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edited Dec 11, 2021 at 15:44

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cv <- structure(list(name = c("AlfF", "AndH", "AntH", "BerG", "BerR","FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KarN", "KerG", "KlaS", "ManS", "MarS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerG", "BerR", "FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW", "KarN", "KerG","KlaS", "ManS", "MarS", "SilN", "TheG", "UweP", "WerT", "AlfF","AndH", "AntH", "BerG", "BerR", "FreZ", "GerB", "GerT", "GueS","GueV", "HanN", "HeiW", "JakW", "KarN", "KerG", "KlaS", "ManS","SilN", "TheG", "UweP", "WerT", "AlfF", "AndH", "AntH", "BerG","BerR","FreZ","GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KarN", "KerG", "KlaS", "ManS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR","FreZ", "GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KlaS", "ManS", "MarH", "PetS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueV", "HanN", "HeiW", "JakW", "KlaS","ManS", "MarH", "PetS", "PetW", "SilN", "SveR", "UweP", "WerT","AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KlaS", "ManS", "MarH", "MicH", "PetS", "SilN", "SveR", "UweP","WerT"), prop_yes = c(0, 0.2, 0.6, 0.1, 0, 0, 0.1, 0, 0.3, 0,0, 0, 0, 0.1, 0.8, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0.3,0.3, 0.5, 0.4, 0.778, 0, 0.05, 0.5, 0, 0, 0.4, 0.2, 0, 0, 0, 0.2, 0.2, 0.3, 0.2, 0.6, 0.2, 0.2, 0.1, 0.1, 0.1, 0, 0.1, 0.3, 0.4, 0.1, 0.111, 0, 0.2, 0.1, 0.2, 0.8, 0, 0.111, 0, 0.1, 0, 0.2, 0.3, 0.1, 0.4, 0.333, 0.2, 0.1, 0.2, 0, 0.2, 0.182, 0, 0.1, 0.364, 0.1, 0.3, 0.375, 0, 0, 0, 0.2, 0, 0.1, 0, 0, 0, 0, 0.1, 0.1, 0, 0.3, 0, 0, 0.3, 0, 0.333, 0, 0, 0.667, 0.2, 0.571, 0.2, 0, 0.2, 0.6, 0.2, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 0, 0.2, 0.3, 0, 0.7, 0.3, 0, 0.2, 0.75, 0.2, 0.1, 0.1, 0.4, 0.1, 0.4, 0.3, 0.222, 0.2, 0.1, 0.1, 0.5, 0.2, 0.6, 0, 0, 0.1, 0.167, 0.333, 0, 0.222, 0.4, 0.5, 0, 0.3, 0.1, 0), size = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 19L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 20L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 10L, 10L, 11L, 10L, 10L, 8L, 7L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 4L, 6L, 10L, 9L, 9L, 10L, 7L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 5L, 10L, 10L, 10L, 10L, 12L, 12L, 10L, 9L, 10L, 10L, 9L, 10L, 10L, 10L), tmean_winter = c(-3.83, -4.31, -3.97, -5.21, -4.6, -4.09, -4.05, -4.09, -4.85, -4.48, -4.77, -6.66, -4.16, -4.68, -4.48, -5.07, -3.83, -4.28, -4.79,  -4.83, -4.09, -4.43, 2.36, 1.47, 2.13, 1.09, 1.93, 2.26, 2.28, 1.98, 1.66, 1.3, 1.69, -1.01, 2.22, 1.89, 2, 1.23, 2.33, 2.1, 1.68, 1.66, 1.95, 1.38, 1.61, 0.86, 1.82, 0.48, 1.45, 1.74, 1.5, 1.78, 1.14, 0.65, 1.17, -1.59, 1.69, 1.55, 1.44, 0.65, 1.59, 1.16, 1.14, 1.23, 0.81, -1.53, -2.61, -1.52, -2.7, -1.77, -1.54, -1.68, -1.32, -2.16, -2.82, -1.95, -4.56, -1.57, -1.77, -1.76, -2.55, -1.51, -1.98, -2.05, -1.97, -2.62, -4.48, -5.25, -4.04, -4.92, -4.59, -5.34, -5.09, -4.12, -4.36, -5.23, -4.94, -4.7, -5.28, -4.55, -7.07, -4.18, -5.17, -4.56, -4.56, -4.74, -4.58, -4.62, -5.08, -5.25, -1.87, -2.67, -2.84, -2.47, -3.11, -2.3, -2.01, -2.05, -2.96, -2.57, -2.75, -2.54, -4.18, -2.07, -3.04, -1.81, -2.39, -2.24, -2.75, -2.75, -2.79, -2.44, -2.85, -0.35, -1.47, -0.43, -1.02, -0.76, -1.23, -1.57, -0.48, -0.65, -1.18, -0.58, -0.92, -1.58, -1.07, -4.05, -0.52, -2.1, -0.36, -0.75, -1.04, -0.67, -1.05, -1.81, -0.61, -1.64)), .Names = c("name", "prop_yes", "size", "tmean_winter"), row.names = c(NA, -158L), class = "data.frame")

glmer(prop_yes~tmean_winter+(1|name), family='binomial', data=cv)
glmer(prop_yes~tmean_winter+(1|name), family='binomial', data=cv, weights=size)

