I looked into what you suggested and got the following results.
> y.hat.new 1 2 3 4 5 6 7
> 8 9 10
1144.675 1190.714 1236.753 1157.829 1210.445 1203.868 1128.232
1282.792 1246.619 1154.540
11 12 13 14 15 16 17
18 19 20
1197.291 1180.848 1253.196 1223.599 1243.330 1207.156 1220.311
1226.888 1164.406 1266.350
21 22 23 24 25 26 27
28 29 30
1161.117 1151.252 1272.927 1147.963 1230.176 1138.097 1286.081
1249.907 1177.560 1167.694
31 32 33 34 35 36 37
38 39 40
1289.369 1269.638 1170.983 1131.520 1240.042 1194.002 1276.215
1305.812 1213.733 1312.389
41 42 43 44 45 46 47
48 49 50
1309.101 1322.255 1121.655 1200.579 1263.061 1184.137 1174.271
1187.425 1259.773 1295.947
51 52 53 54 55 56 57
58 59 60
1233.465 1141.386 1292.658 1217.022 1332.120 1134.809 1124.943
1299.235 1318.966 1256.484
61 62 63 64 65 66 67
68 69 70
1345.274 1325.543 1315.678 1302.524 1279.504 1358.428 1091.997
1138.036 1184.076 1105.151
71 72 73 74 75 76 77
78 79 80
1157.767 1151.190 1075.554 1230.115 1193.941 1101.863 1144.613
1128.171 1200.518 1170.922
81 82 83 84 85 86 87
88 89 90
1190.653 1154.479 1167.633 1174.210 1111.728 1213.672 1108.440
1098.574 1220.249 1095.286
91 92 93 94 95 96 97
98 99 100
1177.499 1085.420 1233.403 1197.230 1124.882 1115.017 1236.692
1216.961 1118.305 1078.843
101 102 103 104 105 106 107
108 109 110
1187.364 1141.325 1223.538 1253.135 1161.056 1259.712 1256.423
1269.577 1068.977 1147.902
111 112 113 114 115 116 117
118 119 120
1210.384 1131.459 1121.594 1134.748 1207.095 1243.269 1180.787
1088.709 1239.981 1164.345
121 122 123 124 125 126 127
128 129 130
1279.443 1082.131 1072.266 1246.558 1266.289 1203.807 1292.597
1272.866 1263.000 1249.846
131 132
1226.826 1305.751
What is this supposed to mean?
mod1 = lm(sold ~ age + gender)
all.x <- expand.grid(age=unique(age), gender=unique(gender))
y.hat.new <- predict(mod1, newdata=all.x)
y.hat.new
On Wed, Dec 21, 2011 at 1:43 AM, Tal Galili <[email protected]> wrote:
> combind
> ?predict
> and:
> expand.grid(weather=1:2,gender=c("male","female"))
>
>
>
>
>
> ----------------Contact
> Details:-------------------------------------------------------
> Contact me: [email protected] | 972-52-7275845
> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
> www.r-statistics.com (English)
>
> ----------------------------------------------------------------------------------------------
>
>
>
>
> On Wed, Dec 21, 2011 at 6:59 AM, Abraham Mathew <[email protected]>wrote:
>
>> Lets say I have a linear model and I want to find the average expented
>> value of the dependent variable. So let's assume that I'm studying the
>> price I pay for coffee.
>>
>> Price = B0 + B1(weather) + B2(gender) + ...
>>
>> What I'm trying to find is the predicted price for every possible
>> combination of values in the independent variables.
>>
>> So Expected price when:
>> weather=1, gender=male
>> weather=1, gender=female
>> weather=2, gender=male
>> etc.
>>
>> Can anyone help with this problem?
>>
>> --
>> *Abraham Mathew
>> Statistical Analyst
>> www.amathew.com
>> 720-648-0108
>> @abmathewks*
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> [email protected] mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
--
*Abraham Mathew
Statistical Analyst
www.amathew.com
720-648-0108
@abmathewks*
[[alternative HTML version deleted]]
______________________________________________
[email protected] mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
