamma = 0.2), trace = TRUE)   ### I get the error below: Error in nls(weight ~ alpha + beta * exp(gamma * week), weightData, start = c(alpha = 0,  :   number of iterations exceeded maximum of 50 M_model ### predict for another 2 weeks newD <- data.frame(week = 1:52);newD newD$pred_wt <- predict(M_model, newD) newD plot(pred_wt ~ week, newD, pch = 4, col = "red", ylab = "Weight", xlab = "Week") with(weightData, points(week, weight,col='blue')) Felipe D. Carrillo Supervisory Fishery Biologi...

...sual, this is what it supposed to look like where d1,d2,d3 and so on up to d1000 are calculated using the equation and the respective a and b values: Code #2 d1 <-runif(33,10,20) d2 <-runif(33,15,20) d3 <-runif(33,18,20) e <-cbind(a,b,d1,d2,d3)   Next, I would like to generate 33 x 1000 newd values again using newa and newb values and the same c values, eg: Code #3 newa <-matrix(seq(16,24, by = sign(24-16)*0.25)) newb <-matrix(seq(24,36, by = sign(36-24)*0.25)) c <-runif(1000,50,60) newd <-exp(-c*newa)+exp(-c*newb)   Next I would like to sum d(ith) and newd(ith) according t...

...p://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. Hello, When creating the new data df, the default check.names = TRUE changes the column name, it is repaired and the hyphen is replaced by a legal dot. # check.names defaults to TRUE newd <- data.frame(`plant-density` = x) # `plant-density` is not a column name head(newd) # check.names set to FALSE newd <- data.frame(`plant-density` = x, check.names = FALSE) # `plant-density` is becomes a column name head(newd) # Use predict to get CIs for a plot # Add CI for regression lin...

...g) y <- f + e Mydata<-data.frame(y=y,x0=x0,x1=x1,x2=x2,x3=x3) remove(list=c("y","x0","x1","x2","x3")) # Note below the syntax of the 3rd variable required for my loop for (i in 4:5){ b<-gam(y~s(x0)+ s(x1)+ ns(Mydata[,i], 3), data=Mydata) newd <- data.frame(x0=(0:399)/30,x1=(0:399)/30,x2=(0:399)/30,x3=(0:399)/30) pred <- predict.gam(b,newd) } Erreur dans model.frame(formula, rownames, variables, varnames, extras, extranames, : type (list) incorrect pour la variable 'Mydata' De plus : Warning message: not all requi...

.../com32/lib/opendir.c @@ -0,0 +1,41 @@ +/* + * opendir.c + */ + +#include <dirent.h> +#include <stdio.h> +#include <errno.h> + +#include <com32.h> +#include <string.h> +#include <unistd.h> +#include <fcntl.h> + + +DIR *opendir(const char *pathname) +{ + DIR *newdir; + com32sys_t regs; + + newdir = NULL; + + strlcpy(__com32.cs_bounce, pathname, __com32.cs_bounce_size); + + regs.eax.w[0] = 0x0020; + regs.esi.w[0] = OFFS(__com32.cs_bounce); + regs.es = SEG(__com32.cs_bounce); + + __com32.cs_intcall(0x22, &regs, &regs); + + if (!(regs.eflags.l & EFL...

.../com32/lib/opendir.c @@ -0,0 +1,41 @@ +/* + * opendir.c + */ + +#include <dirent.h> +#include <stdio.h> +#include <errno.h> + +#include <com32.h> +#include <string.h> +#include <unistd.h> +#include <fcntl.h> + + +DIR *opendir(const char *pathname) +{ + DIR *newdir; + com32sys_t regs; + + newdir = NULL; + + strlcpy(__com32.cs_bounce, pathname, __com32.cs_bounce_size); + + regs.eax.w[0] = 0x001F; + regs.esi.w[0] = OFFS(__com32.cs_bounce); + regs.es = SEG(__com32.cs_bounce); + + __com32.cs_intcall(0x22, &regs, &regs); + + if (!(regs.eflags.l & EFL...

...gh sampling pvec <- c("PR1", "PR2", "PR3", "PR4", "PR5", "PR6", "PR7", "PR8", "PR9", "PR10") fun11 <- function(x){ smpool <- c(1,2,3,3) x <- sample(smpool, 10, replace= TRUE) } newd <- sapply (pvec, fun11) # function to recode this new generated data fun3 <- function(x) { (if ( x ==1){ x = lcd1 }else if (x ==2){ x = lcd2 } else if ( x ==3 ){ x = lcd3 } else x = NA ) return(x) } Applying the f...

...ata set (dataframe) called "mydat". It consist of 3 numerical variable.  They are Centrecode, FSUSN and Round. I want to create unique ID by combining these 3 variables. Follwing commands gives me what I need. mydat1 <- paste(mydat$Centrecode, mydat$FSUSN,mydat$Round,sep="") newds <- data.frame(mydat1)    For a large data set, I don't want to write like this ...    "mydat$Centrecode, mydat$FSUSN,mydat$Round". So,  I tried to automate using following code.   nvar <- paste("mydat","$",names(mydat)[1:3],sep="") mydat1 <- pas...