    cv <- structure(list(name = c("AlfF", "AndH", "AntH", "BerG",  
           "BerR", "FreZ", "GerB","GerT", "GueS", "GueV", "HanN",  
           "HeiW", "JakW","KarN", "KerG", "KlaS", "ManS", "MarS",  
           "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH",  
           "BerG", "BerR", "FreZ", "GerB","GerT", "GueS", "GueV",  
           "HanN", "HeiW", "JakW", "KarN", "KerG", "KlaS", 
           "ManS", "MarS", "SilN", "TheG", "UweP", "WerT", 
           "AlfF", "AndH", "AntH", "BerG", "BerR", "FreZ", 
           "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW", 
           "JakW", "KarN", "KerG", "KlaS", "ManS", "SilN", 
           "TheG", "UweP", "WerT", "AlfF", "AndH", "AntH", 
           "BerG", "BerR", "FreZ", "GerB", "GerT", "GueS", 
           "GueV", "HanN", "HeiW", "JakW", "KarN", "KerG", 
           "KlaS", "ManS", "SilN", "TheG", "UweP", "WerT", 
           "AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", 
           "FraR", "FreZ", "GerB", "GerB", "GerT", "GueS", 
           "GueV", "HanN", "HeiW", "JakW", "KlaS", "ManS", 
           "MarH", "PetS", "SilN", "TheG", "UweP", "WerT", 
           "AlfF", "AndH", "BerK", "BerR", "ChrG", "FraR", 
           "FreZ", "GerB", "GerB", "GerT", "GueV", "HanN", 
           "HeiW", "JakW", "KlaS", "ManS", "MarH", "PetS", 
           "PetW", "SilN", "SveR", "UweP", "WerT", "AlfF", 
           "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR", 
           "FreZ", "GerB", "GerB", "GerT", "GueS", "GueV", 
           "HanN", "HeiW", "JakW", "KlaS", "ManS", "MarH", 
           "MicH", "PetS", "SilN", "SveR", "UweP", "WerT"), 
           prop_yes = c(0, 0.2, 0.6, 0.1, 0, 0, 0.1, 0, 0.3, 0, 
    0, 0, 0, 0.1, 0.8, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 
    0.3, 0.3, 0.5, 0.4, 0.778, 0, 0.05, 0.5, 0, 0, 0.4, 0.2, 0, 
    0, 0, 0.2, 0.2, 0.3, 0.2, 0.6, 0.2, 0.2, 0.1, 0.1, 0.1, 0, 
    0.1, 0.3, 0.4, 0.1, 0.111, 0, 0.2, 0.1, 0.2, 0.8, 0, 0.111, 
    0, 0.1, 0, 0.2, 0.3, 0.1, 0.4, 0.333, 0.2, 0.1, 0.2, 0, 0.2, 
    0.182, 0, 0.1, 0.364, 0.1, 0.3, 0.375, 0, 0, 0, 0.2, 0, 0.1, 
    0, 0, 0, 0, 0.1, 0.1, 0, 0.3, 0, 0, 0.3, 0, 0.333, 0, 0, 
    0.667, 0.2, 0.571, 0.2, 0, 0.2, 0.6, 0.2, 0, 0, 0, 0, 0, 0.2,  
    0, 0, 0, 0, 0.2, 0.3, 0, 0.7, 0.3, 0, 0.2, 0.75, 0.2, 0.1, 
    0.1, 0.4, 0.1, 0.4, 0.3, 0.222, 0.2, 0.1, 0.1, 0.5, 0.2, 0.6,  
    0, 0, 0.1, 0.167, 0.333, 0, 0.222, 0.4, 0.5, 0, 0.3, 0.1, 0),  
    size = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
             19L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
             10L, 9L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 
             10L, 20L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 
             10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L,  
             10L, 10L, 10L, 10L, 10L, 11L, 10L, 10L, 11L, 10L, 
             10L, 8L, 7L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 4L, 6L, 10L, 
             9L, 9L, 10L, 7L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 10L, 10L, 8L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 5L, 10L, 10L,  
             10L, 10L, 12L, 12L, 10L, 9L, 10L, 10L, 9L, 10L, 10L,  
             10L), tmean_winter = c(-3.83, -4.31, -3.97, -5.21,  
             -4.6, -4.09, -4.05, -4.09, -4.85, -4.48, -4.77, 
             -6.66, -4.16, -4.68, -4.48, -5.07, -3.83, -4.28, 
             -4.79,  -4.83, -4.09, -4.43, 2.36, 1.47, 2.13, 1.09,  
             1.93, 2.26, 2.28, 1.98, 1.66, 1.3, 1.69, -1.01, 
             2.22, 1.89, 2, 1.23, 2.33, 2.1, 1.68, 1.66, 1.95, 
             1.38, 1.61, 0.86, 1.82, 0.48, 1.45, 1.74, 1.5, 1.78,  
             1.14, 0.65, 1.17, -1.59, 1.69, 1.55, 1.44, 0.65, 
             1.59, 1.16, 1.14, 1.23, 0.81, -1.53, -2.61, -1.52, 
             -2.7, -1.77, -1.54, -1.68, -1.32, -2.16, -2.82, 
             -1.95, -4.56, -1.57, -1.77, -1.76, -2.55, -1.51, 
             -1.98, -2.05, -1.97, -2.62, -4.48, -5.25, -4.04, 
             -4.92, -4.59, -5.34, -5.09, -4.12, -4.36, -5.23, 
             -4.94, -4.7, -5.28, -4.55, -7.07, -4.18, -5.17, 
             -4.56, -4.56, -4.74, -4.58, -4.62, -5.08, -5.25, 
             -1.87, -2.67, -2.84, -2.47, -3.11, -2.3, -2.01, 
             -2.05, -2.96, -2.57, -2.75, -2.54, -4.18, -2.07, 
             -3.04, -1.81, -2.39, -2.24, -2.75, -2.75, -2.79, 
             -2.44, -2.85, -0.35, -1.47, -0.43, -1.02, -0.76, 
             -1.23, -1.57, -0.48, -0.65, -1.18, -0.58, -0.92, 
             -1.58, -1.07, -4.05, -0.52, -2.1, -0.36, -0.75, 
             -1.04, -0.67, -1.05, -1.81, -0.61, -1.64)), .Names =  
             c("name", "prop_yes", "size", "tmean_winter"), 
             row.names = c(NA, -158L), class = "data.frame")

