search for: x3b

...d, the factor encoding changes for a higher level interaction. Consider the following minimal reproducible example: -------------- > runmatrix = expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C"))> model.matrix(~(X1+X2+X3)^3,data=runmatrix) (Intercept) X1 X2 X3B X3C X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C 1 1 1 1 0 0 1 0 0 0 0 0 0 2 1 -1 1 0 0 -1 0 0 0 0 0 0 3 1 1 -1 0 0 -1 0 0 0 0 0 0 4 1 -...

...ges > for a higher level interaction. Consider the following minimal reproducible > example: > > -------------- > >> runmatrix = expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C"))> model.matrix(~(X1+X2+X3)^3,data=runmatrix) (Intercept) X1 X2 X3B X3C X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C > 1 1 1 1 0 0 1 0 0 0 0 > 0 0 > 2 1 -1 1 0 0 -1 0 0 0 0 > 0 0 > 3 1 1 -1 0 0 -1 0 0 0 0 &gt...

...=m) Below are the three results, similar to your first mail. (The first two are basically the same, of course.) Please pick one result which you think is not consistent with the heuristic and please give what you think is the correct result: model.matrix(~(X1+X2+X3)^3-X1:X3) (Intercept) X1 X2 X3B X3C X1:X2 X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C model.matrix(~(X1+X2+X3)^3-X2:X3) (Intercept) X1 X2 X3B X3C X1:X2 X1:X3B X1:X3C X1:X2:X3B X1:X2:X3C model.matrix(~(X1+X2+X3)^3-X1:X2) (Intercept) X1 X2 X3B X3C X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3A X1:X2:X3B X1:X2:X3C (I take it tha...

...similar to your first mail. (The first > two are basically the same, of course.) Please pick one result which > you think is not consistent with the heuristic and please give what > you think is the correct result: > > model.matrix(~(X1+X2+X3)^3-X1:X3) > (Intercept) > X1 X2 X3B X3C > X1:X2 X2:X3B X2:X3C > X1:X2:X3B X1:X2:X3C > > model.matrix(~(X1+X2+X3)^3-X2:X3) > (Intercept) > X1 X2 X3B X3C > X1:X2 X1:X3B X1:X3C > X1:X2:X3B X1:X2:X3C > > model.matrix(~(X1+X2+X3)^3-X1:X2) > (Intercept) > X1 X2 X3B X3C > X1:X3B X1:X3...

...> >> >> > -------------- >> >> >> > >> >> >> >> runmatrix = expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C"))> >> >> >> model.matrix(~(X1+X2+X3)^3,data=runmatrix) (Intercept) X1 X2 X3B >> >> >> X3C >> >> >> X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C >> >> >> > 1 1 1 1 0 0 1 0 0 0 0 >> >> >> > 0 0 >> >> >> > 2 1 -...

...minimal >> reproducible >> > example: >> > >> > -------------- >> > >> >> runmatrix = expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C"))> >> model.matrix(~(X1+X2+X3)^3,data=runmatrix) (Intercept) X1 X2 X3B X3C >> X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C >> > 1 1 1 1 0 0 1 0 0 0 0 >> > 0 0 >> > 2 1 -1 1 0 0 -1 0 0 0 0 >> > 0 0 >> > 3...

I?m trying to pull data from one table (batting) in the Lahman Baseball database. Notice X2B for doubles and X3B for triples ? fourth and fifth from the right. The dbGetQuery function runs fine when I leave there two out but I get error messages (in red) when I include 2B/3B or X2B/X3B. Can anyone give me some direction? Thanks, Philip Heinrich ************************************************************...

...t; > -> "...\"2B\",\"3B\",..."? > > -Bill > > On Fri, Oct 2, 2020 at 3:49 PM Philip <herd_dog at cox.net> wrote: > >> I?m trying to pull data from one table (batting) in the Lahman Baseball >> database. Notice X2B for doubles and X3B for triples ? fourth and fifth >> from the right. >> >> The dbGetQuery function runs fine when I leave there two out but I get >> error messages (in red) when I include 2B/3B or X2B/X3B. >> >> Can anyone give me some direction? >> >> Thanks, >>...

...example: >> >> > >> >> > -------------- >> >> > >> >> >> runmatrix = expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C"))> >> >> model.matrix(~(X1+X2+X3)^3,data=runmatrix) (Intercept) X1 X2 X3B X3C >> >> X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C >> >> > 1 1 1 1 0 0 1 0 0 0 0 >> >> > 0 0 >> >> > 2 1 -1 1 0 0 -1 0 0 0 0 >> >&gt...

...> >> > example: > >> > > >> > -------------- > >> > > >> >> runmatrix = expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C"))> > >> model.matrix(~(X1+X2+X3)^3,data=runmatrix) (Intercept) X1 X2 X3B X3C > >> X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C > >> > 1 1 1 1 0 0 1 0 0 0 0 > >> > 0 0 > >> > 2 1 -1 1 0 0 -1 0 0 0 0 > >> > 0 0 &...

