sgSEMp2: Semi-supervised Generalized Structural Equation Modelling...
In gSEM: Semi-Supervised Generalized Structural Equation Modeling
Description
Usage
Arguments
Details
Value
See Also
Examples
Description
This function builds an gSEM model using gSEM principle 2. Principle 2 resembles the multiple regression principle in the way multiple predictors are considered simultaneously. Specifically, the first-level predictors to the system level variable, such as, Time and unit level variables, acted on the system level variable collectively by an additive model. This collective additive model can be found with a generalized stepwise variable selection (using the step() function in R, which performs variable selection on the basis of AIC) and this proceeds iteratively.
Usage
1
Arguments
x
A dataframe, requiring at least 2 columns. By default its first column stores the main or primary influencing predictor, or exogenous variable e.g.., time, or a main predictor, the second column stores the response variable, and other columns store intermediate variables.
predictor
A character string of the column name of the system predictor OR a numeric number indexing the column of the main predictor.
response
A character string of the column name of the main response OR a numeric number indexing the column of the system response.
Details
Data is analysed first using Principle 1 to find the best models. If needed, transformations based on the best models are applied to the predictors. Starting from the system response variable, each variable is regressed on all other variables except for the system response in an additive multiple regression model, which is reduced by a stepwise selection using stepAIC(). Then, for each selected variable, fitted regression for 6 selected functional forms and pick the best.
Value
A list of the following items:
"Graph": A network graph that contains the group and individual relationships between response and predictors determined by principle 2.
"res.print": A matrix. For each row, first column is the response variable, second column is the predictor, the other columns show corresponding summary information.
See Also
sgSEMp1() and plot.sgSEMp2()
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
# Using built-in dataset
data(acrylic)
ans <- sgSEMp2(acrylic)
ans$res.print
plot(ans)
## Not run:
# Using simulated data
x4=runif(100,0,2)
x3=1+2.5*x4+rnorm(100,0,0.5)
x1=runif(100,1,4)
x2=-1-x1+x3+rnorm(100,0,0.3)
y=2+2*exp(x1/3)+(x2-1)^2-x3+rnorm(100,0,0.5)
# Check the pairwise plot
sim=cbind(x4,y,x1,x2,x3)
pairs(sim)
ans <- sgSEMp2(as.data.frame(sim))
plot(ans)
## End(Not run)
Example output
Working on YI ~ IrradTot
Working on YI ~ IAD1
Working on YI ~ IAD2
Working on YI ~ IAD2p
Working on YI ~ IAD3
Working on IAD1 ~ IrradTot
Working on IAD1 ~ IAD2
