TODO: make me!
Usage
modelTest(object, ...)
is.modelTest(x)
as.modelTest(x)
# S3 method for vglm
modelTest(object, ...)
# S3 method for lm
modelTest(object, ...)Arguments
- object
A fitted model object.
- ...
Additional arguments passed to specific methods.
- x
A object (e.g., list or a modelTest object) to test or attempt coercing to a modelTest object.
Value
Depends on the method dispatch.
A list with two elements.
Results contains a data table of the actual estimates.
Table contains a nicely formatted character matrix.
A list with two elements.
Results contains a data table of the actual estimates.
Table contains a nicely formatted character matrix.
Examples
mtcars$cyl <- factor(mtcars$cyl)
m <- VGAM::vglm(cyl ~ qsec,
family = VGAM::multinomial(), data = mtcars)
modelTest(m)
#> $FixedEffects
#> Num Names Term Ref Est SE Pval LL
#> <int> <char> <char> <int> <num> <num> <num> <num>
#> 1: 1 qsec qsec 1 -0.5795781 0.4006097 0.147969796 -1.364759
#> 2: 2 qsec qsec 1 -1.2697728 0.4560391 0.005363505 -2.163593
#> 3: 2 qsec qsec 2 -0.6901947 0.4045653 0.088005136 -1.483128
#> UL K Comp Labels
#> <num> <int> <int> <char>
#> 1: 0.2056026 3 2 2 vs. 1
#> 2: -0.3759525 3 3 3 vs. 1
#> 3: 0.1027386 3 3 3 vs. 2
#>
#> $RandomEffects
#> [1] NA
#>
#> $EffectSizes
#> Term Chisq DF Pval Type
#> <char> <num> <num> <num> <char>
#> 1: qsec 14.21315 2 0.0008196964 Fixed
#>
#> $OverallModel
#> [1] NA
#>
#> attr(,"class")
#> [1] "modelTest.vglm" "modelTest"
## clean up
rm(m, mtcars)
if (FALSE) {
mtcars$cyl <- factor(mtcars$cyl)
mtcars$am <- factor(mtcars$am)
m <- VGAM::vglm(cyl ~ qsec,
family = VGAM::multinomial(), data = mtcars)
modelTest(m)
m <- VGAM::vglm(cyl ~ scale(qsec),
family = VGAM::multinomial(), data = mtcars)
modelTest(m)
m2 <- VGAM::vglm(cyl ~ factor(vs) * scale(qsec),
family = VGAM::multinomial(), data = mtcars)
modelTest(m2)
m <- VGAM::vglm(Species ~ Sepal.Length,
family = VGAM::multinomial(), data = iris)
modelTest(m)
set.seed(1234)
sampdata <- data.frame(
Outcome = factor(sample(letters[1:3], 20 * 9, TRUE)),
C1 = rnorm(20 * 9),
D3 = sample(paste0("L", 1:3), 20 * 9, TRUE))
m <- VGAM::vglm(Outcome ~ factor(D3),
family = VGAM::multinomial(), data = sampdata)
modelTest(m)
m <- VGAM::vglm(Outcome ~ factor(D3) + C1,
family = VGAM::multinomial(), data = sampdata)
modelTest(m)
}
m1 <- lm(mpg ~ qsec * hp, data = mtcars)
modelTest(m1)
#> $FixedEffects
#> Term Est LL UL Pval
#> <char> <num> <num> <num> <num>
#> 1: (Intercept) 8.52461247 -17.15039253 34.19961746 0.5020186630
#> 2: qsec 1.47683245 0.08321560 2.87044931 0.0385855703
#> 3: hp 0.23587912 0.08515568 0.38660256 0.0033562173
#> 4: qsec:hp -0.01949155 -0.02855763 -0.01042546 0.0001411028
#>
#> $RandomEffects
#> [1] NA
#>
#> $EffectSizes
#> Term N_Obs AIC BIC LL LLDF Sigma R2
#> <char> <num> <num> <num> <num> <num> <num> <num>
#> 1: qsec 0 -2.977231 -1.511495 2.488615 1 -0.1823263 0.03610200
#> 2: hp 0 -8.004306 -6.538570 5.002153 1 -0.4372429 0.07873595
#> 3: qsec:hp 0 -14.841867 -13.376131 8.420933 1 -0.8177254 0.14859655
#> F2 AdjR2 F FNumDF FDenDF P Type
#> <num> <num> <num> <num> <num> <num> <char>
#> 1: 0.1682869 0.03040174 4.712032 1 28 0.0385855703 Fixed
