similar to: R vs PYTHON vs SAS vs SPSS?

Yes On 30 November 2017 at 22:28, <hotprojects at nyc.rr.com> wrote: > I am a mature learner; 3 masters > some doctoral work ? statistics for social sciences; psychological > statistics ? > worked in spss and sas 2005 ? 2006 > now have forgotten ; relearning > my question is this can I do everything in R and Python and SAS studio > that I did in SPSS and the paid

Hello everyone. My name is Jo?o Paulo I am a master's student in the course of omputer science. I intend to create a package for the R that makes the SQL language commands available for use. I know that there are already some packages that execute SQL commands within R. However, most of these commands require querys to be passed as a string. I wish SQL commands could be used more naturally.

Read the help file for the survival package. Probably use the data function to retrieve it, and write it out using the write.table function. -- Sent from my phone. Please excuse my brevity. On July 29, 2017 8:47:51 PM PDT, Ted via R-help <r-help at r-project.org> wrote: >"Data set flchain available in the survival? package". ?How can?I >get?it (from R)? as Excel file?

"Data set flchain available in the survival? package". ?How can?I get?it (from R)? as Excel file? Thanks! [[alternative HTML version deleted]]

I would like to change where R stores the temporary files to my external hard drive in my iMac. This is important because the temporary files R creates are huge and I do not have enough available space in my internal HD. Again, this is for macOS. This change has to be temporary. Later I need to use the usual location for temp files in the internal HD. Thanks in advance, Kumar Mainali --

Hello,? I want to split the dataframe into 1000 groups based on two column values(max value and second max value). First, I made two lists L1 and L2. ?L1 is the list divided into 100 groups based on the range of max value and L2 is divided into 10 groups based on the second max values. Now I want to do the combinations based on L1 and L2. I want to do a for loop for L1 and for each element in L1,

On 01/12/17 20:33, Hasan Diwan wrote: > Yes. Very true. But some *thinking* is required; that often proves to be a formidable stumbling block. cheers, Rolf Turner > > On 30 November 2017 at 22:28, <hotprojects at nyc.rr.com> wrote: > >> I am a mature learner; 3 masters >> some doctoral work ? statistics for social sciences; psychological >> statistics ?

Dear all, I have a data similar to this: myframe<- data.frame (ID=c("Ernie", "Ernie","Ernie","Ernie"), Timestamp=c("24.09.2012 08:00", "24.09.2012 09:00", "24.09.2012 10:00", "25.09.2012 10:00"), Longitude=c("8.481","8.482","8.483","8.481"),

[answers inline] On 18 August 2017 at 20:08, Dagmar <Ramgad82 at gmx.net> wrote: > > myframe<- data.frame (ID=c("Ernie", "Ernie","Ernie","Ernie"), > Timestamp=c("24.09.2012 08:00", "24.09.2012 09:00", "24.09.2012 10:00", > "25.09.2012 10:00"),

Dear all, I tried to rerun the examples given by Hyndman in otexts but keep on getting errors and I have searched through google but no solution yet. Thank you in advance for any help given. library(fpp3) library(lubridate) library(xts) library(fabletools) library(ggplot2) library(dplyr) library(gridExtra) > dput(head(new_us_retail_employment,100))structure(list(Month = structure(c(-11323,

Hi, This problem may be very easy, but I can't think of how to do it. I have constructed histograms of various variables in my dataset. Some of them are negatively skewed, and hence need data transformations applied. I know that you first need to reflect the negatively skewed data and then apply another transformation such as log, square root etc to bring it towards normailty. How is it

Hi! as my subject says I am struggling with the different of a two-way ANOVA and a (two-way) ANCOVA. I found the following examples from this webpage: http://www.statmethods.net/stats/anova.html # One Way Anova (Completely Randomized Design) fit <- aov(y ~ A, data=mydataframe) # Randomized Block Design (B is the blocking factor) fit <- aov(y ~ A + B, data=mydataframe) # Two Way

Hello to everybody, Does anyone has implemented a function for evaluating models using windowing strategies, such as growing window or sliding window ones? The aim is to evaluate regression models on a time series data. I do not use cross-validation once data sorted in a radom way does not make sense when evaluating time series. Thanks Joao Moreira

Hello, I have a problem with selecting the right type of sums of squares for an ANCOVA for my specific experimental data and hypotheses. I do have a basic understanding of the differences between Type-I, II, and III SSs, have read about the principle of marginality, and read Venable's "Exegeses on Linear Models" (http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf). I am pretty new to

Hi R-Users, I am working with sm.ancova (in the package sm) and I have two problems with the graph, which is automatically generated when sm.ancova() is run. 1-Besides of the fitted lines, the observed data appeared automatically in the graph. I prefer that only fitted lines appear. I check the sm.options, but I could not find the way that the observed data do not appear in the graph. 2-I

I have some data with two categorises plus/minus (p53) and a particular time (Time) and the outcome is a continuous vairable (Result). I set up a maximum model. ancova <- lm(Result~Time*p53) > summary(ancova) .. Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.05919 0.55646 0.106 0.916 Time -0.02134 0.01785 -1.195 0.241 p53plus

Hello, I am VERY new to R, just picking it up infact. I have got my head around the basics of ANOVA with post hoc tests but I am struggling with regression, especially with ANCOVAs. I have two sets of data, one of type A, one of type B. Both have been placed in a wind tunnel and sampled every week. The co variate is of course the days since the start. An example is day A B 0 10.0 10.0 7 9.0

I've run an ancova, edadysexo is a factor with 3 levels,and log(lcc) is the covariate (continous variable) I get this results > ancova<-aov(log(peso)~edadysexo*log(lcc)) > summary(ancova) Df Sum Sq Mean Sq F value Pr(>F) edadysexo 2 31.859 15.9294 803.9843 <2e-16 *** log(lcc) 1 11.389 11.3887 574.8081 <2e-16 ***

Dear R users, i'm using a custom function to fit ancova models to a dataset. The data are divided into 12 groups, with one dependent variable and one covariate. When plotting the data, i'd like to add separate regression lines for each group (so, 12 lines, each with their respective individual slopes). My 'model1' uses the group*covariate interaction term, and so the coefficients

How to Ancova in R? I know this has got to be an FAQ, because I see it asked in the lists, but I haven't seen an answer to it. I see the R-sm has the ancova thing happening, but I kind of doubt that what I'm trying to do is "smoothing"... -- Pete Hurd phurd at uts.cc.utexas.edu http://www.zo.utexas.edu/research/phurd Section of Integrative Biology, University of Texas, Austin