Working on IAD1 ~ IAD2p
Working on IAD1 ~ IAD3
Working on IAD2 ~ IrradTot
Working on IAD2 ~ IAD1
Working on IAD2 ~ IAD2p
Working on IAD2 ~ IAD3
Working on IAD2p ~ IrradTot
Working on IAD2p ~ IAD1
Working on IAD2p ~ IAD2
Working on IAD2p ~ IAD3
Working on IAD3 ~ IrradTot
Working on IAD3 ~ IAD1
Working on IAD3 ~ IAD2
Working on IAD3 ~ IAD2p
resp var ar2 Tran
2 YI IrradTot 0.49 NA
3 YI IAD1 0.49 NA
4 YI IAD2 0.49 NA
5 YI IAD2p 0.49 NA
6 YI IAD3 0.49 NA
7 IAD1 IrradTot 0.86 NA
8 IAD1 IAD2 0.86 NA
9 IAD1 IAD2p 0.86 NA
10 IAD1 IAD3 0.86 NA
11 IAD2 IrradTot 0.99 NA
12 IAD2 IAD1 0.99 NA
13 IAD2 IAD2p 0.99 NA
14 IAD2 IAD3 0.99 NA
15 IAD2p IAD1 0.99 NA
16 IAD2p IAD2 0.99 NA
17 IAD2p IAD3 0.99 NA
18 IAD3 IrradTot 0.37 NA
19 IAD3 IAD1 0.37 NA
20 IAD3 IAD2 0.37 NA
21 IAD3 IAD2p 0.37 NA
GModel
2 YI=6.5e+01+1.3e-02*IrradTot-6.2e+01*e^IAD1+8.2e+02*IAD2-1.0e+03*IAD2p+1.1e+03*IAD3+1.9e+03*IAD2^2-2.6e+03*IAD2p^2-5.3e+04*IAD3^2
3 YI=6.5e+01+1.3e-02*IrradTot-6.2e+01*e^IAD1+8.2e+02*IAD2-1.0e+03*IAD2p+1.1e+03*IAD3+1.9e+03*IAD2^2-2.6e+03*IAD2p^2-5.3e+04*IAD3^2
4 YI=6.5e+01+1.3e-02*IrradTot-6.2e+01*e^IAD1+8.2e+02*IAD2-1.0e+03*IAD2p+1.1e+03*IAD3+1.9e+03*IAD2^2-2.6e+03*IAD2p^2-5.3e+04*IAD3^2
5 YI=6.5e+01+1.3e-02*IrradTot-6.2e+01*e^IAD1+8.2e+02*IAD2-1.0e+03*IAD2p+1.1e+03*IAD3+1.9e+03*IAD2^2-2.6e+03*IAD2p^2-5.3e+04*IAD3^2
6 YI=6.5e+01+1.3e-02*IrradTot-6.2e+01*e^IAD1+8.2e+02*IAD2-1.0e+03*IAD2p+1.1e+03*IAD3+1.9e+03*IAD2^2-2.6e+03*IAD2p^2-5.3e+04*IAD3^2
7 IAD1=-2.1e-01-3.2e-05*IrradTot+7.6e-01*exp$6.4*IAD2$-5.5e-01*exp$8.9*IAD2p$+6.3e-01*IAD3+7.8e-08*IrradTot^2
8 IAD1=-2.1e-01-3.2e-05*IrradTot+7.6e-01*exp$6.4*IAD2$-5.5e-01*exp$8.9*IAD2p$+6.3e-01*IAD3+7.8e-08*IrradTot^2
9 IAD1=-2.1e-01-3.2e-05*IrradTot+7.6e-01*exp$6.4*IAD2$-5.5e-01*exp$8.9*IAD2p$+6.3e-01*IAD3+7.8e-08*IrradTot^2
10 IAD1=-2.1e-01-3.2e-05*IrradTot+7.6e-01*exp$6.4*IAD2$-5.5e-01*exp$8.9*IAD2p$+6.3e-01*IAD3+7.8e-08*IrradTot^2
11 IAD2=-1.2e-05+9.2e-06*IrradTot+1.6e-01*IAD1+1.1e+00*IAD2p-4.4e-01*IAD3-2.6e-08*IrradTot^2-5.0e-01*IAD1^2+1.7e+01*IAD3^2
12 IAD2=-1.2e-05+9.2e-06*IrradTot+1.6e-01*IAD1+1.1e+00*IAD2p-4.4e-01*IAD3-2.6e-08*IrradTot^2-5.0e-01*IAD1^2+1.7e+01*IAD3^2
13 IAD2=-1.2e-05+9.2e-06*IrradTot+1.6e-01*IAD1+1.1e+00*IAD2p-4.4e-01*IAD3-2.6e-08*IrradTot^2-5.0e-01*IAD1^2+1.7e+01*IAD3^2
14 IAD2=-1.2e-05+9.2e-06*IrradTot+1.6e-01*IAD1+1.1e+00*IAD2p-4.4e-01*IAD3-2.6e-08*IrradTot^2-5.0e-01*IAD1^2+1.7e+01*IAD3^2
15 IAD2p=-3.3e-04-1.3e-01*IAD1+8.9e-01*IAD2+6.9e-01*IAD3+3.4e-01*IAD1^2-2.8e+01*IAD3^2
16 IAD2p=-3.3e-04-1.3e-01*IAD1+8.9e-01*IAD2+6.9e-01*IAD3+3.4e-01*IAD1^2-2.8e+01*IAD3^2
17 IAD2p=-3.3e-04-1.3e-01*IAD1+8.9e-01*IAD2+6.9e-01*IAD3+3.4e-01*IAD1^2-2.8e+01*IAD3^2
18 IAD3=-1.4e-05+2.0e-06*IrradTot+1.6e-02*IAD1-1.6e-01*IAD2+2.8e-01*IAD2p+4.1e-01*IAD2p^2
19 IAD3=-1.4e-05+2.0e-06*IrradTot+1.6e-02*IAD1-1.6e-01*IAD2+2.8e-01*IAD2p+4.1e-01*IAD2p^2