#> 2: 0.3670219 0.07597596 10.276613 1 28 0.0033562173 Fixed
#> 3: 0.6926720 0.15065453 19.394816 1 28 0.0001411028 Fixed
#>
#> $OverallModel
#> $Performance
#> Model N_Obs AIC BIC LL LLDF Sigma R2 F2
#> <char> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: lm 32 165.4972 172.8259 -77.74861 5 2.937243 0.7854734 3.661427
#> AdjR2 F FNumDF FDenDF P
#> <num> <num> <num> <num> <num>
#> 1: 0.7624884 34.17332 3 28 1.694676e-09
#>
#> attr(,"class")
#> [1] "modelPerformance.lm" "modelPerformance"
#>
#> attr(,"class")
#> [1] "modelTest.lm" "modelTest"
mtcars$cyl <- factor(mtcars$cyl)
m2 <- lm(mpg ~ cyl, data = mtcars)
modelTest(m2)
#> $FixedEffects
#> Term Est LL UL Pval
#> <char> <num> <num> <num> <num>
#> 1: (Intercept) 26.663636 24.67608 28.651192 2.688358e-22
#> 2: cyl6 -6.920779 -10.10796 -3.733599 1.194696e-04
#> 3: cyl8 -11.563636 -14.21962 -8.907653 8.568209e-10
#>
#> $RandomEffects
#> [1] NA
#>
#> $EffectSizes
#> Term N_Obs AIC BIC LL LLDF Sigma R2 F2
#> <char> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: cyl 0 -38.19157 -35.26009 21.09578 2 -2.803849 0.7324601 2.73776
#> AdjR2 F FNumDF FDenDF P Type
#> <num> <num> <num> <num> <num> <char>
#> 1: 0.714009 39.69752 2 29 4.978919e-09 Fixed
#>
#> $OverallModel
#> $Performance
#> Model N_Obs AIC BIC LL LLDF Sigma R2 F2
#> <char> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: lm 32 170.564 176.4269 -81.28198 4 3.223099 0.7324601 2.73776
#> AdjR2 F FNumDF FDenDF P
#> <num> <num> <num> <num> <num>
#> 1: 0.714009 39.69752 2 29 4.978919e-09
#>
#> attr(,"class")
#> [1] "modelPerformance.lm" "modelPerformance"
#>
#> attr(,"class")
#> [1] "modelTest.lm" "modelTest"
m3 <- lm(mpg ~ hp * cyl, data = mtcars)
modelTest(m3)
#> $FixedEffects
#> Term Est LL UL Pval
#> <char> <num> <num> <num> <num>
#> 1: (Intercept) 35.98302564 27.988911895 43.97713938 1.042337e-09
#> 2: hp -0.11277589 -0.206810088 -0.01874169 2.061364e-02
#> 3: cyl6 -15.30917451 -30.591135429 -0.02721358 4.962243e-02
#> 4: cyl8 -17.90295193 -28.714239963 -7.09166390 2.163657e-03
#> 5: hp:cyl6 0.10516262 -0.035606674 0.24593191 1.367182e-01
#> 6: hp:cyl8 0.09853177 -0.001415964 0.19847950 5.309562e-02
#>
#> $RandomEffects
#> [1] NA
#>
#> $EffectSizes
#> Term N_Obs AIC BIC LL LLDF Sigma R2
#> <char> <num> <num> <num> <num> <num> <num> <num>
#> 1: hp 0 -4.7216325 -3.255897 3.360816 1 -0.2724898 0.04949968
#> 2: cyl 0 -8.3406788 -5.409207 6.170339 2 -0.5104704 0.09965210
#> 3: hp:cyl 0 -0.8128535 2.118618 2.406427 2 -0.1177589 0.03437072
#> F2 AdjR2 F FNumDF FDenDF P Type
#> <num> <num> <num> <num> <num> <num> <char>
#> 1: 0.2337410 0.04748123 6.077266 1 26 0.020613638 Fixed
#> 2: 0.4705643 0.09229362 6.117336 2 26 0.006648256 Fixed
#> 3: 0.1623010 0.02001782 2.109913 2 26 0.141533090 Fixed
#>
#> $OverallModel
#> $Performance
#> Model N_Obs AIC BIC LL LLDF Sigma R2 F2
#> <char> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: lm 32 169.0836 179.3437 -77.5418 7 3.028484 0.7882285 3.722071
#> AdjR2 F FNumDF FDenDF P
#> <num> <num> <num> <num> <num>
#> 1: 0.7475032 19.35477 5 26 5.018882e-08
#>
#> attr(,"class")
#> [1] "modelPerformance.lm" "modelPerformance"
#>
#> attr(,"class")
