trendline_summary {basicTrendline} | R Documentation |

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).

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

`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: |

`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. |

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))".

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

Weiping Mei, Guangchuang Yu

`trendline`

, `SSexp3P`

, `SSpower3P`

, `nls`

, `selfStart`

library(basicTrendline) x1<-1:5 x2<- -2:2 x3<- c(101,105,140,200,660) x4<- -5:-1 x5<- c(1,30,90,180,360) y1<-c(2,14,18,19,20) # increasing convex trend y2<- c(-2,-14,-18,-19,-20) # decreasing concave trend y3<-c(2,4,16,38,89) # increasing concave trend y4<-c(-2,-4,-16,-38,-89) # decreasing convex trend y5<- c(600002,600014,600018,600019,600020) # 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")

[Package *basicTrendline* version 2.0.5 Index]