20 IAD3=-1.4e-05+2.0e-06*IrradTot+1.6e-02*IAD1-1.6e-01*IAD2+2.8e-01*IAD2p+4.1e-01*IAD2p^2
21 IAD3=-1.4e-05+2.0e-06*IrradTot+1.6e-02*IAD1-1.6e-01*IAD2+2.8e-01*IAD2p+4.1e-01*IAD2p^2
Gpvalue GR2aR2
2 0 0 0 0 0 0 0 0.003 0 0.505 0.493
3 0 0 0 0 0 0 0 0.003 0 0.505 0.493
4 0 0 0 0 0 0 0 0.003 0 0.505 0.493
5 0 0 0 0 0 0 0 0.003 0 0.505 0.493
6 0 0 0 0 0 0 0 0.003 0 0.505 0.493
7 0 0.016 0 0 0.052 0.035 0.864 0.862
8 0 0.016 0 0 0.052 0.035 0.864 0.862
9 0 0.016 0 0 0.052 0.035 0.864 0.862
10 0 0.016 0 0 0.052 0.035 0.864 0.862
11 0.912 0 0 0 0 0 0 0.003 0.993 0.993
12 0.912 0 0 0 0 0 0 0.003 0.993 0.993
13 0.912 0 0 0 0 0 0 0.003 0.993 0.993
14 0.912 0 0 0 0 0 0 0.003 0.993 0.993
15 0 0 0 0 0 0 0.99 0.99
16 0 0 0 0 0 0 0.99 0.99
17 0 0 0 0 0 0 0.99 0.99
18 0.878 0 0.072 0 0 0.005 0.383 0.374
19 0.878 0 0.072 0 0 0.005 0.383 0.374
20 0.878 0 0.072 0 0 0.005 0.383 0.374
21 0.878 0 0.072 0 0 0.005 0.383 0.374
IModel Ipvalue
2 YI=2.6+0.031*IrradTot-5e-05*IrradTot^2 0 0 0
3 YI=-26+30*e^IAD1 0.002 0
4 YI=4.4+67*IAD2+432*IAD2^2 0 0 0.017
5 YI=4.5+68*IAD2p+537*IAD2p^2 0 0.004 0.131
6 YI=4.5+710*IAD3-31084*IAD3^2 0 0 0.001
7 IAD1=0.0047+8.8e-05*IrradTot-2.8e-07*IrradTot^2 0.005 0.009 0.003
8 IAD1=-0.2+0.21*e^6.4IAD2 0 0 0
9 IAD1=-0.17+0.18*e^8.9IAD2p 0 0 0
10 IAD1=0.0068+8.5*IAD3-382*IAD3^2 0 0 0
11 IAD2=-0.00084+4.1e-05*IrradTot-1.1e-07*IrradTot^2 0.492 0.097 0.114
12 IAD2=-0.0019+0.56*IAD1-3.1*IAD1^2 0 0 0
13 IAD2=0.0013+1.3*IAD2p 0 0
14 IAD2=0.00073+9.8*IAD3-459*IAD3^2 0.134 0 0
15 IAD2p=-0.002+0.38*IAD1-2.5*IAD1^2 0 0 0
16 IAD2p=-0.001+0.76*IAD2 0 0
17 IAD2p=-0.00046+8.1*IAD3-377*IAD3^2 0.171 0 0
18 IAD3=-0.00014+2.4e-06*IrradTot 0.162 0
19 IAD3=-2.7e-05+0.025*IAD1-0.11*IAD1^2 0.74 0 0.024
20 IAD3=1.5e-05+0.066*IAD2+0.18*IAD2^2 0.822 0 0.021
21 IAD3=9.4e-05+0.098*IAD2p+0.43*IAD2p^2 0.15 0 0.004
IR2aR2 Rank
2 0.335 0.331 1
3 0.038 0.035 7
4 0.049 0.044 3
5 0.027 0.021 2
6 0.065 0.06 4
7 0.029 0.023 3
8 <NA> 1
9 <NA> 2
10 0.226 0.222 5
11 0.008 0.002 5
12 0.77 0.769 2
13 0.967 0.967 1
14 0.57 0.567 6
15 0.637 0.635 2
16 0.967 0.967 1
17 0.653 0.651 3
18 0.036 0.033 2
19 0.126 0.121 5
20 0.273 0.269 3
21 0.322 0.318 1
Gcoeff
2 6.5e+01 1.3e-02 -6.2e+01 8.2e+02 -1.0e+03 1.1e+03 1.9e+03 -2.6e+03 -5.3e+04
3 6.5e+01 1.3e-02 -6.2e+01 8.2e+02 -1.0e+03 1.1e+03 1.9e+03 -2.6e+03 -5.3e+04
4 6.5e+01 1.3e-02 -6.2e+01 8.2e+02 -1.0e+03 1.1e+03 1.9e+03 -2.6e+03 -5.3e+04
5 6.5e+01 1.3e-02 -6.2e+01 8.2e+02 -1.0e+03 1.1e+03 1.9e+03 -2.6e+03 -5.3e+04
6 6.5e+01 1.3e-02 -6.2e+01 8.2e+02 -1.0e+03 1.1e+03 1.9e+03 -2.6e+03 -5.3e+04
7 -2.1e-01 -3.2e-05 7.6e-01 -5.5e-01 6.3e-01 7.8e-08
8 -2.1e-01 -3.2e-05 7.6e-01 -5.5e-01 6.3e-01 7.8e-08
9 -2.1e-01 -3.2e-05 7.6e-01 -5.5e-01 6.3e-01 7.8e-08
10 -2.1e-01 -3.2e-05 7.6e-01 -5.5e-01 6.3e-01 7.8e-08
