search for: g_i

Hi, is there a R function that order a matrix according to some criteria based on the rows(or cols) of that matrix? For example, let's say that my matrix S is composed by n rows S_1, S_2,.., S_n and that I compute some real value g_i=g(S_i) for each row. Then I want to order this set of g_i (from smaller to bigger) and order the correspondent row to the new position. Is it possible (apart from looping on the index) to do this with some predefined R function? Thanks, Francesco

Hi everyone, I am trying to compare proportions among groups using the logistic regression approach as follows: 1) Fit the model log(p_i/(1-p_i)) = M + G_i, where p_i is the probability of success in group i and G_i is the effect of group i, i=1,..,I. 2) Test the hypotheses: Ho: G_1 = G_2 = ... = G_I (the probability of success is the same for all groups) versus Ha: at least two G_i's are different (at least two groups have different p...

Hi all, I want to do plot() in a loop to make 10 graphs, so I have some code like for (i in 1:10) { plot(... ... , xlab = expression(g[i]) ) } I expect g_1, g_2, and so on appear on x labels, but it simply prints g_i for each graph. Does anybody know how to get around this problem? Thanks. NL

Here is a programming question. The code I am using is quite slow and I was wondering if there is a way to get rid of the for loop. I am dealing with "interaction" in 2x2 table, and am using Edwards's G_I (Likelihood, p. 194). I label the cells in the table as follows stim response "y" "n" total -------------------------------- y hit miss nsignal -------------------------------- n false correct nnoise alarm rejection --------------------------------- Gsens<-functi...

...l My situation is different in that I have only 2 predictors (coordinates x,y) for local regression but a number of global ("parametric") offsets that I need to consider. Essentially, we have a spatial distortion s(x,y) overlaid over a number of measurements z: z_i = s(x_i,y_i) + v_{g_i} These measurements z can be grouped by the same underlying undistorted measurement value v for each group g. The group membership g_i is known for each measurement, but the underlying undistorted measurement values v_g for the groups are not known and should be determined by (global, not local) re...

...o use index.cond for that. Several attempts didn't deliver the correct results. Now, I noticed the following: > p <- xyplot(dur~roi|trial, data) > p$index.cond [[1]] [1] 1 2 3 4 5 6 7 8 9 10 These numbers are "valid indexing vector for the integer vector '1:nlevels(g_i)'" but levels(data$trial)[p$index.cond[[1]]] does not yield the correct level names of the data points in data. (Actually the first ten levels are not used in data.) I was expecting that this should plot the panels in the order in which the levels occur in the data frame: xyplot(du...

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Greetings, If this is not the appropriate place to post this question please let me know where to post it. I have a package under development which fits models of the form $$ f(t)=\sum_i B_iG_i(t,\omega) $$ depending on a parameter vector $\omega$ of arbitrary dimension to data (one dimensional time series) in the general framework of the data = deterministic signal + Gaussian noise in the spirit of Bretthorst, G. Larry, 1988, "Bayesian Spectrum Analysis and Parameter Estimation,&q...

...the formulae and then I'll paste my code. If anyone can spot something wrong with my implementation it would really make my day. (1) dV/dt = (I_ext - I_int-I_coup)/C I_ext = injected current I_int = Sum of all ion currents I_coup = coupling current (but we're not using it here ) (2) I_i = g_i * m_i^pi * h_i^pi(V-E) i identifies the ion, thus I_K would be Potassium current. (3) dm/dt = (m_inf*V - m)/tau_m (4) dh/dt = (h_inf*V-h)/tau_h (5) The Nernst equation is used to calculate reversal potential for Ca: Eca = 12.2396 * log(13000/Ca2+) (6) d[Ca_2+]/dt = (F*I_Ca - [Ca2+] + C0)/Tau_Ca...

Dear R-users I have 5 dependent variables (y1 to y5) and one independent variable (x) and 3 conditioning variables (m, n, and 0). Each of the conditioning variables has 2 levels. I created 2*4 panel plots. xyplot(y1+y2+y3+y4+y5 ~ x | m*n*o,layout = c(4,2)) I would like to reorder the 8 panels. I tried to use index.cond (e.g., index.cond = list(c(1,3,2,4,5,7,6,8)) but it didn't work out.

> -----Original Message----- > From: llvm-dev <llvm-dev-bounces at lists.llvm.org> On Behalf Of Kai Peter Nacke > via llvm-dev > Sent: Tuesday, June 16, 2020 8:51 AM > To: Corentin <corentin.jabot at gmail.com> > Cc: llvm-dev at lists.llvm.org > Subject: Re: [llvm-dev] RFC: Adding support for the z/OS platform to LLVM and > clang > > > > 2) Add

...mail.gmail.com> Content-Type: text/plain; charset=ISO-8859-1 Hi, is there a R function that order a matrix according to some criteria based on the rows(or cols) of that matrix? For example, let's say that my matrix S is composed by n rows S_1, S_2,.., S_n and that I compute some real value g_i=g(S_i) for each row. Then I want to order this set of g_i (from smaller to bigger) and order the correspondent row to the new position. Is it possible (apart from looping on the index) to do this with some predefined R function? Thanks, Francesco ------------------------------ Message: 55 Da...