predict

I want to determine the marginal effects of each dependent variable in a probit regression as follows: predict the (base) probability with the mean of each variable for each variable, predict the change in probability compared to the base probability if the variable takes the value of mean + 1x standard deviation of the variable In one of my regressions, I have a multiplicative variable, as follows: my_probit <- glm(a ~ b + c + I(b*c), family = binomial(link = "probit"), data=data) Two questions: When I determine the marginal effects using the approach above, will the value of the

Here is my data: a <- c(60, 65, 70, 75, 80, 85, 90, 95, 100, 105) b <- c(26, 24.7, 20, 16.1, 12.6, 10.6, 9.2, 7.6, 6.9, 6.9) a_b <- cbind(a,b) plot(a,b, col = "purple") abline(lm(b ~ a),col="red") reg <- lm(b ~ a) I would like to use the predict function in order to compute the standard error for the predicted b value at 110. z <- predict(reg, newdata=data.frame(year=110), se.fit=TRUE) This is the output I get, but I think this is just giving me the standard errors for my 10 time points, but not the new 11th data point: z $fit 1 2 3 4 5 6 7 8 9 10 24.456364 22.146061 19.835758 17.525455 15

I am new to R and I am trying to do linear prediction. Here is some simple data: test.frame<-data.frame(year=8:11, value= c(12050,15292,23907,33991)) Say if I want to predict the value for year=12 . This is what I am doing (experimenting with different commands): lma=lm(test.frame$value~test.frame$year) # let's get a linear fit summary(lma) # let's see some parameters attributes(lma) # let's see what parameters we can call lma$coefficients # I get the intercept and gradient predict(lm(test.frame$value~test.frame$year)) newyear <- 12 # new value for year predict.lm(lma, newyear) # predicted

I have run the multinom() function in R, but when I try to predict on a new sample, it keeps giving an error. this is the code: library(nnet) dta=data.frame(replicate(10,runif(10))) names(dta)=c('y',paste0('x',1:9)) res4 <- multinom(y ~ as.matrix(dta[2:10]) , data=dta) #make new data to predict nd<-0.1*dta[1,2:10] pred<-predict(res4, newdata=nd) and this is the error: Error in predict.multinom(res4, newdata = nd) : NAs are not allowed in subscripted assignments I think it has to do with the intercept being included in the analysis, but not in the new prediction input. I tried to set it