similar to: Computing stats on common parts of multiple dataframes

aa <- (structure(list(X.0.85 = c(-1.02, -1.17, -1.29, -1.39, -1.46, -1.5, -1.52, -1.5, -1.46, -1.39, -1.3, -1.19, -1.07, -0.93, -0.79, -0.65, -0.5, -0.36, -0.22, -0.08, 0.05, 0.18, 0.3, 0.41, 0.52, 0.62, 0.72, 0.81, 0.89, 0.98, 1.05, 1.13, 1.19, 1.25, 1.29, 1.31, 1.31, 1.29, 1.24, 1.16, 1.06, 0.93, 0.77, 0.58, 0.38, 0.16, -0.07, -0.31, -0.89, -1.05, -1.19, -1.31, -1.41, -1.47, -1.51, -1.51,

I've a matrix like this: 1985 1.38 1.27 1.84 2.10 0.59 3.47 1986 1.05 1.13 1.21 1.54 0.21 2.14 1987 1.33 1.21 1.77 1.44 0.27 2.85 1988 1.86 1.06 2.33 2.14 0.55 1.40 1989 2.10 0.65 2.74 2.43 1.19 1.45 1990 1.55 0.00 1.59 1.94 0.99 2.14 1991 0.92

*Hola!!! resulta que tengo unos datos de divisas ordenados por fechas (días) los que he convertido a formato tipo YYYY-MM-DD donde DD siempre es 01:* * * * EUR.resto$date<-as.Date(EUR.resto$date) EUR.resto$mo <- substr(EUR.resto$date,6,7) EUR.resto$yr <- substr(EUR.resto$date, 1,4)

I have a problem with R. I try to compute the confidence interval for my df. When I want to create the plot I have this problem: Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' and 'y' lengths differ. I try this code: library(dplR) df.rwi <- detrend(rwl = df, method = "Spline",nyrs=NULL) write.table(df.rwi,file="rwi.txt",quote=FALSE,row.names=TRUE)

I am a new user of R so please bear with me. I have reviewed some R books, FAQs and such but the volume of material is great. I am in the process of porting my current SAS and SVS Script code to Lotus Approach, R and WordPerfect. My question is, can you help me determine the best R method to implement the Hoaglin Outlier Method? It is used in the Appendix A and B of the fo llowing link.

Hi samba community. I'm having a problem with the smb_auth authentication method. Everything looks like normal, but everytime I try to use smb_auth it returns ERR. I will show here some commands output to secure that all configuration is correct, and if anyone can help me to investigate what's happend I'll thanks. I'm using: Debian lenny, updated. ii samba 2:3.2.3-1

Hi, I'm trying to use sapply to compute the min of several variables, each of them stored in data.frames, grouped as a list: Is it normal that mean() and min() produce different objects dimensions? > str(dats) List of 5 $ log20:'data.frame': 83 obs. of 5 variables: ..$ DATE : int [1:83] 2001081500 2001081512 2001081600 2001081612 2001081700 2001081712

Hi all. I'm working on a LLVM based compiler for the D programming language by Walter Bright. D allows static arrays up to 16MB in size. I'm initializing global static arrays with a constant initializer. But D requires that (per default) static arrays are initialized with the default initializer for the element type. For float this happens to be NaN, so I cannot use a

All, I'm witnessing some strange behavior when checking the values of one of my variables. My guess is that it has something to do with the number of significant digits being represented, but perhaps not as my variable was created from other variables that only had one decimal place. See below. I'm sure this is something basic, any suggestions much appreciated. Cheers, David >

R users, I intention is to calculate some summary statistics across factor levels. I know that in Hmisc package there is a summary function which produces neat summary statistics when using "cross" option. I would like to produce similar output with N and Missing columns but produce a data.frame. Is there any built-in function for that? #example data install.packages("Hmisc")

Hi, so I don't speak computer and I have no idea what this code is telling the program to do, but I apparently need to be able to find and isolate influencial observations. Problem, I have no idea what the error means and where it may be from in the code. error I get is below the code { ## OLS results NameC<- lm(gpanew~female+female:lastinit+agenew+canadian+mom_ed+yearstudy) ## default:

No no, eso lo he sacaod, es decir, tengo la matriz de confusión para las OK/NOK, lo que no entiendo es como extraer las conclusiones sobre el modelo, de cara a como afectan las variables. He seguido dos estrategias: 1-Crear arboles de clasificacion con las variables más importantes del random Forest, pero el modelo se empobrece bastante. 2- Sacar los partialPlot, para ver la influencia de cada

Hello! We have a w2k8 R2 64-bit machine in a Samba 3.4.4 controlled domain. When logging in as a domain user there is a ~30 sec delay waiting for the User Profile Service. This has nothing to do with the size of the user profile. Looking at the network traffic I can see 2 gaps, the first about 11 sek and the second about 19 sek with no packets sent except one TCP ACK (from the client) in each

Dear R forum I have data.frame as df = data.frame(rate_name = c("USD_1w", "USD_1w", "USD_1w", "USD_1w", "USD_1m", "USD_1m", "USD_1m", "USD_1m", "USD_2m", "USD_2m", "USD_2m", "USD_2m",  "GBP_1w", "GBP_1w", "GBP_1w", "GBP_1w",

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Mi matriz de datos inicial estaba muy desbalanceada (5% de la clase minoritaria), por lo que he creado con el algoritmo SMOTE un dataset balanceado con el que he creado el modelo, y luego sobre ese modelo he creado la matriz de confusión con los datos originales. Respecto a lo que me comentas, Carlos, creo que además de todo lo que comentas, que está bien, en micaso es necesario también saber no

Gracias por la pronta respuesta, pero tras leer la contestación de la gente, sigo sin entender muy bien la explicación. Le responden lo siguiente: "Each point on the partial dependence plot is the average vote percentage in favor of the "Yes trees" class across all observations, given a fixed level of TRI. It's not a probability of correct classification. It has absolutely

Hello List, My goal is to create a 5 category variable (p1_st_data$ob_mrj_cat), based on the p1_st_data$obt_mrj_p variable, using the following code for 50 States and District of Columbia (N=51). p1_st_data$ob_mrj_cat <- cut (p1_st_data$obt_mrj_p, quantile (p1_st_data$obt_mrj_p, (0:5/5), include.lowest=TRUE)) The issue is that, for Utah, I am getting an <NA> instead of (42,48.7] in

Hi Elisa, Try this: date1<-format(seq.Date(as.Date("1991.1.1",format="%Y.%m.%d"),as.Date("1996.12.31",format="%Y.%m.%d"),by="day"),"%Y.%m.%d") ?length(date1) #[1] 2192 mat1<-matrix(c(.314,.314,.273,.273,.236,.236,.236,.236,.273,.314,.403,.314),ncol=1) res1<-

the 'hivex' had been installed actually, hivex.x86_64 0:1.2.4-3.el6 and perl-hivex.x86_64 0:1.2.4-3.el6, however it just could not be detected.Maybe i should compile one rather than just yum. I`m not familiar with hivex,as far as i know, it can be used to modify windows registry, but why the inspection API had been binded with hivex when we might use inspection api solely with linux os