search for: lm3

Hello, I have done a scatterplot and now would like to add its regression line but it does not show. Below, the code I have used. lm3 <- lm(data$S_pH_KCl2.5_BCx~data$B_OleicoPF_BCx_per) plot(data$S_pH_KCl2.5_BCx, data$B_OleicoPF_BCx_per) abline(lm3) I have been able to do the complete operation using the software STATISTICA but it would be great to do it with R. If you require more details please get in touch. Thanks a lot!...

...1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1) e<-c(1739,1633,1481,1837,1780,2073,1374,1629,1555,1385,1756,1522,1566,1643,1939,1615,1475,1759,1388,1483,1127,1682,1542,1247,1235,1605,1598,1718 ) Data<-data.frame(subject=as.factor(a), drug=as.factor(b), period=as.factor(c), sequence=as.factor(d), Max=e) lm3<- lm(Max ~subject*sequence + sequence + period + drug, data=Data) print(lm3) anova(lm3) When I use lm to fit the data, there are some problems in ?subject*sequence?. I have use GLM in SPSS to fit the same data, and it seems there is no problem. I don?t know where my problem is. How can I g...

Dear all, I am using poly() in lm() in the following form. 1> DelsDPWOS.lm3 <- lm(DelsPDWOS[,1] ~ poly(DelsPDWOS[,4],3)) 2> DelsDPWOS.I.lm3 <- lm(DelsPDWOS[,1] ~ poly(I(DelsPDWOS[,4]),3)) 3> DelsDPWOS.2.lm3 <- lm(DelsPDWOS[,1]~DelsPDWOS[,4]+I(DelsPDWOS[,4]^2)+I(DelsPDWOS[,4]^3)) 1 and 2 lead to identical but wrong results. 3 is correct. Surprisingly (t...

...uss the relationship between taking part in sports, GPA, and income for these data. The R code I used so far is Does sports predict GPA? > lm1<-lm(GPA~sports) > summary(lm1) Does sports predict income? > lm2<-lm(income~sports) > summary(lm2) Does GPA predict income? > lm3<-lm(income~GPA) > summary(lm3) Does sports predict income after accounting for GPA? > lm4<-lm(income~GPA+sports) > summary(lm4) Can someone let me know is the above is correct? I am not sure if to keep all four regressions or only 1 and 4. Also, I need to plot the data on a graph...

...ón puede utilizarse con el pvalor de esa variable. Por ejemplo: Lm1=lmer(rt_ln ~ (fre_ln * Z_nsize * frebase_ln + (1|word), data = x) Lm2= lmer(rt_ln ~ (Z_nsize * frebase_ln + (1|word), data = x) anova(Lm1,Lm2, test="Chisq") #Obtiens el pvalor de la variable ?fre_ln?   Lm3=lmer(rt_ln ~ (fre_ln * frebase_ln + (1|word), data = x) anova(Lm1,Lm3, test="Chisq") #Obtiens el pvalor de la variable ? Z_nsize?   Espero que te sea de utilidad, Un saludo Manuel El 13 de junio de 2014, 12:25, Miguel Lázaro <lazarolv en yahoo.es> escribió: Hol...

...m14<-c(1:nitns) for(itn in 1:nitns){ g<-rbinom(nsims,1,0.5) b<-rnorm(nsims,0,1)*10 rn<-rnorm(nsims,0,1)*10 a<-b*r+rn*(1-r^2)^0.5 a<-round(a)+50 a<-a-g*5 b<-round(b)+50 abs.2<-function(x) ifelse(x<1,1,x) b<-abs.2(b) c<-b-a p<-c/b lm1<-lm(a~g) lm2<-lm(c~g) lm3<-lm(p~g) lm4<-lm(a~b+g) gr<-c(g,g) occasion<-rep(0:1,c(nsims,nsims)) occ<-occasion-0.5 ppd<-c(b,a) h<-rep(0,nsims) mb<-mean(b) bppd<-b-mb bappd<-c(h,bppd) occgr<-occ*gr subject<-c(1:nsims) sub<-c(subject,subject) library(nlme) lm5<-lme(ppd~occ+gr+occgr,rand...

Hi, When I use Anova(car) to produce type III SS, 'Sum Sq' is reported in integers: > Anova(bot.lm3, type ="III") Anova Table (Type III tests) Response: bottemp Sum Sq Df F value Pr(>F) (Intercept) 45295 1 29436.4440 < 2e-16 fungroup 3 2 0.8259 0.44006 numsp.fun 11 2 3.4503 0.03460 b...

...ically linear and or quadratic. I found out that the following code produces the same p-value for Observation as the Anova() function. However, the result also does not include any p-values for the linear or quadratic effect: lm2 <- lm(log(Variable) ~ Observation + COV1 + COV2, data = df1) lm3 <- lm(log(Variable) ~ COV1 + COV2, data = df1) anova(lm2, lm3) Does anybody know how to conduct a Type II anova and the contrasts function to obtain the p-values for the linear and quadratic effects? Help would be very much appreciated. Best Peter [[alternative HTML version deleted]]

Hola a todos, quería preguntaros un medio para obtener los valores p usando lmer. He tratado con pvals.fnc, que es lo que me habían recomendado, pero por algún motivo no está ya disponible etc. Ésta es la función que tengo, pero da las "t", sin los valores p. Aunque Baayen indica que valores por encima de 2 son significativos necesito saber las p. resultado = lmer(rt_ln ~ (fre_ln *

...t; > data = x) > > > > Lm2= lmer(rt_ln ~ (Z_nsize > > * frebase_ln + (1|word), data = > > x) > > > > anova(Lm1,Lm2, > > test="Chisq") #Obtiens el pvalor de > > la variable "fre_ln" > > > > > > > > Lm3=lmer(rt_ln ~ (fre_ln * > > frebase_ln + (1|word), data = x) > > > > anova(Lm1,Lm3, > > test="Chisq") #Obtiens el pvalor de > > la variable " Z_nsize" > > > > > > > > Espero que te sea de > > utilidad, > > &g...

