Define your new_df before you calculate temp_var, and give it to predict so
predict doesn't start from your original x values. Add temp_var to new_df.
Read ?predict.lm.
Don't create matrices with cbind before creating data frames... data frames can
have a variety of data storage modes (types) for the columns, matrices cannot.
fit <-lm(y~x)
new_df <- data.frame(x,y)
new_df$temp_var <- predict(fit, newdata=new_df, interval="prediction")
--
Sent from my phone. Please excuse my brevity.
On March 14, 2017 4:39:51 AM PDT, Andras Farkas via R-help
<r-help@r-project.org> wrote:
>Dear All,
>
>would you have some thoughts on how to extend the prediction interval
>lines to beyond the "range of data"?
>
>
>example:
>
>y <-c(0.4316092,0.4156757,0.3517915,0.3669508,0.3899471,0.3964143,
>0.4001074,0.3851003,0.4222451,0.375324,0.3652045,0.3376978,0.383012,
>0.3763665,0.3550609,0.2958678,0.3726571,0.3442298
>#,0.3403275,0.2973978
>)*100
>x <-seq(1,length(y),1)
>
>z<-c("07/01/2015","08/01/2015","09/01/2015","10/01/2015","11/01/2015",
>"12/01/2015","01/01/2016","02/01/2016","03/01/2016","04/01/2016","05/01/2016",
>
>"06/01/2016","07/01/2016","08/01/2016","09/01/2016","10/01/2016","11/01/2016",
>
>"12/01/2016","01/01/2017","02/01/2017")
>
>fit <-lm(y~x)
>
>temp_var <- predict(fit, interval="prediction")
>
>new_df <- data.frame(cbind(x,y, temp_var))
>#new_df$x<-factor(new_df$x, ordered = T)
>
>library(ggplot2)
>ggplot(new_df, aes(x,y))+
>geom_point() +
>theme(panel.background = element_rect(fill = 'white', colour =
>'black'))+
>geom_line(aes(y=lwr), color = "black", linetype = "dashed",size=0.75)+
>geom_line(aes(y=upr), color = "black", linetype = "dashed",size=0.75)+
>scale_x_discrete(limits=z)+
>theme(axis.text.x = element_text(angle = 45, hjust = 1))+
>theme(panel.grid.major=element_line(colour = "grey"))+
>lims(y=c(0,50))+
>geom_smooth(method=lm,
>se=TRUE,fullrange=TRUE,fill="darkgrey",col="black")+labs(title =
>paste("Adj R2 = ",signif(summary(fit)$adj.r.squared, 4),
>"Intercept =",signif(fit$coef[[1]],4 ),
>" Slope =",signif(fit$coef[[2]], 4)
># " P =",signif(summary(fit)$coef[2,4], 3)
>))+
>ggtitle("Consumption Over Time") +
>theme(plot.title = element_text(hjust = 0.5))+
>labs(y="y",x="x")+
>geom_point(shape=15,aes(x=c(7),y=new_df[,2][7]), color="black",cex=4)+
>geom_point(shape=15,aes(x=c(8),y=new_df[,2][8]), color="black",cex=4)+
>geom_point(shape=17,aes(x=c(19),y=0.3403275*100), color="black",cex=4)+
>
>geom_point(shape=17,aes(x=c(20),y=0.2973978*100), color="black",cex=4)
>
>
>as you will see the regresssion line and confidence interval is
>extended, but would also want to extend the prediction interval lines
>to the "same length"... Wonder if you have any insights to this
>question...
>
>
>appreciate the help,
>
>
>Andras Farkas
>
>______________________________________________
>R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>https://stat.ethz.ch/mailman/listinfo/r-help
>PLEASE do read the posting guide
>http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
