trendline_summary: Summarized Results of Each Regression Model

In PhDMeiwp/basicTrendline: Add Trendline and Confidence Interval of Basic Regression Models to Plot

Summarized Results of Each Regression Model

Description

Summarizing the results of each regression model which built in the 'trendline()' function. The function includes the following models in the latest version: "line2P" (formula as: y=a*x+b), "line3P" (y=a*x^2+b*x+c), "log2P" (y=a*ln(x)+b), "exp2P" (y=a*exp(b*x)),"exp3P" (y=a*exp(b*x)+c), "power2P" (y=a*x^b), and "power3P" (y=a*x^b+c).

Usage

trendline_summary(
  x,
  y,
  model = "line2P",
  Pvalue.corrected = TRUE,
  summary = TRUE,
  eDigit = 5
)

Arguments

x, y	the x and y arguments provide the x and y coordinates for the plot. Any reasonable way of defining the coordinates is acceptable.
model	select which model to fit. Default is "line2P". The "model" should be one of c("line2P", "line3P", "log2P", "exp2P", "exp3P", "power2P", "power3P"), their formulas are as follows: "line2P": y=a*x+b "line3P": y=a*x^2+b*x+c "log2P": y=a*ln(x)+b "exp2P": y=a*exp(b*x) "exp3P": y=a*exp(b*x)+c "power2P": y=a*x^b "power3P": y=a*x^b+c
Pvalue.corrected	if P-value corrected or not, the vlaue is one of c("TRUE", "FALSE").
summary	summarizing the model fits. Default is TRUE.
eDigit	the numbers of digits for summarized results. Default is 5.

Details

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))".

Value

R^2, indicates the R-Squared value of each regression model.

p, indicates the p-value of each regression model.

N, indicates the sample size.

AIC, AICc, or BIC, indicate the Akaike's Information Criterion (AIC), the second-order AIC (AICc) for small samples, or Bayesian Information Criterion (BIC) for fitted model. Click AIC for details. The smaller the AIC, AICc or BIC, the better the model.

RSS, indicate the value of "Residual Sum of Squares".

Note

If the output of 'AICc' is 'Inf', not an exact number, please try to expand the sample size of your dataset to >=6.

Author(s)

Weiping Mei, Guangchuang Yu

See Also

trendline, SSexp3P, SSpower3P, nls, selfStart, AICc

Examples

library(basicTrendline)
x1<-1:6
x2<- -2:3
x3<- c(101,105,140,200,660,1000)
x4<- -6:-1
x5<- c(1,30,90,180,360,800)

y1<-c(2,14,18,19,20,36)        # increasing convex trend
y2<- c(-2,-14,-18,-19,-20,-21)  # decreasing concave trend
y3<-c(2,4,16,38,89,160)         # increasing concave trend
y4<-c(-2,-4,-16,-38,-89,-160)    # decreasing convex trend
y5<- c(600002,600014,600018,600019,600020,600022) # high y values with low range.

trendline_summary(x1,y1,model="line2P",summary=TRUE,eDigit=10)
trendline_summary(x2,y2,model="line3P",summary=FALSE)
trendline_summary(x3,y3,model="log2P")
trendline_summary(x4,y4,model="exp3P")
trendline_summary(x5,y5,model="power3P")

PhDMeiwp/basicTrendline documentation built on May 7, 2022, 9:38 a.m.