How to create confidence Interval plot in R - r

Question

b
DF1 = read.csv("Nerlove.3.csv",header=TRUE)
head(DF1, n=5)
split = round(nrow(DF1) * 0.60)
train = (DF1[1:split, ])
test = (DF1[(split + 1):nrow(DF1), ])
model = lm(output ~ ., train)
summary(model)
plot(train$cost, train$output, ylab = "Output", xlab = "Cost",main = "....")
abline(model, col=2)
c
plot(test$cost, test$output, ylab = "Output", xlab = "Cost",main = "....")
model1 = lm(output ~ ., test)
abline(model, col=2)
prediction = predict(model, test)
plot(prediction, main = "....")
abline(model1, col=2)
summary(model1)
d
library(stats)
X_0 = data.frame(cost = test$cost)
FI_mean = predict(model, newdata = X_0, interval="confidence", level = 0.95)
FI_ind = predict(model,newdata = X_0, interval = "prediction")
plot(test$cost, test$output, ylab = "Output", xlab = "Cost",main = "....")
abline(model, col=2)
min = test$cost
max = test$cost
newx = seq(min,max)
matlines(newx, FI_mean[,2:3], col = "blue", lty=2)
I need to plot the Confidence interval result I found around the regression line, but I'm getting an error. can anybody please help me to fix this. Thanks
This is the link for my data. I have edited it and only using the cost and output data in my dataframe

Answer 1

You are doing it wrong because you are using all the variables while developing the linear model by the command model = lm(output ~ ., train). But during plotting, you are using cost vs. output plotting (as in case of b and c) and in case of d, you are trying to predict using only one variable i.e. cost. Regression plot should be made between observed output vs. predicted output. For that, you can use the following code
library(lattice)
library(mosaic)
#> Loading required package: dplyr
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
#> Loading required package: ggformula
#> Loading required package: ggplot2
#> Loading required package: ggstance
#>
#> Attaching package: 'ggstance'
#> The following objects are masked from 'package:ggplot2':
#>
#> geom_errorbarh, GeomErrorbarh
#>
#> New to ggformula? Try the tutorials:
#> learnr::run_tutorial("introduction", package = "ggformula")
#> learnr::run_tutorial("refining", package = "ggformula")
#> Loading required package: mosaicData
#> Loading required package: Matrix
#> Registered S3 method overwritten by 'mosaic':
#> method from
#> fortify.SpatialPolygonsDataFrame ggplot2
#>
#> The 'mosaic' package masks several functions from core packages in order to add
#> additional features. The original behavior of these functions should not be affected by this.
#>
#> Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.
#>
#> Attaching package: 'mosaic'
#> The following object is masked from 'package:Matrix':
#>
#> mean
#> The following object is masked from 'package:ggplot2':
#>
#> stat
#> The following objects are masked from 'package:dplyr':
#>
#> count, do, tally
#> The following objects are masked from 'package:stats':
#>
#> binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
#> quantile, sd, t.test, var
#> The following objects are masked from 'package:base':
#>
#> max, mean, min, prod, range, sample, sum
DF1 = read.csv("Nerlove.csv",header=TRUE)
head(DF1, n=5)
#> cost output pl sl pk sk pf sf
#> 1 0.082 2 2.09 0.3164 183 0.4521 17.9 0.2315
#> 2 0.661 3 2.05 0.2073 174 0.6676 35.1 0.1251
#> 3 0.990 4 2.05 0.2349 171 0.5799 35.1 0.1852
#> 4 0.315 4 1.83 0.1152 166 0.7857 32.2 0.0990
#> 5 0.197 5 2.12 0.2300 233 0.3841 28.6 0.3859
split = round(nrow(DF1) * 0.60)
train = (DF1[1:split, ])
test = (DF1[(split + 1):nrow(DF1), ])
model = lm(output ~ ., train)
summary(model)
#>
#> Call:
#> lm(formula = output ~ ., data = train)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -409.65 -112.20 -4.61 94.20 430.76
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 1182.296 1861.876 0.635 0.527
#> cost 146.231 6.127 23.866 < 2e-16 ***
#> pl 14.897 80.142 0.186 0.853
#> sl -2471.312 1930.034 -1.280 0.204
#> pk 1.477 1.011 1.460 0.148
#> sk -869.468 1874.585 -0.464 0.644
#> pf -13.701 2.365 -5.794 1.08e-07 ***
#> sf -947.958 1861.490 -0.509 0.612
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 169.3 on 87 degrees of freedom
#> Multiple R-squared: 0.9111, Adjusted R-squared: 0.904
#> F-statistic: 127.4 on 7 and 87 DF, p-value: < 2.2e-16
#Calibration plotting
pred_cal <- predict(model, newdata=train)
df_cal <- data.frame(Observed=train$output, Predicted=pred_cal)
xyplot(Predicted ~ Observed, data = df_cal, pch = 19, panel=panel.lmbands,
band.lty = c(conf =2, pred = 1))
#Validation plottig
pred_val <- predict(model, newdata=test)
df_val <- data.frame(Observed=test$output, Predicted=pred_val)
xyplot(Predicted ~ Observed, data = df_val, pch = 19, panel=panel.lmbands,
band.lty = c(conf =2, pred = 1))
Created on 2020-01-07 by the reprex package (v0.3.0)