    glmer(prop_yes~tmean_winter+(1|name), family='binomial', 
           data=cv)
    glmer(prop_yes~tmean_winter+(1|name), family='binomial', 
           data=cv, weights=size)

cv <- structure(list(name = c("AlfF", "AndH", "AntH", "BerG", "BerR","FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KarN", "KerG", "KlaS", "ManS", "MarS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerG", "BerR", "FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW", "KarN", "KerG","KlaS", "ManS", "MarS", "SilN", "TheG", "UweP", "WerT", "AlfF","AndH", "AntH", "BerG", "BerR", "FreZ", "GerB", "GerT", "GueS","GueV", "HanN", "HeiW", "JakW", "KarN", "KerG", "KlaS", "ManS","SilN", "TheG", "UweP", "WerT", "AlfF", "AndH", "AntH", "BerG","BerR","FreZ","GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KarN", "KerG", "KlaS", "ManS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR","FreZ", "GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KlaS", "ManS", "MarH", "PetS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueV", "HanN", "HeiW", "JakW", "KlaS","ManS", "MarH", "PetS", "PetW", "SilN", "SveR", "UweP", "WerT","AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KlaS", "ManS", "MarH", "MicH", "PetS", "SilN", "SveR", "UweP","WerT"), prop_yes = c(0, 0.2, 0.6, 0.1, 0, 0, 0.1, 0, 0.3, 0,0, 0, 0, 0.1, 0.8, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0.3,0.3, 0.5, 0.4, 0.778, 0, 0.05, 0.5, 0, 0, 0.4, 0.2, 0, 0, 0, 0.2, 0.2, 0.3, 0.2, 0.6, 0.2, 0.2, 0.1, 0.1, 0.1, 0, 0.1, 0.3, 0.4, 0.1, 0.111, 0, 0.2, 0.1, 0.2, 0.8, 0, 0.111, 0, 0.1, 0, 0.2, 0.3, 0.1, 0.4, 0.333, 0.2, 0.1, 0.2, 0, 0.2, 0.182, 0, 0.1, 0.364, 0.1, 0.3, 0.375, 0, 0, 0, 0.2, 0, 0.1, 0, 0, 0, 0, 0.1, 0.1, 0, 0.3, 0, 0, 0.3, 0, 0.333, 0, 0, 0.667, 0.2, 0.571, 0.2, 0, 0.2, 0.6, 0.2, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 0, 0.2, 0.3, 0, 0.7, 0.3, 0, 0.2, 0.75, 0.2, 0.1, 0.1, 0.4, 0.1, 0.4, 0.3, 0.222, 0.2, 0.1, 0.1, 0.5, 0.2, 0.6, 0, 0, 0.1, 0.167, 0.333, 0, 0.222, 0.4, 0.5, 0, 0.3, 0.1, 0), size = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 19L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 20L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 10L, 10L, 11L, 10L, 10L, 8L, 7L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 4L, 6L, 10L, 9L, 9L, 10L, 7L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 5L, 10L, 10L, 10L, 10L, 12L, 12L, 10L, 9L, 10L, 10L, 9L, 10L, 10L, 10L), tmean_winter = c(-3.83, -4.31, -3.97, -5.21, -4.6, -4.09, -4.05, -4.09, -4.85, -4.48, -4.77, -6.66, -4.16, -4.68, -4.48, -5.07, -3.83, -4.28, -4.79,  -4.83, -4.09, -4.43, 2.36, 1.47, 2.13, 1.09, 1.93, 2.26, 2.28, 1.98, 1.66, 1.3, 1.69, -1.01, 2.22, 1.89, 2, 1.23, 2.33, 2.1, 1.68, 1.66, 1.95, 1.38, 1.61, 0.86, 1.82, 0.48, 1.45, 1.74, 1.5, 1.78, 1.14, 0.65, 1.17, -1.59, 1.69, 1.55, 1.44, 0.65, 1.59, 1.16, 1.14, 1.23, 0.81, -1.53, -2.61, -1.52, -2.7, -1.77, -1.54, -1.68, -1.32, -2.16, -2.82, -1.95, -4.56, -1.57, -1.77, -1.76, -2.55, -1.51, -1.98, -2.05, -1.97, -2.62, -4.48, -5.25, -4.04, -4.92, -4.59, -5.34, -5.09, -4.12, -4.36, -5.23, -4.94, -4.7, -5.28, -4.55, -7.07, -4.18, -5.17, -4.56, -4.56, -4.74, -4.58, -4.62, -5.08, -5.25, -1.87, -2.67, -2.84, -2.47, -3.11, -2.3, -2.01, -2.05, -2.96, -2.57, -2.75, -2.54, -4.18, -2.07, -3.04, -1.81, -2.39, -2.24, -2.75, -2.75, -2.79, -2.44, -2.85, -0.35, -1.47, -0.43, -1.02, -0.76, -1.23, -1.57, -0.48, -0.65, -1.18, -0.58, -0.92, -1.58, -1.07, -4.05, -0.52, -2.1, -0.36, -0.75, -1.04, -0.67, -1.05, -1.81, -0.61, -1.64)), .Names = c("name", "prop_yes", "size", "tmean_winter"), row.names = c(NA, -158L), class = "data.frame")

glmer(prop_yes~tmean_winter+(1|name), family='binomial', data=cv)
glmer(prop_yes~tmean_winter+(1|name), family='binomial', data=cv, weights=size)

