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]