11 -1.2e-05 9.2e-06 1.6e-01 1.1e+00 -4.4e-01 -2.6e-08 -5.0e-01 1.7e+01
12 -1.2e-05 9.2e-06 1.6e-01 1.1e+00 -4.4e-01 -2.6e-08 -5.0e-01 1.7e+01
13 -1.2e-05 9.2e-06 1.6e-01 1.1e+00 -4.4e-01 -2.6e-08 -5.0e-01 1.7e+01
14 -1.2e-05 9.2e-06 1.6e-01 1.1e+00 -4.4e-01 -2.6e-08 -5.0e-01 1.7e+01
15 -3.3e-04 -1.3e-01 8.9e-01 6.9e-01 3.4e-01 -2.8e+01
16 -3.3e-04 -1.3e-01 8.9e-01 6.9e-01 3.4e-01 -2.8e+01
17 -3.3e-04 -1.3e-01 8.9e-01 6.9e-01 3.4e-01 -2.8e+01
18 -1.4e-05 2.0e-06 1.6e-02 -1.6e-01 2.8e-01 4.1e-01
19 -1.4e-05 2.0e-06 1.6e-02 -1.6e-01 2.8e-01 4.1e-01
20 -1.4e-05 2.0e-06 1.6e-02 -1.6e-01 2.8e-01 4.1e-01
21 -1.4e-05 2.0e-06 1.6e-02 -1.6e-01 2.8e-01 4.1e-01
GModelB
2 YI=IrradTot+e^IAD1+IAD2+IAD2p+IAD3+IAD2^2+IAD2p^2+IAD3^2
3 YI=IrradTot+e^IAD1+IAD2+IAD2p+IAD3+IAD2^2+IAD2p^2+IAD3^2
4 YI=IrradTot+e^IAD1+IAD2+IAD2p+IAD3+IAD2^2+IAD2p^2+IAD3^2
5 YI=IrradTot+e^IAD1+IAD2+IAD2p+IAD3+IAD2^2+IAD2p^2+IAD3^2
6 YI=IrradTot+e^IAD1+IAD2+IAD2p+IAD3+IAD2^2+IAD2p^2+IAD3^2
7 IAD1=IrradTot+exp$6.4*IAD2$+exp$8.9*IAD2p$+IAD3+IrradTot^2
8 IAD1=IrradTot+exp$6.4*IAD2$+exp$8.9*IAD2p$+IAD3+IrradTot^2
9 IAD1=IrradTot+exp$6.4*IAD2$+exp$8.9*IAD2p$+IAD3+IrradTot^2
10 IAD1=IrradTot+exp$6.4*IAD2$+exp$8.9*IAD2p$+IAD3+IrradTot^2
11 IAD2=IrradTot+IAD1+IAD2p+IAD3+IrradTot^2+IAD1^2+IAD3^2
12 IAD2=IrradTot+IAD1+IAD2p+IAD3+IrradTot^2+IAD1^2+IAD3^2
13 IAD2=IrradTot+IAD1+IAD2p+IAD3+IrradTot^2+IAD1^2+IAD3^2
14 IAD2=IrradTot+IAD1+IAD2p+IAD3+IrradTot^2+IAD1^2+IAD3^2
15 IAD2p=IAD1+IAD2+IAD3+IAD1^2+IAD3^2
16 IAD2p=IAD1+IAD2+IAD3+IAD1^2+IAD3^2
17 IAD2p=IAD1+IAD2+IAD3+IAD1^2+IAD3^2
18 IAD3=IrradTot+IAD1+IAD2+IAD2p+IAD2p^2
19 IAD3=IrradTot+IAD1+IAD2+IAD2p+IAD2p^2
20 IAD3=IrradTot+IAD1+IAD2+IAD2p+IAD2p^2
21 IAD3=IrradTot+IAD1+IAD2+IAD2p+IAD2p^2
The cutoff value is lower than all of the adjusted R-sqr values: Only solid lines
Working on y ~ x4
Working on y ~ x1
Working on y ~ x2
Working on y ~ x3
Working on x1 ~ x4
Working on x1 ~ x2
Working on x1 ~ x3
Working on x2 ~ x4
Working on x2 ~ x1
Working on x2 ~ x3
Working on x3 ~ x4
Working on x3 ~ x1
Working on x3 ~ x2
The cutoff value is lower than all of the adjusted R-sqr values: Only solid lines
gSEM documentation built on May 2, 2019, 11:27 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value See Also Examples
Description
This function builds an gSEM model using gSEM principle 2. Principle 2 resembles the multiple regression principle in the way multiple predictors are considered simultaneously. Specifically, the first-level predictors to the system level variable, such as, Time and unit level variables, acted on the system level variable collectively by an additive model. This collective additive model can be found with a generalized stepwise variable selection (using the step() function in R, which performs variable selection on the basis of AIC) and this proceeds iteratively.