    cv <- structure(list(name = c("AlfF", "AndH", "AntH", "BerG",  
           "BerR", "FreZ", "GerB","GerT", "GueS", "GueV", "HanN",  
           "HeiW", "JakW","KarN", "KerG", "KlaS", "ManS", "MarS",  
           "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH",  
           "BerG", "BerR", "FreZ", "GerB","GerT", "GueS", "GueV",  
           "HanN", "HeiW", "JakW", "KarN", "KerG", "KlaS", 
           "ManS", "MarS", "SilN", "TheG", "UweP", "WerT", 
           "AlfF", "AndH", "AntH", "BerG", "BerR", "FreZ", 
           "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW", 
           "JakW", "KarN", "KerG", "KlaS", "ManS", "SilN", 
           "TheG", "UweP", "WerT", "AlfF", "AndH", "AntH", 
           "BerG", "BerR", "FreZ", "GerB", "GerT", "GueS", 
           "GueV", "HanN", "HeiW", "JakW", "KarN", "KerG", 
           "KlaS", "ManS", "SilN", "TheG", "UweP", "WerT", 
           "AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", 
           "FraR", "FreZ", "GerB", "GerB", "GerT", "GueS", 
           "GueV", "HanN", "HeiW", "JakW", "KlaS", "ManS", 
           "MarH", "PetS", "SilN", "TheG", "UweP", "WerT", 
           "AlfF", "AndH", "BerK", "BerR", "ChrG", "FraR", 
           "FreZ", "GerB", "GerB", "GerT", "GueV", "HanN", 
           "HeiW", "JakW", "KlaS", "ManS", "MarH", "PetS", 
           "PetW", "SilN", "SveR", "UweP", "WerT", "AlfF", 
           "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR", 
           "FreZ", "GerB", "GerB", "GerT", "GueS", "GueV", 
           "HanN", "HeiW", "JakW", "KlaS", "ManS", "MarH", 
           "MicH", "PetS", "SilN", "SveR", "UweP", "WerT"), 
           prop_yes = c(0, 0.2, 0.6, 0.1, 0, 0, 0.1, 0, 0.3, 0, 
    0, 0, 0, 0.1, 0.8, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 
    0.3, 0.3, 0.5, 0.4, 0.778, 0, 0.05, 0.5, 0, 0, 0.4, 0.2, 0, 
    0, 0, 0.2, 0.2, 0.3, 0.2, 0.6, 0.2, 0.2, 0.1, 0.1, 0.1, 0, 
    0.1, 0.3, 0.4, 0.1, 0.111, 0, 0.2, 0.1, 0.2, 0.8, 0, 0.111, 
    0, 0.1, 0, 0.2, 0.3, 0.1, 0.4, 0.333, 0.2, 0.1, 0.2, 0, 0.2, 
    0.182, 0, 0.1, 0.364, 0.1, 0.3, 0.375, 0, 0, 0, 0.2, 0, 0.1, 
    0, 0, 0, 0, 0.1, 0.1, 0, 0.3, 0, 0, 0.3, 0, 0.333, 0, 0, 
    0.667, 0.2, 0.571, 0.2, 0, 0.2, 0.6, 0.2, 0, 0, 0, 0, 0, 0.2,  
    0, 0, 0, 0, 0.2, 0.3, 0, 0.7, 0.3, 0, 0.2, 0.75, 0.2, 0.1, 
    0.1, 0.4, 0.1, 0.4, 0.3, 0.222, 0.2, 0.1, 0.1, 0.5, 0.2, 0.6,  
    0, 0, 0.1, 0.167, 0.333, 0, 0.222, 0.4, 0.5, 0, 0.3, 0.1, 0),  
    size = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
             19L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
             10L, 9L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 
             10L, 20L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 
             10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L,  
             10L, 10L, 10L, 10L, 10L, 11L, 10L, 10L, 11L, 10L, 
             10L, 8L, 7L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 4L, 6L, 10L, 
             9L, 9L, 10L, 7L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 10L, 10L, 8L, 10L, 10L, 10L, 10L, 
             10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 5L, 10L, 10L,  
             10L, 10L, 12L, 12L, 10L, 9L, 10L, 10L, 9L, 10L, 10L,  
             10L), tmean_winter = c(-3.83, -4.31, -3.97, -5.21,  
             -4.6, -4.09, -4.05, -4.09, -4.85, -4.48, -4.77, 
             -6.66, -4.16, -4.68, -4.48, -5.07, -3.83, -4.28, 
             -4.79,  -4.83, -4.09, -4.43, 2.36, 1.47, 2.13, 1.09,  
             1.93, 2.26, 2.28, 1.98, 1.66, 1.3, 1.69, -1.01, 
             2.22, 1.89, 2, 1.23, 2.33, 2.1, 1.68, 1.66, 1.95, 
             1.38, 1.61, 0.86, 1.82, 0.48, 1.45, 1.74, 1.5, 1.78,  
             1.14, 0.65, 1.17, -1.59, 1.69, 1.55, 1.44, 0.65, 
             1.59, 1.16, 1.14, 1.23, 0.81, -1.53, -2.61, -1.52, 
             -2.7, -1.77, -1.54, -1.68, -1.32, -2.16, -2.82, 
             -1.95, -4.56, -1.57, -1.77, -1.76, -2.55, -1.51, 
             -1.98, -2.05, -1.97, -2.62, -4.48, -5.25, -4.04, 
             -4.92, -4.59, -5.34, -5.09, -4.12, -4.36, -5.23, 
             -4.94, -4.7, -5.28, -4.55, -7.07, -4.18, -5.17, 
             -4.56, -4.56, -4.74, -4.58, -4.62, -5.08, -5.25, 
             -1.87, -2.67, -2.84, -2.47, -3.11, -2.3, -2.01, 
             -2.05, -2.96, -2.57, -2.75, -2.54, -4.18, -2.07, 
             -3.04, -1.81, -2.39, -2.24, -2.75, -2.75, -2.79, 
             -2.44, -2.85, -0.35, -1.47, -0.43, -1.02, -0.76, 
             -1.23, -1.57, -0.48, -0.65, -1.18, -0.58, -0.92, 
             -1.58, -1.07, -4.05, -0.52, -2.1, -0.36, -0.75, 
             -1.04, -0.67, -1.05, -1.81, -0.61, -1.64)), .Names =  
             c("name", "prop_yes", "size", "tmean_winter"), 
             row.names = c(NA, -158L), class = "data.frame")

