similar to: Text mining: Narrowing a field of 27, 855 predictors using semi-partial correlations or some other means

In linear regression, regression weights of x1 on Y given x2 and x3 should be mathematically identical to the semipartial correlations between x1 and Y, given x2 and x3. However, I do not obtain identical results, so apparently I'm doing something wrong in R. Data preparation: data<-read.csv("file.csv", head=T) data <- subset(data, select=c(age,s1,s2,s3,s5,s10,WSAS01)) data

Dear fellow R-users I've always used Prof Harrell's rms package to calculate/visualize partial R2 values from a standard regression analysis (below is a quick example on how I've done so). Recently, I calcuated partial R2 values using the ppcor and gRbase packages. As it turns out, ppcor and gRbase agree with each other but the values generated from these packages tended to be greater

Hi, I want to know how i could calculate semi correlation with R. Is there any package for it? [[alternative HTML version deleted]]

Our backend only does 32 bit loads. I noticed some loads were narrowed to 8 bits and then had to be reverted back to 32 bit loads. This patch prevents the initial lowering. I've only tested this on my backend. Can someone review and apply this or should I submit a bug? Index: CodeGen/SelectionDAG/TargetLowering.cpp =================================================================== ---

I have a platform that I am targeting that has 64-bit general purpose registers but uses 32-bit pointers (ie, sizeof(void*) == 4) but unfortunately it also requires that the top 32-bits of the pointer value be clear. This sometimes becomes a problem with certain pointer calculations such as using a 32-bit negative value for indexing into an array can cause the upper bits to be set, generating

On Wed, Jul 27, 2011 at 2:28 PM, Matt Johnson <johnso87 at crhc.illinois.edu> wrote: > Hi All, >     I'm writing a backend for a target which only supports 4-byte, > 4-byte-aligned loads and stores.  I custom-lower all {*EXT}LOAD and > STORE nodes in TargetISelLowering.cpp to take advantage of all alignment > information available to the backend, rather than treat each

Dear all, I'm writing my manuscript to publish after analysis my final data with ANOVA, ANCOVA, MANCOVA. In a section of my result, I did correlation of my data (2 categirical factors with 2 levels: Quantity & Quality; 2 dependent var: Irid.area & Casa.PC1, and 1 co-var: SL). But as some traits (here Irid.area) are significantly influenced by the covariate (standard length, SL), I

Hi All, I'm writing a backend for a target which only supports 4-byte, 4-byte-aligned loads and stores. I custom-lower all {*EXT}LOAD and STORE nodes in TargetISelLowering.cpp to take advantage of all alignment information available to the backend, rather than treat each load and store conservatively, which takes O(10) instructions. My target's allowsUnalignedMemoryOperations()

On Wed, Jul 27, 2011 at 3:50 PM, Matt Johnson <johnso87 at crhc.illinois.edu> wrote: > Hi Eli, > > On 07/27/2011 04:59 PM, Eli Friedman wrote: >> >> On Wed, Jul 27, 2011 at 2:28 PM, Matt Johnson >> <johnso87 at crhc.illinois.edu>  wrote: >>> >>> Hi All, >>>     I'm writing a backend for a target which only supports 4-byte,

Hi all, recently when working with GlobalISel we have often encountered cases in the legalizer where instructions could not be narrowed because the narrowing code relies on G_UNMERGE_VALUES and therefore requires the source type to be a multiple of the narrow type. Often times these instructions can be widened without any problem to a fitting type. This has us writing legalization rules like

Hi Eli, On 07/27/2011 04:59 PM, Eli Friedman wrote: > On Wed, Jul 27, 2011 at 2:28 PM, Matt Johnson > <johnso87 at crhc.illinois.edu> wrote: >> Hi All, >> I'm writing a backend for a target which only supports 4-byte, >> 4-byte-aligned loads and stores. I custom-lower all {*EXT}LOAD and >> STORE nodes in TargetISelLowering.cpp to take advantage of

Hi Matt, thanks for responding. I left a couple of comments down below. Am 24.03.20 um 18:26 schrieb Matt Arsenault: > >> On Mar 24, 2020, at 12:24, Dominik Montada via llvm-dev <llvm-dev at lists.llvm.org> wrote: >> >> Hi all, >> >> recently when working with GlobalISel we have often encountered cases in the legalizer where instructions could not be

Dear R-user, Could anybody tell me of the key difference between data mining and text mining? Please make a list for packages about data/text mining. And give me an example of text mining with R (any relating materials will be highly appreciated), because a vignette written by Ingo Feinerer seems too concise for me. Thanks _____________________________________________ Dr.Ruixin ZHU Shanghai

Hi, folks, Happy work in weekends >_< My question is how to plot the dependent variable against one of the predictors with other predictors as constant. Not for the original data, but after prediction. It means y is the predicted value of the dependent variables. The constane value of the other predictors may be the average or some fixed value. ####### y=1:10 x=10:1 z=2:11

Hi everyone, I woudl like to apply a Tukey post hoc after a repeated measure ANOVA. I followed the suggestions that I found in this help -list especially this one: /[R] Tukey HSD (or other post hoc tests) following repeated measures ANOVA You want to use lme() in package nlme, then glht() in the multcomp package. This will give you multiplicity adjusted p-values and confidence intervals.

Hello! I am trying test my observed data against the null-hypothesis that different items from a psychological questionnaire contribute equally to the metric dependent variable that measures problems (sum score of a questionnaire). That is, I am interested in relative strength of the predictors. Predictor items of the questionnaire are on a scale from 0-3, and technically ordinal, although most

It always helps to check whether you got the data into R correctly. Hint: What does str(credit) tell you? Andy > From: Melanie Vida > > Hi All, > > My question is in regards to an error generated when using > randomForest > in R. Is there a special way to format the data in order to > avoid this > error, or am I completely confused on what the error implies?

My question is related to plot( ) in the mgcv package. Before modelling the data, a few predictors were transformed to normalize them. Therefore, the x-axes in the plots show transformed predictor values. How do I back-transform the predictors so that the plots are easier to interpret? Thanks in advance, Suzan -- Suzan Pool Oregon State University Cooperative Institute for Marine

Hello everybody, in the past months I have developed a model to predict the mortality rate of Acute Myocardial Infarction Patients. My evaluation was mainly based on ROC Curves etc. I have now been asked to calculate the R-Square of my model, another existing model in regards to the actual mortality rate. So far I have only found a function calculating a regression, and then the R-Square of the

Dear R-help, I am using R-3.3.2 on Windows 10. As per my previous post today, I teach on a course which has 4 computer practical sessions related to the development and validation of clinical prediction models. These are currently written for Stata and I am in the process of writing them for use in R too (as I far prefer R to Stata!) Part of the practical requires the student to fit a flexible