search for: f_j

...combination of only X3B and X3C implies contrasts encoding, with respect to X3A.) Thanks in advance, Arie On Sat, Nov 4, 2017 at 5:33 PM, Tyler <tylermw at gmail.com> wrote: > Hi Arie, > > I understand what you're saying. The following excerpt out of the book shows > that F_j does not refer exclusively to categorical factors: "...the rule > does not do anything special for them, and it remains valid, in a trivial > sense, whenever any of the F_j is numeric rather than categorical." Since > F_j refers to both categorical and numeric variables, the beha...

Hello Tyler, I rephrase my previous mail, as follows: In your example, T_i = X1:X2:X3. Let F_j = X3. (The numerical variables X1 and X2 are not encoded at all.) Then T_{i(j)} = X1:X2, which in the example is dropped from the model. Hence the X3 in T_i must be encoded by dummy variables, as indeed it is. Arie On Thu, Nov 2, 2017 at 4:11 PM, Tyler <tylermw at gmail.com> wrote: >...

Hi Arie, I understand what you're saying. The following excerpt out of the book shows that F_j does not refer exclusively to categorical factors: "...the rule does not do anything special for them, and it remains valid, in a trivial sense, whenever any of the F_j is numeric rather than categorical." Since F_j refers to both categorical and numeric variables, the behavior of model.m...

...oded with dummy variables. In reality, it is encoded by dummy variables only when the numeric:numeric interaction is missing, and by contrasts for the other two. The heuristic does not specify separate behavior for numeric vs categorical factors (When the author of Statistical Models in S refers to F_j as a "factor", it is a more general usage than the R type "factor" and includes numeric variables--the language used later on in the chapter on page 40 confirms this): when there is a missing marginal term in the formula, the higher-order interaction should be coded by dummy var...

Hello Tyler, Thank you for searching for, and finding, the basic description of the behavior of R in this matter. I think your example is in agreement with the book. But let me first note the following. You write: "F_j refers to a factor (variable) in a model and not a categorical factor". However: "a factor is a vector object used to specify a discrete classification" (start of chapter 4 of "An Introduction to R".) You might also see the description of the R function factor(). You note...

Hi Arie, The book out of which this behavior is based does not use factor (in this section) to refer to categorical factor. I will again point to this sentence, from page 40, in the same section and referring to the behavior under question, that shows F_j is not limited to categorical factors: "Numeric variables appear in the computations as themselves, uncoded. Therefore, the rule does not do anything special for them, and it remains valid, in a trivial sense, whenever any of the F_j is numeric rather than categorical." Note the ".....

Hello Tyler, I want to bring to your attention the following document: "What happens if you omit the main effect in a regression model with an interaction?" (https://stats.idre.ucla.edu/stata/faq/what-happens-if-you-omit-the-main-effect-in-a-regression-model-with-an-interaction). This gives a useful review of the problem. Your example is Case 2: a continuous and a categorical regressor.

...o increment the stack pointer by 'amount' (i.e. sp = sp + amount). During assembly generation, the string that gets emitted is "ADS $SP" rather than "ADS 10", for example, if I want to increment it by 10. Can somebody show me what I'm doing wrong? Thanks. def ADS: F_J<0b100001,(outs),(ins i32imm:$amount),"ADS $amount",[(set SP, (add SP, i32imm:$amount))] > { bits<10> amount; let Inst{9-0} = amount; let isReMaterializable = 1; let mayLoad = 0; let mayStore = 0; let Defs = [SP]; let Us...

...hose pages could made unavailable by Amazon at any time, at the very least we have an problem with a lack of documentation. However, I still believe there is a bug when comparing R's implementation to the heuristic described in the book. From Statistical Models in S, page 38-39: "Suppose F_j is any factor included in term T_i. Let T_{i(j)} denote the margin of T_i for factor F_j--that is, the term obtained by dropping F_j from T_i. We say that T_{i(j)} has appeared in the formula if there is some term T_i' for i' < i such that T_i' contains all the factors appearing in T...

...#39; (i.e. sp = sp + amount). During assembly generation, the string that gets emitted is "ADS $SP" rather than "ADS 10", for example, if I want to increment it by 10. >> >> Can somebody show me what I'm doing wrong? >> Thanks. >> >> def ADS: F_J<0b100001,(outs),(ins i32imm:$amount),"ADS $amount",[(set SP, (add SP, i32imm:$amount))] > { >> >> bits<10> amount; >> >> let Inst{9-0} = amount; >> let isReMaterializable = 1; >> let mayLoad = 0; >>...

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Saludo cordial para cada uno. Les pido ayuda para generar números aleatorios de una mixtura k-puntos. Sabemos que la función de distribución F es una mixtura k-puntos si es de la forma F(x) = p_1 F_1(x) + p_2 F_2(x) + … + p_k F_k(x), donde F_j es una función de distribución de probabilidad, p_j > 0 y suma(p_j) = 1, para j = 1, 2, …, k. En mi caso particular F es la suavización de la función de probabilidad discreta {p_j} mediante kernel gamma. En el siguiente código F es la función Keg: # Distribución empírica suavizada # Val...

...fit simultaneously. Here \eta_j is the linear predictor, the logodds of outcome j vs the reference outcome. I cannot see how to fit exactly this. Instead I seem to be able to do the following: vgam(formula = y ~ s(x), family = multinomial) fits the model \eta_j = \beta_{ (j) 0 } + \beta_{ (j) x } f_j (x) i.e. a different function f_j (x) is fit for each outcome. vgam(formula = y ~ s(x), family = multinomial, constraints = list(`(Intercept)`= diag(1,3), 's(x)' = matrix(c(1,1,1),3,1)) ) fits the model \eta_j = \beta_{ (j) 0 } + f (x) i.e. a single function f (x) is fit, but scaled the s...

Hi folks, I am having a question about efficiently finding the integrals of a list of functions. To be specific, here is a simple example showing my question. Suppose we have a function f defined by f<-function(x,y,z) c(x,y^2,z^3) Thus, f is actually corresponding to three uni-dimensional functions f_1(x)=x, f_2(y)=y^2 and f_3(z)=z^3. What I am looking for are the integrals of these three