diff --git a/README.Rmd b/README.Rmd index a035dee..e20386c 100755 --- a/README.Rmd +++ b/README.Rmd @@ -20,7 +20,7 @@ knitr::opts_chunk$set( [![Travis build status](https://travis-ci.org/fchamroukhi/MEteorits.svg?branch=master)](https://travis-ci.org/fchamroukhi/MEteorits) -[![CRAN versions](https://www.r-pkg.org/badges/version/meteorits)](https://cran.r-project.org/web/packages/meteorits/index.html) +[![CRAN versions](https://www.r-pkg.org/badges/version/meteorits)](https://CRAN.R-project.org/package=meteorits) MEteorits is an open source toolbox (available in R and Matlab) containing diff --git a/README.md b/README.md index 638635e..a289020 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ [![Travis build status](https://travis-ci.org/fchamroukhi/MEteorits.svg?branch=master)](https://travis-ci.org/fchamroukhi/MEteorits) [![CRAN -versions](https://www.r-pkg.org/badges/version/meteorits)](https://cran.r-project.org/web/packages/meteorits/index.html) +versions](https://www.r-pkg.org/badges/version/meteorits)](https://CRAN.R-project.org/package=meteorits) MEteorits is an open source toolbox (available in R and Matlab) @@ -85,34 +85,38 @@ p <- 1 # Order of the polynomial regression (regressors/experts) q <- 1 # Order of the logistic regression (gating network) nmoe <- emNMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE) -#> EM NMoE: Iteration: 1 | log-likelihood: -838.447445867875 -#> EM NMoE: Iteration: 2 | log-likelihood: -837.37508383732 -#> EM NMoE: Iteration: 3 | log-likelihood: -835.008915590507 -#> EM NMoE: Iteration: 4 | log-likelihood: -829.943909664411 -#> EM NMoE: Iteration: 5 | log-likelihood: -819.958468854358 -#> EM NMoE: Iteration: 6 | log-likelihood: -802.818824558949 -#> EM NMoE: Iteration: 7 | log-likelihood: -779.021919164906 -#> EM NMoE: Iteration: 8 | log-likelihood: -754.538237668832 -#> EM NMoE: Iteration: 9 | log-likelihood: -736.534509726297 -#> EM NMoE: Iteration: 10 | log-likelihood: -726.212790007082 -#> EM NMoE: Iteration: 11 | log-likelihood: -720.943485298157 -#> EM NMoE: Iteration: 12 | log-likelihood: -718.420540639761 -#> EM NMoE: Iteration: 13 | log-likelihood: -717.284851785052 -#> EM NMoE: Iteration: 14 | log-likelihood: -716.787481789878 -#> EM NMoE: Iteration: 15 | log-likelihood: -716.568613503038 -#> EM NMoE: Iteration: 16 | log-likelihood: -716.469812658632 -#> EM NMoE: Iteration: 17 | log-likelihood: -716.423289855584 -#> EM NMoE: Iteration: 18 | log-likelihood: -716.400062499913 -#> EM NMoE: Iteration: 19 | log-likelihood: -716.387586176496 -#> EM NMoE: Iteration: 20 | log-likelihood: -716.380317685929 -#> EM NMoE: Iteration: 21 | log-likelihood: -716.375734832227 -#> EM NMoE: Iteration: 22 | log-likelihood: -716.372643683173 -#> EM NMoE: Iteration: 23 | log-likelihood: -716.370448852322 -#> EM NMoE: Iteration: 24 | log-likelihood: -716.368833541125 -#> EM NMoE: Iteration: 25 | log-likelihood: -716.367616262241 -#> EM NMoE: Iteration: 26 | log-likelihood: -716.366684945501 -#> EM NMoE: Iteration: 27 | log-likelihood: -716.365965564047 -#> EM NMoE: Iteration: 28 | log-likelihood: -716.365406507273 +#> EM NMoE: Iteration: 1 | log-likelihood: -850.659719240158 +#> EM NMoE: Iteration: 2 | log-likelihood: -850.524629010475 +#> EM NMoE: Iteration: 3 | log-likelihood: -850.430788051698 +#> EM NMoE: Iteration: 4 | log-likelihood: -850.283793706938 +#> EM NMoE: Iteration: 5 | log-likelihood: -849.97811162098 +#> EM NMoE: Iteration: 6 | log-likelihood: -849.309846170774 +#> EM NMoE: Iteration: 7 | log-likelihood: -847.853073877546 +#> EM NMoE: Iteration: 8 | log-likelihood: -844.760254765814 +#> EM NMoE: Iteration: 9 | log-likelihood: -838.538908952736 +#> EM NMoE: Iteration: 10 | log-likelihood: -827.124841419721 +#> EM NMoE: Iteration: 11 | log-likelihood: -809.002195790739 +#> EM NMoE: Iteration: 12 | log-likelihood: -786.082845509062 +#> EM NMoE: Iteration: 13 | log-likelihood: -765.697860048611 +#> EM NMoE: Iteration: 14 | log-likelihood: -753.84437315637 +#> EM NMoE: Iteration: 15 | log-likelihood: -748.545284749922 +#> EM NMoE: Iteration: 16 | log-likelihood: -746.181369709665 +#> EM NMoE: Iteration: 17 | log-likelihood: -745.062227019926 +#> EM NMoE: Iteration: 18 | log-likelihood: -744.517209155278 +#> EM NMoE: Iteration: 19 | log-likelihood: -744.248035626126 +#> EM NMoE: Iteration: 20 | log-likelihood: -744.113273238347 +#> EM NMoE: Iteration: 21 | log-likelihood: -744.04458797388 +#> EM NMoE: Iteration: 22 | log-likelihood: -744.008709857418 +#> EM NMoE: Iteration: 23 | log-likelihood: -743.989337491229 +#> EM NMoE: Iteration: 24 | log-likelihood: -743.978422442498 +#> EM NMoE: Iteration: 25 | log-likelihood: -743.971951246252 +#> EM NMoE: Iteration: 26 | log-likelihood: -743.967895060795 +#> EM NMoE: Iteration: 27 | log-likelihood: -743.965208755974 +#> EM NMoE: Iteration: 28 | log-likelihood: -743.963339864259 +#> EM NMoE: Iteration: 29 | log-likelihood: -743.961986174011 +#> EM NMoE: Iteration: 30 | log-likelihood: -743.960975097926 +#> EM NMoE: Iteration: 31 | log-likelihood: -743.960202991077 +#> EM NMoE: Iteration: 32 | log-likelihood: -743.959604173327 