similar to: : for factors (PR#139)

Hi, Please look at this c code: typedef struct _PB { void* data; /* required.*/ int f1_; float f2_; } PB; PB** bar(PB** t); void qux(PB* c) { bar(&c); /* c is escaped because of bar */ c->f1_ = 0; c->f2_ = 0.f; } // gcc-5.2.1 with -fno-strict-aliasing -O2 on x86 call bar movq 8(%rsp), %rax movl $0, 8(%rax) movl $0x00000000, 12(%rax) // llvm

Agree, and I did : ) Please refer to this mailing list: https://gcc.gnu.org/ml/gcc/2016-03/msg00179.html On Sat, Mar 19, 2016 at 1:25 AM, Daniel Berlin <dberlin at dberlin.org> wrote: > I suspect you should just go ask #1 on the gcc mailing list and see what > the answer is. > We are basically trying to figure out their reasoning, but we should > instead just go ask what it is

On 2016.03.17 at 16:35 -0700, Chris Lattner via llvm-dev wrote: > > > On Mar 15, 2016, at 7:58 AM, Chuang-Yu Cheng via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > > > Hi, > > > > Please look at this c code: > > > > typedef struct _PB { > > void* data; /* required.*/ > > int f1_; > > float f2_; > > } PB;

Hello, I'd like to produce a ggplot where the order of factors within facets is based on the average of another variable. Here's a reproducible example. My problem is that the factors are ordered similarly in both facets. I would like to have, within each facet of `f1', boxplots for 'x' within each factor `f2', where the boxplots are ordered based on the average of x

1. Same question as David, why &c - 8 is invalid? Is it related to below statements In C99 standard? 6.5.3.3: "Among the invalid values for dereferencing a pointer by the unary * operator are a null pointer, an address inappropriately aligned for the type of object pointed to, and the address of an object after the end of its lifetime." 2. We are trying to preserve 1st load and

Hi I know this is a basic question and I know I have done it before but I can't find the answer any more. I have a data set, say: F1, F2, Value F1 and F2 are Factors. I would like to plot plot(TheCombinationOf(F1, F2), Value) I remember there was a function for TheCombinationOf() but I don't remember the name. I could create a new factor based on F1 and F2, but I would prefer the

U = c("b", "b", "b", "c", "d", "e", "e") F1 = factor( U, levels=c("a", "b", "c", "d", "e") ) as.numeric(F1) [1] 2 2 2 3 4 5 5 Here, the integer code of "b" in F1 is 2 K = factor( levels(F1) ) as.numeric(K) [1] 1 2 3 4 5 K [1] a b c d e Levels: a

Hello, I use the function 'aggregate' a lot. One small annoyance is that it is necessary to name the factors in the 'by' list to get the names in the resulting data.frame (else, they appear as Group.1, Group.2...etc). For example, I am forced to write: aggregate(y,list(f1=f1,f2=f2),mean) instead of aggregate(y,list(f1,f2),mean) (for two factors with short names, it is not such

Hi all, I hope it's not too trivial for the list - I'm trying to concatenate two factor arrays, and obtain the following: > f1<-factor(c("a","a","b")) > f1 [1] a a b Levels: a b > f2<-factor(c("b","b","a")) > f2 [1] b b a Levels: a b > c(f1,f2) [1] 1 1 2 2 2 1 Instead of getting: [1] a a b b b a Levels: a

Greetings, I am attempting to run a function, which produces a vector and requires two input variables, across two nested factor levels. I can do this using by(X, list(factor1, factor2), function), however I haven't found a simple way to extract the list output into an organized vector form. I can do this using nested loops but it isn't exactly an optimal approach. Thank you

I'm interested in plotting a y with an x factor as the combination of 2 factors and colour with respect to a third, which the code below does with interaction.plot(). However, this is because I redefine the x to be 1 factor. Is there a way of getting it to plot without redefining it, and ideally to not join up the lines BETWEEN levels a and b, but just join those between after and before for

hello, I am trying to use predict.lm to make point forecasts based on a model with continuous and categorical independent variables I have no problems fitting the model using lm, but when I try to use predict to make point predictions. it reverts back to the original dataframe and gives me the point predictions for the fitted data rather than for the new data, I imagine that I am missing

Hi everyone, I'm fairly new to R, and I don't have a background in statistics, so please bear with me. ;-) I'm dealing with 2^k factorial designs, and I was just wondering if there's any way to analyze more than two factors of a gage R&R study in R. For example, Minitab has an "expanded gage R&R" function that lets you include up to eight additional factors

Hi, I have a data frame with two factors, and I would like to create a new one by "combining" them. I have already a solution, but it is very "heavy", and I'm sure there is a basic function which can do the same. I tried to find it in the R-help, but without result. See the example : # just to create a dataframe f1 <- factor(rep(c("A","B"),3)) f2

Dear R helpers: Sorry to bother for a basic question about model.matrix. Basically, I want to apply the dummy coding rule in a dataframe with complete factor levels to another dataframe with incomplete factor levels. I used model.matrix, but could not get what I want. The following is an example. #Suppose I have two dataframe A and B

#I have a dataset with two factor. I want to combine those factors into a single factor and count the number of data values for each new factor. The following gives a comparable dataframe: a <- rep(c("a", "b"), c(6,6)) b <- rep(c("c", "d"), c(6,6)) df <- data.frame(f1=a, f2=b, d=rnorm(12)) df # I use the 'interaction' function to combine

Hi, Is there any way to suppress plotting of panels that don't actually contain any information? I have tried using 'drop.unused.levels=TRUE', but there doesn't seem to be any effect. Here is an example: library(lattice) # some fake data: d <- data.frame(x=runif(20), x.class=rep(letters[1:5], each=4), f1=rep(letters[1:2], each=10), f2=rep(letters[10:19], each=2) ) # plot

I'm using specify.model for the sem package. I can't figure out how to represent the residual errors for the observed variables for a CFA model. (Once I get this working I need to add some further constraints.) Here is what I've tried: model.sa <- specify.model() F1 -> X1,l11, NA F1 -> X2,l21, NA F1 -> X3,l31, NA F1 -> X4,l41, NA F1 -> X5, NA, 0.20

Norm Josephy <NJOSEPHY@bentley.edu> writes: > Sir: > > I hope you do not mind me sending e-mail directly to you > rather than to the developer's list. I wish to avoid > unnecessary traffic on the list when the issue I raise may > have already been fixed. As a matter of principle: I *do* mind. One of the purposes of having a list is that not everyone has the time

Norm Josephy <NJOSEPHY@bentley.edu> writes: > Sir: > > I hope you do not mind me sending e-mail directly to you > rather than to the developer's list. I wish to avoid > unnecessary traffic on the list when the issue I raise may > have already been fixed. As a matter of principle: I *do* mind. One of the purposes of having a list is that not everyone has the time