#> [1] "modelTest.lm" "modelTest"
m4 <- lm(sqrt(mpg) ~ hp * cyl, data = mtcars)
modelTest(m4)
#> $FixedEffects
#> Term Est LL UL Pval
#> <char> <num> <num> <num> <num>
#> 1: (Intercept) 6.052499736 5.170711347 6.9342881254 1.070676e-13
#> 2: hp -0.010956546 -0.021328961 -0.0005841313 3.922162e-02
#> 3: cyl6 -1.512749623 -3.198421874 0.1729226268 7.650843e-02
#> 4: cyl8 -1.808475093 -3.001011071 -0.6159391140 4.419878e-03
#> 5: hp:cyl6 0.010146472 -0.005381043 0.0256739883 1.908190e-01
#> 6: hp:cyl8 0.009178964 -0.001845741 0.0202036696 9.891437e-02
#>
#> $RandomEffects
#> [1] NA
#>
#> $EffectSizes
#> Term N_Obs AIC BIC LL LLDF Sigma R2
#> <char> <num> <num> <num> <num> <num> <num> <num>
#> 1: hp 0 -3.3324188 -1.866683 2.666209 1 -0.022238466 0.03882660
#> 2: cyl 0 -6.5881559 -3.656684 5.294078 2 -0.045762925 0.08397830
#> 3: hp:cyl 0 0.4535321 3.385004 1.773234 2 -0.006189775 0.02509585
#> F2 AdjR2 F FNumDF FDenDF P Type
#> <num> <num> <num> <num> <num> <num> <char>
#> 1: 0.1813267 0.035123021 4.714493 1 26 0.03922162 Fixed
#> 2: 0.3921925 0.074740039 5.098503 2 26 0.01354906 Fixed
#> 3: 0.1172017 0.009548751 1.523623 2 26 0.23674952 Fixed
#>
#> $OverallModel
#> $Performance
#> Model N_Obs AIC BIC LL LLDF Sigma R2 F2
#> <char> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: lm 32 27.99504 38.25519 -6.99752 7 0.3340561 0.7858748 3.670165
#> AdjR2 F FNumDF FDenDF P
#> <num> <num> <num> <num> <num>
#> 1: 0.7446969 19.08486 5 26 5.77082e-08
#>
#> attr(,"class")
#> [1] "modelPerformance.lm" "modelPerformance"
#>
#> attr(,"class")
#> [1] "modelTest.lm" "modelTest"
m5 <- lm(mpg ~ sqrt(hp) * cyl, data = mtcars)
modelTest(m5)
#> $FixedEffects
#> Term Est LL UL Pval
#> <char> <num> <num> <num> <num>
#> 1: (Intercept) 45.320923 30.0821810 60.5596654 1.839533e-06
#> 2: sqrt(hp) -2.067923 -3.7442683 -0.3915773 1.757488e-02
#> 3: cyl6 -23.458371 -54.5954487 7.6787057 1.335616e-01
#> 4: cyl8 -23.624030 -44.7466708 -2.5013883 2.979562e-02
#> 5: sqrt(hp):cyl6 1.875526 -1.0975943 4.8486458 2.061304e-01
#> 6: sqrt(hp):cyl8 1.608904 -0.3488383 3.5666459 1.031270e-01
#>
#> $RandomEffects
#> [1] NA
#>
#> $EffectSizes
#> Term N_Obs AIC BIC LL LLDF Sigma
#> <char> <num> <num> <num> <num> <num> <num>
#> 1: sqrt(hp) 0 -5.0712752 -3.6055393 3.535638 1 -0.28847604
#> 2: cyl 0 -2.5498555 0.3816163 3.274928 2 -0.20284492
#> 3: sqrt(hp):cyl 0 0.3345454 3.2660172 1.832727 2 -0.06140898
#> R2 F2 AdjR2 F FNumDF FDenDF P Type
#> <num> <num> <num> <num> <num> <num> <num> <char>
#> 1: 0.05161694 0.2472952 0.05004665 6.429675 1 26 0.01757488 Fixed
#> 2: 0.04740919 0.2271360 0.03471263 2.952768 2 26 0.06988678 Fixed
#> 3: 0.02533174 0.1213636 0.01026974 1.577727 2 26 0.22557763 Fixed
#>
#> $OverallModel
#> $Performance
#> Model N_Obs AIC BIC LL LLDF Sigma R2 F2
#> <char> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: lm 32 168.6201 178.8802 -77.31003 7 3.00663 0.791274 3.790969
#> AdjR2 F FNumDF FDenDF P
#> <num> <num> <num> <num> <num>
#> 1: 0.7511343 19.71304 5 26 4.179396e-08
#>
#> attr(,"class")
#> [1] "modelPerformance.lm" "modelPerformance"
#>
#> attr(,"class")
#> [1] "modelTest.lm" "modelTest"
## cleanup
rm(m1, m2, m3, m4, m5, mtcars)