    glmer(prop_yes~tmean_winter+(1|name), family='binomial', 
           data=cv)
    glmer(prop_yes~tmean_winter+(1|name), family='binomial', 
           data=cv, weights=size)

edited tags; edited title

Link

edited Sep 5, 2016 at 10:53

amoeba

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Weights argument in glmer(), when predicting proportion data: why is it needed when all weights are around the same?

r mixed-model glmer lme4 glmm lme4-nlme

Tweeted twitter.com/StackStats/status/734630941361065984

occurred May 23, 2016 at 6:24

light editing & formatting

Source Link

edited Aug 26, 2013 at 15:49

gung - Reinstate Monica

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weights Weights argument in glmer(), proportion data

Here (http://comments.gmane.org/gmane.comp.lang.r.lme4.devel/10160A thread on r-sig-mixed models) and in github pages, there seems to be an issue with weights argument in glmer, but since my knowledge of mixed-models is only weeks old, I am not able to follow it.

My data:

cv<-structure(list(name = c("AlfF", "AndH", "AntH", "BerG", "BerR","FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KarN", "KerG", "KlaS", "ManS", "MarS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerG", "BerR", "FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW", "KarN", "KerG","KlaS", "ManS", "MarS", "SilN", "TheG", "UweP", "WerT", "AlfF","AndH", "AntH", "BerG", "BerR", "FreZ", "GerB", "GerT", "GueS","GueV", "HanN", "HeiW", "JakW", "KarN", "KerG", "KlaS", "ManS","SilN", "TheG", "UweP", "WerT", "AlfF", "AndH", "AntH", "BerG","BerR","FreZ","GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KarN", "KerG", "KlaS", "ManS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR","FreZ", "GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KlaS", "ManS", "MarH", "PetS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueV", "HanN", "HeiW", "JakW", "KlaS","ManS", "MarH", "PetS", "PetW", "SilN", "SveR", "UweP", "WerT","AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KlaS", "ManS", "MarH", "MicH", "PetS", "SilN", "SveR", "UweP","WerT"), prop_yes = c(0, 0.2, 0.6, 0.1, 0, 0, 0.1, 0, 0.3, 0,0, 0, 0, 0.1, 0.8, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0.3,0.3, 0.5, 0.4, 0.778, 0, 0.05, 0.5, 0, 0, 0.4, 0.2, 0, 0, 0, 0.2, 0.2, 0.3, 0.2, 0.6, 0.2, 0.2, 0.1, 0.1, 0.1, 0, 0.1, 0.3, 0.4, 0.1, 0.111, 0, 0.2, 0.1, 0.2, 0.8, 0, 0.111, 0, 0.1, 0, 0.2, 0.3, 0.1, 0.4, 0.333, 0.2, 0.1, 0.2, 0, 0.2, 0.182, 0, 0.1, 0.364, 0.1, 0.3, 0.375, 0, 0, 0, 0.2, 0, 0.1, 0, 0, 0, 0, 0.1, 0.1, 0, 0.3, 0, 0, 0.3, 0, 0.333, 0, 0, 0.667, 0.2, 0.571, 0.2, 0, 0.2, 0.6, 0.2, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 0, 0.2, 0.3, 0, 0.7, 0.3, 0, 0.2, 0.75, 0.2, 0.1, 0.1, 0.4, 0.1, 0.4, 0.3, 0.222, 0.2, 0.1, 0.1, 0.5, 0.2, 0.6, 0, 0, 0.1, 0.167, 0.333, 0, 0.222, 0.4, 0.5, 0, 0.3, 0.1, 0), size = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 19L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 20L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 10L, 10L, 11L, 10L, 10L, 8L, 7L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 4L, 6L, 10L, 9L, 9L, 10L, 7L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 5L, 10L, 10L, 10L, 10L, 12L, 12L, 10L, 9L, 10L, 10L, 9L, 10L, 10L, 10L), tmean_winter = c(-3.83, -4.31, -3.97, -5.21, -4.6, -4.09, -4.05, -4.09, -4.85, -4.48, -4.77, -6.66, -4.16, -4.68, -4.48, -5.07, -3.83, -4.28, -4.79,  -4.83, -4.09, -4.43, 2.36, 1.47, 2.13, 1.09, 1.93, 2.26, 2.28, 1.98, 1.66, 1.3, 1.69, -1.01, 2.22, 1.89, 2, 1.23, 2.33, 2.1, 1.68, 1.66, 1.95, 1.38, 1.61, 0.86, 1.82, 0.48, 1.45, 1.74, 1.5, 1.78, 1.14, 0.65, 1.17, -1.59, 1.69, 1.55, 1.44, 0.65, 1.59, 1.16, 1.14, 1.23, 0.81, -1.53, -2.61, -1.52, -2.7, -1.77, -1.54, -1.68, -1.32, -2.16, -2.82, -1.95, -4.56, -1.57, -1.77, -1.76, -2.55, -1.51, -1.98, -2.05, -1.97, -2.62, -4.48, -5.25, -4.04, -4.92, -4.59, -5.34, -5.09, -4.12, -4.36, -5.23, -4.94, -4.7, -5.28, -4.55, -7.07, -4.18, -5.17, -4.56, -4.56, -4.74, -4.58, -4.62, -5.08, -5.25, -1.87, -2.67, -2.84, -2.47, -3.11, -2.3, -2.01, -2.05, -2.96, -2.57, -2.75, -2.54, -4.18, -2.07, -3.04, -1.81, -2.39, -2.24, -2.75, -2.75, -2.79, -2.44, -2.85, -0.35, -1.47, -0.43, -1.02, -0.76, -1.23, -1.57, -0.48, -0.65, -1.18, -0.58, -0.92, -1.58, -1.07, -4.05, -0.52, -2.1, -0.36, -0.75, -1.04, -0.67, -1.05, -1.81, -0.61, -1.64)), .Names = c("name", "prop_yes", "size", "tmean_winter"), row.names = c(NA, -158L), class = "data.frame")
