Get the released version from CRAN:
Or the development version from github:
install.packages("devtools") devtools::install_github("PhDMeiwp/basicTrendline@master", force = TRUE)
- add several arguments to
trendline()function, including show.equation, show.Rpvalue, Rname, Pname, xname, yname, yhat, CI.fill, CI.level, CI.alpha, CI.color, CI.lty, CI.lwd, ePos.x, ePos.y, las.
- enable to draw confidence interval for regression models (arguments CI.fill, CI.level, etc.)
- add ‘show.equation’ and show.Rpvale’ arguments to enable to choose which parameter to show
- add ‘Rname’ and ‘Pname’ arguments to specify the character of R-square and P-vlaue (i.e. R^2 or r^2; P or p)
- add ‘xname’ and ‘ynameto’ arguments to specify the character of ‘x’ and ‘y’ in the equation
- add ‘yhat’ argument to enable to add a hat symbol on the top of ‘y’ in the equation
- add ‘ePos.x’ and ‘ePos.y’ arguments to specify the x and y co-ordinates of equation’s position
- deleted the ‘ePos’ argument
- add “Residual Sum of Squares” to the output of ‘trendline_summary()’ function
library(basicTrendline) x <- c(1, 3, 6, 9, 13, 17) y <- c(5, 8, 11, 13, 13.2, 13.5)
trendline(x, y, model="line2P", ePos.x = "topleft", summary=TRUE, eDigit=5)
trendline(x, y, model="line3P", CI.fill = FALSE, CI.color = "black", CI.lty = 2, linecolor = "blue")
trendline(x, y, model="log2P", ePos.x= "top", linecolor = "red", CI.color = NA)
trendline(x, y, model="exp2P", show.equation = TRUE, show.Rpvalue = FALSE)
see Arguments c(‘xname’, ‘yname’, ‘yhat’, ‘Rname’, ‘Pname’)
trendline(x, y, model=”exp3P”, xname=”T”, yname=paste(delta^15,”N”),
yhat=FALSE, Rname=1, Pname=0, ePos.x = “bottom”)
trendline(x, y, model="power2P", eDigit = 3, eSize = 1.4, text.col = "blue")
trendline(x, y, model="power3P", ePos.x = NA)
### NOT RUN par(mgp=c(1.5,0.4,0), mar=c(3,3,1,1), tck=-0.01, cex.axis=0.9) trendline(x, y) # dev.off() ### END (NOT RUN)
Plot, draw regression line and confidence interval, and show regression equation, R-square and P-value, as simple as possible,
by using different models built in the ‘trendline()’ function. The function includes the following models in the latest version:
“line2P” (formula as: y=a*x+b),
Besides, the summarized results of each fitted model are also output by default.
trendline(x, y, model = "line2P", Pvalue.corrected = TRUE, linecolor = "blue", lty = 1, lwd = 1, show.equation = TRUE, show.Rpvalue = TRUE, Rname = 1, Pname = 0, xname = "x", yname = "y", yhat = FALSE, summary = TRUE, ePos.x = NULL, ePos.y = NULL, text.col = "black", eDigit = 5, eSize = 1, CI.fill = TRUE, CI.level = 0.95, CI.color = "grey", CI.alpha = 1, CI.lty = 1, CI.lwd = 1, las = 1, xlab = NULL, ylab = NULL, ...)
the x and y arguments provide the x and y coordinates for the plot. Any reasonable way of defining the coordinates is acceptable.
select which model to fit. Default is “line2P”. The “model” should be one of c(“line2P”, “line3P”, “log2P”, “exp3P”, “power3P”), their formulas are as follows:
if P-value corrected or not, the vlaue is one of c(“TRUE”, “FALSE”).
color of regression line.
line type. lty can be specified using either text c(“blank”,”solid”,”dashed”,”dotted”,”dotdash”,”longdash”,”twodash”) or number c(0, 1, 2, 3, 4, 5, 6). Note that lty = “solid” is identical to lty=1.
