similar to: Predicting probabilities in ordinal probit analysis in R

When I try to run the program Knytt Stories, I get an error window that says: "Cannot Load DirectoryPacker.mfx. This object might need an external program or library not yet installed." Wine helper says, Code: err:module:import_dll Library MSVCP60.dll (which is needed by L"C:\\windows\\temp\\mrt188.tmp\\DirectoryPacker.mfx") not found fixme:heap:RtlCompactHeap (0x110000,

Dear all, I am trying to estimate the marginal effects of a logit regression using the mfx package. It is crucial that the standard errors are clustered at the year level. Hence, the code looks as follows: marginal.t24.2<-logitmfx(stock.market.crash~crash.t24+bubble.t24+RV.t24,data=Data_logitregression_lags, clustervar1 = "year")

I have a question regarding calculating marginal effects (change in the probability due to a one unit change in x) for a probit or for that matter any discrete choice model. Does R have a function that will essentially do this: dE[y|x] ------- = f(x'beta)*beta dx Stata has a command called "mfx" that calculates the marginal effects at the mean (of the x

Dear R-help, my aim is to fit a constrained 2 component gamma mixture using mix within package mixdist. The constraints being fixing both of the means and both of the sigmas. Thus the only unconstrained parameter I wish to fit is pi the proportions of the mixtures. This is the code I used: PP2Pars<-data.frame(c(0.23,0.77), c(9.174,29.54), c(9.983,12.369));

Hi R Users, I have been trying to work out how to rename column names using grep, basically I have generated these column names using tapply: [1] "NAME" "X1.1" "X2.1" "X3.1" "X4.1" "X5.1" "X6.1" "X7.1" "X8.1" [10] "X1.2" "X2.2" "X3.2" "X4.2"

Salut Adrien, On 13 May 2020 at 14:19, Adrien FABRE wrote: | I have upgraded R (from 3.6 to 4.0) and RStudio (from 1.1 to 1.2.5) a few | days ago, and Ubuntu from 18.04 to 20.04 yesterday. | | Since then, R sometimes never terminates when executing certain commands: | ivreg (from package AER), summary (of a logit regression) and logitmfx | (from package mfx). Sometimes these commands run fine,

I have upgraded R (from 3.6 to 4.0) and RStudio (from 1.1 to 1.2.5) a few days ago, and Ubuntu from 18.04 to 20.04 yesterday. Since then, R sometimes never terminates when executing certain commands: ivreg (from package AER), summary (of a logit regression) and logitmfx (from package mfx). Sometimes these commands run fine, but most of the time I have to kill the process because R won't

Has anyone gotten centos 4 to print on an officejet 7310? I have tried jetdirect with pcl and postcript no go... I have tried smb but it does seem to be browsable as its not listed and when I specify I enter the IP address for server and I dont know what the share is. Windows XP connects and prints fine. THanks, Jerry

Dear ALL, I'm trying to figure out what the percentage effects are in a logistic regression. To be more clear, I'm not interested in the effect on y of a 1-unit increase in x, but on the percentage effect on y of a 1% increase in x (in economics this is also often called an "elasticity"). For example, if my independent variables are in logs, the betas can be directly

Hi, You might need to check library(boot).? I have never used that before.? So, I can't comment much.? It is better to post on R-help list.? I had seen your postings on Nabble in the past.? Unfortunately those postings were not accepted in R-help.? You have to directly post at ? r-help at r-project.org after registering at: https://stat.ethz.ch/mailman/listinfo/r-help ?

Dear List, I have fitted a spherical function to my variogram using "variofit(...)" from GeoR. Now I would like to predict some data with the function "predict(object,...)" from package stats. Does anyone know wether this works and if it does how to do it? Thanks a lot! Anna

Basically all I want to do is predict a scalar response using some curves. I've got as far as doing a regression (using fRegress from the fda package) but have no idea how to apply the results to a NEW set of curves (for prediction). I have N=536 curves, and 536 scalar responses. Here's what I've done so far: * I've created a basis for the curves. * I've created a

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 What are the best tools (models within R) for use in predicting stock market trading values? Currently I'm using the smooth.spline but are there any others used in practice? Do I find the mathematical formulations of R-functions somewhere (I now I could deduct it from the source code algorithms but ...)? Of the smooth.spline() for example? Thank

Could anybody help me out please? > cl<-as.factor(traindata[,13]) > knn(traindata[1:295,2], newdata[1:32,2], cl,k=2, prob=TRUE) Error in knn(traindata[1:295, 2], newdata[1:32, 2], cl, k = m, prob = TRUE) : Dims of test and train differ Both traindata and newdata have 13 elements. Only one of the first 12 elemnets is needed to predict the 13 element. What's the problem of

Dear listers, I experinced a problem prdicting the values using the LME with multilevel data. I have NA's in my dependent variable and the model is fitted only on the completed cases. I want to estimate the predicted values for the rest of the data (those cases with missing dep. variable) I extracted a subset from the original file containing the variables used in the model as well as the

Hello together, I would like to predict my fitted values on a new dataset. The original dataset consists of the variable a and b (data.frame(a,b)). The dataset for prediction consists of the same variables, but variable b has a constant value (x) added towards it (data.frame (a,b+x). The prediction command returns the identical set of predicted values as for the original dataset yet I would have

Hi, I noticed the following fortune in R 2.7 and 2.6.2: fortune('Spreads') If anything, there should be a Law: Thou Shalt Not Even Think Of Producing A Graph That Looks Like Anything From A Spreadsheet. -- Ted Harding (in a discussion about producing graphics) R-help (August 2008) Just wondering, what function and library gave this detailed prediction? Cheers

Lets say I have a linear model and I want to find the average expented value of the dependent variable. So let's assume that I'm studying the price I pay for coffee. Price = B0 + B1(weather) + B2(gender) + ... What I'm trying to find is the predicted price for every possible combination of values in the independent variables. So Expected price when: weather=1, gender=male weather=1,

I fit a coxph model: coxphfit <- coxph(Surv(sampledLifetime, !sampledCensoredQ) ~ curpbc6 + prevpbc6, sampledTimeSeries) Now I'm trying to predict the expected number of events using a new dataset. The documentation suggests that coxPred <- predict(coxphfit, newdata = testTimeSeries, type="expected") will do what I want, but I get the error Error in

Happy Friday Everyone, ? Hope Friday afternoon doesn't turn out to be a terrible time to post a question. I've been doing a little data mining of patient text medical records as of late. I started out trying to predict whether or not cancer patients had received KRAS mutation testing and did quite well with that. Now I'm trying to predict the results of KRAS testing (mutated?vs. wild