cv <- structure(list(name = c("AlfF", "AndH", "AntH", "BerG", "BerR","FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KarN", "KerG", "KlaS", "ManS", "MarS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerG", "BerR", "FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW", "KarN", "KerG","KlaS", "ManS", "MarS", "SilN", "TheG", "UweP", "WerT", "AlfF","AndH", "AntH", "BerG", "BerR", "FreZ", "GerB", "GerT", "GueS","GueV", "HanN", "HeiW", "JakW", "KarN", "KerG", "KlaS", "ManS","SilN", "TheG", "UweP", "WerT", "AlfF", "AndH", "AntH", "BerG","BerR","FreZ","GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KarN", "KerG", "KlaS", "ManS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR","FreZ", "GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KlaS", "ManS", "MarH", "PetS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueV", "HanN", "HeiW", "JakW", "KlaS","ManS", "MarH", "PetS", "PetW", "SilN", "SveR", "UweP", "WerT","AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KlaS", "ManS", "MarH", "MicH", "PetS", "SilN", "SveR", "UweP","WerT"), prop_yes = c(0, 0.2, 0.6, 0.1, 0, 0, 0.1, 0, 0.3, 0,0, 0, 0, 0.1, 0.8, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0.3,0.3, 0.5, 0.4, 0.778, 0, 0.05, 0.5, 0, 0, 0.4, 0.2, 0, 0, 0, 0.2, 0.2, 0.3, 0.2, 0.6, 0.2, 0.2, 0.1, 0.1, 0.1, 0, 0.1, 0.3, 0.4, 0.1, 0.111, 0, 0.2, 0.1, 0.2, 0.8, 0, 0.111, 0, 0.1, 0, 0.2, 0.3, 0.1, 0.4, 0.333, 0.2, 0.1, 0.2, 0, 0.2, 0.182, 0, 0.1, 0.364, 0.1, 0.3, 0.375, 0, 0, 0, 0.2, 0, 0.1, 0, 0, 0, 0, 0.1, 0.1, 0, 0.3, 0, 0, 0.3, 0, 0.333, 0, 0, 0.667, 0.2, 0.571, 0.2, 0, 0.2, 0.6, 0.2, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 0, 0.2, 0.3, 0, 0.7, 0.3, 0, 0.2, 0.75, 0.2, 0.1, 0.1, 0.4, 0.1, 0.4, 0.3, 0.222, 0.2, 0.1, 0.1, 0.5, 0.2, 0.6, 0, 0, 0.1, 0.167, 0.333, 0, 0.222, 0.4, 0.5, 0, 0.3, 0.1, 0), size = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 19L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 20L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 10L, 10L, 11L, 10L, 10L, 8L, 7L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 4L, 6L, 10L, 9L, 9L, 10L, 7L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 5L, 10L, 10L, 10L, 10L, 12L, 12L, 10L, 9L, 10L, 10L, 9L, 10L, 10L, 10L), tmean_winter = c(-3.83, -4.31, -3.97, -5.21, -4.6, -4.09, -4.05, -4.09, -4.85, -4.48, -4.77, -6.66, -4.16, -4.68, -4.48, -5.07, -3.83, -4.28, -4.79,  -4.83, -4.09, -4.43, 2.36, 1.47, 2.13, 1.09, 1.93, 2.26, 2.28, 1.98, 1.66, 1.3, 1.69, -1.01, 2.22, 1.89, 2, 1.23, 2.33, 2.1, 1.68, 1.66, 1.95, 1.38, 1.61, 0.86, 1.82, 0.48, 1.45, 1.74, 1.5, 1.78, 1.14, 0.65, 1.17, -1.59, 1.69, 1.55, 1.44, 0.65, 1.59, 1.16, 1.14, 1.23, 0.81, -1.53, -2.61, -1.52, -2.7, -1.77, -1.54, -1.68, -1.32, -2.16, -2.82, -1.95, -4.56, -1.57, -1.77, -1.76, -2.55, -1.51, -1.98, -2.05, -1.97, -2.62, -4.48, -5.25, -4.04, -4.92, -4.59, -5.34, -5.09, -4.12, -4.36, -5.23, -4.94, -4.7, -5.28, -4.55, -7.07, -4.18, -5.17, -4.56, -4.56, -4.74, -4.58, -4.62, -5.08, -5.25, -1.87, -2.67, -2.84, -2.47, -3.11, -2.3, -2.01, -2.05, -2.96, -2.57, -2.75, -2.54, -4.18, -2.07, -3.04, -1.81, -2.39, -2.24, -2.75, -2.75, -2.79, -2.44, -2.85, -0.35, -1.47, -0.43, -1.02, -0.76, -1.23, -1.57, -0.48, -0.65, -1.18, -0.58, -0.92, -1.58, -1.07, -4.05, -0.52, -2.1, -0.36, -0.75, -1.04, -0.67, -1.05, -1.81, -0.61, -1.64)), .Names = c("name", "prop_yes", "size", "tmean_winter"), row.names = c(NA, -158L), class = "data.frame")

glmer(prop_yes~tmean_winter+(1|name), family='binomial', data=cv)
glmer(prop_yes~tmean_winter+(1|name), family='binomial',weights=size, data=cv, weights=size)

weights argument in glmer, proportion data

Here (http://comments.gmane.org/gmane.comp.lang.r.lme4.devel/10160) and in github pages, there seems to be an issue with weights argument in glmer, but since my knowledge of mixed-models is only weeks old, I am not able to follow it.