line width. Default is 1.
whether to show the regression equation, the value is one of c(“TRUE”, “FALSE”).
whether to show the R-square and P-value, the value is one of c(“TRUE”, “FALSE”).
to specify the character of R-square, the value is one of c(0, 1), corresponding to c(r^2, R^2).
to specify the character of P-value, the value is one of c(0, 1), corresponding to c(p, P).
to specify the character of “x” in equation, see Examples [case 5].
to specify the character of “y” in equation, see Examples [case 5].
whether to add a hat symbol (^) on the top of “y” in equation. Default is FALSE.
summarizing the model fits. Default is TRUE.
equation position. Default as ePos.x = “topleft”. If no need to show equation, set ePos.x = NA. It’s same as those in legend.
the color used for the legend text.
the numbers of digits for equation parameters. Default is 5.
font size in percentage of equation. Default is 1.
fill the confidance interval? (TRUE by default, see ‘CI.level’ to control)
level of confidence interval to use (0.95 by default)
line or fill color of confidence interval.
alpha value of fill color of confidence interval.
line type of confidence interval.
line width of confidence interval.
style of axis labels. (0=parallel, 1=all horizontal, 2=all perpendicular to axis, 3=all vertical)
labels of x- and y-axis.
additional parameters to plot,such as type, main, sub, xlab, ylab, col.
The linear models (line2P, line3P, log2P) in this package are estimated by lm function, while the nonlinear models (exp2P, exp3P, power2P, power3P) are estimated by nls function (i.e., least-squares method).
The argument ‘Pvalue.corrected’ is workful for non-linear regression only.
If “Pvalue.corrected = TRUE”, the P-vlaue is calculated by using “Residual Sum of Squares” and “Corrected Total Sum of Squares (i.e. sum((y-mean(y))^2))”.
If “Pvalue.corrected = TRUE”, the P-vlaue is calculated by using “Residual Sum of Squares” and “Uncorrected Total Sum of Squares (i.e. sum(y^2))”.
Confidence intervals for nonlinear regression (i.e., objects of class nls) are based on the linear approximation described in Bates & Watts (2007).
Bates, D. M., and Watts, D. G. (2007) Nonlinear Regression Analysis and its Applications. Wiley.
Greenwell B. M., and Schubert-Kabban, C. M. (2014) investr: An R Package for Inverse Estimation. The R Journal, 6(1), 90-100.
R2, indicates the R-Squared value of each regression model.
p, indicates the p-value of each regression model.
AIC or BIC, indicate the Akaike’s Information Criterion or Bayesian Information Criterion for fitted model. Click AIC for details. The smaller the AIC or BIC, the better the model.
RSS, indicates the “Residual Sum of Squares” of regression model.
To see examples on how to use “basicTrendline” in R software, you can run the following R code if you have the “basicTrendline” package installed:
We would like to express my special thanks to Uwe Ligges, Swetlana Herbrandt, and CRAN team for their very valuable comments to the ‘basicTrendline’ package.
Our thanks also go to those who contributed R codes by:
- adding conficende interval for both lm and nls objects: https://github.com/bgreenwell/investr
- adding-regression-line-equation-and-r2-on-graph-1: http://blog.sciencenet.cn/blog-267448-1021594.html
- adding-regression-line-equation-and-r2-on-graph-2: https://stackoverflow.com/questions/7549694/adding-regression-line-equation-and-r2-on-graph
- What is non-linear regression?: https://datascienceplus.com/first-steps-with-non-linear-regression-in-r/
- adding regression line for nonlinear regression: http://blog.sciencenet.cn/blog-651374-1014133.html
- R codes for ‘print.summary.nls of exp3P and power3P’ cite from https://github.com/SurajGupta/r-source/blob/master/src/library/stats/R/nls.R
If you have any question or comment to this package, tell me at here.
The PDF file of this R package is available at https://cran.r-project.org/web/packages/basicTrendline/index.html
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