nmoe$summary() #> ------------------------------------------ @@ -121,24 +125,24 @@ nmoe$summary() #> #> NMoE model with K = 2 experts: #> -#> log-likelihood df AIC BIC ICL -#> -716.3654 8 -724.3654 -741.2238 -791.7005 +#> log-likelihood df AIC BIC ICL +#> -743.9596 8 -751.9596 -768.818 -827.3815 #> #> Clustering table (Number of observations in each expert): #> #> 1 2 -#> 246 254 +#> 292 208 #> #> Regression coefficients: #> #> Beta(k = 1) Beta(k = 2) -#> 1 -0.1606128 0.2682631 -#> X^1 2.1096154 -3.0300456 +#> 1 0.01265767 -0.1734812 +#> X^1 2.26644322 -2.4105137 #> #> Variances: #> #> Sigma2(k = 1) Sigma2(k = 2) -#> 0.8940933 0.9745745 +#> 1.103732 0.8591557 nmoe$plot() ``` @@ -157,69 +161,61 @@ p <- 1 # Order of the polynomial regression (regressors/experts) q <- 1 # Order of the logistic regression (gating network) nmoe <- emNMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE) -#> EM NMoE: Iteration: 1 | log-likelihood: 48.5243125778593 -#> EM NMoE: Iteration: 2 | log-likelihood: 48.6364284693695 -#> EM NMoE: Iteration: 3 | log-likelihood: 48.6616321314588 -#> EM NMoE: Iteration: 4 | log-likelihood: 48.6773675575729 -#> EM NMoE: Iteration: 5 | log-likelihood: 48.7053927789753 -#> EM NMoE: Iteration: 6 | log-likelihood: 48.7766445918878 -#> EM NMoE: Iteration: 7 | log-likelihood: 48.979273648811 -#> EM NMoE: Iteration: 8 | log-likelihood: 49.5734534492088 -#> EM NMoE: Iteration: 9 | log-likelihood: 51.2630597928685 -#> EM NMoE: Iteration: 10 | log-likelihood: 55.4549828103761 -#> EM NMoE: Iteration: 11 | log-likelihood: 62.7516702581651 -#> EM NMoE: Iteration: 12 | log-likelihood: 69.5411819493645 -#> EM NMoE: Iteration: 13 | log-likelihood: 73.0749147960322 -#> EM NMoE: Iteration: 14 | log-likelihood: 74.837792021492 -#> EM NMoE: Iteration: 15 | log-likelihood: 76.2129545969553 -#> EM NMoE: Iteration: 16 | log-likelihood: 77.6343116555625 -#> EM NMoE: Iteration: 17 | log-likelihood: 79.2632002850807 -#> EM NMoE: Iteration: 18 | log-likelihood: 81.2952991145554 -#> EM NMoE: Iteration: 19 | log-likelihood: 84.0745195970881 -#> EM NMoE: Iteration: 20 | log-likelihood: 88.0339783350761 -#> EM NMoE: Iteration: 21 | log-likelihood: 92.5687702928963 -#> EM NMoE: Iteration: 22 | log-likelihood: 95.1575440541629 -#> EM NMoE: Iteration: 23 | log-likelihood: 95.9544677788821 -#> EM NMoE: Iteration: 24 | log-likelihood: 96.2167807441089 -#> EM NMoE: Iteration: 25 | log-likelihood: 96.3362330013093 -#> EM NMoE: Iteration: 26 | log-likelihood: 96.4162095777963 -#> EM NMoE: Iteration: 27 | log-likelihood: 96.487298270972 -#> EM NMoE: Iteration: 28 | log-likelihood: 96.5599476764925 -#> EM NMoE: Iteration: 29 | log-likelihood: 96.6385230689988 -#> EM NMoE: Iteration: 30 | log-likelihood: 96.7252408349824 -#> EM NMoE: Iteration: 31 | log-likelihood: 96.8212095715313 -#> EM NMoE: Iteration: 32 | log-likelihood: 96.9265765099347 -#> EM NMoE: Iteration: 33 | log-likelihood: 97.0404258022096 -#> EM NMoE: Iteration: 34 | log-likelihood: 97.1607190162368 -#> EM NMoE: Iteration: 35 | log-likelihood: 97.284450499809 -#> EM NMoE: Iteration: 36 | log-likelihood: 97.4081040246956 -#> EM NMoE: Iteration: 37 | log-likelihood: 97.5283679903711 -#> EM NMoE: Iteration: 38 | log-likelihood: 97.6429650181185 -#> EM NMoE: Iteration: 39 | log-likelihood: 97.7513229032921 -#> EM NMoE: Iteration: 40 | log-likelihood: 97.8548491584134 -#> EM NMoE: Iteration: 41 | log-likelihood: 97.9566464084193 -#> EM NMoE: Iteration: 42 | log-likelihood: 98.0607458023894 -#> EM NMoE: Iteration: 43 | log-likelihood: 98.1710900362685 -#> EM NMoE: Iteration: 44 | log-likelihood: 98.290611490955 -#> EM NMoE: Iteration: 45 | log-likelihood: 98.4207438657233 -#> EM NMoE: Iteration: 46 | log-likelihood: 98.5615661926316 -#> EM NMoE: Iteration: 47 | log-likelihood: 98.7125004516639 -#> EM NMoE: Iteration: 48 | log-likelihood: 98.8731603656299 -#> EM NMoE: Iteration: 49 | log-likelihood: 99.0440269125487 -#> EM NMoE: Iteration: 50 | log-likelihood: 99.2267748343605 -#> EM NMoE: Iteration: 51 | log-likelihood: 99.424428011894 -#> EM NMoE: Iteration: 52 | log-likelihood: 99.6416387004919 -#> EM NMoE: Iteration: 53 | log-likelihood: 99.8853574869513 -#> EM NMoE: Iteration: 54 | log-likelihood: 100.166052835318 -#> EM NMoE: Iteration: 55 | log-likelihood: 100.499426246299 -#> EM NMoE: Iteration: 56 | log-likelihood: 100.907332702804 -#> EM NMoE: Iteration: 57 | log-likelihood: 101.40983714996 -#> EM NMoE: Iteration: 58 | log-likelihood: 101.979036782566 -#> EM NMoE: Iteration: 59 | log-likelihood: 102.45723924927 -#> EM NMoE: Iteration: 60 | log-likelihood: 102.68727086043 -#> EM NMoE: Iteration: 61 | log-likelihood: 102.715880882474 -#> EM NMoE: Iteration: 62 | log-likelihood: 102.721454317169 -#> EM NMoE: Iteration: 63 | log-likelihood: 102.721454641665 +#> EM NMoE: Iteration: 1 | log-likelihood: 48.3988726040827 +#> EM NMoE: Iteration: 2 | log-likelihood: 48.9326207295142 +#> EM NMoE: Iteration: 3 | log-likelihood: 50.051039377426 +#> EM NMoE: Iteration: 4 | log-likelihood: 52.9250961462781 +#> EM NMoE: Iteration: 5 | log-likelihood: 59.1669854674966 +#> EM NMoE: Iteration: 6 | log-likelihood: 67.5520185593279 +#> EM NMoE: Iteration: 7 | log-likelihood: 73.0997722565129 +#> EM NMoE: Iteration: 8 | log-likelihood: 75.5728843281524 +#> EM NMoE: Iteration: 9 | log-likelihood: 77.1804335125676 +#> EM NMoE: Iteration: 10 | log-likelihood: 78.8228583260898 +#> EM NMoE: Iteration: 11 | log-likelihood: 80.7994256495649 +#> EM NMoE: Iteration: 12 | log-likelihood: 83.4327216902578 +#> EM NMoE: Iteration: 13 | log-likelihood: 87.167207159755 +#> EM NMoE: Iteration: 14 | log-likelihood: 91.7548816275664 +#> EM NMoE: Iteration: 15 | log-likelihood: 94.8386054468416 +#> EM NMoE: Iteration: 16 | log-likelihood: 95.8702965168198 +#> EM NMoE: Iteration: 17 | log-likelihood: 96.201217475001 +#> EM NMoE: Iteration: 18 | log-likelihood: 96.3427273583883 +#> EM NMoE: Iteration: 19 | log-likelihood: 96.4312445403178 +#> EM NMoE: Iteration: 20 | log-likelihood: 96.5068035716238 +#> EM NMoE: Iteration: 21 | log-likelihood: 96.5827848006443 +#> EM NMoE: Iteration: 22 | log-likelihood: 96.664497621724 +#> EM NMoE: Iteration: 23 | log-likelihood: 96.7544065779447 +#> EM NMoE: Iteration: 24 | log-likelihood: 96.8535649805854 +#> EM NMoE: Iteration: 25 | log-likelihood: 96.9618980067147 +#> EM NMoE: Iteration: 26 | log-likelihood: 97.0781807281132 +#> EM NMoE: Iteration: 27 | log-likelihood: 97.2000668915646 +#> EM NMoE: Iteration: 28 | log-likelihood: 97.3243471857001 +#> EM NMoE: Iteration: 29 | log-likelihood: 97.4475005220902 +#> EM NMoE: Iteration: 30 | log-likelihood: 97.566473896656 +#> EM NMoE: Iteration: 31 | log-likelihood: 97.6794841146006 +#> EM NMoE: Iteration: 32 | log-likelihood: 97.7865826549208 +#> EM NMoE: Iteration: 33 | log-likelihood: 97.8897593890552 +#> EM NMoE: Iteration: 34 | log-likelihood: 97.9924846700633 +#> EM NMoE: Iteration: 35 | log-likelihood: 98.0988320818964 +#> EM NMoE: Iteration: 36 | log-likelihood: 98.2124589670307 +#> EM NMoE: Iteration: 37 | log-likelihood: 98.3358032691223 +#> EM NMoE: Iteration: 38 | log-likelihood: 98.4698046243747 +#> EM NMoE: Iteration: 39 | log-likelihood: 98.6142554980094 +#> EM NMoE: Iteration: 40 | log-likelihood: 98.7685998935106 +#> EM NMoE: Iteration: 41 | log-likelihood: 98.9327260646186 +#> EM NMoE: Iteration: 42 | log-likelihood: 99.1075255399307 +#> EM NMoE: Iteration: 43 | log-likelihood: 99.2951330061669 +#> EM NMoE: Iteration: 44 | log-likelihood: 99.4990978545361 +#> EM NMoE: Iteration: 45 | log-likelihood: 99.724781385219 +#> EM NMoE: Iteration: 46 | log-likelihood: 99.9802114334364 +#> EM NMoE: Iteration: 47 | log-likelihood: 100.277506353508 +#> EM NMoE: Iteration: 48 | log-likelihood: 100.634603770888 +#> EM NMoE: Iteration: 49 | log-likelihood: 101.074685777405 +#> EM NMoE: Iteration: 50 | log-likelihood: 101.609342261681 +#> EM NMoE: Iteration: 51 | log-likelihood: 102.167518045425 +#> EM NMoE: Iteration: 52 | log-likelihood: 102.591482251134 +#> EM NMoE: Iteration: 53 | log-likelihood: 102.692086561759 +#> EM NMoE: Iteration: 54 | log-likelihood: 102.721983731666 +#> EM NMoE: Iteration: 55 | log-likelihood: 102.721991417921 nmoe$summary() #> ------------------------------------------ @@ -229,23 +225,23 @@ nmoe$summary() #> NMoE model with K = 2 experts: #> #> log-likelihood df AIC BIC ICL -#> 102.7215 8 94.72145 83.07084 83.17754 +#> 102.722 8 94.72199 83.07137 83.17998 #> #> Clustering table (Number of observations in each expert): #> #> 1 2 -#> 84 52 +#> 52 84 #> #> Regression coefficients: #> -#> Beta(k = 1) Beta(k = 2) -#> 1 -12.667329479 -42.36183259 -#> X^1 0.006474827 0.02149254 +#> Beta(k = 1) Beta(k = 2) +#> 1 -42.36252836 -12.667270814 +#> X^1 0.02149289 0.006474796 #> #> Variances: #> #> Sigma2(k = 1) Sigma2(k = 2) -#> 0.01352345 0.01193119 +#> 0.01193084 0.01352335 nmoe$plot() ``` @@ -278,49 +274,36 @@ p <- 1 # Order of the polynomial regression (regressors/experts) q <- 1 # Order of the logistic regression (gating network) tmoe <- emTMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE) -#> EM - tMoE: Iteration: 1 | log-likelihood: -716.644178973591 -#> EM - tMoE: Iteration: 2 | log-likelihood: -697.818351225002 -#> EM - tMoE: Iteration: 3 | log-likelihood: -687.824933585738 -#> EM - tMoE: Iteration: 4 | log-likelihood: -681.4257226434 -#> EM - tMoE: Iteration: 5 | log-likelihood: -677.118395646965 -#> EM - tMoE: Iteration: 6 | log-likelihood: -674.120244375149 -#> EM - tMoE: Iteration: 7 | log-likelihood: -671.980986801128 -#> EM - tMoE: Iteration: 8 | log-likelihood: -670.42469452345 -#> EM - tMoE: Iteration: 9 | log-likelihood: -669.274679785116 -#> EM - tMoE: Iteration: 10 | log-likelihood: -668.413720185928 -#> EM - tMoE: Iteration: 11 | log-likelihood: -667.762090603242 -#> EM - tMoE: Iteration: 12 | log-likelihood: -667.264312597848 -#> EM - tMoE: Iteration: 13 | log-likelihood: -666.88104924334 -#> EM - tMoE: Iteration: 14 | log-likelihood: -666.583952172541 -#> EM - tMoE: Iteration: 15 | log-likelihood: -666.352300872008 -#> EM - tMoE: Iteration: 16 | log-likelihood: -666.170761347961 -#> EM - tMoE: Iteration: 17 | log-likelihood: -666.027761772033 -#> EM - tMoE: Iteration: 18 | log-likelihood: -665.914885646052 -#> EM - tMoE: Iteration: 19 | log-likelihood: -665.825372738855 -#> EM - tMoE: Iteration: 20 | log-likelihood: -665.75415676862 -#> EM - tMoE: Iteration: 21 | log-likelihood: -665.697332523945 -#> EM - tMoE: Iteration: 22 | log-likelihood: -665.651819830029 -#> EM - tMoE: Iteration: 23 | log-likelihood: -665.615383512261 -#> EM - tMoE: Iteration: 24 | log-likelihood: -665.5861081626 -#> EM - tMoE: Iteration: 25 | log-likelihood: -665.562514318331 -#> EM - tMoE: Iteration: 26 | log-likelihood: -665.543514176513 -#> EM - tMoE: Iteration: 27 | log-likelihood: -665.528156065136 -#> EM - tMoE: Iteration: 28 | log-likelihood: -665.515748043735 -#> EM - tMoE: Iteration: 29 | log-likelihood: -665.505701334283 -#> EM - tMoE: Iteration: 30 | log-likelihood: -665.497557314352 -#> EM - tMoE: Iteration: 31 | log-likelihood: -665.490941463575 -#> EM - tMoE: Iteration: 32 | log-likelihood: -665.485574162205 -#> EM - tMoE: Iteration: 33 | log-likelihood: -665.481211208667 -#> EM - tMoE: Iteration: 34 | log-likelihood: -665.477661961738 -#> EM - tMoE: Iteration: 35 | log-likelihood: -665.47477266997 -#> EM - tMoE: Iteration: 36 | log-likelihood: -665.472419151465 -#> EM - tMoE: Iteration: 37 | log-likelihood: -665.470497127234 -#> EM - tMoE: Iteration: 38 | log-likelihood: -665.468932993849 -#> EM - tMoE: Iteration: 39 | log-likelihood: -665.467656958995 -#> EM - tMoE: Iteration: 40 | log-likelihood: -665.466615520844 -#> EM - tMoE: Iteration: 41 | log-likelihood: -665.465765226306 -#> EM - tMoE: Iteration: 42 | log-likelihood: -665.465070754519 -#> EM - tMoE: Iteration: 43 | log-likelihood: -665.464503373372 +#> EM - tMoE: Iteration: 1 | log-likelihood: -552.125213974242 +#> EM - tMoE: Iteration: 2 | log-likelihood: -547.987183857056 +#> EM - tMoE: Iteration: 3 | log-likelihood: -546.40733469181 +#> EM - tMoE: Iteration: 4 | log-likelihood: -544.898386695277 +#> EM - tMoE: Iteration: 5 | log-likelihood: -543.502686575021 +#> EM - tMoE: Iteration: 6 | log-likelihood: -542.283105674398 +#> EM - tMoE: Iteration: 7 | log-likelihood: -541.266467232123 +#> EM - tMoE: Iteration: 8 | log-likelihood: -540.450661063362 +#> EM - tMoE: Iteration: 9 | log-likelihood: -539.815711994686 +#> EM - tMoE: Iteration: 10 | log-likelihood: -539.333458769544 +#> EM - tMoE: Iteration: 11 | log-likelihood: -538.974215771526 +#> EM - tMoE: Iteration: 12 | log-likelihood: -538.710672092328 +#> EM - tMoE: Iteration: 13 | log-likelihood: -538.519646653311 +#> EM - tMoE: Iteration: 14 | log-likelihood: -538.38248504553 +#> EM - tMoE: Iteration: 15 | log-likelihood: -538.284724625379 +#> EM - tMoE: Iteration: 16 | log-likelihood: -538.215449987784 +#> EM - tMoE: Iteration: 17 | log-likelihood: -538.166584335222 +#> EM - tMoE: Iteration: 18 | log-likelihood: -538.132238929576 +#> EM - tMoE: Iteration: 19 | log-likelihood: -538.108167974741 +#> EM - tMoE: Iteration: 20 | log-likelihood: -538.09133618607 +#> EM - tMoE: Iteration: 21 | log-likelihood: -538.07958783267 +#> EM - tMoE: Iteration: 22 | log-likelihood: -538.071399628517 +#> EM - tMoE: Iteration: 23 | log-likelihood: -538.065699459315 +#> EM - tMoE: Iteration: 24 | log-likelihood: -538.061735113966 +#> EM - tMoE: Iteration: 25 | log-likelihood: -538.058980140461 +#> EM - tMoE: Iteration: 26 | log-likelihood: -538.05706681974 +#> EM - tMoE: Iteration: 27 | log-likelihood: -538.055738714103 +#> EM - tMoE: Iteration: 28 | log-likelihood: -538.054817220152 +#> EM - tMoE: Iteration: 29 | log-likelihood: -538.054178073834 +#> EM - tMoE: Iteration: 30 | log-likelihood: -538.053734891082 tmoe$summary() #> ------------------------------------- @@ -330,7 +313,7 @@ tmoe$summary() #> tMoE model with K = 2 experts: #> #> log-likelihood df AIC BIC ICL -#> -665.4645 10 -675.4645 -696.5375 -696.5316 +#> -538.0537 10 -548.0537 -569.1268 -569.1248 #> #> Clustering table (Number of observations in each expert): #> @@ -340,13 +323,13 @@ tmoe$summary() #> Regression coefficients: #> #> Beta(k = 1) Beta(k = 2) -#> 1 0.08405958 0.03574341 -#> X^1 2.56854853 -2.46216566 +#> 1 0.1725939 -0.08414846 +#> X^1 2.7387008 -2.33997997 #> #> Variances: #> #> Sigma2(k = 1) Sigma2(k = 2) -#> 0.3172393 0.04147353 +#> 0.2727009 0.4847398 tmoe$plot() ``` @@ -366,40 +349,43 @@ p <- 2 # Order of the polynomial regression (regressors/experts) q <- 1 # Order of the logistic regression (gating network) tmoe <- emTMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE) -#> EM - tMoE: Iteration: 1 | log-likelihood: -585.222191741282 -#> EM - tMoE: Iteration: 2 | log-likelihood: -580.714594960129 -#> EM - tMoE: Iteration: 3 | log-likelihood: -578.941952744992 -#> EM - tMoE: Iteration: 4 | log-likelihood: -576.278694880256 -#> EM - tMoE: Iteration: 5 | log-likelihood: -569.71678286666 -#> EM - tMoE: Iteration: 6 | log-likelihood: -562.247850043825 -#> EM - tMoE: Iteration: 7 | log-likelihood: -558.350949524939 -#> EM - tMoE: Iteration: 8 | log-likelihood: -557.202669120524 -#> EM - tMoE: Iteration: 9 | log-likelihood: -556.418031795557 -#> EM - tMoE: Iteration: 10 | log-likelihood: -555.57930505681 -#> EM - tMoE: Iteration: 11 | log-likelihood: -554.66788465451 -#> EM - tMoE: Iteration: 12 | log-likelihood: -553.688789078615 -#> EM - tMoE: Iteration: 13 | log-likelihood: -552.708369658539 -#> EM - tMoE: Iteration: 14 | log-likelihood: -551.889328179308 -#> EM - tMoE: Iteration: 15 | log-likelihood: -551.313179959412 -#> EM - tMoE: Iteration: 16 | log-likelihood: -550.923396387858 -#> EM - tMoE: Iteration: 17 | log-likelihood: -550.661283189432 -#> EM - tMoE: Iteration: 18 | log-likelihood: -550.488241921989 -#> EM - tMoE: Iteration: 19 | log-likelihood: -550.375736326336 -#> EM - tMoE: Iteration: 20 | log-likelihood: -550.303080492558 -#> EM - tMoE: Iteration: 21 | log-likelihood: -550.256179815335 -#> EM - tMoE: Iteration: 22 | log-likelihood: -550.225765799227 -#> EM - tMoE: Iteration: 23 | log-likelihood: -550.205900021734 -#> EM - tMoE: Iteration: 24 | log-likelihood: -550.192803094992 -#> EM - tMoE: Iteration: 25 | log-likelihood: -550.184072825929 -#> EM - tMoE: Iteration: 26 | log-likelihood: -550.178178304445 -#> EM - tMoE: Iteration: 27 | log-likelihood: -550.174139357111 -#> EM - tMoE: Iteration: 28 | log-likelihood: -550.171324909373 -#> EM - tMoE: Iteration: 29 | log-likelihood: -550.16932617621 -#> EM - tMoE: Iteration: 30 | log-likelihood: -550.167876656148 -#> EM - tMoE: Iteration: 31 | log-likelihood: -550.166801414743 -#> EM - tMoE: Iteration: 32 | log-likelihood: -550.165984766164 -#> EM - tMoE: Iteration: 33 | log-likelihood: -550.165349570913 -#> EM - tMoE: Iteration: 34 | log-likelihood: -550.1648439081 +#> EM - tMoE: Iteration: 1 | log-likelihood: -605.266571357791 +#> EM - tMoE: Iteration: 2 | log-likelihood: -599.044701698548 +#> EM - tMoE: Iteration: 3 | log-likelihood: -595.501279714269 +#> EM - tMoE: Iteration: 4 | log-likelihood: -593.009530361222 +#> EM - tMoE: Iteration: 5 | log-likelihood: -590.714969153092 +#> EM - tMoE: Iteration: 6 | log-likelihood: -587.897449166264 +#> EM - tMoE: Iteration: 7 | log-likelihood: -583.582012360803 +#> EM - tMoE: Iteration: 8 | log-likelihood: -578.122132426342 +#> EM - tMoE: Iteration: 9 | log-likelihood: -573.081475929554 +#> EM - tMoE: Iteration: 10 | log-likelihood: -570.74014908355 +#> EM - tMoE: Iteration: 11 | log-likelihood: -569.7657737772 +#> EM - tMoE: Iteration: 12 | log-likelihood: -568.885074316649 +#> EM - tMoE: Iteration: 13 | log-likelihood: -568.011955227929 +#> EM - tMoE: Iteration: 14 | log-likelihood: -567.159312820848 +#> EM - tMoE: Iteration: 15 | log-likelihood: -566.350991948378 +#> EM - tMoE: Iteration: 16 | log-likelihood: -565.616862268021 +#> EM - tMoE: Iteration: 17 | log-likelihood: -564.990448386782 +#> EM - tMoE: Iteration: 18 | log-likelihood: -564.496384022067 +#> EM - tMoE: Iteration: 19 | log-likelihood: -564.13571445338 +#> EM - tMoE: Iteration: 20 | log-likelihood: -563.887578265863 +#> EM - tMoE: Iteration: 21 | log-likelihood: -563.72301337972 +#> EM - tMoE: Iteration: 22 | log-likelihood: -563.61586828125 +#> EM - tMoE: Iteration: 23 | log-likelihood: -563.546554999698 +#> EM - tMoE: Iteration: 24 | log-likelihood: -563.501679965445 +#> EM - tMoE: Iteration: 25 | log-likelihood: -563.472480239373 +#> EM - tMoE: Iteration: 26 | log-likelihood: -563.453334332534 +#> EM - tMoE: Iteration: 27 | log-likelihood: -563.440660583559 +#> EM - tMoE: Iteration: 28 | log-likelihood: -563.43217720637 +#> EM - tMoE: Iteration: 29 | log-likelihood: -563.426425658754 +#> EM - tMoE: Iteration: 30 | log-likelihood: -563.422468915477 +#> EM - tMoE: Iteration: 31 | log-likelihood: -563.41970146878 +#> EM - tMoE: Iteration: 32 | log-likelihood: -563.417729585165 +#> EM - tMoE: Iteration: 33 | log-likelihood: -563.416295552506 +#> EM - tMoE: Iteration: 34 | log-likelihood: -563.415229512982 +#> EM - tMoE: Iteration: 35 | log-likelihood: -563.414418669214 +#> EM - tMoE: Iteration: 36 | log-likelihood: -563.413787491396 +#> EM - tMoE: Iteration: 37 | log-likelihood: -563.413284930069 tmoe$summary() #> ------------------------------------- @@ -409,24 +395,24 @@ tmoe$summary() #> tMoE model with K = 4 experts: #> #> log-likelihood df AIC BIC ICL -#> -550.1648 26 -576.1648 -613.7394 -613.7359 +#> -563.4133 26 -589.4133 -626.9878 -626.9753 #> #> Clustering table (Number of observations in each expert): #> #> 1 2 3 4 -#> 28 37 31 37 +#> 28 36 32 37 #> #> Regression coefficients: #> #> Beta(k = 1) Beta(k = 2) Beta(k = 3) Beta(k = 4) -#> 1 -1.038103332 1097.88043 -1816.583171 335.9625237 -#> X^1 -0.111460165 -115.59851 111.938322 -14.0024442 -#> X^2 -0.007713605 2.74875 -1.678539 0.1439691 +#> 1 -1.037712416 1774.38349 -1434.398457 292.6068438 +#> X^1 -0.111685768 -189.85966 84.930824 -12.1664690 +#> X^2 -0.007693142 4.74843 -1.205771 0.1248612 #> #> Variances: #> #> Sigma2(k = 1) Sigma2(k = 2) Sigma2(k = 3) Sigma2(k = 4) -#> 1.586136 379.9208 560.9809 331.5414 +#> 1.585304 30.88009 588.3835 572.0153 tmoe$plot() ``` @@ -460,232 +446,116 @@ p <- 1 # Order of the polynomial regression (regressors/experts) q <- 1 # Order of the logistic regression (gating network) snmoe <- emSNMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE) -#> EM - SNMoE: Iteration: 1 | log-likelihood: -496.091531688357 -#> EM - SNMoE: Iteration: 2 | log-likelihood: -492.042904737495 -#> EM - SNMoE: Iteration: 3 | log-likelihood: -492.000411302195 -#> EM - SNMoE: Iteration: 4 | log-likelihood: -491.988908917768 -#> EM - SNMoE: Iteration: 5 | log-likelihood: -491.984073920568 -#> EM - SNMoE: Iteration: 6 | log-likelihood: -491.980747916387 -#> EM - SNMoE: Iteration: 7 | log-likelihood: -491.977808459843 -#> EM - SNMoE: Iteration: 8 | log-likelihood: -491.975048213171 -#> EM - SNMoE: Iteration: 9 | log-likelihood: -491.97241849584 -#> EM - SNMoE: Iteration: 10 | log-likelihood: -491.969912463406 -#> EM - SNMoE: Iteration: 11 | log-likelihood: -491.9675333639 -#> EM - SNMoE: Iteration: 12 | log-likelihood: -491.965270559466 -#> EM - SNMoE: Iteration: 13 | log-likelihood: -491.963107973331 -#> EM - SNMoE: Iteration: 14 | log-likelihood: -491.961040157006 -#> EM - SNMoE: Iteration: 15 | log-likelihood: -491.959066081795 -#> EM - SNMoE: Iteration: 16 | log-likelihood: -491.957175204774 -#> EM - SNMoE: Iteration: 17 | log-likelihood: -491.955354446886 -#> EM - SNMoE: Iteration: 18 | log-likelihood: -491.953587316706 -#> EM - SNMoE: Iteration: 19 | log-likelihood: -491.951878310094 -#> EM - SNMoE: Iteration: 20 | log-likelihood: -491.950225391248 -#> EM - SNMoE: Iteration: 21 | log-likelihood: -491.948630793558 -#> EM - SNMoE: Iteration: 22 | log-likelihood: -491.947078457867 -#> EM - SNMoE: Iteration: 23 | log-likelihood: -491.945556473792 -#> EM - SNMoE: Iteration: 24 | log-likelihood: -491.944059202704 -#> EM - SNMoE: Iteration: 25 | log-likelihood: -491.942586570455 -#> EM - SNMoE: Iteration: 26 | log-likelihood: -491.941145612824 -#> EM - SNMoE: Iteration: 27 | log-likelihood: -491.939711770964 -#> EM - SNMoE: Iteration: 28 | log-likelihood: -491.938285845065 -#> EM - SNMoE: Iteration: 29 | log-likelihood: -491.936860963434 -#> EM - SNMoE: Iteration: 30 | log-likelihood: -491.93543818584 -#> EM - SNMoE: Iteration: 31 | log-likelihood: -491.93401434269 -#> EM - SNMoE: Iteration: 32 | log-likelihood: -491.9325869148 -#> EM - SNMoE: Iteration: 33 | log-likelihood: -491.931152291809 -#> EM - SNMoE: Iteration: 34 | log-likelihood: -491.929702129278 -#> EM - SNMoE: Iteration: 35 | log-likelihood: -491.92823525641 -#> EM - SNMoE: Iteration: 36 | log-likelihood: 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-498.267901191242 snmoe$summary() #> ----------------------------------------------- @@ -694,24 +564,24 @@ snmoe$summary() #> #> SNMoE model with K = 2 experts: #> -#> log-likelihood df AIC BIC ICL -#> -491.54 10 -501.54 -522.613 -522.6593 +#> log-likelihood df AIC BIC ICL +#> -498.2679 10 -508.2679 -529.3409 -529.3804 #> #> Clustering table (Number of observations in each expert): #> #> 1 2 -#> 253 247 +#> 249 251 #> #> Regression coefficients: #> #> Beta(k = 1) Beta(k = 2) -#> 1 0.8362846 0.8901115 -#> X^1 2.5401126 -2.6757700 +#> 1 0.9709634 1.021977 +#> X^1 2.6703213 -2.736127 #> #> Variances: #> #> Sigma2(k = 1) Sigma2(k = 2) -#> 0.3704868 0.4742813 +#> 0.4324076 0.4345685 snmoe$plot() ``` @@ -730,44 +600,193 @@ p <- 1 # Order of the polynomial regression (regressors/experts) q <- 1 # Order of the logistic regression (gating network) snmoe <- emSNMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE) -#> EM - SNMoE: Iteration: 1 | log-likelihood: 84.8852795716429 -#> EM - SNMoE: 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log-likelihood: 90.8185290095565 +#> EM - SNMoE: Iteration: 163 | log-likelihood: 90.818718303285 +#> EM - SNMoE: Iteration: 164 | log-likelihood: 90.8189892103138 +#> EM - SNMoE: Iteration: 165 | log-likelihood: 90.8192039239698 +#> EM - SNMoE: Iteration: 166 | log-likelihood: 90.8193641478361 +#> EM - SNMoE: Iteration: 167 | log-likelihood: 90.8196070442012 +#> EM - SNMoE: Iteration: 168 | log-likelihood: 90.8197949331449 +#> EM - SNMoE: Iteration: 169 | log-likelihood: 90.81997483171 +#> EM - SNMoE: Iteration: 170 | log-likelihood: 90.8201473016286 +#> EM - SNMoE: Iteration: 171 | log-likelihood: 90.8203136768737 +#> EM - SNMoE: Iteration: 172 | log-likelihood: 90.8204744695155 +#> EM - SNMoE: Iteration: 173 | log-likelihood: 90.820628259348 +#> EM - SNMoE: Iteration: 174 | log-likelihood: 90.8207754470074 +#> EM - SNMoE: Iteration: 175 | log-likelihood: 90.8209167839697 +#> EM - SNMoE: Iteration: 176 | log-likelihood: 90.8210525848093 +#> EM - SNMoE: Iteration: 177 | log-likelihood: 90.8211826587277 +#> EM - SNMoE: Iteration: 178 | log-likelihood: 90.8213073953412 +#> EM - SNMoE: Iteration: 179 | log-likelihood: 90.8214281260729 +#> EM - SNMoE: Iteration: 180 | log-likelihood: 90.8215444184688 +#> EM - SNMoE: Iteration: 181 | log-likelihood: 90.821656408958 +#> EM - SNMoE: Iteration: 182 | log-likelihood: 90.8217642942328 +#> EM - SNMoE: Iteration: 183 | log-likelihood: 90.8218682729254 +#> EM - SNMoE: Iteration: 184 | log-likelihood: 90.8219689323786 +#> EM - SNMoE: Iteration: 185 | log-likelihood: 90.8220661409908 +#> EM - SNMoE: Iteration: 186 | log-likelihood: 90.8221825393501 +#> EM - SNMoE: Iteration: 187 | log-likelihood: 90.8222726222892 snmoe$summary() #> ----------------------------------------------- @@ -776,24 +795,24 @@ snmoe$summary() #> #> SNMoE model with K = 2 