My data

cv<-structure(list(name = c("AlfF", "AndH", "AntH", "BerG", "BerR","FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KarN", "KerG", "KlaS", "ManS", "MarS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerG", "BerR", "FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW", "KarN", "KerG","KlaS", "ManS", "MarS", "SilN", "TheG", "UweP", "WerT", "AlfF","AndH", "AntH", "BerG", "BerR", "FreZ", "GerB", "GerT", "GueS","GueV", "HanN", "HeiW", "JakW", "KarN", "KerG", "KlaS", "ManS","SilN", "TheG", "UweP", "WerT", "AlfF", "AndH", "AntH", "BerG","BerR","FreZ","GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KarN", "KerG", "KlaS", "ManS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR","FreZ", "GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KlaS", "ManS", "MarH", "PetS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueV", "HanN", "HeiW", "JakW", "KlaS","ManS", "MarH", "PetS", "PetW", "SilN", "SveR", "UweP", "WerT","AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KlaS", "ManS", "MarH", "MicH", "PetS", "SilN", "SveR", "UweP","WerT"), prop_yes = c(0, 0.2, 0.6, 0.1, 0, 0, 0.1, 0, 0.3, 0,0, 0, 0, 0.1, 0.8, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0.3,0.3, 0.5, 0.4, 0.778, 0, 0.05, 0.5, 0, 0, 0.4, 0.2, 0, 0, 0, 0.2, 0.2, 0.3, 0.2, 0.6, 0.2, 0.2, 0.1, 0.1, 0.1, 0, 0.1, 0.3, 0.4, 0.1, 0.111, 0, 0.2, 0.1, 0.2, 0.8, 0, 0.111, 0, 0.1, 0, 0.2, 0.3, 0.1, 0.4, 0.333, 0.2, 0.1, 0.2, 0, 0.2, 0.182, 0, 0.1, 0.364, 0.1, 0.3, 0.375, 0, 0, 0, 0.2, 0, 0.1, 0, 0, 0, 0, 0.1, 0.1, 0, 0.3, 0, 0, 0.3, 0, 0.333, 0, 0, 0.667, 0.2, 0.571, 0.2, 0, 0.2, 0.6, 0.2, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 0, 0.2, 0.3, 0, 0.7, 0.3, 0, 0.2, 0.75, 0.2, 0.1, 0.1, 0.4, 0.1, 0.4, 0.3, 0.222, 0.2, 0.1, 0.1, 0.5, 0.2, 0.6, 0, 0, 0.1, 0.167, 0.333, 0, 0.222, 0.4, 0.5, 0, 0.3, 0.1, 0), size = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 19L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 20L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 10L, 10L, 11L, 10L, 10L, 8L, 7L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 4L, 6L, 10L, 9L, 9L, 10L, 7L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 5L, 10L, 10L, 10L, 10L, 12L, 12L, 10L, 9L, 10L, 10L, 9L, 10L, 10L, 10L), tmean_winter = c(-3.83, -4.31, -3.97, -5.21, -4.6, -4.09, -4.05, -4.09, -4.85, -4.48, -4.77, -6.66, -4.16, -4.68, -4.48, -5.07, -3.83, -4.28, -4.79,  -4.83, -4.09, -4.43, 2.36, 1.47, 2.13, 1.09, 1.93, 2.26, 2.28, 1.98, 1.66, 1.3, 1.69, -1.01, 2.22, 1.89, 2, 1.23, 2.33, 2.1, 1.68, 1.66, 1.95, 1.38, 1.61, 0.86, 1.82, 0.48, 1.45, 1.74, 1.5, 1.78, 1.14, 0.65, 1.17, -1.59, 1.69, 1.55, 1.44, 0.65, 1.59, 1.16, 1.14, 1.23, 0.81, -1.53, -2.61, -1.52, -2.7, -1.77, -1.54, -1.68, -1.32, -2.16, -2.82, -1.95, -4.56, -1.57, -1.77, -1.76, -2.55, -1.51, -1.98, -2.05, -1.97, -2.62, -4.48, -5.25, -4.04, -4.92, -4.59, -5.34, -5.09, -4.12, -4.36, -5.23, -4.94, -4.7, -5.28, -4.55, -7.07, -4.18, -5.17, -4.56, -4.56, -4.74, -4.58, -4.62, -5.08, -5.25, -1.87, -2.67, -2.84, -2.47, -3.11, -2.3, -2.01, -2.05, -2.96, -2.57, -2.75, -2.54, -4.18, -2.07, -3.04, -1.81, -2.39, -2.24, -2.75, -2.75, -2.79, -2.44, -2.85, -0.35, -1.47, -0.43, -1.02, -0.76, -1.23, -1.57, -0.48, -0.65, -1.18, -0.58, -0.92, -1.58, -1.07, -4.05, -0.52, -2.1, -0.36, -0.75, -1.04, -0.67, -1.05, -1.81, -0.61, -1.64)), .Names = c("name", "prop_yes", "size", "tmean_winter"), row.names = c(NA, -158L), class = "data.frame")

glmer(prop_yes~tmean_winter+(1|name),family='binomial',data=cv)
glmer(prop_yes~tmean_winter+(1|name),family='binomial',weights=size,data=cv)

Weights argument in glmer(), proportion data

A thread on r-sig-mixed models and in github pages, there seems to be an issue with weights argument in glmer, but since my knowledge of mixed-models is only weeks old, I am not able to follow it.

