search for: pmodel

Dear R users, I'm a little lost on how to define pmodels for the DRC package. My goals are to produce isoboles of binary toxicity data. any tips? I really just need to know what pmodels refers to. Cheers, Pat -- View this message in context: http://r.789695.n4.nabble.com/pmodels-in-DRC-tp4190567p4190567.html Sent from the R help mailing list archi...

...ta, the process is strangely slow and I usually terminate it either after ~15min or when my CPU hits 100C. This is similar to what I mentioned on the list some time ago [1]. [1] http://www.mail-archive.com/r-help at r-project.org/msg89421.html Unfortunately I get the same slow behaviour when using pmodel.response(x, model="within", effect="twoways") to compute teh within R-sq. Below is an example that approximates the structure of my data. ### define fun to compute the within R-sq pmodel.response<-plm:::pmodel.response.plm plmr2 <- function(x, adj=TRUE, effect="...

Dear R users, I wish to manually demean a panel over time and entities. I tried to code the Wansbeek and Kapteyn (1989) transformation (from Baltagi's book Ch. 9). As a benchmark I use both the pmodel.response() and model.matrix() functions in package plm and the results from using dummy variables. As far as I understood the transformation (Ch.3), Q%*%y (with y being the dependent variable) should yield the demeaned series. However, ... ...I find that the results do not match, if I do so....

Quiero comparar varias dosis letales 50% (LD50) usando análisis probit. He seguido un ejemplo que viene en paquete DRC, pero no obtengo el resultado esperado. Lo que quiero es saber si las LD50s, son diferentes y si la diferencias son estadísticamente significativas. Gracias de antemano. José Arturo e-mail. jafarfan@uady.mx <grejon@uady.mx> e-mail alterno. jafarfan@gmail.com

...mesy : 'names' attribute [996] must be the same length as the vector [0] I know the data recognizes that I have 5 columns. I also know that there's nothing wrong with row 996 (I even want back and checked for hidden characters in the original .csv file). traceback() was useless: 4: pmodel.response.pFormula(formula, data, model = model, effect = effect, theta = theta) 3: pmodel.response(formula, data, model = model, effect = effect, theta = theta) 2: plm.fit(formula, data, model, effect, random.method, inst.method) 1: plm(h ~ o + m + a, data = drugsXX, index = c(&quot...

Hi there, Maybe people who know both R and econometrics will be able to answer my questions. I want to run panel regressions in R with fixed-effect. I know two ways to do it. First, I can include factor(grouping_variable) in my regression equation. Second, I plan to subtract group mean from my variables and run OLS panel regression with function lm(). I plan to do it with the second way because

...ethods that the function can carry out. > library(plm) > example(plm) > class(zz) [1] "plm" "panelmodel" > methods(class = "plm") [1] ercomp fixef has.intercept model.matrix [5] pFtest plmtest plot pmodel.response [9] pooltest predict residuals summary [13] vcovBK vcovDC vcovG vcovHC [17] vcovNW vcovSCC see '?methods' for accessing help and source code > methods(class = "panelmodel") [1] deviance df.residual fitt...

Hi, How would one go about determining if the slope terms from an analysis of covariance model are different from eachother? Based on the example from MASS: library(MASS) # parallel slope model l.para <- lm(Temp ~ Gas + Insul, data=whiteside) # multiple slope model l.mult <- lm(Temp ~ Insul/Gas -1, data=whiteside) # compare nested models: anova(l.para, l.mult) Analysis of Variance

...e,index = c("id")) re=plm(gd ~ l+g+o+c+g1+h+n+r, model = "random", data = new.frame,index = c("id"),random.method="amemiya") then I wrote the following function for the Hausman Test using an auxiliary regression method: hmtest=function(re=0,fe=0){ y.re=pmodel.response(re) X.re=model.matrix(re) X.fe=model.matrix(fe) auxdata<-data.frame(cbind(y.re,X.re,X.fe)) colnames(auxdata)<-c("y", paste("x", 1:17, sep="")) auxmod<-lm(y~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10+x11+x12+x13+x14+x15+x16+x17-1, auxdata)...

Hello, I am trying to conduct a Hausman test to choose between FE estimators and RE estimators. When I try to run: library(plm) fixed <- plm(ROS ~ DiffClosenessC +ZZiele + AggSK + nRedundantStrecken + Degree + KantenGew + BetweennessC + SitzKappazitaet, data=Panel,index=c("id","time"),model="within") summary(fixed) fixef(fixed) random <-plm(ROS ~

...> >> library(plm) >> example(plm) > >> class(zz) > [1] "plm" "panelmodel" >> methods(class = "plm") > [1] ercomp fixef has.intercept model.matrix > [5] pFtest plmtest plot pmodel.response > [9] pooltest predict residuals summary > [13] vcovBK vcovDC vcovG vcovHC > [17] vcovNW vcovSCC > see '?methods' for accessing help and source code >> methods(class = "panelmodel") > [1] devia...

...ell as among the variables. However, I find that extracting the demeaned data from plm(), variables demXt$d and demXt$e (i.e. the demeaned variables) have sd()s that are very small compared to those of dem_yt and demXt$c (approx. by factor 1e-15). I extract the demeaned data as follows: dem_yt<-pmodel.response(res) demXt<-model.matrix(res) How is this possible? What is it that plm() does with my data so that the standard deviations change? ## it demeans them... (although the scale of the reduction is impressive, yet you're estimating out 1500 constants!) I suspect effect="twoway...

Hello, I am using {plm} to estimate panel models. I want to estimate a model that includes fixed effects for time and individual, but has a random individual effect for the coefficient on the independent variable. That is, I would like to estimate the model: Y_it = a_i + a_t + B_i * X_it + e_it Where i denotes individuals, t denotes time, X is my independent variable, and B (beta) is the

...sure; in this case, vcovHAC should be applied this way (here: the famous Munnell data, see example(plm)) > library(plm) > fm<-log(gsp)~log(pcap)+log(pc)+log(emp)+unemp > data(Produc) > ## est. FE model > femod<-plm(fm, Produc) > ## extract time-demeaned data > demy<-pmodel.response(femod, model="within") demX<-model.matrix(femod, > model="within") ## estimate lm model on demeaned data ## (equivalent > to FE, but makes a 'lm' object) > demod<-lm(demy~demX-1) > library(sandwich) > library(lmtest) > ## apply HAC cova...

...dea where to find such data? Nevertheless you should be able to follow my subsequent thoughts: # I should be able to get my (time- and sectionally) demeaned series as follows: res1<-plm(x~c+v,data=pdata_frame, effect="twoways", model="within", na.action=na.omit)) dem_yt<-pmodel.response(res) demXt<-model.matrix(res) # Given the demeaned series, I need to set the first observation(s) in each cross-section to NA in order to avoid inter-sectional links in the lagged residuals (i.e. in the MA component). #Note: Delete the first n observations per section for a MA(n) regr...

...sure; in this case, vcovHAC should be applied this way (here: the famous Munnell data, see example(plm)) > library(plm) > fm<-log(gsp)~log(pcap)+log(pc)+log(emp)+unemp > data(Produc) > ## est. FE model > femod<-plm(fm, Produc) > ## extract time-demeaned data > demy<-pmodel.response(femod, model="within") > demX<-model.matrix(femod, model="within") > ## estimate lm model on demeaned data > ## (equivalent to FE, but makes a 'lm' object) > demod<-lm(demy~demX-1) > library(sandwich) > library(lmtest) > ## apply HAC c...