Usage
1 |
Arguments
x |
A dataframe, requiring at least 2 columns. By default its first column stores the main or primary influencing predictor, or exogenous variable e.g.., time, or a main predictor, the second column stores the response variable, and other columns store intermediate variables. |
predictor |
A character string of the column name of the system predictor OR a numeric number indexing the column of the main predictor. |
response |
A character string of the column name of the main response OR a numeric number indexing the column of the system response. |
Details
Data is analysed first using Principle 1 to find the best models. If needed, transformations based on the best models are applied to the predictors. Starting from the system response variable, each variable is regressed on all other variables except for the system response in an additive multiple regression model, which is reduced by a stepwise selection using stepAIC(). Then, for each selected variable, fitted regression for 6 selected functional forms and pick the best.
Value
A list of the following items:
"Graph": A network graph that contains the group and individual relationships between response and predictors determined by principle 2.
"res.print": A matrix. For each row, first column is the response variable, second column is the predictor, the other columns show corresponding summary information.
See Also
sgSEMp1() and plot.sgSEMp2()
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # Using built-in dataset
data(acrylic)
ans <- sgSEMp2(acrylic)
ans$res.print
plot(ans)
## Not run:
# Using simulated data
x4=runif(100,0,2)
x3=1+2.5*x4+rnorm(100,0,0.5)
x1=runif(100,1,4)
x2=-1-x1+x3+rnorm(100,0,0.3)
y=2+2*exp(x1/3)+(x2-1)^2-x3+rnorm(100,0,0.5)
# Check the pairwise plot
sim=cbind(x4,y,x1,x2,x3)
pairs(sim)
ans <- sgSEMp2(as.data.frame(sim))
plot(ans)
## End(Not run)
|
Example output
Working on YI ~ IrradTot
Working on YI ~ IAD1
Working on YI ~ IAD2
Working on YI ~ IAD2p
Working on YI ~ IAD3
Working on IAD1 ~ IrradTot
Working on IAD1 ~ IAD2
Working on IAD1 ~ IAD2p
Working on IAD1 ~ IAD3
Working on IAD2 ~ IrradTot
Working on IAD2 ~ IAD1
Working on IAD2 ~ IAD2p
Working on IAD2 ~ IAD3
Working on IAD2p ~ IrradTot
Working on IAD2p ~ IAD1
Working on IAD2p ~ IAD2
Working on IAD2p ~ IAD3
Working on IAD3 ~ IrradTot
Working on IAD3 ~ IAD1
Working on IAD3 ~ IAD2
Working on IAD3 ~ IAD2p
resp var ar2 Tran
2 YI IrradTot 0.49 NA
3 YI IAD1 0.49 NA