...y well. Hence, the R-squared value is close to 1. For the second example it also makes sense that the p-value is almost 1 (p=0.9221) as there is hardly any correlation between scores and height. What is not clear to me is shown in my 3rd linear model which includes both weight and height. > (lm3 <- summary(lm(scores ~ weight + height))) Call: lm(formula = scores ~ weight + height) Residuals: 1 2 3 4 5 6 1.189e+00 -1.946e+00 -2.165e-15 4.865e-01 -1.081e+00 1.351e+00 Coefficients: Estimate Std. Error t value Pr(>...

...## 3. delete all objects in function call stack > lmtest3 <- lmfunc(add = TRUE, gr = TRUE) > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 210766 5.7 467875 12.5 467875 12.5 Vcells 3615863 27.6 6933688 52.9 6898609 52.7 > save(lmtest3, file = "lm3.RData") > system("ls -s lm3.RData") 320 lm3.RData ## A minimal increase in Vcells; .RData object is now 320Kb > sapply(ls(pattern = "lmtest*"), function(x) object.size(get(x, envir = .GlobalEnv))) lmtest1 lmtest2 lmtest3 358428 358428 358428 ## all obj...

Hi all, how can I remove objects from the workspace which starts with a certain pattern, e.g. lm (lm1, lm2, lm3 etc). Wildcards won?t work. Thanks, Sven -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not t...

Dear friends. I use nls() and encounter the following puzzling problem: I have a function f(a,b,c,x), I have a data vector of x and a vectory y of realized value of f. Case1 I tried to estimate c with (a=0.3, b=0.5) fixed: nls(y~f(a,b,c,x), control=list(maxiter = 100000, minFactor=0.5 ^2048),start=list(c=0.5)). The error message is: "number of iterations exceeded maximum of

> lm2 <- function(...) lm(...) > lm2(mpg ~ wt, data=mtcars) Call: lm(formula = ..1, data = ..2) Coefficients: (Intercept) wt 37.285 -5.344 > lm2(mpg ~ wt, weights=cyl, data=mtcars) Error in eval(expr, envir, enclos) : ..2 used in an incorrect context, no ... to look in Can anyone explain why this is happening? (Obviously this is a manufactured example, but it

...lm glm and gls? I try the following codes, but the results are different. > lm1 Call: lm(formula = y ~ x) Coefficients: (Intercept) x 0.1183 7.3075 > lm2 Call: lm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.04193 7.30660 > lm3 Call: lm(formula = ys ~ Xs - 1) Coefficients: Xs Xsx 0.04193 7.30660 Here ys= y*sqrt(W), Xs<- sqrt(W)*cbind(1,x) So we can see weights here for lm means the scale for X and y. But for glm and gls I try > glm1 Call: glm(formula = y ~ x) Coefficients: (Intercept)...

...mcReg # in which one of the newmcReg happen to be all true or false. # # God I wish I had taken more then a Trig course. Where was I? # # Yes that phantom 'i' you see is that this is all in a big loop # for 6 possible models # lm1 = all regressors w/ intercept # lm2 = lm1 stepwise removal # lm3 = all regressors wo/ intercept # lm4 = lm3 stepwise removal # lm5 = Hand Tuned # lm6 = lm5 stepwise removal ################################################### toPurge=c() for(k in names(newmcReg[[i]])) { print (paste("check to see if",k,"is a useless regressors for model",i))...

...these individually against the explanatory variables, is there a way to 'group' all response and explanatory variables and then look at all the linear relationship amongst all these variables? I have tried: my.group1 <- c(a,b,c,d,e,f,g,h,i,j) my.group2 <- c(k,l,m,n,o,p,q,r,s,t) my.lm3 <- lm(my.group1 ~ my.group2) to no avail. Thanks for your help, Andy

...1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,5,4,4,4,4,4,3,3,3,6,6,6,6,6,6,6,6,5,5,5,5) > names(site.codings) <- c( "WM1", "WM2", "WM3", "NM1", "NM2", "NM3", "KH1", "KH2", "KH3", "LM1" ,"LM2" ,"LM3", "DB1" ,"DB2" ,"DB3", "DM1" , "DM2" , "DM3" , "FI1", "FI2", "BKI1", "BKI2", "BKO1", "BKO2", "BKO3", "SUR1","MI1","MI2","MI3&qu...

...7.8887 [10,] 201.3 24.01 317.8470 [11,] 203.6 25.14 322.4460 [12,] 204.6 26.57 327.9783 [13,] 209.5 28.49 334.9553 [14,] 208.6 27.76 332.3596 [15,] 210.7 29.04 336.8674 [16,] 211.9 29.88 339.7189 [17,] 212.2 30.06 340.3195 > lm3 Call: lm(formula = log.pressure ~ boil.pt, subset = c(-12)) Coefficients: (Intercept) boil.pt -95.176624 2.051855 > forbes.pred boil.pt 190 190 200 200 > predict(lm3,newdata=forbes.pred) Error in if (object$intercept) X <- cbind(rep(...