Hey List, I did a multiple regression and my final model looks as follows: model9<-lm(calP ~ nsP + I(st^2) + distPr + I(distPr^2)) Now I tried to predict the values for calP from this model using the following function: xv<-seq(0,89,by=1) yv<-predict(model9,list(distPr=xv,st=xv,nsP=xv)) The predicted values are however strange. Now I do not know weather just the model does not fit

I am having trouble using back ticks with the R extractor function 'predict' and an lm() model.? I'm trying too construct some nice vectors that can be used for plotting the two types of regression intervals.? I think it works with normal column heading names but it fails when I have "special" back-tick names.? Can anyone help with how I would reference these?? Short of

...02-19:15:00 20.1 15 97 2005-02-20:18:00 16.5 95 95 2005-03-03:18:00 10.3 95 95 2005-03-04:00:00 13.4 13 95 2005-10-22:15:00 11.3 13 95 2005-10-25:15:00 10.3 2 2 I want to create another dataframe made up of the values of dataframe1 which are not common with dataframe2, ie. newD = D1 - (D1 intersection D2) that is, newD: dates ws wc pwc 2005-10-19:12:00 10.8 80 81 2005-10-20:12:00 12.3 5 15 2005-10-21:15:00 12.3 3 15 2005-10-23:12:00 12.3 13 2 2005-10-24:15:00 10.3 2 95 Thanks for any help you can provide, Augusto ------------...

...;smooth", lty=2, col="red") panel.xyplot(x[order(x)], lower[subscripts][order(x)], type = "smooth", lty=2, col="red") #panel.loess(x,y,...) #panel.rug(x = x[is.na(y)], y = y[is.na(x)]) } ) # by newdata = ... # take a cross-section of data - reduces the amount of data to be plotted mx = aggregate(Q95 ~ super.end.group, spring, max, na.rm=T) newlst <- do.call(rbind, lapply(1:6, function(j) expand.grid(Q95 = seq(0,mx[j,2], length = 50), super.end.group = LETTERS[j])...

Dear all,? I hope that you can help me on this. I have been struggling to figure this out but I haven't found any solution. I am running a generalised mixed effect model, gamm4, for an ecology project. Below is the code for the model: model<-gamm4(LIFE.OE_spring~s(Q95, by=super.end.group)+Year+Hms_Rsctned+Hms_Poaching+X.broadleaved_woodland? ? ? ? ? ? ?+X.urban.suburban+X.CapWks,

Here's my little discussion example for a quadratic regression: http://pj.freefaculty.org/R/WorkingExamples/regression-quadratic-1.R Students press me to know the benefits of poly() over the more obvious regression formulas. I think I understand the theory on why poly() should be more numerically stable, but I'm having trouble writing down an example that proves the benefit of this. I

...it. [R version 1.5.1 and at least 1.4.1, probably all versions at all:] For these, R 1.5.x does not differ from 1.4.1 : Inspite of R 1.5.0's "NEWS" entry and the ?Safepredict help page, predict() doesn't (always?) work correctly for (some cases of) predict(lm(y ~ poly(....)), newdata = .....) or predict(lm(y ~ ns(....)), newdata = .....) Here is a simple (longer than necessary) example #### This modified from the last part of example(cars) {of R 1.5.1}: data(cars) ## An example of polynomial regression ## -- to show the bug, the linear example is sufficient cars.1...

Dear all, I'm a fairly new R user having two questions regarding gam: 1. The prediction example on p. 38 in the mgcv manual. In order to get predictions based on the original data set, by leaving out the 'newdata' argument ("newd" in the example), I get an error message "Warning message: the condition has length > 1 and only the first element will be used in: if (object$dim == 0) m <- 0 else m <- length(object$sp)" I suspected that it had somthing to do with the data...

I am working on Johansen cointegration test, using urca and var package. in the selection of var, I have got following results. >VARselect(newd, lag.max = 10,type = "none") $selection AIC(n) HQ(n) SC(n) FPE(n) 6 6 6 5 $criteria 1 2 3 4 5 6 7 8 9 AIC(n) -3.818646e+01 -3.864064e+01 -3.833435e+01 -4.089169e+01 NaN -Inf -Inf -Inf -Inf HQ(n)...

...obtain the difference in the predicted logodds using the predict() function, and thus get a point-estimate OR. But I can't see how to obtain the confidence interval for such an OR. For example: model <- glm(chd ~age.cat + male + lowed, family=binomial(logit)) pred1 <- predict(model, newdata=data.frame(age.cat=1,male=1,lowed=1)) pred2 <- predict(model, newdata=data.frame(age.cat=2,male=0,lowed=0)) OR <- exp(pred2-pred1) Thanks [[alternative HTML version deleted]]