experts: #> -#> log-likelihood df AIC BIC ICL -#> 89.96844 10 79.96844 65.40517 65.289 +#> log-likelihood df AIC BIC ICL +#> 90.82227 10 80.82227 66.259 66.16274 #> #> Clustering table (Number of observations in each expert): #> #> 1 2 -#> 70 66 +#> 69 67 #> #> Regression coefficients: #> #> Beta(k = 1) Beta(k = 2) -#> 1 -14.041895089 -33.78757649 -#> X^1 0.007207881 0.01719685 +#> 1 -14.217412214 -32.63731250 +#> X^1 0.007303448 0.01668922 #> #> Variances: #> #> Sigma2(k = 1) Sigma2(k = 2) -#> 0.01461443 0.01719955 +#> 0.01492812 0.03739716 snmoe$plot() ``` @@ -828,115 +847,172 @@ p <- 1 # Order of the polynomial regression (regressors/experts) q <- 1 # Order of the logistic regression (gating network) stmoe <- emStMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE) -#> EM - StMoE: Iteration: 1 | log-likelihood: -394.559227946017 -#> EM - StMoE: Iteration: 2 | log-likelihood: -375.361815343331 -#> EM - StMoE: Iteration: 3 | log-likelihood: -372.84273063518 -#> EM - StMoE: Iteration: 4 | log-likelihood: -371.294656889484 -#> EM - StMoE: Iteration: 5 | log-likelihood: -370.449213171171 -#> EM - StMoE: Iteration: 6 | log-likelihood: 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Iteration: 153 | log-likelihood: -302.286015388739 +#> EM - StMoE: Iteration: 154 | log-likelihood: -302.286484497098 +#> EM - StMoE: Iteration: 155 | log-likelihood: -302.286935951393 +#> EM - StMoE: Iteration: 156 | log-likelihood: -302.287370547269 +#> EM - StMoE: Iteration: 157 | log-likelihood: -302.287789015395 +#> EM - StMoE: Iteration: 158 | log-likelihood: -302.288192033309 +#> EM - StMoE: Iteration: 159 | log-likelihood: -302.288580234002 +#> EM - StMoE: Iteration: 160 | log-likelihood: -302.288954212308 +#> EM - StMoE: Iteration: 161 | log-likelihood: -302.28931452978 +#> EM - StMoE: Iteration: 162 | log-likelihood: -302.289661718499 +#> EM - StMoE: Iteration: 163 | log-likelihood: -302.289996284107 +#> EM - StMoE: Iteration: 164 | log-likelihood: -302.290318708259 +#> EM - StMoE: Iteration: 165 | log-likelihood: -302.290629450648 +#> EM - StMoE: Iteration: 166 | log-likelihood: -302.290928950694 stmoe$summary() #> ------------------------------------------ @@ -945,29 +1021,35 @@ stmoe$summary() #> #> StMoE model with K = 2 experts: #> -#> log-likelihood df AIC BIC ICL -#> -295.9285 12 -307.9285 -333.2162 -333.6106 +#> log-likelihood df AIC BIC ICL +#> -302.2909 12 -314.2909 -339.5786 -339.576 #> #> Clustering table (Number of observations in each expert): #> #> 1 2 -#> 250 250 +#> 249 251 #> #> Regression coefficients: #> #> Beta(k = 1) Beta(k = 2) -#> 1 0.004172274 -0.1101857 -#> X^1 2.628202793 -2.5297250 +#> 1 0.06643398 -0.02736487 +#> X^1 2.57061178 -2.64710637 #> #> Variances: #> #> Sigma2(k = 1) Sigma2(k = 2) -#> 0.5303111 0.6466242 +#> 0.1031365 0.6024446 stmoe$plot() ``` - + + + #> Warning in sqrt(stat$Vary): production de NaN + + #> Warning in sqrt(stat$Vary): production de NaN + + ``` r # Applicartion to a real data set @@ -982,31 +1064,98 @@ p <- 2 # Order of the polynomial regression (regressors/experts) q <- 1 # Order of the logistic regression (gating network) stmoe <- emStMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE) -#> EM - StMoE: Iteration: 1 | log-likelihood: -596.437715374398 -#> EM - StMoE: Iteration: 2 | log-likelihood: -584.33102169765 -#> EM - StMoE: Iteration: 3 | log-likelihood: -584.272117037533 -#> EM - StMoE: Iteration: 4 | log-likelihood: -582.419529508737 -#> EM - StMoE: Iteration: 5 | log-likelihood: -577.297713042123 -#> EM - StMoE: Iteration: 6 | log-likelihood: -569.630947666291 -#> EM - StMoE: Iteration: 7 | log-likelihood: -565.159627283255 -#> EM - StMoE: Iteration: 8 | log-likelihood: -562.714071372189 -#> EM - StMoE: Iteration: 9 | log-likelihood: -561.741270986392 -#> EM - StMoE: Iteration: 10 | log-likelihood: -561.264453844157 -#> EM - StMoE: Iteration: 11 | log-likelihood: -560.853653903939 -#> EM - StMoE: Iteration: 12 | log-likelihood: -560.416434152859 -#> EM - StMoE: Iteration: 13 | log-likelihood: -559.981925254891 -#> EM - StMoE: Iteration: 14 | log-likelihood: -559.601355272617 -#> EM - StMoE: Iteration: 15 | log-likelihood: -559.284822567346 -#> EM - StMoE: Iteration: 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log-likelihood: -562.893400819209 +#> EM - StMoE: Iteration: 22 | log-likelihood: -562.886326886739 +#> EM - StMoE: Iteration: 23 | log-likelihood: -562.869734547993 +#> EM - StMoE: Iteration: 24 | log-likelihood: -562.843373338597 +#> EM - StMoE: Iteration: 25 | log-likelihood: -562.806530223593 +#> EM - StMoE: Iteration: 26 | log-likelihood: -562.759209213378 +#> EM - StMoE: Iteration: 27 | log-likelihood: -562.701759556614 +#> EM - StMoE: Iteration: 28 | log-likelihood: -562.637851562422 +#> EM - StMoE: Iteration: 29 | log-likelihood: -562.578679951567 +#> EM - StMoE: Iteration: 30 | log-likelihood: -562.544706088763 +#> EM - StMoE: Iteration: 31 | log-likelihood: -562.547659760017 +#> EM - StMoE: Iteration: 32 | log-likelihood: -562.573594841724 +#> EM - StMoE: Iteration: 33 | log-likelihood: -562.606224655412 +#> EM - StMoE: Iteration: 34 | log-likelihood: -562.639170956927 +#> EM - StMoE: Iteration: 35 | log-likelihood: -562.670887429489 +#> EM - StMoE: Iteration: 36 | log-likelihood: -562.700977889776 +#> EM - StMoE: Iteration: 37 | log-likelihood: -562.729333904045 +#> EM - StMoE: Iteration: 38 | log-likelihood: -562.75594302018 +#> EM - StMoE: Iteration: 39 | log-likelihood: -562.780915483106 +#> EM - StMoE: Iteration: 40 | log-likelihood: -562.804273764516 +#> EM - StMoE: Iteration: 41 | log-likelihood: -562.826081748726 +#> EM - StMoE: Iteration: 42 | log-likelihood: -562.846465069854 +#> EM - StMoE: Iteration: 43 | log-likelihood: -562.865494990344 +#> EM - StMoE: Iteration: 44 | log-likelihood: -562.883363535599 +#> EM - StMoE: Iteration: 45 | log-likelihood: -562.899766649106 +#> EM - StMoE: Iteration: 46 | log-likelihood: -562.915105887419 +#> EM - StMoE: Iteration: 47 | log-likelihood: -562.929369415829 +#> EM - StMoE: Iteration: 48 | log-likelihood: -562.942618350082 +#> EM - StMoE: Iteration: 49 | log-likelihood: -562.954914681938 +#> EM - StMoE: Iteration: 50 | log-likelihood: -562.966324704433 +#> EM - StMoE: Iteration: 51 | log-likelihood: -562.976892924208 +#> EM - StMoE: Iteration: 52 | log-likelihood: -562.986679129858 +#> EM - StMoE: Iteration: 53 | log-likelihood: -562.995698141401 +#> EM - StMoE: Iteration: 54 | log-likelihood: -563.004199322622 +#> EM - StMoE: Iteration: 55 | log-likelihood: -563.011948719677 +#> EM - StMoE: Iteration: 56 | log-likelihood: -563.019092394262 +#> EM - StMoE: Iteration: 57 | log-likelihood: -563.025788220585 +#> EM - StMoE: Iteration: 58 | log-likelihood: -563.032130750582 +#> EM - StMoE: Iteration: 59 | log-likelihood: -563.038101658285 +#> EM - StMoE: Iteration: 60 | log-likelihood: -563.043686700587 +#> EM - StMoE: Iteration: 61 | log-likelihood: -563.048913316641 +#> EM - StMoE: Iteration: 62 | log-likelihood: -563.053800034428 +#> EM - StMoE: Iteration: 63 | log-likelihood: -563.058367081312 +#> EM - StMoE: Iteration: 64 | log-likelihood: -563.062634411041 +#> EM - StMoE: Iteration: 65 | log-likelihood: -563.066621029848 +#> EM - StMoE: Iteration: 66 | log-likelihood: -563.070344865861 +#> EM - StMoE: Iteration: 67 | log-likelihood: -563.073822774497 +#> EM - StMoE: Iteration: 68 | log-likelihood: -563.077068884576 +#> EM - StMoE: Iteration: 69 | log-likelihood: -563.080101318079 +#> EM - StMoE: Iteration: 70 | log-likelihood: -563.082932976016 +#> EM - StMoE: Iteration: 71 | log-likelihood: -563.085576456654 +#> EM - StMoE: Iteration: 72 | log-likelihood: -563.088043769262 +#> EM - StMoE: Iteration: 73 | log-likelihood: -563.090354748117 +#> EM - StMoE: Iteration: 74 | log-likelihood: -563.092543476789 +#> EM - StMoE: Iteration: 75 | log-likelihood: -563.094432674549 +#> EM - StMoE: Iteration: 76 | log-likelihood: -563.09630477819 +#> EM - StMoE: Iteration: 77 | log-likelihood: -563.098074257544 +#> EM - StMoE: Iteration: 78 | log-likelihood: -563.099724441976 +#> EM - StMoE: Iteration: 79 | log-likelihood: -563.101258071476 +#> EM - StMoE: Iteration: 80 | log-likelihood: -563.102682505525 +#> EM - StMoE: Iteration: 81 | log-likelihood: -563.104005588245 +#> EM - StMoE: Iteration: 82 | log-likelihood: -563.105234621045 +#> EM - StMoE: Iteration: 83 | log-likelihood: -563.106376189189 +#> EM - StMoE: Iteration: 84 | log-likelihood: -563.107436197855 +#> EM - StMoE: Iteration: 85 | log-likelihood: -563.108419942485 +#> EM - StMoE: Iteration: 86 | log-likelihood: -563.109332171131 +#> EM - StMoE: Iteration: 87 | log-likelihood: -563.110177132063 +#> EM - StMoE: Iteration: 88 | log-likelihood: -563.11095860863 +#> EM - StMoE: Iteration: 89 | log-likelihood: -563.11167994535 +#> EM - StMoE: Iteration: 90 | log-likelihood: -563.112344067246 +#> EM - StMoE: Iteration: 91 | log-likelihood: -563.112953493273 +#> EM - StMoE: Iteration: 92 | log-likelihood: -563.113510345457 stmoe$summary() #> ------------------------------------------ @@ -1016,7 +1165,7 @@ stmoe$summary() #> StMoE model with K = 4 experts: #> #> log-likelihood df AIC BIC ICL -#> -558.1841 30 -588.1841 -631.5393 -631.5358 +#> -563.1135 30 -593.1135 -636.4687 -636.4969 #> #> Clustering table (Number of observations in each expert): #> @@ -1025,15 +1174,15 @@ stmoe$summary() #> #> Regression coefficients: #> -#> Beta(k = 1) Beta(k = 2) Beta(k = 3) Beta(k = 4) -#> 1 -3.56982092 1254.435645 -1593.34768 302.0990820 -#> X^1 0.89734058 -135.908885 94.30706 -12.5548183 -#> X^2 -0.08296406 3.309847 -1.36735 0.1288317 +#> Beta(k = 1) Beta(k = 2) Beta(k = 3) Beta(k = 4) +#> 1 -3.52358475 996.077085 -1616.483001 134.35786999 +#> X^1 0.88184631 -104.419255 95.549943 -6.74970173 +#> X^2 -0.08184845 2.446371 -1.386852 0.07092188 #> #> Variances: #> #> Sigma2(k = 1) Sigma2(k = 2) Sigma2(k = 3) Sigma2(k = 4) -#> 14.3463 1075.172 1173.423 520.9005 +#> 14.09186 448.3051 1404.488 1385.116 stmoe$plot() ``` diff --git a/man/figures/README-unnamed-chunk-10-1.png b/man/figures/README-unnamed-chunk-10-1.png index d5e726a..f553fe5 100644 Binary files a/man/figures/README-unnamed-chunk-10-1.png and b/man/figures/README-unnamed-chunk-10-1.png 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