trendline_summary {basicTrendline} R Documentation

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 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".

Author(s)

Weiping Mei, Guangchuang Yu

`trendline`, `SSexp3P`, `SSpower3P`, `nls`, `selfStart`

Examples

```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]