4 YI IAD2 0.49 NA
5 YI IAD2p 0.49 NA
6 YI IAD3 0.49 NA
7 IAD1 IrradTot 0.86 NA
8 IAD1 IAD2 0.86 NA
9 IAD1 IAD2p 0.86 NA
10 IAD1 IAD3 0.86 NA
11 IAD2 IrradTot 0.99 NA
12 IAD2 IAD1 0.99 NA
13 IAD2 IAD2p 0.99 NA
14 IAD2 IAD3 0.99 NA
15 IAD2p IAD1 0.99 NA
16 IAD2p IAD2 0.99 NA
17 IAD2p IAD3 0.99 NA
18 IAD3 IrradTot 0.37 NA
19 IAD3 IAD1 0.37 NA
20 IAD3 IAD2 0.37 NA
21 IAD3 IAD2p 0.37 NA
GModel
2 YI=6.5e+01+1.3e-02*IrradTot-6.2e+01*e^IAD1+8.2e+02*IAD2-1.0e+03*IAD2p+1.1e+03*IAD3+1.9e+03*IAD2^2-2.6e+03*IAD2p^2-5.3e+04*IAD3^2
3 YI=6.5e+01+1.3e-02*IrradTot-6.2e+01*e^IAD1+8.2e+02*IAD2-1.0e+03*IAD2p+1.1e+03*IAD3+1.9e+03*IAD2^2-2.6e+03*IAD2p^2-5.3e+04*IAD3^2
4 YI=6.5e+01+1.3e-02*IrradTot-6.2e+01*e^IAD1+8.2e+02*IAD2-1.0e+03*IAD2p+1.1e+03*IAD3+1.9e+03*IAD2^2-2.6e+03*IAD2p^2-5.3e+04*IAD3^2
5 YI=6.5e+01+1.3e-02*IrradTot-6.2e+01*e^IAD1+8.2e+02*IAD2-1.0e+03*IAD2p+1.1e+03*IAD3+1.9e+03*IAD2^2-2.6e+03*IAD2p^2-5.3e+04*IAD3^2
6 YI=6.5e+01+1.3e-02*IrradTot-6.2e+01*e^IAD1+8.2e+02*IAD2-1.0e+03*IAD2p+1.1e+03*IAD3+1.9e+03*IAD2^2-2.6e+03*IAD2p^2-5.3e+04*IAD3^2
7 IAD1=-2.1e-01-3.2e-05*IrradTot+7.6e-01*exp$6.4*IAD2$-5.5e-01*exp$8.9*IAD2p$+6.3e-01*IAD3+7.8e-08*IrradTot^2
8 IAD1=-2.1e-01-3.2e-05*IrradTot+7.6e-01*exp$6.4*IAD2$-5.5e-01*exp$8.9*IAD2p$+6.3e-01*IAD3+7.8e-08*IrradTot^2
9 IAD1=-2.1e-01-3.2e-05*IrradTot+7.6e-01*exp$6.4*IAD2$-5.5e-01*exp$8.9*IAD2p$+6.3e-01*IAD3+7.8e-08*IrradTot^2
10 IAD1=-2.1e-01-3.2e-05*IrradTot+7.6e-01*exp$6.4*IAD2$-5.5e-01*exp$8.9*IAD2p$+6.3e-01*IAD3+7.8e-08*IrradTot^2
11 IAD2=-1.2e-05+9.2e-06*IrradTot+1.6e-01*IAD1+1.1e+00*IAD2p-4.4e-01*IAD3-2.6e-08*IrradTot^2-5.0e-01*IAD1^2+1.7e+01*IAD3^2
12 IAD2=-1.2e-05+9.2e-06*IrradTot+1.6e-01*IAD1+1.1e+00*IAD2p-4.4e-01*IAD3-2.6e-08*IrradTot^2-5.0e-01*IAD1^2+1.7e+01*IAD3^2
13 IAD2=-1.2e-05+9.2e-06*IrradTot+1.6e-01*IAD1+1.1e+00*IAD2p-4.4e-01*IAD3-2.6e-08*IrradTot^2-5.0e-01*IAD1^2+1.7e+01*IAD3^2
14 IAD2=-1.2e-05+9.2e-06*IrradTot+1.6e-01*IAD1+1.1e+00*IAD2p-4.4e-01*IAD3-2.6e-08*IrradTot^2-5.0e-01*IAD1^2+1.7e+01*IAD3^2
15 IAD2p=-3.3e-04-1.3e-01*IAD1+8.9e-01*IAD2+6.9e-01*IAD3+3.4e-01*IAD1^2-2.8e+01*IAD3^2
16 IAD2p=-3.3e-04-1.3e-01*IAD1+8.9e-01*IAD2+6.9e-01*IAD3+3.4e-01*IAD1^2-2.8e+01*IAD3^2
17 IAD2p=-3.3e-04-1.3e-01*IAD1+8.9e-01*IAD2+6.9e-01*IAD3+3.4e-01*IAD1^2-2.8e+01*IAD3^2
18 IAD3=-1.4e-05+2.0e-06*IrradTot+1.6e-02*IAD1-1.6e-01*IAD2+2.8e-01*IAD2p+4.1e-01*IAD2p^2
19 IAD3=-1.4e-05+2.0e-06*IrradTot+1.6e-02*IAD1-1.6e-01*IAD2+2.8e-01*IAD2p+4.1e-01*IAD2p^2
