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• Guide Binära val Fagersta ##### Stata function in R

Hi guys, somebody knows if there's an equivalence of the next function from Stata in R:
In stata after running the MCA command i can use the predict command like this:
MCA var1 var2 predict var3 if e(sample)
In R i have been trying the next:
MCA1 <- MCA(df, var1, var2) predict.mca(MCA1, df)
But I don't get the same results that i get from Stata Somebody knows how I can emulate the e(sample) function?
Thanks a lot

##### Is it possible to use ln / exp as functions in formula?

I know the js math module has it, but can I just write javascript in the formula field?

##### Something similar to a windows function in STATA?

I have two columns, one of "Months" (1999m1, 1999m2, 1999m3, etc) and another of "Events" which indicate whether an event occurred which month it corresponded with (0,0,0,1,0,0).
My goal is to define a two-year event window, and a three-year window, meaning, I want to flag the rows that have the event-time variable, between -24 and 23, or -36 and 35. This is with the final goal of creating a buffer that returns positive (0) or negative (1) whether there is another event that exists within that buffer.
How can I do this in STATA?

##### Stata lincom function equivalent in R. P- values calculation ?

Can't figure out an equivalent function to "lincom" command in R ?Hence cannot really calculate p- values.If at all someone can help me with which stat-test to use to calculate p-values ?Variables -disease = numeric =0,1race= char= European, Indian, AsianAge1=num= 0,1sex =factor= M,F
glm disease i.raceage1 sex, link(identity) family(binomial) testparm X di "Omnibus test of age1race interaction p = " %8.6f r(p) lincom age1 di %6.4f r(p) " | | " lincom age1 + _IraceXage1__i' di %6.4f r(p) " | " _continue lincom _IraceXage__i' di %6.4f r(p) " | "
So far, in R have reached till here any help would be appreciated -

model1<-glm(formula = disease ~ race * age1 + sex, family = binomial(link = "identity"), data = MORT) summary(model1) fit2<-augment (model1, newdata = MORT, type.predict = "response") fit2$.fitted<- fit2$.fitted*100 install.packages("remotes") remotes::install_github("bmckuw/UWbe536")library(UWbe536) lincom(model1, lc = "age1 == 0") lincom(model1, lc = "race+age1 = 0")

##### Parametric survival analysis with proportional hazard function in R and STATA

I have a question concerning the coefficients in the two programs, when estimating a proportional hazard model with a Weibull baseline hazard function.
When using the same dataset i get different coefficients, but almost identical p-values and standard errors.
Actually, the signs of the coefficients are exactly opposite across the two programs, which can have quite the consequences, if you want to conclude anything.
I am using it for my bachelor thesis on costumer churn, so instead of uploading manipulated results and data, I have found two videos on Youtube, which does exactly the same with identical data and still gets opposite results.

##### Stata lincom function equivalent in R. P- values calculation ?

Can't figure out an equivalent function to "lincom" command in R ?
Hence cannot really calculate p- values.
If at all someone can help me with which stat-test to use to calculate p-values ?
Variables -
disease = numeric =0,1
race= char= European, Indian, Asian
Age1=num= 0,1
sex =factor= M,F
glm disease i.raceage1 sex, link(identity) family(binomial) testparm X di "Omnibus test of age1race interaction p = " %8.6f r(p) lincom age1 di %6.4f r(p) " | | " lincom age1 + _IraceXage1__i' di %6.4f r(p) " | " _continue lincom _IraceXage__i' di %6.4f r(p) " | "
So far, in R have reached till here any help would be appreciated -
model1<-glm(formula = disease ~ race * age1 + sex, family = binomial(link = "identity"), data = MORT) summary(model1) fit2<-augment (model1, newdata = MORT, type.predict = "response") fit2$.fitted<- fit2$.fitted*100 install.packages("remotes") remotes:: install_github("bmckuw/UWbe536")library(UWbe536) lincom(model1, lc = "age1 == 0") lincom(model1, lc = "race+age1 = 0")

##### Parametric survival analysis with proportional hazard function in R and STATA

I have a question concerning the coefficients in the two programs, when estimating a proportional hazard model with a Weibull baseline hazard function.
When using the same dataset i get different coefficients, but almost identical p-values and standard errors.
Actually, the signs of the coefficients are exactly opposite across the two programs, which can have quite the consequences, if you want to conclude anything.
I am using it for my bachelor thesis on costumer churn, so instead of uploading manipulated results and data, I have found two videos on Youtube, which does exactly the same with identical data and still gets opposite results.

##### Parametric survival analysis with proportional hazard function in R and STATA

I have a question concerning the coefficients in the two programs, when estimating a proportional hazard model with a Weibull baseline hazard function.
When using the same dataset i get different coefficients, but almost identical p-values and standard errors.
Actually, the signs of the coefficients are exactly opposite across the two programs, which can have quite the consequences, if you want to conclude anything.
I am using it for my bachelor thesis on costumer churn, so instead of uploading manipulated results and data, I have found two videos on Youtube, which does exactly the same with identical data and still gets opposite results.

##### Link between constant elasticity (ln) model versus variable elasticity model in Stata

I was trying to understand how the elasticity from a double logged model (i.e., the observed coefficient in that model) relates to the elasticities at the means calculated after a normal (variable elasticity) model using margins in Stata. Stata support seems to suggest hey should be the same, yet in practice this is never true. Does anyone have an idea how both relate to each other? A simple sample code shows that they are very different:
** SAMPLE CODE ** sysuse auto, clear keep mpg weight price * Constant elasticity model gen lnmpg=ln(mpg) gen lnweight=ln(weight) gen lnprice=ln(price) regress lnmpg lnwei lnprice * Price elasticity = -.1113206 * Varying elasticity model, elasticity at the mean regress mpg weight price margins, eyex(price) atmeans * Price elasticity = -.0270703

##### Cannot find the 'Automatic Recode' function in Stata 15.1

Currently, I have a string variable indicating categories. However, since Stata cannot analyse well with string variables, I need these to be converted into numerical variables.
E.g. "poor" = 0, "mediocre" = 1 and "good" = 2
Some googling found that the function 'Automatic Recode' is exactly what I need. Unfortunately I can't seem to find its location in Stata 15.1, since all the instructions online are for earlier versions following an nonexistent path:
transform recode automatic recode

##### [Grade 12 Math: Logarithmic Functions] Can someone explain Log and Ln and e in the simplest terms possible?

I know it’s not that hard but I don’t remember it and even after watching videos I can’t understand it one bit.

##### What Stata functions would you want to see in R?

Hi all,
I'm an R user who frequently has to work with Stata users. Some of them are making an effort to switch to R, but are finding that some convenient Stata functions are cumbersome in R. I'm an R elitist (fight me), but I agree one some points.
I'm currently building an R package with some custom functions that basically replicate Stata commands in R. I currently have a nice tabstat and isid function.
I know the overlap may be small, but I'm curious if any of you have Stata commands you'd like to see in R?      