My data:

cv <- structure(list(name = c("AlfF", "AndH", "AntH", "BerG", "BerR","FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KarN", "KerG", "KlaS", "ManS", "MarS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerG", "BerR", "FreZ", "GerB","GerT", "GueS", "GueV", "HanN", "HeiW", "JakW", "KarN", "KerG","KlaS", "ManS", "MarS", "SilN", "TheG", "UweP", "WerT", "AlfF","AndH", "AntH", "BerG", "BerR", "FreZ", "GerB", "GerT", "GueS","GueV", "HanN", "HeiW", "JakW", "KarN", "KerG", "KlaS", "ManS","SilN", "TheG", "UweP", "WerT", "AlfF", "AndH", "AntH", "BerG","BerR","FreZ","GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KarN", "KerG", "KlaS", "ManS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR","FreZ", "GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW","JakW", "KlaS", "ManS", "MarH", "PetS", "SilN", "TheG", "UweP","WerT", "AlfF", "AndH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueV", "HanN", "HeiW", "JakW", "KlaS","ManS", "MarH", "PetS", "PetW", "SilN", "SveR", "UweP", "WerT","AlfF", "AndH", "AntH", "BerK", "BerR", "ChrG", "FraR", "FreZ","GerB", "GerB", "GerT", "GueS", "GueV", "HanN", "HeiW", "JakW","KlaS", "ManS", "MarH", "MicH", "PetS", "SilN", "SveR", "UweP","WerT"), prop_yes = c(0, 0.2, 0.6, 0.1, 0, 0, 0.1, 0, 0.3, 0,0, 0, 0, 0.1, 0.8, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0.3,0.3, 0.5, 0.4, 0.778, 0, 0.05, 0.5, 0, 0, 0.4, 0.2, 0, 0, 0, 0.2, 0.2, 0.3, 0.2, 0.6, 0.2, 0.2, 0.1, 0.1, 0.1, 0, 0.1, 0.3, 0.4, 0.1, 0.111, 0, 0.2, 0.1, 0.2, 0.8, 0, 0.111, 0, 0.1, 0, 0.2, 0.3, 0.1, 0.4, 0.333, 0.2, 0.1, 0.2, 0, 0.2, 0.182, 0, 0.1, 0.364, 0.1, 0.3, 0.375, 0, 0, 0, 0.2, 0, 0.1, 0, 0, 0, 0, 0.1, 0.1, 0, 0.3, 0, 0, 0.3, 0, 0.333, 0, 0, 0.667, 0.2, 0.571, 0.2, 0, 0.2, 0.6, 0.2, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 0, 0.2, 0.3, 0, 0.7, 0.3, 0, 0.2, 0.75, 0.2, 0.1, 0.1, 0.4, 0.1, 0.4, 0.3, 0.222, 0.2, 0.1, 0.1, 0.5, 0.2, 0.6, 0, 0, 0.1, 0.167, 0.333, 0, 0.222, 0.4, 0.5, 0, 0.3, 0.1, 0), size = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 19L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 20L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 10L, 10L, 11L, 10L, 10L, 8L, 7L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 4L, 6L, 10L, 9L, 9L, 10L, 7L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 5L, 10L, 10L, 10L, 10L, 12L, 12L, 10L, 9L, 10L, 10L, 9L, 10L, 10L, 10L), tmean_winter = c(-3.83, -4.31, -3.97, -5.21, -4.6, -4.09, -4.05, -4.09, -4.85, -4.48, -4.77, -6.66, -4.16, -4.68, -4.48, -5.07, -3.83, -4.28, -4.79,  -4.83, -4.09, -4.43, 2.36, 1.47, 2.13, 1.09, 1.93, 2.26, 2.28, 1.98, 1.66, 1.3, 1.69, -1.01, 2.22, 1.89, 2, 1.23, 2.33, 2.1, 1.68, 1.66, 1.95, 1.38, 1.61, 0.86, 1.82, 0.48, 1.45, 1.74, 1.5, 1.78, 1.14, 0.65, 1.17, -1.59, 1.69, 1.55, 1.44, 0.65, 1.59, 1.16, 1.14, 1.23, 0.81, -1.53, -2.61, -1.52, -2.7, -1.77, -1.54, -1.68, -1.32, -2.16, -2.82, -1.95, -4.56, -1.57, -1.77, -1.76, -2.55, -1.51, -1.98, -2.05, -1.97, -2.62, -4.48, -5.25, -4.04, -4.92, -4.59, -5.34, -5.09, -4.12, -4.36, -5.23, -4.94, -4.7, -5.28, -4.55, -7.07, -4.18, -5.17, -4.56, -4.56, -4.74, -4.58, -4.62, -5.08, -5.25, -1.87, -2.67, -2.84, -2.47, -3.11, -2.3, -2.01, -2.05, -2.96, -2.57, -2.75, -2.54, -4.18, -2.07, -3.04, -1.81, -2.39, -2.24, -2.75, -2.75, -2.79, -2.44, -2.85, -0.35, -1.47, -0.43, -1.02, -0.76, -1.23, -1.57, -0.48, -0.65, -1.18, -0.58, -0.92, -1.58, -1.07, -4.05, -0.52, -2.1, -0.36, -0.75, -1.04, -0.67, -1.05, -1.81, -0.61, -1.64)), .Names = c("name", "prop_yes", "size", "tmean_winter"), row.names = c(NA, -158L), class = "data.frame")

glmer(prop_yes~tmean_winter+(1|name), family='binomial', data=cv)
glmer(prop_yes~tmean_winter+(1|name), family='binomial', data=cv, weights=size)