20 IAD3=-1.4e-05+2.0e-06*IrradTot+1.6e-02*IAD1-1.6e-01*IAD2+2.8e-01*IAD2p+4.1e-01*IAD2p^2
21 IAD3=-1.4e-05+2.0e-06*IrradTot+1.6e-02*IAD1-1.6e-01*IAD2+2.8e-01*IAD2p+4.1e-01*IAD2p^2
Gpvalue GR2aR2
2 0 0 0 0 0 0 0 0.003 0 0.505 0.493
3 0 0 0 0 0 0 0 0.003 0 0.505 0.493
4 0 0 0 0 0 0 0 0.003 0 0.505 0.493
5 0 0 0 0 0 0 0 0.003 0 0.505 0.493
6 0 0 0 0 0 0 0 0.003 0 0.505 0.493
7 0 0.016 0 0 0.052 0.035 0.864 0.862
8 0 0.016 0 0 0.052 0.035 0.864 0.862
9 0 0.016 0 0 0.052 0.035 0.864 0.862
10 0 0.016 0 0 0.052 0.035 0.864 0.862
11 0.912 0 0 0 0 0 0 0.003 0.993 0.993
12 0.912 0 0 0 0 0 0 0.003 0.993 0.993
13 0.912 0 0 0 0 0 0 0.003 0.993 0.993
14 0.912 0 0 0 0 0 0 0.003 0.993 0.993
15 0 0 0 0 0 0 0.99 0.99
16 0 0 0 0 0 0 0.99 0.99
17 0 0 0 0 0 0 0.99 0.99
18 0.878 0 0.072 0 0 0.005 0.383 0.374
19 0.878 0 0.072 0 0 0.005 0.383 0.374
20 0.878 0 0.072 0 0 0.005 0.383 0.374
21 0.878 0 0.072 0 0 0.005 0.383 0.374
IModel Ipvalue
2 YI=2.6+0.031*IrradTot-5e-05*IrradTot^2 0 0 0
3 YI=-26+30*e^IAD1 0.002 0
4 YI=4.4+67*IAD2+432*IAD2^2 0 0 0.017
5 YI=4.5+68*IAD2p+537*IAD2p^2 0 0.004 0.131
6 YI=4.5+710*IAD3-31084*IAD3^2 0 0 0.001
7 IAD1=0.0047+8.8e-05*IrradTot-2.8e-07*IrradTot^2 0.005 0.009 0.003
8 IAD1=-0.2+0.21*e^6.4IAD2 0 0 0
9 IAD1=-0.17+0.18*e^8.9IAD2p 0 0 0
10 IAD1=0.0068+8.5*IAD3-382*IAD3^2 0 0 0
11 IAD2=-0.00084+4.1e-05*IrradTot-1.1e-07*IrradTot^2 0.492 0.097 0.114
12 IAD2=-0.0019+0.56*IAD1-3.1*IAD1^2 0 0 0
13 IAD2=0.0013+1.3*IAD2p 0 0
14 IAD2=0.00073+9.8*IAD3-459*IAD3^2 0.134 0 0
15 IAD2p=-0.002+0.38*IAD1-2.5*IAD1^2 0 0 0
16 IAD2p=-0.001+0.76*IAD2 0 0
17 IAD2p=-0.00046+8.1*IAD3-377*IAD3^2 0.171 0 0
18 IAD3=-0.00014+2.4e-06*IrradTot 0.162 0
19 IAD3=-2.7e-05+0.025*IAD1-0.11*IAD1^2 0.74 0 0.024
20 IAD3=1.5e-05+0.066*IAD2+0.18*IAD2^2 0.822 0 0.021
21 IAD3=9.4e-05+0.098*IAD2p+0.43*IAD2p^2 0.15 0 0.004
IR2aR2 Rank
2 0.335 0.331 1
3 0.038 0.035 7
4 0.049 0.044 3
5 0.027 0.021 2
6 0.065 0.06 4
7 0.029 0.023 3
8 <NA> 1
9 <NA> 2
10 0.226 0.222 5
11 0.008 0.002 5
12 0.77 0.769 2
13 0.967 0.967 1
14 0.57 0.567 6
15 0.637 0.635 2
16 0.967 0.967 1
17 0.653 0.651 3
18 0.036 0.033 2
19 0.126 0.121 5
20 0.273 0.269 3
21 0.322 0.318 1
Gcoeff
2 6.5e+01 1.3e-02 -6.2e+01 8.2e+02 -1.0e+03 1.1e+03 1.9e+03 -2.6e+03 -5.3e+04
3 6.5e+01 1.3e-02 -6.2e+01 8.2e+02 -1.0e+03 1.1e+03 1.9e+03 -2.6e+03 -5.3e+04
4 6.5e+01 1.3e-02 -6.2e+01 8.2e+02 -1.0e+03 1.1e+03 1.9e+03 -2.6e+03 -5.3e+04
5 6.5e+01 1.3e-02 -6.2e+01 8.2e+02 -1.0e+03 1.1e+03 1.9e+03 -2.6e+03 -5.3e+04