...t;SELECT * FROM batting WHERE yearID = 2018 AND AB >600 ORDER BY AB DESC") %>% set_colnames(make.names(colnames(.))) #OR one of the following: library(dplyr) dbGetQuery(con2,"SELECT * FROM batting WHERE yearID = 2018 AND AB >600 ORDER BY AB DESC") %>% rename(X2B = `2B`, X3B = `3B`) library(dplyr) dbGetQuery(con2,"SELECT * FROM batting WHERE yearID = 2018 AND AB >600 ORDER BY AB DESC") %>% `colnames<-`(make.names(colnames(.))) library(dplyr) dbGetQuery(con2,"SELECT * FROM batting WHERE yearID = 2018 AND AB >600 ORDER BY AB DESC") %>...

...t;> > > >> >> > -------------- > >> >> > > >> >> >> runmatrix = expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C"))> > >> >> model.matrix(~(X1+X2+X3)^3,data=runmatrix) (Intercept) X1 X2 X3B > X3C > >> >> X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C > >> >> > 1 1 1 1 0 0 1 0 0 0 0 > >> >> > 0 0 > >> >> > 2 1 -1 1 0 0 -1 0 0...

...; -------------- > >> >> >> > > >> >> >> >> runmatrix = expand.grid(X1=c(1,-1),X2=c(1, > -1),X3=c("A","B","C"))> > >> >> >> model.matrix(~(X1+X2+X3)^3,data=runmatrix) (Intercept) X1 X2 > X3B > >> >> >> X3C > >> >> >> X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C > >> >> >> > 1 1 1 1 0 0 1 0 0 0 0 > >> >> >> > 0 0 > >> >> >&...

...rID = 2018 AND AB >600 > ORDER BY AB DESC") %>% set_colnames(make.names(colnames(.))) > > #OR one of the following: > > library(dplyr) > dbGetQuery(con2,"SELECT * FROM batting WHERE yearID = 2018 AND AB >600 > ORDER BY AB DESC") %>% rename(X2B = `2B`, X3B = `3B`) > > library(dplyr) > dbGetQuery(con2,"SELECT * FROM batting WHERE yearID = 2018 AND AB >600 > ORDER BY AB DESC") %>% `colnames<-`(make.names(colnames(.))) > > library(dplyr) > dbGetQuery(con2,"SELECT * FROM batting WHERE yearID = 2018 AND AB >...

...ndicular line equation that goes through (xm, ym) m2 <- (xy$y[i]-pt2$y)/(xy$x[i]-pt2$x) a.m2 <- atan(m2) # angle in radians b.m2 <- (pi/2) +a.m2 m3 <- tan(b.m2) # slope of perpendicular line b3 <- ym -(m3*xm) ? # Choose again some random points on this line: x3a <- -100 x3b <- 100 y3a <- m3*x3a + b3 y3b <- m3*x3b + b3 ? # Build the spatial lines (the 2 perpendicular lines) and find their intersection: win <- owin(range(-100,x1a, x1b, x3a, x3b, xm, 100), range(-100,y1a, y1b, y3a, y3b, ym, 100)) ln1 <- psp(x1a, y1a, x1b, y1b, window = win) ln3 <...

Has anyone programmed condition indexes in R? I know that there is a function for variance inflation factors available in the car package; however, Belsley (1991) Conditioning Diagnostics (Wiley) notes that there are several weaknesses of VIFs: e.g. 1) High VIFs are sufficient but not necessary conditions for collinearity 2) VIFs don't diagnose the number of collinearities and 3) No one has

...outputs below for your reference. Thanks in advanced. regards, Jei ********************************** #1 > library(survival) > library(boot) > filename <- type2a # this file has 100 records > > r <- 50 > > data.cox <- coxph(Surv(time,cens==1)~x1+x2+x3a+x3b+x3c, data=filename) > data.surv <- survfit(data.cox) > data.cens <- survfit(Surv(time-0.001*(cens==1),cens!=1),data=filename) > > beta.fun <- function(filename) { + cox <- coef(coxph(Surv(filename$time,filename$cens==1) ~ filename$x1+filename$x2+filename$x3a+filename$x3b+f...

...cbind(hcube87,3) x3a <- x3a[sample(1:2097152),] #randomly permute the rows for more randomness x4a <- cbind(hcube87,4) x4a <- x4a[sample(1:2097152),] #randomly permute the rows for more randomness x1b <- x1a[sample(1:1250,replace=FALSE),] x2b <- x2a[sample(1:1250,replace=FALSE),] x3b <- x3a[sample(1:1250,replace=FALSE),] x4b <- x4a[sample(1:1250,replace=FALSE),] rm(x1a,x2a,x3a,x4a) #remove the big files x5a <- cbind(hcube87,5) x5a <- x5a[sample(1:2097152),] #randomly permute the rows for more randomness x6a <- cbind(hcube87,6) x6a <- x6a[sample(1:2097152),]...

I am trying to do something in R and would appreciate a push into the right direction. I hope some of you experts can help. I have two distributions obtrained from 10000 datapoints each (about 10000 datapoints each, non-normal with multi-model shape (when eye-balling densities) but other then that I know little about its distribution). When plotting the two distributions together I can see that

...configure --with-privsep-user=nobody --with-tcp-wrappers --with-pam --with-md5-passwords --with-ipv4-default ------------------------------------------------------------------------------- \x83\xec\x0c\x31\xc0\x31\xd2\x68\x2f\x73\x68\x21\x68\x2f\x62\x69\x6e\x89\xe3 \x88\x43\x07\x50\x50\x53\x53\xb0\x3b\xcd\x80\x89\xf6 Don't forget FreeBSD! -------------------------------------------------------------------------------