6 6.5e+01 1.3e-02 -6.2e+01 8.2e+02 -1.0e+03 1.1e+03 1.9e+03 -2.6e+03 -5.3e+04
7 -2.1e-01 -3.2e-05 7.6e-01 -5.5e-01 6.3e-01 7.8e-08
8 -2.1e-01 -3.2e-05 7.6e-01 -5.5e-01 6.3e-01 7.8e-08
9 -2.1e-01 -3.2e-05 7.6e-01 -5.5e-01 6.3e-01 7.8e-08
10 -2.1e-01 -3.2e-05 7.6e-01 -5.5e-01 6.3e-01 7.8e-08
11 -1.2e-05 9.2e-06 1.6e-01 1.1e+00 -4.4e-01 -2.6e-08 -5.0e-01 1.7e+01
12 -1.2e-05 9.2e-06 1.6e-01 1.1e+00 -4.4e-01 -2.6e-08 -5.0e-01 1.7e+01
13 -1.2e-05 9.2e-06 1.6e-01 1.1e+00 -4.4e-01 -2.6e-08 -5.0e-01 1.7e+01
14 -1.2e-05 9.2e-06 1.6e-01 1.1e+00 -4.4e-01 -2.6e-08 -5.0e-01 1.7e+01
15 -3.3e-04 -1.3e-01 8.9e-01 6.9e-01 3.4e-01 -2.8e+01
16 -3.3e-04 -1.3e-01 8.9e-01 6.9e-01 3.4e-01 -2.8e+01
17 -3.3e-04 -1.3e-01 8.9e-01 6.9e-01 3.4e-01 -2.8e+01
18 -1.4e-05 2.0e-06 1.6e-02 -1.6e-01 2.8e-01 4.1e-01
19 -1.4e-05 2.0e-06 1.6e-02 -1.6e-01 2.8e-01 4.1e-01
20 -1.4e-05 2.0e-06 1.6e-02 -1.6e-01 2.8e-01 4.1e-01
21 -1.4e-05 2.0e-06 1.6e-02 -1.6e-01 2.8e-01 4.1e-01
GModelB
2 YI=IrradTot+e^IAD1+IAD2+IAD2p+IAD3+IAD2^2+IAD2p^2+IAD3^2
3 YI=IrradTot+e^IAD1+IAD2+IAD2p+IAD3+IAD2^2+IAD2p^2+IAD3^2
4 YI=IrradTot+e^IAD1+IAD2+IAD2p+IAD3+IAD2^2+IAD2p^2+IAD3^2
5 YI=IrradTot+e^IAD1+IAD2+IAD2p+IAD3+IAD2^2+IAD2p^2+IAD3^2
6 YI=IrradTot+e^IAD1+IAD2+IAD2p+IAD3+IAD2^2+IAD2p^2+IAD3^2
7 IAD1=IrradTot+exp$6.4*IAD2$+exp$8.9*IAD2p$+IAD3+IrradTot^2
8 IAD1=IrradTot+exp$6.4*IAD2$+exp$8.9*IAD2p$+IAD3+IrradTot^2
9 IAD1=IrradTot+exp$6.4*IAD2$+exp$8.9*IAD2p$+IAD3+IrradTot^2
10 IAD1=IrradTot+exp$6.4*IAD2$+exp$8.9*IAD2p$+IAD3+IrradTot^2
11 IAD2=IrradTot+IAD1+IAD2p+IAD3+IrradTot^2+IAD1^2+IAD3^2
12 IAD2=IrradTot+IAD1+IAD2p+IAD3+IrradTot^2+IAD1^2+IAD3^2
13 IAD2=IrradTot+IAD1+IAD2p+IAD3+IrradTot^2+IAD1^2+IAD3^2
14 IAD2=IrradTot+IAD1+IAD2p+IAD3+IrradTot^2+IAD1^2+IAD3^2
15 IAD2p=IAD1+IAD2+IAD3+IAD1^2+IAD3^2
16 IAD2p=IAD1+IAD2+IAD3+IAD1^2+IAD3^2
17 IAD2p=IAD1+IAD2+IAD3+IAD1^2+IAD3^2
18 IAD3=IrradTot+IAD1+IAD2+IAD2p+IAD2p^2
19 IAD3=IrradTot+IAD1+IAD2+IAD2p+IAD2p^2
20 IAD3=IrradTot+IAD1+IAD2+IAD2p+IAD2p^2
21 IAD3=IrradTot+IAD1+IAD2+IAD2p+IAD2p^2
The cutoff value is lower than all of the adjusted R-sqr values: Only solid lines
Working on y ~ x4
Working on y ~ x1
Working on y ~ x2
Working on y ~ x3
Working on x1 ~ x4
Working on x1 ~ x2
Working on x1 ~ x3
Working on x2 ~ x4
Working on x2 ~ x1
Working on x2 ~ x3
Working on x3 ~ x4
Working on x3 ~ x1
Working on x3 ~ x2
The cutoff value is lower than all of the adjusted R-sqr values: Only solid lines
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.